40:null 41:["$","title",null,{"children":"Understand Heat Stress on Producers | 360ground | Observable"}] 47:Td15,// define choices for the data value viewof selectGeoDataType = { const options = [ { key: "ag_workforce_data_2", // lookup id for data option dataColumn: "corrected_ag_pop", // data column label: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_2.label), // label labelTip: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_2.labelTip), // label for tooltip labelLegend: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_2.labelLegend), // label for legend formatFunc: formatNumCompactShort({locale: language.locale}), // formatting function for raw data value colorRange: colorScales.range.yellowGreen, // color range colorUnknown: colorScales.unknown, // unknown fill color }, { key: "ag_workforce_data_1", dataColumn: "agw_total_mid", label: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_1.label), labelTip: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_1.labelTip), labelLegend: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_1.labelLegend), formatFunc: d => `${formatNumCompactShort({locale: language.locale})(d)}%`, colorRange: colorScales.range.redYellowGreen, colorUnknown: colorScales.unknown, }, { key: "ag_workforce_data", dataColumn: "agw_total", label: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data.label), labelTip: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data.labelTip), labelLegend: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data.labelLegend), formatFunc: d => `${formatNumCompactShort({locale: language.locale})(d)}%`, colorRange: colorScales.range.redYellowGreen, colorUnknown: colorScales.unknown, }, { key: "ag_workforce_data_15", dataColumn: "agw_total_mid_abs", label: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_15.label), labelTip: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_15.labelTip), labelLegend: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_15.labelLegend), formatFunc: formatNumCompactShort({locale: language.locale}), colorRange: colorScales.range.yellowGreen, colorUnknown: colorScales.unknown, }, { key: "ag_workforce_data_10", dataColumn: "agw_total_abs", label: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_10.label), labelTip: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_10.labelTip), labelLegend: _lang(nbText.sections.futureWorkforce.map.mapLayer.values.ag_workforce_data_10.labelLegend), formatFunc: formatNumCompactShort({locale: language.locale}), colorRange: colorScales.range.yellowGreen, colorUnknown: colorScales.unknown, }, ]; return Inputs.radio(options.sort((a,b) => b.key.localeCompare(a.key)), { width: 300, label: _lang(nbText.sections.futureWorkforce.map.mapLayer.title), format: (x) => x.label, value: options.find((t) => t.key === "ag_workforce_data_2") }); }48:T1075,plotChoroplethGeoImpact = { const data = mapDataGeoImpact; const selector = selectGeoDataType; const allValues = data.features.flatMap((d) => d.properties.data && d.properties.data[selector.dataColumn] !== undefined ? [Math.abs(d.properties.data[selector.dataColumn])] : [] ); const caption = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.futureWorkforce.map.caption), [{ name: "selector", value: selector.label }] ); // return plot return Plot.plot({ width: mapWidth, height: 600, caption, color: { legend: true, label: selector.labelLegend, range: selector.colorRange, unknown: selector.colorUnknown, tickFormat: (d) => { // Check if dataColumn is one of the specified types if ( selector.dataColumn === "corrected_ag_pop" || selector.dataColumn === "agw_total_mid_abs" || selector.dataColumn === "agw_total_abs" ) { // Use the provided formatFunc and append a "%" symbol for these types return selector.formatFunc(d); } else { // For other types, just use the formatFunc without appending a "%" symbol return `${selector.formatFunc(d)}`; } }, domain: selector.dataColumn === "corrected_ag_pop" || selector.dataColumn === "agw_total_mid_abs" || selector.dataColumn === "agw_total_abs" ? [0, Math.max(...allValues)] : [-Math.max(...allValues), Math.max(...allValues)] }, marks: [ // geo data Plot.geo(data.features, { fill: (d) => { const dataColumn = selector.dataColumn; const fillValue = d.properties.data ? d.properties.data[dataColumn] : null; // handle missing data return fillValue; }, stroke: "#fff", strokeWidth: 0.5 }), // admin3 highlight // if Admin selection includes admin3, highlight specific woreda Plot.geo( adminSelections.selectAdmin3.value ? data.features.filter( (d) => d.properties.admin3_name == adminSelections.selectAdmin3.value ) : [], { fill: null, stroke: "#333", strokeWidth: 1.5 } ), // admin2 highlight (only if admin3 is not selected) // if Admin selection includes admin2 but not admin3, highlight section Plot.geo( adminSelections.selectAdmin2.value && !adminSelections.selectAdmin3.value ? data.features.filter( (d) => d.properties.admin2_name == adminSelections.selectAdmin2.value ) : [], { fill: null, stroke: "#333", strokeWidth: 1.5 } ), // geo pointer Plot.geo( data, Plot.pointer( Plot.centroid({ stroke: "#333", strokeWidth: 1.5 }) ) ), // tooltip Plot.tip( data.features, Plot.pointer( Plot.centroid({ channels: { name: { // region label, based on selection label: Lang.toTitleCase(getLowerLevelAdminLabel()), // plot-level so inputs don't reload value: (d) => { // Show admin3_name if available, otherwise fall back to admin_name if (d.properties.admin3_name) { return d.properties.admin3_name; } return _lang(td.admin0_name.values?.[d.properties.admin_name]) ?? d.properties.admin_name; } }, data: { label: selector.labelTip, value: (d) => { const dataColumn = selector.dataColumn; const data = d.properties.data ? d.properties.data[dataColumn] : undefined; return data; } } }, format: { name: true, data: (d) => `${selector.formatFunc(d)}` } }) ) ) ] }); }49:T5362,viewof exportPlotChoroplethGeoImpact = { const t = nbText.export.geoImpact; // Helper: Download blob const downloadBlob = (blob, filename) => { const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; a.download = filename; a.style.display = 'none'; document.body.appendChild(a); a.click(); document.body.removeChild(a); setTimeout(() => URL.revokeObjectURL(url), 100); }; // Helper: Show notification const showNotification = (message, isError = false) => { const notification = html`

${message}

`; document.body.appendChild(notification); setTimeout(() => { if (notification.parentNode) { notification.parentNode.removeChild(notification); } }, isError ? 5000 : 3000); }; // Helper: Download as SVG const downloadAsSvg = (svg, filename) => { const clone = svg.cloneNode(true); const serializer = new XMLSerializer(); const svgString = serializer.serializeToString(clone); const blob = new Blob([svgString], { type: 'image/svg+xml' }); downloadBlob(blob, filename); }; // Helper: Download as raster (PNG/JPEG) const downloadAsRaster = (svg, filename, format) => { return new Promise((resolve, reject) => { const clone = svg.cloneNode(true); const serializer = new XMLSerializer(); let svgString = serializer.serializeToString(clone); if (!svgString.includes('\n' + svgString; } const svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' }); const url = URL.createObjectURL(svgBlob); const img = new Image(); const rect = svg.getBoundingClientRect(); const width = rect.width || 900; const height = rect.height || 600; const canvas = document.createElement('canvas'); const ctx = canvas.getContext('2d'); canvas.width = width; canvas.height = height; // Fill with white background for both PNG and JPEG ctx.fillStyle = 'white'; ctx.fillRect(0, 0, width, height); img.onload = () => { try { ctx.drawImage(img, 0, 0, width, height); URL.revokeObjectURL(url); canvas.toBlob( (blob) => { if (blob) { downloadBlob(blob, filename); resolve(); } else { reject(new Error('Failed to create blob from canvas')); } }, format === 'png' ? 'image/png' : 'image/jpeg', format === 'jpeg' || format === 'jpg' ? 0.92 : 1.0 ); } catch (drawError) { URL.revokeObjectURL(url); reject(new Error(`Failed to draw image: ${drawError.message}`)); } }; img.onerror = () => { URL.revokeObjectURL(url); reject(new Error('Failed to load SVG image')); }; img.src = url; }); }; // Helper: Download as CSV const downloadAsCsv = (data, filename) => { if (!data || data.length === 0) { throw new Error(_lang(t.noDataAvailable)); } const headers = Object.keys(data[0]); const csvRows = []; csvRows.push(headers.join(',')); for (const row of data) { const values = headers.map(header => { const value = row[header]; if (value === null || value === undefined) return ''; const stringValue = String(value); if (stringValue.includes(',') || stringValue.includes('"') || stringValue.includes('\n')) { return `"${stringValue.replace(/"/g, '""')}"`; } return stringValue; }); csvRows.push(values.join(',')); } const csvContent = csvRows.join('\n'); const blob = new Blob([csvContent], { type: 'text/csv;charset=utf-8;' }); downloadBlob(blob, filename); }; // Helper: Download as JSON const downloadAsJson = (data, filename) => { const jsonString = JSON.stringify(data, null, 2); const blob = new Blob([jsonString], { type: 'application/json' }); downloadBlob(blob, filename); }; // Helper: Download as GeoJSON const downloadAsGeoJson = (geojson, filename) => { const jsonString = JSON.stringify(geojson, null, 2); const blob = new Blob([jsonString], { type: 'application/geo+json' }); downloadBlob(blob, filename); }; // Helper: Download as Shapefile (pure-JS binary generation) const downloadAsShapefile = (geojson, filename) => { const crcTable = (() => { const tb = new Uint32Array(256); for (let i = 0; i < 256; i++) { let c = i; for (let j = 0; j < 8; j++) c = (c & 1) ? (0xEDB88320 ^ (c >>> 1)) : (c >>> 1); tb[i] = c; } return tb; })(); const crc32 = (data) => { let crc = 0xFFFFFFFF; for (let i = 0; i < data.length; i++) crc = crcTable[(crc ^ data[i]) & 0xFF] ^ (crc >>> 8); return (crc ^ 0xFFFFFFFF) >>> 0; }; const enc = new TextEncoder(); const buildShapefiles = (features) => { const geomType = (() => { for (const f of features) { if (!f.geometry) continue; const tp = f.geometry.type; if (tp === 'Point' || tp === 'MultiPoint') return 1; if (tp === 'LineString' || tp === 'MultiLineString') return 3; if (tp === 'Polygon' || tp === 'MultiPolygon') return 5; } return 5; })(); const records = []; let bbox = [Infinity, Infinity, -Infinity, -Infinity]; for (const f of features) { const geom = f.geometry; if (!geom) { records.push(null); continue; } let rings = []; if (geomType === 5) { if (geom.type === 'Polygon') rings = geom.coordinates; else if (geom.type === 'MultiPolygon') rings = geom.coordinates.flat(1); } else if (geomType === 3) { if (geom.type === 'LineString') rings = [geom.coordinates]; else if (geom.type === 'MultiLineString') rings = geom.coordinates; } else { rings = [geom.type === 'Point' ? [geom.coordinates] : geom.coordinates]; } const allPts = rings.flat(); let fb = [Infinity, Infinity, -Infinity, -Infinity]; for (const [x, y] of allPts) { fb[0] = Math.min(fb[0], x); fb[1] = Math.min(fb[1], y); fb[2] = Math.max(fb[2], x); fb[3] = Math.max(fb[3], y); bbox[0] = Math.min(bbox[0], x); bbox[1] = Math.min(bbox[1], y); bbox[2] = Math.max(bbox[2], x); bbox[3] = Math.max(bbox[3], y); } records.push({ rings, bbox: fb, numPts: allPts.length }); } if (!isFinite(bbox[0])) bbox = [0, 0, 0, 0]; const shxSize = 100 + records.length * 8; let shpContentLen = 100; for (const r of records) { if (!r) { shpContentLen += 12; continue; } if (geomType === 1) { shpContentLen += 4 + 16; } else { shpContentLen += 8 + 4 + 32 + 8 + r.rings.length * 4 + r.numPts * 16; } } const shp = new DataView(new ArrayBuffer(shpContentLen)); const shx = new DataView(new ArrayBuffer(shxSize)); const writeHeader = (dv, fileLen) => { dv.setInt32(0, 9994, false); dv.setInt32(24, fileLen / 2, false); dv.setInt32(28, 1000, true); dv.setInt32(32, geomType, true); dv.setFloat64(36, bbox[0], true); dv.setFloat64(44, bbox[1], true); dv.setFloat64(52, bbox[2], true); dv.setFloat64(60, bbox[3], true); }; writeHeader(shp, shpContentLen); writeHeader(shx, shxSize); let shpOff = 100, shxOff = 100; for (let i = 0; i < records.length; i++) { const r = records[i]; const recNum = i + 1; shx.setInt32(shxOff, shpOff / 2, false); if (!r) { shp.setInt32(shpOff, recNum, false); shp.setInt32(shpOff + 4, 2, false); shp.setInt32(shpOff + 8, 0, true); shx.setInt32(shxOff + 4, 2, false); shpOff += 12; shxOff += 8; continue; } if (geomType === 1) { const cLen = (4 + 16) / 2; shp.setInt32(shpOff, recNum, false); shp.setInt32(shpOff + 4, cLen, false); shp.setInt32(shpOff + 8, 1, true); shp.setFloat64(shpOff + 12, r.rings[0][0][0], true); shp.setFloat64(shpOff + 20, r.rings[0][0][1], true); shx.setInt32(shxOff + 4, cLen, false); shpOff += 8 + cLen * 2; shxOff += 8; } else { const numParts = r.rings.length, numPts = r.numPts; const cBytes = 4 + 32 + 8 + numParts * 4 + numPts * 16; const cLen = cBytes / 2; shp.setInt32(shpOff, recNum, false); shp.setInt32(shpOff + 4, cLen, false); let p = shpOff + 8; shp.setInt32(p, geomType, true); p += 4; shp.setFloat64(p, r.bbox[0], true); p += 8; shp.setFloat64(p, r.bbox[1], true); p += 8; shp.setFloat64(p, r.bbox[2], true); p += 8; shp.setFloat64(p, r.bbox[3], true); p += 8; shp.setInt32(p, numParts, true); p += 4; shp.setInt32(p, numPts, true); p += 4; let ptIdx = 0; for (const ring of r.rings) { shp.setInt32(p, ptIdx, true); p += 4; ptIdx += ring.length; } for (const ring of r.rings) for (const [x, y] of ring) { shp.setFloat64(p, x, true); p += 8; shp.setFloat64(p, y, true); p += 8; } shx.setInt32(shxOff + 4, cLen, false); shpOff += 8 + cBytes; shxOff += 8; } } return { shp: new Uint8Array(shp.buffer), shx: new Uint8Array(shx.buffer) }; }; const buildDbf = (features) => { const allKeys = new Set(); for (const f of features) if (f.properties) Object.keys(f.properties).forEach(k => allKeys.add(k)); const fields = Array.from(allKeys).map(k => ({ name: k.substring(0, 10), key: k })); const headerSize = 32 + fields.length * 32 + 1; const recordSize = 1 + fields.length * 10; const buf = new Uint8Array(headerSize + features.length * recordSize); const dv = new DataView(buf.buffer); buf[0] = 3; const now = new Date(); buf[1] = now.getFullYear() - 1900; buf[2] = now.getMonth() + 1; buf[3] = now.getDate(); dv.setUint32(4, features.length, true); dv.setUint16(8, headerSize, true); dv.setUint16(10, recordSize, true); let off = 32; for (const f of fields) { const nb = enc.encode(f.name); buf.set(nb.subarray(0, Math.min(nb.length, 10)), off); buf[off + 11] = 67; dv.setUint8(off + 16, 10); off += 32; } buf[off++] = 0x0D; for (const f of features) { buf[off++] = 0x20; for (const field of fields) { const val = f.properties ? String(f.properties[field.key] ?? '') : ''; const vb = enc.encode(val.substring(0, 10).padEnd(10, ' ')); buf.set(vb, off); off += 10; } } return buf; }; const buildPrj = () => enc.encode('GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]'); const buildZip = (files) => { const localHeaders = []; let off = 0; const parts = []; for (const file of files) { const nameBuf = enc.encode(file.name); const crc = crc32(file.data); const lh = new Uint8Array(30 + nameBuf.length); const lv = new DataView(lh.buffer); lv.setUint32(0, 0x04034b50, true); lv.setUint16(4, 20, true); lv.setUint32(14, crc, true); lv.setUint32(18, file.data.length, true); lv.setUint32(22, file.data.length, true); lv.setUint16(26, nameBuf.length, true); lh.set(nameBuf, 30); localHeaders.push({ offset: off, nameBuf, crc, size: file.data.length }); parts.push(lh, file.data); off += lh.length + file.data.length; } const cdOffset = off; for (let i = 0; i < files.length; i++) { const { offset, nameBuf, crc, size } = localHeaders[i]; const ch = new Uint8Array(46 + nameBuf.length); const cv = new DataView(ch.buffer); cv.setUint32(0, 0x02014b50, true); cv.setUint16(4, 20, true); cv.setUint16(6, 20, true); cv.setUint32(16, crc, true); cv.setUint32(20, size, true); cv.setUint32(24, size, true); cv.setUint16(28, nameBuf.length, true); cv.setUint32(42, offset, true); ch.set(nameBuf, 46); parts.push(ch); off += ch.length; } const cdSize = off - cdOffset; const eocd = new Uint8Array(22); const ev = new DataView(eocd.buffer); ev.setUint32(0, 0x06054b50, true); ev.setUint16(8, files.length, true); ev.setUint16(10, files.length, true); ev.setUint32(12, cdSize, true); ev.setUint32(16, cdOffset, true); parts.push(eocd); const total = parts.reduce((s, p) => s + p.length, 0); const zip = new Uint8Array(total); let pos = 0; for (const p of parts) { zip.set(p, pos); pos += p.length; } return zip; }; const { shp, shx } = buildShapefiles(geojson.features); const dbf = buildDbf(geojson.features); const prj = buildPrj(); const base = filename.replace(/\.[^.]+$/, ''); const zipData = buildZip([{ name: `${base}.shp`, data: shp }, { name: `${base}.shx`, data: shx }, { name: `${base}.dbf`, data: dbf }, { name: `${base}.prj`, data: prj }]); downloadBlob(new Blob([zipData], { type: 'application/zip' }), `${base}.zip`); showNotification(_lang(t.shapefileSuccess)); }; // Find Map SVG - simplified approach const findMap = () => { // First, try to find in the immediately previous cell const cells = Array.from(document.querySelectorAll('.observablehq--cell')); let currentCellIndex = -1; // Find the current export cell for (let i = 0; i < cells.length; i++) { if (cells[i].contains(select)) { currentCellIndex = i; break; } } // Look in the immediately previous cell first if (currentCellIndex > 0) { const prevCell = cells[currentCellIndex - 1]; if (prevCell) { const svg = prevCell.querySelector('svg'); if (svg) { // Check if it looks like a map (has many paths or is wide) const paths = svg.querySelectorAll('path'); const rect = svg.getBoundingClientRect(); // Maps typically have many path elements or are wide if (paths.length > 10 || rect.width > 500) { return svg; } } } } // Fallback: find all SVGs and pick the one that looks most like a map const allSvgs = Array.from(document.querySelectorAll('svg')); if (allSvgs.length > 0) { // Score each SVG based on map-like characteristics const scoredSvgs = allSvgs.map(svg => { const paths = svg.querySelectorAll('path'); const rect = svg.getBoundingClientRect(); const score = paths.length * 2 + (rect.width > 600 ? 100 : 0) + (rect.height > 400 ? 50 : 0); return { svg, score }; }); // Return the highest scoring SVG scoredSvgs.sort((a, b) => b.score - a.score); if (scoredSvgs[0].score > 50) { return scoredSvgs[0].svg; } } return null; }; // Get GeoJSON from mapDataGeoImpact const getGeoJson = () => { const selector = selectGeoDataType; const geojson = { type: "FeatureCollection", features: mapDataGeoImpact.features.map(feature => ({ type: "Feature", geometry: feature.geometry, properties: { ...feature.properties, // Include data values in properties for GIS software ...(feature.properties.data ? feature.properties.data : {}), // Preserve admin names admin0_name: feature.properties.admin0_name, admin1_name: feature.properties.admin1_name, admin2_name: feature.properties.admin2_name, admin3_name: feature.properties.admin3_name, admin_name: feature.properties.admin_name, // Include the selected data column value for easy styling in GIS [selector.dataColumn]: feature.properties.data && feature.properties.data[selector.dataColumn] !== undefined ? feature.properties.data[selector.dataColumn] : null } })) }; return geojson; }; // Get CSV data from GeoJSON features const getCsvData = () => { const selector = selectGeoDataType; return mapDataGeoImpact.features.map(feature => { const props = feature.properties; const data = props.data || {}; return { admin0_name: props.admin0_name || '', admin1_name: props.admin1_name || '', admin2_name: props.admin2_name || '', admin3_name: props.admin3_name || '', admin_name: props.admin_name || '', [selector.dataColumn]: data[selector.dataColumn] !== undefined ? data[selector.dataColumn] : null, // Include all other data columns ...Object.keys(data).reduce((acc, key) => { acc[`data_${key}`] = data[key]; return acc; }, {}) }; }); }; // Get JSON data (tabular format) const getJsonData = () => { return getCsvData(); }; // Main download function const downloadChart = async (format) => { // Add delay before finding SVG for image formats if (format === 'png' || format === 'jpeg' || format === 'jpg' || format === 'svg') { await new Promise(resolve => setTimeout(resolve, 300)); } const timestamp = new Date().toISOString().replace(/[:.]/g, '-').split('T')[0]; const selector = selectGeoDataType; const baseFilename = `geo-impact-map-${selector.dataColumn}-${timestamp}`; if (format === 'csv') { try { const csvData = getCsvData(); const filename = `${baseFilename}.csv`; downloadAsCsv(csvData, filename); showNotification(_lang(t.csvSuccess)); } catch (error) { console.error('CSV export failed:', error); showNotification(`${_lang(t.csvFailed)} ${error.message || error}`, true); } return; } if (format === 'json') { try { const jsonData = getJsonData(); const filename = `${baseFilename}.json`; downloadAsJson(jsonData, filename); showNotification(_lang(t.jsonSuccess)); } catch (error) { console.error('JSON export failed:', error); showNotification(`${_lang(t.jsonFailed)} ${error.message || error}`, true); } return; } if (format === 'geojson') { try { const geojson = getGeoJson(); const filename = `${baseFilename}.geojson`; downloadAsGeoJson(geojson, filename); showNotification(_lang(t.geojsonSuccess)); } catch (error) { console.error('GeoJSON export failed:', error); showNotification(`${_lang(t.geojsonFailed)} ${error.message || error}`, true); } return; } if (format === 'shp' || format === 'shapefile') { try { const geojson = getGeoJson(); downloadAsShapefile(geojson, baseFilename); } catch (error) { console.error('Shapefile export failed:', error); showNotification(`${_lang(t.shapefileFailed)} ${error.message || error}`, true); } return; } // Image formats (PNG, JPEG, SVG) const svg = findMap(); if (!svg) { const errorMsg = _lang(t.mapNotFound); console.error(errorMsg); showNotification(errorMsg, true); return; } const filename = `${baseFilename}.${format}`; try { if (format === 'svg') { downloadAsSvg(svg, filename); showNotification(_lang(t.exportSuccess)); } else { await downloadAsRaster(svg, filename, format); showNotification(_lang(t.exportSuccess)); } } catch (error) { console.error('Download failed:', error); showNotification(`${_lang(t.downloadFailed)} ${error.message || error}`, true); } }; // Create select element const select = html``; select.onchange = async (event) => { const format = event.target.value; if (format) { try { await downloadChart(format); event.target.value = ''; } catch (error) { console.error('Export error:', error); } } }; return html`

${_lang(t.title)}

${select}

• ${_lang(t.hintPng)}

• ${_lang(t.hintJpeg)}

• ${_lang(t.hintSvg)}

• ${_lang(t.hintCsv)}

• ${_lang(t.hintJson)}

• ${_lang(t.hintGeojson)}

• ${_lang(t.hintShp)}

`; } 4a:Td85,// text insights for geo impact textDynamicInsights_geoImpact = { const adminSelection = getAdmin0WithAdminSubParens({articleField: "article2"}); const i = dynamicInsights_geoImpact.insight; const currentAgPop = i.totalAgPop_noformat; // totalVop is the projected change in agricultural population by 2050 const projectedChange2050 = i.totalAgPop_2_50_noformat; const projectedChange2030 = i.totalAgPop_2_30_noformat; // string for percent change const percChangeType = (d) => d >= 0 ? _lang(nbText.sections.futureWorkforce.quickInsights.direction.inc) : _lang(nbText.sections.futureWorkforce.quickInsights.direction.dec); // Calculate the new total agricultural population for 2050 const newTotalAgPop2050 = currentAgPop + projectedChange2050; // Calculate the percentage change let percentChange50 = ((projectedChange2050 - currentAgPop) / currentAgPop) * 100; let changeType50 = percChangeType(percentChange50); percentChange50 = Math.abs(percentChange50).toFixed(1); // Keeping two decimals for readability let percentChange30 = ((projectedChange2030 - currentAgPop) / currentAgPop) * 100; let changeType30 = percChangeType(percentChange30); percentChange30 = Math.abs(percentChange30).toFixed(1); // Keeping two decimals for readability let percentChange_5 = ((i.totalAgPop_5_50_noformat - currentAgPop) / currentAgPop) * 100; let changeType_5 = percChangeType(percentChange_5); percentChange_5 = Math.abs(percentChange_5).toFixed(1); // Keeping two decimals for readability let percentChange30_5 = ((i.totalAgPop_5_30_noformat - currentAgPop) / currentAgPop) * 100; let changeType30_5 = percChangeType(percentChange30_5); percentChange30_5 = Math.abs(percentChange30_5).toFixed(1); // Keeping two decimals for readability const selectedCountry = getAdminSelection0().value; // Function to get the selected country const countriesWithoutData = [ "Burundi", "Rwanda", "Lesotho", "Gabon", "Eswatini", "Equatorial Guinea" ]; const insightBase = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.futureWorkforce.quickInsights.basePop), [ { name: "geo", value: adminSelection }, { name: "total", value: i.totalAgPop } ] ); const insightPopChange = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.futureWorkforce.quickInsights.popChange), [ { name: "change2030", value: i.totalAgPop_2_30 }, { name: "change2050", value: i.totalAgPop_2_50 }, { name: "direction2030", value: changeType30 }, { name: "direction2050", value: changeType50 }, { name: "perc2030", value: percentChange30 }, { name: "perc2050", value: percentChange50 } ] ); const insightPopScenario = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.futureWorkforce.quickInsights.popScenario), [ { name: "pop2030", value: i.totalAgPop_5_30 }, { name: "pop2050", value: i.totalAgPop_5_50 }, { name: "direction", value: changeType30_5 }, { name: "perc2030", value: percentChange30_5 }, { name: "perc2050", value: percentChange_5 } ] ) // Check if the selected country is in the list of countries without data if (countriesWithoutData.includes(selectedCountry)) { return md`${_lang(nbText.sections.quickInsightsGeneral.noDataBlurb)}`; } else { return md`${styling}${insightBase} ${insightPopChange} ${insightPopScenario}`; } }4b:T15ca,HeatStressChart = { // Determine grouping keys - include admin3_name if admin3 is selected const groupByKeys = adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia" ? ["admin0_name", "admin1_name", "admin2_name", "admin3_name", "scenario", "timeframe", "threshold", "crop"] : ["admin0_name", "admin1_name", "admin2_name", "scenario", "timeframe", "threshold", "crop"]; const _anyData = T.tidy( data_hazardVoP, T.groupBy( groupByKeys, [ T.summarize({ vop_any: T.sum("vop") }) ] ) ); // Determine join keys - include admin3_name if admin3 is selected const joinByKeys = adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia" ? ["admin0_name", "admin1_name", "admin2_name", "admin3_name", "scenario", "timeframe", "crop", "threshold"] : ["admin0_name", "admin1_name", "admin2_name", "scenario", "timeframe", "crop", "threshold"]; const _dataWithAnyColumn = T.tidy( data_hazardVoP, T.leftJoin(_anyData, { by: joinByKeys }) ); const data = _dataWithAnyColumn; const dataAny = data.filter((d) => d.hazard == "any"); // combined const dataBars = [ ...data.filter((d) => d.hazard !== "any" && d.vop !== 0) // remove the combined hazard ]; const _formatNumber = formatNumCompactShort({locale: language.locale}); const xDomain = cropNames .filter(d => { const availableCrops = _.uniq(dataBars.map(d => d.crop)); return availableCrops.includes(d.crop); }) .map(d => { const upperBound = parseInt(d.cropName, 10); // Assuming cropName is the upper bound const lowerBound = Math.max(0, upperBound - 5); return `${lowerBound}-${upperBound}%`; }); const maxValue = d3.max(dataBars, (d) => d.vop) * 1.5; // adding 10% buffer return Plot.plot({ // caption: `To ensure that the details across all bins are visible without being dominated by the initial range, the 0-5% bin has been intentionally excluded from this visualization.`, caption: _lang(nbText.sections.heatStress.barChart.caption), width, height: 600, marginLeft: 40, marginRight: 40, marginBottom: 40, color: { domain: hazardPlotLookup.color.domain, range: hazardPlotLookup.color.range, legend: true }, x: { domain: xDomain, label: _lang(nbText.sections.heatStress.barChart.xLabel), labelStyle: { fontSize: '36px' }, // Hypothetical API }, y: { nice: true, grid: true, label: _lang(nbText.sections.heatStress.barChart.yLabel), tickFormat: d => _formatNumber(d), }, marks: [ // main x-axis Plot.axisX({ tickSize: 0, // lineWidth: 3, }), // pointer white-out Plot.axisX( Plot.pointerX({ label: null, fill: "white", textStroke: "white", textStrokeWidth: 1, tickSize: 0 }) ), // bold pointer Plot.axisX( Plot.pointerX({ fontWeight: "bold", label: null, tickSize: 0 }) ), // hazard component bars Plot.barY( dataBars, Plot.stackY( { order: hazardPlotLookup.color.domain }, { y: (d) => d.vop, x: d => { const upperBound = parseInt(hazardPlotLookup.crops[d.crop], 10); // Assuming this gives the upper bound as before const lowerBound = Math.max(0, upperBound - 5); return `${lowerBound}-${upperBound}%`; }, fill: (d) => hazardPlotLookup.names[d.hazard], stroke: "#fff", strokeWidth: 0.25 } ) ), // tooltip Plot.tip( dataBars, Plot.pointerX( Plot.stackY( { order: hazardPlotLookup.color.domain }, { y: (d) => d.vop, x: d => { const upperBound = parseInt(hazardPlotLookup.crops[d.crop], 10); // Assuming this gives the upper bound as before const lowerBound = Math.max(0, upperBound - 5); return `${lowerBound}-${upperBound}%`; }, fill: (d) => hazardPlotLookup.names[d.hazard], channels: { hazard: { label: _lang(nbText.sections.heatStress.barChart.tooltip.period), value: (d) => hazardPlotLookup.names[d.hazard] }, vop: { label: _lang(nbText.sections.heatStress.barChart.tooltip.exposed), value: (d) => { const vop = d.vop; return `(${vop})`; }, value: (d) => _formatNumber(d.vop) } }, format: { x: false, y: false, fill: false, vop: true, hazard: true, perc: true } } ) ) ), // total VoP, sidebar highlight Plot.textY( dataAny, Plot.pointer({ y: maxValue, x: d => { const upperBound = parseInt(hazardPlotLookup.crops[d.crop], 10); // Assuming this gives the upper bound as before const lowerBound = Math.max(0, upperBound - 5); return `${lowerBound}-${upperBound}%`; }, text: (d) => { const value = d.vop_total; return `${value}`; }, textAnchor: "start", dx: 6 }) ) ] }); } 4c:T2db2,viewof exportHeatStressChart ={ const t = nbText.export.heatStressChart; // Helper: Download blob const downloadBlob = (blob, filename) => { const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; a.download = filename; a.style.display = 'none'; document.body.appendChild(a); a.click(); document.body.removeChild(a); setTimeout(() => URL.revokeObjectURL(url), 100); }; // Helper: Show notification const showNotification = (message, isError = false) => { const notification = html`

${message}

${_lang(t.title)}

${select}

• ${_lang(t.hintPng)}

• ${_lang(t.hintJpeg)}

• ${_lang(t.hintSvg)}

• ${_lang(t.hintCsv)}

• ${_lang(t.hintJson)}

`; } 4d:T1b02,{ const crop = insight_totalExposedVop.filter( (d) => d.totalExposedVop !== 0 && d.cropType == "crop" )?.[0]?.["totalExposedVop"]; const totalExposedVopBaseline = insight_totalExposedVop.find( (d) => d.cropType === "crop" && d.hazard === "baseline" )?.totalExposedVop || "no data"; const totalExposedVopProjection = insight_totalExposedVop.find( (d) => d.cropType === "crop" && d.hazard === "projection" )?.totalExposedVop || "no data"; const totalExposedVopOver30 = insight_totalExposedVop.find((d) => d.hazard === "projection") ?.totalExposedVopOver30 || 0; const totalExposedVopOver50 = insight_totalExposedVop.find((d) => d.hazard === "projection") ?.totalExposedVopOver50 || 0; const totalExposedVopOver30_b = insight_totalExposedVop.find((d) => d.hazard === "baseline") ?.totalExposedVopOver30 || 0; const totalExposedVopOver50_b = insight_totalExposedVop.find((d) => d.hazard === "baseline") ?.totalExposedVopOver50 || 0; const formatVop = (vop) => vop === "no data" ? vop : formatNumCompactShort({ locale: language.locale })(vop); function findTopCropByHazard(hazardType) { const filteredCrops = T.tidy( data_selectedHazardCropType, T.select(["cropType", "crop", "vop", "hazard"]), T.filter((d) => d.cropType == "crop" && d.hazard == hazardType), T.sliceMax(1, "vop") ); return filteredCrops[0]; // Assuming there's at least one entry after filtering } // Calculate topCrop_b and topCrop_p using the adjusted function const topCrop_b = findTopCropByHazard("baseline"); const topCrop_p = findTopCropByHazard("projection"); const lowerBound_b = hazardPlotLookup.crops[topCrop_b?.["crop"]] - 5; const upperBound_b = hazardPlotLookup.crops[topCrop_b?.["crop"]]; const lowerBound_p = hazardPlotLookup.crops[topCrop_p?.["crop"]] - 5; const upperBound_p = hazardPlotLookup.crops[topCrop_p?.["crop"]]; const missingValue = "--- no data for that bin ---"; function showResult(value, format) { if (value === null || value === undefined) return missingValue; return format(value); } function findMedianCropWithVop(data) { const sortedData = [...data] .filter((d) => !isNaN(d.vop)) .sort((a, b) => a.vop - b.vop); const medianIndex = Math.floor(sortedData.length / 2); if (sortedData.length === 0) return { crop: undefined, vop: undefined }; return sortedData.length % 2 !== 0 ? sortedData[medianIndex] : sortedData[medianIndex]; } const baselineData = data_selectedHazardCropType.filter( (d) => d.hazard === "baseline" && d.cropType == "crop" ); const projectionData = data_selectedHazardCropType.filter( (d) => d.hazard === "projection" ); const medianCropInfoBaseline = findMedianCropWithVop(baselineData); const medianCropInfoProjection = findMedianCropWithVop(projectionData); const getBinBounds = (cropValue) => { // Assuming cropValue directly relates to the bin, adjust as necessary for your logic const lowerBound = cropValue - (cropValue % 5); // Adjust based on how bins are defined const upperBound = lowerBound + 5; return { lowerBound, upperBound }; }; const medianCropBaselineBounds = getBinBounds(medianCropInfoBaseline.crop); const medianCropProjectionBounds = getBinBounds( medianCropInfoProjection.crop ); // Example function to convert percentages to months function percentToMonths(percent) { return (percent * 12) / 100; } // Calculate months for baseline and projection median bins const baselineMonthsLower = percentToMonths( medianCropBaselineBounds.lowerBound ); const baselineMonthsUpper = percentToMonths( medianCropBaselineBounds.upperBound ); const projectionMonthsLower = percentToMonths( medianCropProjectionBounds.lowerBound ); const projectionMonthsUpper = percentToMonths( medianCropProjectionBounds.upperBound ); let percentChange = ((totalExposedVopOver50 - totalExposedVopOver50_b) / totalExposedVopOver50_b) * 100; percentChange = Math.abs(percentChange).toFixed(1); let changeType = percentChange >= 0 ? "an increase" : "a decrease"; let percentChange2 = ((totalExposedVopOver30 - totalExposedVopOver30_b) / totalExposedVopOver30_b) * 100; percentChange2 = Math.abs(percentChange2).toFixed(1); let changeType2 = percentChange >= 0 ? "an increase" : "a decrease"; const scenarioLabel = selectScenarioAndTimeframe.scenario === "SSP2" ? "SSP2 4.5" : "SSP5 8.5"; // const over30Insight = `For the baseline period, **${formatVop( // totalExposedVopOver50_b // )}** people experienced unsafe working conditions for at least **half of the days** in the year. Under the scenario **${scenarioLabel}** and year **${ // selectScenarioAndTimeframe.timeframe // }**, it's expected to rise to **${formatVop( // totalExposedVopOver50 // )}** people.`; const insightOver30 = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.heatStress.quickInsights.overview), [ { name: "baseRisk", value: formatVop(totalExposedVopOver30_b) }, { name: "scenario", value: scenarioLabel }, { name: "year", value: selectScenarioAndTimeframe.timeframe }, { name: "projectionRisk", value: formatVop(totalExposedVopOver30) } ] ); const insightOver50 = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.heatStress.quickInsights.half), [ { name: "baseline", value: formatVop(totalExposedVopOver50_b) }, { name: "scenario", value: scenarioLabel }, { name: "year", value: selectScenarioAndTimeframe.timeframe }, { name: "projection", value: formatVop(totalExposedVopOver50) } ] ); // const over50Insight = `For over **100 days** in a year, the people at risk for the baseline were **${formatVop( // totalExposedVopOver30_b // )}**; under the scenario **${scenarioLabel}** and year **${ // selectScenarioAndTimeframe.timeframe // }**, it ascends to **${formatVop(totalExposedVopOver30)}**.`; const medianExplanation = `When we say we're 'looking at the median', we mean focusing on the group in the exact middle when arranging everyone from the least to the most exposed to heat stress over the year. It's like finding the middle point in a line of people sorted by how long they're exposed to high heat.`; const selectedCountry = getAdminSelection0().value; // Function to get the selected country const countriesWithoutData = [ "Burundi", "Rwanda", "Lesotho", "Gabon", "Eswatini", "Equatorial Guinea" ]; // Check if the selected country is in the list of countries without data if (countriesWithoutData.includes(selectedCountry)) { return md`${_lang(nbText.sections.quickInsightsGeneral.noDataBlurb)}`; } else { return md`${insightOver30} ${insightOver50}`; } }4e:T13d4,plotHourSeasonLoss ={ console.log("DEBUG _plotHourSeasonLoss:", { admin3: adminSelections.selectAdmin3.value, dataLength: data_hourlyStress.length, sampleData: data_hourlyStress[0], hourlyNamesLength: hourlyNames.length, radiosValue: radios.value }); // Determine grouping keys - include admin3_name if admin3 is selected const groupByKeys = adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia" ? ["admin0_name", "admin1_name", "admin2_name", "admin3_name", "scenario", "timeframe", "threshold", "hour_day"] : ["admin0_name", "admin1_name", "admin2_name", "scenario", "timeframe", "threshold", "hour_day"]; const _anyData = T.tidy( data_hourlyStress, T.groupBy( groupByKeys, [ T.summarize({ vop_any: T.mean("delta") }) ] ) ); // Determine join keys - include admin3_name if admin3 is selected const joinByKeys = adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia" ? ["admin0_name", "admin1_name", "admin2_name", "admin3_name", "scenario", "timeframe", "hour_day", "threshold"] : ["admin0_name", "admin1_name", "admin2_name", "scenario", "timeframe", "hour_day", "threshold"]; const _dataWithAnyColumn = T.tidy( data_hourlyStress, T.leftJoin(_anyData, { by: joinByKeys }) ); const data = _dataWithAnyColumn.filter(d => d.hazard == radios.value); const dataAny = data.filter((d) => d.hazard == "any"); // combined const dataBars = [ ...data.filter((d) => d.hazard !== "any" && d.delta !== 0) // remove the combined hazard ]; const _formatPercent = (num) => { return `${num.toFixed(1)}%`; }; const xDomain = hourlyNames.map(d => d.hourlyName); const maxValue = d3.max(dataBars, (d) => d.delta) * 1.75; // adding 10% buffer return Plot.plot({ // caption: `The data on the plot above is stacked for visualization purposes, but percentages are based on different parts of the year, depending on the season.`, width: 800, height: 500, marginLeft: 40, marginRight: 5, marginBottom: 35, color: { domain: hourlyPlotLookup.color.domain, range: hourlyPlotLookup.color.range, legend: false }, x: { domain: xDomain, label: _lang(nbText.sections.workingHours.barChart.xLabel) }, y: { domain: [0, maxValue], grid: true, label: _lang(nbText.sections.workingHours.barChart.yLabel), // tickSize: 0, // Set tick size to 0 to hide the ticks // tickFormat: () => "", // Return an empty string to hide tick labels }, marks: [ // main y-axis Plot.axisX({ tickSize: 0, }), // pointer white-out Plot.axisX( Plot.pointerX({ label: null, fill: "white", textStroke: "white", textStrokeWidth: 2, tickSize: 0 }) ), // bold pointer Plot.axisX( Plot.pointerX({ label: null, fontWeight: "bold", tickSize: 0 }) ), // hazard component bars Plot.barY( dataBars, Plot.stackY( { order: hourlyPlotLookup.color.domain }, { y: (d) => d.delta, x: (d) => hourlyPlotLookup.hours[d.hour_day], fill: (d) => hourlyPlotLookup.names[d.hazard], stroke: "#fff", strokeWidth: 0.1, width: 1 // Adjust this value to control the thickness of the bars } ) ), // tooltip Plot.tip( dataBars, Plot.pointerX( Plot.stackY( { order: hourlyPlotLookup.color.domain }, { y: (d) => d.delta, x: (d) => hourlyPlotLookup.hours[d.hour_day], fill: (d) => hourlyPlotLookup.names[d.hazard], channels: { hazard: { label: _lang(nbText.sections.workingHours.barChart.tooltip.period), value: (d) => hourlyPlotLookup.names[d.hazard] }, delta: { label: _lang(nbText.sections.workingHours.barChart.tooltip.perc), value: (d) => { const delta_num = d.delta; return `(${delta_num})`; }, value: (d) => _formatPercent(d.delta) } }, format: { x: false, y: false, fill: false, delta: true, hazard: true, perc: true } } ) ) ), // total VoP, sidebar highlight Plot.textY( dataAny, Plot.pointerX({ y: maxValue, x: (d) => hourlyPlotLookup.hours[d.hour_day], text: (d) => { const value = d.delta_total; return `${value}`; }, textAnchor: "start", dx: 5 }) ) ] }); } 4f:T24d6,viewof exportPlotHourSeasonLoss = { const t = nbText.export.hourSeasonLoss; // Helper: Download blob const downloadBlob = (blob, filename) => { const url = URL.createObjectURL(blob); const a = document.createElement('a'); a.href = url; a.download = filename; a.style.display = 'none'; document.body.appendChild(a); a.click(); document.body.removeChild(a); setTimeout(() => URL.revokeObjectURL(url), 100); }; // Helper: Show notification const showNotification = (message, isError = false) => { const notification = html`

${message}

${_lang(t.title)}

${select}

• ${_lang(t.hintPng)}

• ${_lang(t.hintJpeg)}

• ${_lang(t.hintSvg)}

• ${_lang(t.hintCsv)}

`; } 50:T13d6,insightsHourSeasonLoss = { const i = dynamicInsights_geoImpact.insight; const currentAgPop = i.totalAgPop_noformat; // totalVop is the projected change in agricultural population by 2050 const projectedChange2050 = i.totalAgPop_5_50_noformat; const projectedChange2030 = i.totalVopMid_noformat; const pop_2050 = currentAgPop + projectedChange2050; const crop = insight_totalExposedHourly.filter( (d) => d.totalExposedHourly !== 0 && d.hour_dayType == "hour_day" )?.[0]?.["totalExposedHourly"]; const pop_sum2 = insight_totalExposedHourly.filter( (d) => d.corrected_pop_sum !== 0 && d.hour_dayType == "hour_day" )?.[0]?.["corrected_pop_sum"]; const totalPercentHarvesting = insight_totalExposedHourly.find( (d) => d.hour_dayType === "hour_day" && d.hazard === "harvesting" )?.totalExposedHourly || "no data"; const totalPercentPlanting = insight_totalExposedHourly.find( (d) => d.hour_dayType === "hour_day" && d.hazard === "planting" )?.totalExposedHourly || "no data"; const formatVop = (vop) => vop === "no data" ? vop : formatNumCompactShort({ locale: language.locale })(vop); const topHour_day = insight_topHourly.filter( (d) => d.delta !== 0 && d.hour_dayType == "hour_day" )?.[0]; const missingValue = "--- no data for that bin ---"; function showResult(value, format) { if (value === null || value === undefined) return missingValue; return format(value); } // Find a row for each hazard type to get period and total_days const harvestingInsight = insight_totalExposedHourly.find( (d) => d.hazard === "harvesting" ); const plantingInsight = insight_totalExposedHourly.find( (d) => d.hazard === "planting" ); const periodHarvesting = insight_totalExposedHourly.find( (d) => d.hour_dayType === "hour_day" && d.hazard === "harvesting" )?.period || "no data"; const totalDaysHarvesting = harvestingInsight ? harvestingInsight.days_period : "no data"; const periodPlanting = plantingInsight ? plantingInsight.period : "no data"; const totalDaysPlanting = plantingInsight ? plantingInsight.days_period : "no data"; const pop_sum = harvestingInsight ? harvestingInsight.corrected_pop_sum : "no data"; const delta_mean = harvestingInsight ? harvestingInsight.delta_pop_mean : "no data"; const totalPercentHarvestingNumeric = totalPercentHarvesting !== "no data" ? parseFloat(totalPercentHarvesting) : 0; const totalDaysHarvestingNumeric = totalDaysHarvesting !== "no data" ? parseFloat(totalDaysHarvesting) : 0; const totalPercentPlantingNumeric = totalPercentPlanting !== "no data" ? parseFloat(totalPercentPlanting) : 0; const totalDaysPlantingNumeric = totalDaysPlanting !== "no data" ? parseFloat(totalDaysPlanting) : 0; const pop_sumNumeric = pop_2050 !== "no data" ? parseFloat(pop_2050) : 0; const totalHoursLostHarvest = (totalPercentHarvestingNumeric / 100) * totalDaysHarvestingNumeric * pop_sumNumeric; const totalHoursLostPlanting = (totalPercentPlantingNumeric / 100) * totalDaysPlantingNumeric * pop_sumNumeric; const totalWorkingDaysLostHarvest = totalHoursLostHarvest / 8; // assuming 8h working day const totalWorkingDaysLostPlanting = totalHoursLostPlanting / 8; // assuming 8h working day const totalWorkingDaysLostHarvestFormatted = formatVop( totalWorkingDaysLostHarvest.toFixed(1) ); // formatting const totalWorkingDaysLostPlantingFormatted = formatVop( totalWorkingDaysLostPlanting.toFixed(1) ); // formatting const insightIntro = _lang(nbText.sections.workingHours.quickInsights.intro); const insightBody = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.workingHours.quickInsights.body), [ { name: "scenario", value: selectScenarioAndTimeframe2.scenario }, { name: "year", value: selectScenarioAndTimeframe2.timeframe }, { name: "totalHours", value: formatVop(totalHoursLostHarvest) }, { name: "daysHarvest", value: formatVop(totalDaysHarvesting) }, { name: "wholeWorkingDays", value: totalWorkingDaysLostHarvestFormatted } ] ); const insightLostDays = Lang.reduceReplaceTemplateItems( _lang(nbText.sections.workingHours.quickInsights.lostDays), [ { name: "period", value: formatVop(totalDaysPlanting) }, { name: "geo", value: getAdmin0WithAdminSubParens({articleField: "article1"}) }, { name: "lostDays", value: totalWorkingDaysLostPlantingFormatted } ] ); const selectedCountry = getAdminSelection0().value; // Function to get the selected country const countriesWithoutData = [ "Burundi", "Rwanda", "Lesotho", "Gabon", "Eswatini", "Equatorial Guinea" ]; // Check if the selected country is in the list of countries without data if (countriesWithoutData.includes(selectedCountry)) { return md`${_lang(nbText.sections.quickInsightsGeneral.noDataBlurb)}`; } else { return md` ${insightIntro} ${insightBody} ${insightLostDays} `; } }51:Tba1,nbTextLegend=({ legends: { language: { en: "language", am: "ቋንቋ", or: "Afaan", ti: "ቋንቋ", so: "Luqad", af: "Af" }, toc: { en: "in this notebook", am: "በዚህ ኖትቡክ ውስጥ", or: "boo'anna kana keessatti", ti: "ኣብዚ ናይ ምዝገባ ደገፍ", so: "buuggankani", af: "maktaba kana keessa" }, adminSelections: { country: { en: "country", am: "አገር", or: "biyya", ti: "ሃገር", so: "dalka", af: "dala" }, region: { en: "region", am: "ክልል", or: "naannoo", ti: "ክልል", so: "gobol", af: "gabaa" }, subregion: { en: "subregion", am: "ንዑስ ክልል", or: "naannoo giddu galeessa", ti: "ንኡስ ክልል", so: "gobol-hoosaad", af: "gaba-dhiha" }, woreda: { en: "woreda", am: "ወረዳ", or: "aanaa", ti: "ወረዳ", so: "degmo", af: "woreda" }, allRegions: { en: "All regions", am: "ሁሉም ክልሎች", or: "Aanaa hunda", ti: "ኩላት ክልላት", so: "Gobollada oo dhan", af: "Qadaanaa kee num kaysa" }, allSubregions: { en: "All subregions", am: "ሁሉም ንዑስ ክልሎች", or: "Aanaa gurguddaa hunda", ti: "ኩላት ንኡስ ክልላት", so: "Gobollada hoose oo dhan", af: "Qadaanaa diifaa kee num kaysa" }, allWoredas: { en: "All woredas", am: "ሁሉም ወረዳዎች", or: "Aanaa dachaa hunda", ti: "ኩላት ወረዳታት", so: "Degmooyinka oo dhan", af: "Qadaanaa kee num kaysa" } }, scenario: { en: "scenario", am: "እቅድ", or: "haala", ti: "ስነ-ስርዓት", so: "mawduuc", af: "sheeko" }, threshold: { en: "threshold", am: "ደረጃ", or: "saffisa", ti: "ደረጃ", so: "xad", af: "kalaqa" }, season: { en: "season", am: "ወቅት", or: "ayyaana", ti: "ወቕቲ", so: "xilli", af: "ayyaana" }, seasonTypes: { planting: { en: "planting", am: "መትከል", or: "kuufachiisuu", ti: "ኣተንክሮ", so: "beerista", af: "beerista" }, harvesting: { en: "harvesting", am: "ማጨድ", or: "qabsiisuu", ti: "ኣእክዮ", so: "goosashada", af: "argita" } } } })52:T9443,nbTextSection1=({ sections:{ quickInsightsGeneral: { header: { en: "Quick Insights for :::selection:::", am: "ፈጣን ግንዛቤዎች ለ :::selection:::", or: "Hubannoo Dadhabbii :::selection:::", ti: "ቀልጢፍ ርእይቶታት ን :::selection:::", so: "Aragtiyaha Degdeg ah :::selection:::", af: "Hubannoo Lafaa :::selection:::" }, noDataBlurb: { en: "Currently, there is no available data for this country. Please, select another country.", am: "በአሁኑ ጊዜ ለዚህ አገር የሚገኝ ውሂብ የለም። እባክዎ ሌላ አገር ይምረጡ።", or: "Amma, biyyi kun qabxii hin qabu. Maaloo, biroo filadhaa.", ti: "ሕጂ፡ ነዚ ሃገር ዘለዎ ዳታ የለን። በጃኻ፡ ካልእ ሃገር ምረጽ።", so: "Hadda, macluumaadka dalkaan waa lahayn. Fadlan, dooro dal kale.", af: "Amma, data dalaa kanaa hin jiru. Maaloo, dala biraa filadhaa." } }, overview: { title: { en: "Overview", am: "አጠቃላይ እይታ", or: "Ijjechaa", ti: "ኣጠቓላላይ ኣርእስቲ", so: "Dulmarka", af: "Dubbisa" }, intro: { en: "African food systems depend on manual labour for production, distribution, and processing. Economic development is leading to changes in the structure and size of the labour force. This change, in the number and size of the agricultural workforce, is happening in parallel to climate change. The intersection of these two drivers together pose a range of risks to the African continent's food systems. This notebook aims to communicate the size of the agricultural workforce at risk to extreme heat today, and in the future. It presents a new insight into this risk leveraging a range of new datasets and highlights how the workday for different labourers will be disrupted by climate change in the future. You can follow the narrative for the continent as a whole, or select individual countries for geography-specific statistics.", am: "የአፍሪካ የምግብ ስርዓቶች ለማምረት፣ ለማከፋፈል እና ለማቀነባበር በእጅ ጉልበት ላይ የተመሰረቱ ናቸው። ኢኮኖሚያዊ ልማት የሰው ኃይል መዋቅር እና መጠን ለውጥ እያምጣ ነው። ይህ ለውጥ፣ በየወቅቱ ሰራተኞች ቁጥር እና መጠን፣ ከአየር ንብረት ለውጥ ጋር ትይዩ እየተከሰተ ነው። የእነዚህ ሁለት አንገብጋቢ ሁኔታዎች መገናኛ ለአፍሪካ አህጉር የምግብ ስርዓቶች ተለያይ ተጽዕኖዎችን ያስከትላል። ይህ ኖትቡክ በአሁኑ ጊዜ እና በወደፊቱ ለከፍተኛ ሙቀት በሚጋልጡ የወቅቱ ሰራተኞች መጠን ለመግለጽ የተዘጋጀ ነው። አዳዲስ የውሂብ ስብስቦችን በመጠቀም ለዚህ አደጋ አዳዲስ ግንዛቤዎችን ያቀርባል እንዲሁም የተለያዩ ሠራተኞች የሥራ ቀን በወደፊቱ በአየር ንብረት ለውጥ እንዴት እንደሚበላሽ ያብራራል። ለአህጉሩ በሙሉ ያለውን ዘገባ ሊከተሉ ወይም ለተወሰኑ አገራት የተለየ ስታቲስቲክስ ሊመርጡ ይችላሉ።", or: "Siistemiin nyaataa Aafrikaa oomishaa, qoqsaa fi hojii keessatti hojii harka irratti hirkatu. Fooyya'ina daldalaa qaama hojii ijaarsaa fi bal'ina keessatti jijjiirama geessaa jira. Jijjiiramni kun, lakkoofsa fi bal'ina qaama hojii qonnaa, jijjiirama qilleensaa waliin wal qabatee raawwata. Wal qunnamtii dhimmiwwan lamaani kun siistemiin nyaataa kontinentii Aafrikaa keessatti balaa adda addaa dhiyeessu. Boo'annaan kun bal'ina qaama hojii qonnaa ho'aa guddaa keessatti jiru ammaafi fuulduraatti dubbachuuf qophaa'e. Bal'aasaa irratti hubannoo haaraa gabaasaa haaraa adda addaa fayyadamuun dhiyeessaa fi hojii guyyaa hojjettoota adda addaa jijjiirama qilleensaa fuulduraatti akkamitti akka jijjiiramu ibsa. Seenaa kontinentii guutuuf hordoftuu ykn daldala adda addaa arifannaa alaa alaa argachuuf filuu dandeessa.", ti: "ስርዓታት መግቢ ኣፍሪቃ ኣብ ፍርያት፡ ኣተንክሮ፡ ከምኡውን ኣቀነባብራ ኣብ ስራሕ ኢድ የተመርኮሱ እዮም። ልምምድ ስነ-ንግዲ ኣብ ንድፊን መጠንን ሓይሊ ስራሕ ለውጢ ከም ዝመጽእ እዩ። እዚ ለውጢ፡ ብቝጽርን መጠንን ሓይሊ ስራሕ ግብርና፡ ኣብ ጎኒ ለውጢ ኩነታት ኣየር እዩ ዘጋጥም። ምትእስሳር እዚ ክልተ ዋንነታት እዚ ንስርዓታት መግቢ ሓጺር ኣፍሪቃ ኣብ ሓደ ስፍሓት ሓደጋታት ከም ዝነብር ይገብር። እዚ መዝገብ እዚ መጠን ሓይሊ ስራሕ ግብርና ብማዕርነት ካብ ሙቀት ዘለዎ ሕማም ሎሚን ኣብ መጻኢን ንኸም ምግላጽ ዝተዳለወ እዩ። ኣብ ነዚ ሓደጋ እዚ ሓድሽ ርእይቶታት ብምጥቃም ሓድሽ ዳታ ስብስብታት የቕርብ፡ ከምኡውን መዓልታዊ ስራሕ ንፈላስያን ሰራሕተኛታት ብለውጢ ኩነታት ኣየር ኣብ መጻኢ ብኸመይ ከም ዝተበላሽዎ የዘርዝር። ንተርኢ እቲ ሓጺር ብምስማዕ ወይ ንፈላስያን ሃገራት ንኸፍልፍል ስታቲስቲክስ ብምምራጽ ክትከታተል ትኽእል።", so: "Nidaamyada cuntada Afrikaanka waxay ku tiirsan yihiin shaqada gacanta soo saarista, qaybis iyo habaynta. Horumar dhaqaale ayaa horseedaya isbedello qaabka iyo baaxada shaqaalaha. Isbedelladan, tirada iyo baaxada shaqaalaha beeraha, waxay dhacayaan isla waqtiga ay isbeddelka cimiladu dhacayo. Is-dhex-galka labadan arrimood wuxuu keenaa khatar kala duwan oo ku saabsan nidaamyada cuntada qaaradda Afrika. Buuggankani wuxuu ujeeddo u leeyahay inuu sheego tirada shaqaalaha beeraha ee khatarta kulka daran ee hadda iyo mustaqbalka. Wuxuu soo bandhigaa aragti cusub oo ku saabsan khatartan iyadoo la isticmaalayo xog urur cusub, waxaana uu muujinayaa sida maalinta shaqada uu u qasbayo isbeddelka cimilada mustaqbalka. Waxaad raaci kartaa sheekada qaaradda oo dhan, ama dooro dalal gaar ah si aad u hesho tirakoob goobo gaar ah.", af: "Sisitemii nyaataa Afriikaa oomishaa, bobbaasaa fi qopheessaa keessatti hojii harka irratti hirkatu. Jireenya daldalaa qaama hojii ijaarsaa fi ballina isaa keessatti jijjiirama fiduu dha. Jijjiiramni kun, lakkoofsa fi ballina qaama hojii qonnaa, jijjiirama qilleensaa waliin wal qabatee raawwata. Wal qunnamtii warreen lamaani kun sisitemii nyaataa Afriikaa keessatti balaa adda addaa dhiyeessu. Maktabaan kun ballina qaama hojii qonnaa ho'aa guddaa keessatti jiru ammaafi fuulduraatti dubbachuuf qophaa'e. Balaa kana irratti hubannoo haaraa gabaasaa haaraa adda addaa fayyadamuun dhiyeessaa fi hojii guyyaa hojjettoota adda addaa jijjiirama qilleensaa fuulduraatti akkamitti akka jijjiiramu ibsa. Seenaa Afriikaa guutuuf hordoftuu ykn daldala adda addaa arifannaa alaa alaa argachuuf filuu dandeessa." } }, futureWorkforce: { title: { en: "The Future of the Agricultural Workforce", am: "የወደፊቱ የወቅቱ ሰራተኞች", or: "Fuuldura Qaama Hojii Qonnaa", ti: "ወደፊት ሓይሊ ስራሕ ግብርና", so: "Mustaqbalka Shaqaalaha Beeraha", af: "Fuuldura Qaama Hojii Qonnaa" }, intro: { en: "The agricultural workforce covers the entire employed portion of the employable population involved in the agriculture sector. In the African continent, the vast majority of these are farmers. However, the number of farmers and the size of the workforce is not static, and depends on structural factors which drive rural population growth, push or pull the rural population towards urban areas over time. Here, you can explore how the size of the agricultural workforce today and how it will change as a result of these factors in the future.", am: "የወቅቱ ሰራተኞች በወቅቱ ስራ ውስጥ የሚሳተፉ የሥራ ክህሎት ያላቸው ሕዝብ ሙሉ ክፍል ይጨምራሉ። በአፍሪካ አህጉር አብዛኞቻቸው አርሶ አደሮች ናቸው። ሆኖም የአርሶ አደሮች ቁጥር እና የሰራተኞች መጠን የማይለወጥ አይደለም፣ እና የገጠር ሕዝብ እድገትን የሚያጋጥሙ፣ የገጠር ሕዝብን በጊዜ ሂደት ወደ ከተማ አካባቢዎች የሚጎትቱ ወይም የሚጎትቱ መዋቅራዊ ሁኔታዎች ላይ የተመሰረተ ነው። እዚህ የወቅቱ ሰራተኞች መጠን አሁን እንዴት እና በወደፊቱ በእነዚህ ሁኔታዎች ምክንያት እንዴት እንደሚቀየር ማሰስ ይችላሉ።", or: "Qaama hojii qonnaa qooda hojii hunda kan ummata hojii qonnaa irraa argamu hunda dabarsa. Kontinentii Aafrikaa keessatti, baay'inni isaanii qotiyoota. Haa ta'u malee, lakkoofsi qotiyootaa fi bal'ina qaama hojii hin jijjiiramne, haala ijaarsaa kan dagaagina ummata baadiyyaa dhaqa, ummata baadiyyaa gara naannoo magaalaatti yeroo darban deebisu ykn fiixu irratti hirkata. Asii, akkaataa bal'ina qaama hojii qonnaa amma akkamitti akka ta'e fi fuuldura keessatti haala kanaan akkamitti akka jijjiiramu qoruu dandeessa.", ti: "ሓይሊ ስራሕ ግብርና ንኹሉ ክፍሊ ሓይሊ ስራሕ ዘለዎ ህዝቢ ኣብ ዘርኢ ንግዲ ግብርና ዘሎ ይሽፍን። ኣብ ሓጺር ኣፍሪቃ፡ መብዛሕትኦም እዚኦም ኣርሶ ኣነድሮ እዮም። ይኹን እምበር፡ ብዝሒ ኣርሶ ኣነድሮን መጠን ሓይሊ ስራሕን ኣይንክን እዩ፡ ነቲ ህዝቢ ገጠር ዝዕብዮ፡ ህዝቢ ገጠር ንግዜ ኣብ ዝሓልፍ ናብ ከተማታት ዝወስዶ ወይ ዝስሕቦ ኣቃውማ ስርዓት ላዕሊ ይውከል። ኣብዚ እዋን፡ መጠን ሓይሊ ስራሕ ግብርና ሎሚ ብኸመይ ከም ዘሎን ኣብ መጻኢ በቲ ኣቃውማ እዚ ብኸመይ ከም ዝበላሽዎን ክትመርምር ትኽእል።", so: "Shaqaalaha beeraha waxay ka kooban yihiin dhamaan qeybta shaqaalaha ee dadka la shaqeeya ee ka shaqeeya baahida beeraha. Qaaradda Afrikada, badankoodu waxay yihiin beeraleeyaal. Si kastaba ha ahaatee, tirada beeraleeyaasha iyo baaxada shaqaalaha ma ahan mid joogto ah, waxayna ku xiran yihiin arrimaha qaab dhismeedka ee koritaanka dadka reer miyiga, dhiirigelinta ama jiidista dadka reer miyiga ee u kacaya magaalooyinka waqti ku darso. Halkan, waxaad ka heli kartaa sida baaxada shaqaalaha beeraha ee maanta ay u tahay iyo sida ay u bedeli doonto sababtoo ah arrimahan mustaqbalka.", af: "Qaama hojii qonnaa qooda hojii hunda kan ummata hojii qonnaa irraa argamu hunda dabarsa. Afriikaa keessatti, baay'inni isaanii qotiyoota. Haa ta'u malee, lakkoofsi qotiyootaa fi ballina qaama hojii hin jijjiiramne, haala ijaarsaa kan dagaagina ummata baadiyyaa dhaqa, ummata baadiyyaa gara naannoo magaalaatti yeroo darban deebisu ykn fiixu irratti hirkata. Asii, akkaataa ballina qaama hojii qonnaa amma akkamitti akka ta'e fi fuuldura keessatti haala kanaan akkamitti akka jijjiiramu qoruu dandeessa." }, map: { caption: { en: `Grey regions signal lack of :::selector::: data`, am: `ግራጫ ክልሎች የ :::selector::: ውሂብ እጥረትን ያመለክታሉ`, or: `Moggaa diimaa :::selector::: data hin jirreetti mallatteessu`, ti: `ኣንጻር ኣህጽር ኣገባብ :::selector::: ዳታ ከይተረኽበ`, so: `Gobollada cirdan waxay calaamadeeyaan in :::selector::: xogta ay lahayn`, af: `Naannoon diimaa :::selector::: data hin jirreetti mallatteessu` }, mapLayer: { title: { en: "Map Layer", am: "የካርታ ንብርብር", or: "Saala Kaartaa", ti: "ቀትሪ ካርታ", so: "Dul kaaraha", af: "Saala Kaartaa" }, values: { ag_workforce_data_2: { label: { en: "Ag population baseline (circa 2005)", am: "የወቅቱ ሕዝብ መሠረት (በ2005 ግድም)", or: "Bu'uura ummata qonnaa (waggaa 2005)", ti: "ኣብ መሰረት ህዝቢ ግብርና (ከም 2005)", so: "Aasaaska dadka beeraha (2005kii)", af: "Bu'uura ummata qonnaa (waggaa 2005)" }, labelTip: { en: "Ag population", am: "የወቅቱ ሕዝብ", or: "Ummata qonnaa", ti: "ህዝቢ ግብርና", so: "Dadka beeraha", af: "Ummata qonnaa" }, labelLegend: { en: "Ag population", am: "የወቅቱ ሕዝብ", or: "Ummata qonnaa", ti: "ህዝቢ ግብርና", so: "Dadka beeraha", af: "Ummata qonnaa" } }, ag_workforce_data_1: { label: { en: "Percentage change by 2030", am: "እስከ 2030 የሚከሰት መቶኛ ለውጥ", or: "Jijjiirama lakkaawwii waggaa 2030tti", ti: "ኣብ 2030 መበገሲ ሚእቲ", so: "Isbeddelka boqolkiiba ee ilaa 2030", af: "Jijjiirama lakkaawwii waggaa 2030tti" }, labelTip: { en: "Population change by 2030", am: "እስከ 2030 የሚከሰት የሕዝብ ለውጥ", or: "Jijjiirama ummata waggaa 2030tti", ti: "ለውጢ ህዝቢ ኣብ 2030", so: "Isbeddelka dadka ilaa 2030", af: "Jijjiirama ummata waggaa 2030tti" }, labelLegend: { en: "Population change by 2030 (%)", am: "እስከ 2030 የሚከሰት የሕዝብ ለውጥ (%)", or: "Jijjiirama ummata waggaa 2030tti (%)", ti: "ለውጢ ህዝቢ ኣብ 2030 (%)", so: "Isbeddelka dadka ilaa 2030 (%)", af: "Jijjiirama ummata waggaa 2030tti (%)" } }, ag_workforce_data: { label: { en: "Percentage change by 2050", am: "እስከ 2050 የሚከሰት መቶኛ ለውጥ", or: "Jijjiirama lakkaawwii waggaa 2050tti", ti: "ኣብ 2050 መበገሲ ሚእቲ", so: "Isbeddelka boqolkiiba ee ilaa 2050", af: "Jijjiirama lakkaawwii waggaa 2050tti" }, labelTip: { en: "Population change by 2050", am: "እስከ 2050 የሚከሰት የሕዝብ ለውጥ", or: "Jijjiirama ummata waggaa 2050tti", ti: "ለውጢ ህዝቢ ኣብ 2050", so: "Isbeddelka dadka ilaa 2050", af: "Jijjiirama ummata waggaa 2050tti" }, labelLegend: { en: "Population change by 2050 (%)", am: "እስከ 2050 የሚከሰት የሕዝብ ለውጥ (%)", or: "Jijjiirama ummata waggaa 2050tti (%)", ti: "ለውጢ ህዝቢ ኣብ 2050 (%)", so: "Isbeddelka dadka ilaa 2050 (%)", af: "Jijjiirama ummata waggaa 2050tti (%)" } }, ag_workforce_data_15: { label: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030", or: "Ummata qonnaa waggaa 2030tti", ti: "ህዝቢ ግብርና ኣብ 2030", so: "Dadka beeraha 2030kii", af: "Ummata qonnaa waggaa 2030tti" }, labelTip: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030", or: "Ummata qonnaa waggaa 2030tti", ti: "ህዝቢ ግብርና ኣብ 2030", so: "Dadka beeraha 2030kii", af: "Ummata qonnaa waggaa 2030tti" }, labelLegend: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030", or: "Ummata qonnaa waggaa 2030tti", ti: "ህዝቢ ግብርና ኣብ 2030", so: "Dadka beeraha 2030kii", af: "Ummata qonnaa waggaa 2030tti" } }, ag_workforce_data_10: { label: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050", or: "Ummata qonnaa waggaa 2050tti", ti: "ህዝቢ ግብርና ኣብ 2050", so: "Dadka beeraha 2050kii", af: "Ummata qonnaa waggaa 2050tti" }, labelTip: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050", or: "Ummata qonnaa waggaa 2050tti", ti: "ህዝቢ ግብርና ኣብ 2050", so: "Dadka beeraha 2050kii", af: "Ummata qonnaa waggaa 2050tti" }, labelLegend: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050", or: "Ummata qonnaa waggaa 2050tti", ti: "ህዝቢ ግብርና ኣብ 2050", so: "Dadka beeraha 2050kii", af: "Ummata qonnaa waggaa 2050tti" } } } } }, quickInsights: { direction: { inc: { en: "an increase", am: "ጭማሪ", or: "dagaagina", ti: "ወሰኽ", so: "kordhin", af: "dagaagina" }, dec: { en: "a decrease", am: "ቅነሳ", or: "hir'isaa", ti: "ንእሽቶ", so: "hoos u dhig", af: "hir'isaa" } }, basePop: { en: `**:::geo:::**'s total agricultural population is **:::total:::** for the baseline year, circa 2005.`, am: `የ**:::geo:::** አጠቃላይ የወቅቱ ሕዝብ ለመሠረታዊው ዓመት፣ በ2005 ግድም **:::total:::** ነው።`, or: `Ummata qonnaa guutu **:::geo:::** waggaa bu'uuraa, waggaa 2005, **:::total:::** ta'a.`, ti: `ጠቕላላ ህዝቢ ግብርና **:::geo:::** ንመሰረት ዓመት፡ ከም 2005፡ **:::total:::** እዩ።`, so: `Wadarta guud ee dadka beeraha **:::geo:::** ee sanadka aasaasiga ah, 2005kii, waa **:::total:::**.`, af: `Ummata qonnaa guutu **:::geo:::** waggaa bu'uuraa, waggaa 2005, **:::total:::** ta'a.` }, popChange: { en: `The total agricultural population is expected to change to **:::change2030:::** by 2030, and to **:::change2050:::** by 2050, under SSP245. These represent **:::direction2030:::** of **:::perc2030:::%** and **:::direction2050:::** of **:::perc2050:::%**, respectively.`, am: `አጠቃላይ የወቅቱ ሕዝብ በSSP245 ስር እስከ 2030 ዓ.ም **:::change2030:::** እና እስከ 2050 ዓ.ም **:::change2050:::** እንደሚለወጥ ይጠበቃል። እነዚህ በቅደም ተከተል **:::direction2030:::** **:::perc2030:::%** እና **:::direction2050:::** **:::perc2050:::%** ይወክላሉ።`, or: `Ummata qonnaa guutu **:::change2030:::** waggaa 2030tti, **:::change2050:::** waggaa 2050tti, SSP245 jala jijjiiramu ni eegama. Kunnis **:::direction2030:::** **:::perc2030:::%** fi **:::direction2050:::** **:::perc2050:::** tiin wal qixxee.`, ti: `ጠቕላላ ህዝቢ ግብርና ኣብ SSP245 ትሕቲ ናብ **:::change2030:::** ኣብ 2030፡ ናብ **:::change2050:::** ኣብ 2050 ክቕየር እዩ ዝተጸበየ። እዚ ን**:::direction2030:::** ዝኾነ **:::perc2030:::%** ን**:::direction2050:::** ዝኾነ **:::perc2050:::%** ብተኸታታሊ የውክል።`, so: `Wadarta guud ee dadka beeraha waxaa la filayaa inay u bedeleeso **:::change2030:::** ilaa 2030, iyo **:::change2050:::** ilaa 2050, hoosteeda SSP245. Kuwani waxay u taagan yihiin **:::direction2030:::** oo ah **:::perc2030:::%** iyo **:::direction2050:::** oo ah **:::perc2050:::%**, si taxaddar leh.`, af: `Ummata qonnaa guutu **:::change2030:::** waggaa 2030tti, **:::change2050:::** waggaa 2050tti, SSP245 jala jijjiiramu ni eegama. Kunnis **:::direction2030:::** **:::perc2030:::%** fi **:::direction2050:::** **:::perc2050:::** tiin wal qixxee.` }, popScenario: { en: `Under SSP585, the total agricultural population will be **:::pop2030:::** by 2030 and **:::pop2050:::** by 2050. These represent **:::direction:::** of **:::perc2030:::%** and **:::perc2050:::%**, respectively.`, am: `በSSP585 ስር፣ አጠቃላይ የወቅቱ ሕዝብ እስከ 2030 ዓ.ም **:::pop2030:::** እና እስከ 2050 ዓ.ም **:::pop2050:::** ይሆናል። እነዚህ በቅደም ተከተል **:::direction:::** **:::perc2030:::%** እና **:::perc2050:::%** ይወክላሉ።`, or: `SSP585 jala, ummata qonnaa guutu **:::pop2030:::** waggaa 2030tti fi **:::pop2050:::** waggaa 2050tti ta'a. Kun **:::direction:::** **:::perc2030:::%** fi **:::perc2050:::** tiin wal qixxee.`, ti: `ኣብ SSP585 ትሕቲ፡ ጠቕላላ ህዝቢ ግብርና **:::pop2030:::** ኣብ 2030 ክኸውን እዩ፡ **:::pop2050:::** ኣብ 2050። እዚ ን**:::direction:::** ዝኾነ **:::perc2030:::%** ን**:::perc2050:::%** ብተኸታታሊ የውክል።`, so: `Hoosteeda SSP585, wadarta guud ee dadka beeraha waxay noqon doontaa **:::pop2030:::** ilaa 2030 iyo **:::pop2050:::** ilaa 2050. Kuwani waxay u taagan yihiin **:::direction:::** oo ah **:::perc2030:::%** iyo **:::perc2050:::%**, si taxaddar leh.`, af: `SSP585 jala, ummata qonnaa guutu **:::pop2030:::** waggaa 2030tti fi **:::pop2050:::** waggaa 2050tti ta'a. Kun **:::direction:::** **:::perc2030:::%** fi **:::perc2050:::** tiin wal qixxee.` } } }, heatStress: { title: { en: "Heat Stress in Numbers", am: "የሙቀት ጫና በቁጥር", or: "Cimdii Ho'a Lakkaawwii", ti: "ጸቕጢ ሙቀት ኣብ ቁጽሪ", so: "Cadaadiska Kulka Tiradeeda", af: "Cimdii Ho'a Lakkaawwii" }, intro: { en: "An important question is how the increases in agricultural workforce on the African continent will be affected by climate change. A key indicator of climate change impacts on human well being is heat stress. The level of heat stress experienced by a person depends not only on temperature but also on humidity, which are together often represented in terms of Wet Bulb Globe Temperature, where thresholds exceeding 28°C are considered harmful, and those exceeding 32°C extremely harmful. Use the dropdowns below to explore how many days in the year the agricultural workforce will be exposed to harmful heat stress for specific geographies, temperature thresholds and climate change scenarios.", am: "አስፈላጊ ጥያቄ በአፍሪካ አህጉር ላይ ያለው የወቅቱ ሰራተኞች ጭማሪ በአየር ንብረት ለውጥ እንዴት እንደሚጎዳ ነው። የአየር ንብረት ለውጥ በሰው ደህንነት ላይ ያለው ዋና አመላካች የሙቀት ጫና ነው። የሙቀት ጫና ደረጃ በአንድ ሰው ላይ በሚኖረው ብቻ ሳይሆን በእርጥበት ላይም የተመሠረተ ነው፣ እነዚህም በአብዛኛው አንጻራዊ ሙቀት እንደ የሚወከሉ ሲሆን ከ28°ሴ በላይ የሆኑ ደረጃዎች ጎጂ እንደሆኑ እና ከ32°ሴ በላይ የሆኑት እጅግ ጎጂ እንደሆኑ ይቆጠራሉ። በዓመቱ ውስጥ የወቅቱ ሰራተኞች ለተወሰኑ ጂኦግራፊዎች፣ ለሙቀት ደረጃዎች እና ለአየር ንብረት ለውጥ እቅዶች ለጎጂ የሙቀት ጫና ለምን ያህል ቀናት እንደሚጋለጡ ለማሰስ ከዚህ በታች ያሉትን የዝርዝር ምረጥ ይጠቀሙ።", or: "Gaaffiin barbaachisaa akkaataa dagaagina qaama hojii qonnaa kontinentii Aafrikaa jijjiirama qilleensaan akkamitti akka dhiyaatu. Mallattoo guddaan jijjiirama qilleensaa qaama namaa irratti cimdii ho'a. Sadarkaan cimdii ho'a nama irratti ta'e ho'aa malee, dhangala'uu irrattis hirkata, kan lakkoofsaan Wet Bulb Globe Temperature jedhamuun walitti qabamu, 28°C oliin miidhaa jedhamu, 32°C oliin immoo miidhaa guddaa jedhamu. Asii gadi bu'iinsa armaan gadii fayyadamtee guyyaa waggaa keessaa meeqaatama qaama hojii qonnaa cimdii ho'a miidhaa alaa alaa, safartuu ho'aa fi haala jijjiirama qilleensaa adda addaa keessatti akka dhiyaatu ilaali.", ti: "ሕቶ ኣገዳሲ እንታይ እዩ ከምቲ ዝዕበየ ሓይሊ ስራሕ ግብርና ኣብ ሓጺር ኣፍሪቃ ብለውጢ ኩነታት ኣየር ብኸመይ ከም ዝጎድእ። መወከሲት ኣገዳሲ ለውጢ ኩነታት ኣየር ኣብ ጥዕና ሰብ ጸቕጢ ሙቀት እዩ። ደረጃ ጸቕጢ ሙቀት ኣብ ሓደ ሰብ ዝርከብ ጥራይ ዘይኮነስ ኣብ ውሑጅነት ደኣ ይውከል፣ እዚ ድማ ብግምት Wet Bulb Globe Temperature ኣብ ዝርከብ ሙቀት ዝሕተም፣ 28°ሴ ካብ ዝሓለፈ ደረጃ ጉዳእ ከም ዘለዎ ይቑጸር፣ 32°ሴ ካብ ዝሓለፈ ድማ ኣዝዩ ጉዳእ ከም ዘለዎ። ኣብ ዝስዕብ ዝወረደ ምረጺ ተጠቐም ንኸተመርኮሶ ኣብ ዓመት ንኽንደይ መዓልቲ ሓይሊ ስራሕ ግብርና ንፈላስያን ኣገባብ ጂኦግራፍያዊ፣ ደረጃ ሙቀትን ስነ-ስርዓት ለውጢ ኩነታት ኣየርን ንጉዳእ ዘለዎ ጸቕጢ ሙቀት ከም ዝጋለጥ ንምፍታሽ።", so: "Su'aal muhiim ah waa sida koritaanka shaqaalaha beeraha ee qaaradda Afrika ay u saamayn doonto isbeddelka cimilada. Astaanta ugu muhiimsan ee saameynta isbeddelka cimilada ee nolosha aadanaha waa cadaadiska kulka. Heerka cadaadiska kulka ee qofku la kulmo ma ku xiraneyn oo kaliya heerkulka, laakiin sidoo kale qoyanka, kuwaas oo inta badan lagu matalayo heerkulka Globe ee Bulb ee qoyan, halkaasoo xadadka ka sarreeya 28°C loo tixgeliyo inay waxyeello leeyihiin, kuwa ka sarreeya 32°Cna waxyeello aad u xun. Isticmaal soo-socda hoose si aad u ogaato inta maalmood ee sanadka shaqaalaha beeraha ay ku kulmi doonaan cadaadis kul oo waxyeello leh goobo gaar ah, xadadka heerkulka iyo go'aamada isbeddelka cimilada.", af: "Gaaffiin barbaachisaa akkaataa dagaagina qaama hojii qonnaa Afriikaa jijjiirama qilleensaan akkamitti akka dhiyaatu. Mallattoo guddaan jijjiirama qilleensaa qaama namaa irratti cimdii ho'a. Sadarkaan cimdii ho'a nama irratti ta'e ho'aa malee, dhangala'uu irrattis hirkata, kan lakkoofsaan Wet Bulb Globe Temperature jedhamuun walitti qabamu, 28°C oliin miidhaa jedhamu, 32°C oliin immoo miidhaa guddaa jedhamu. Asii gadi bu'iinsa armaan gadii fayyadamtee guyyaa waggaa keessaa meeqaatama qaama hojii qonnaa cimdii ho'a miidhaa alaa alaa, safartuu ho'aa fi haala jijjiirama qilleensaa adda addaa keessatti akka dhiyaatu ilaali." }, barChart: { caption: { en: "To ensure that the details across all bins are visible without being dominated by the initial range, the 0-5% bin has been intentionally excluded from this visualization.", am: "በሁሉም መደርደሪያዎች ላይ ያሉ ዝርዝሮች በመጀመሪያው ክልል ሳይቆጣጠሩ እንዲታዩ ለማድረግ ፣ 0-5% መደርደሪያ በማሰብ ከዚህ ምስላዊ አቀራረብ ውጭ ተወግዷል።", or: "Hundeeffama gatiin bin 0-5% fakkaataa kanaa irraa kan of eeggannoon haqamuuf, akka ibsaan bin hundaa mul'atuu malee dheerinni jalqabaa irratti hin hirkanne.", ti: "ንምስጢር እቲ ኣብ ኩሎም ሳንዱቅ ዘሎ ዝርዝር ብዘርእዮ መጀመርታ ደረጃ ተገዚኡ ክርአ እንከሎ ፣ 0-5% ሳንዱቅ ብሓሳብ ካብዚ ኣርእስቲ ኣውጺኡ ተባሪሩ ኣሎ።", so: "Si loo hubiyo in faahfaahinta dhammaan qaybaha ay muuqato iyadoon aan la hayn qaybta horumarinta, qaybta 0-5% waxaa si agoon leh looga reebay muujinta.", af: "Hundeeffama gatiin bin 0-5% fakkaataa kanaa irraa kan of eeggannoon haqamuuf, akka ibsaan bin hundaa mul'atuu malee dheerinni jalqabaa irratti hin hirkanne." }, group: { baseline: { en: "Baseline", am: "መሠረት", or: "Bu'uura", ti: "መሰረት", so: "Aasaaska", af: "Bu'uura" }, projection: { en: "Projection", am: "ትንበያ", or: "Ibsa", ti: "ትንበያ", so: "Saadaalinta", af: "Ibsa" } }, xLabel: { en: "Percent of Year Exceeding Safe Working Conditions", am: "የሚጣሱ ደህንነታዊ የሥራ ሁኔታዎች የዓመት መቶኛ", or: "Lakkaawwii Waggaa Haala Hojii Nageenyaa Irra Geessu", ti: "ኣብ ዓመት መበገሲ ካብ ደህንነታዊ ስራሕ ክልተ ዝሓልፍ", so: "Boqolkiiba sanadka ee ka sarreeya xaaladaha shaqo ee ammaanka ah", af: "Lakkaawwii Waggaa Haala Hojii Nageenyaa Irra Geessu" }, yLabel: { en: "People Exposed", am: "ተጋላጭ ሰዎች", or: "Nama Dhiyaate", ti: "ተጋላጽነት ሰባት", so: "Dadka la saameeyay", af: "Nama Dhiyaate" }, tooltip: { exposed: { en: "Ag pop exposed", am: "ተጋላጭ የወቅቱ ሕዝብ", or: "Ummata qonnaa dhiyaate", ti: "ህዝቢ ግብርና ተጋላጺ", so: "Dadka beeraha la saameeyay", af: "Ummata qonnaa dhiyaate" }, period: { en: "Period", am: "ጊዜ", or: "Yeroo", ti: "እዋን", so: "Waqti", af: "Yeroo" } } }, figureBody: { en: "The figure presents the count of agricultural workers subject to unsafe Wet Bulb Globe Temperature (WBGT) levels across the year, based on both baseline data and projections. For instance, if a projection displays 16 million people within the 45%-50% bin, it means that these individuals are exposed to unsafe working conditions for a portion of their day, 45% to 50% of the days in the year.", am: "ስዕሉ በመሠረታዊ ውሂብ እና በትንበያዎች ላይ በመመርኮዝ በዓመቱ ውስጥ ለአደገኛ የእርጥበት ግሎበ ሙቀት (WBGT) ደረጃዎች የተጋለጡ የወቅቱ ሰራተኞችን ቁጥር ያቀርባል። ለምሳሌ፣ አንድ ትንበያ 16 ሚሊዮን ሰዎችን በ45%-50% መደርደሪያ ውስጥ ከሚያሳይ ከሆነ፣ ይህ ማለት እነዚህ ሰዎች ለቀናቸው አንድ ክፍል፣ በዓመቱ 45% እስከ 50% ያሉ ቀናት ለአደገኛ የሥራ ሁኔታዎች ይጋለጣሉ ማለት ነው።", or: "Figaariin lakkoofsa hojjettoota qonnaa sadarka Wet Bulb Globe Temperature (WBGT) nageenyaa hin qabne waggaa guutuu keessatti dhiyaatan, bu'uura dataa fi ibsaa irratti hundaa'uun dhiyeessa. Fakkeenyaaf, ibsiin namoota 16 miliyoona bin 45%-50% keessatti yoo agarsiise, kun namootni kun guyyaa isaanii qooda, waggaa 45% hanga 50% keessatti haala hojii nageenyaa hin qabne keessatti dhiyaatamu jechuudha.", ti: "እቲ ስእሊ ቁጽሪ ሰራሕተኛታት ግብርና ንደረጃ Wet Bulb Globe Temperature (WBGT) ዘይክልተ ኣብ ዓመት ከተጋላጽዎ ፣ ብመሰረት ዳታን ትንበያታትን ተመርኲሱ የቕርብ። ንኣብነት ፣ ትንበያ 16 ሚሊዮን ሰባት ኣብ ሳንዱቅ 45%-50% እንተ ዘርኢ ፣ እዚ ማለት እዞም ሰባት ንኽፋል መዓልቶም ፣ 45% ክሳዕ 50% ኣብ ዓመት ንዘይክልተ ስራሕ ከም ዝጋለጡ እዩ።", so: "Jaantusku wuxuu soo bandhigaa tirinta shaqaalaha beeraha ee hoos u dhaca heerarka Wet Bulb Globe Temperature (WBGT) ee aan ammaan ahayn sanadka oo dhan, iyadoo la salaynayo labada xogta aasaasiga ah iyo saadaalinta. Tusaale ahaan, haddii saadaalinta ay muujiso 16 milyan oo qof oo ku jira qaybta 45%-50%, waxay la micne tahay in dadkani ay ku kulmaan xaaladaha shaqo ee aan ammaan ahayn qayb maalintooda, 45% ilaa 50% maalmaha sanadka.", af: "Figaariin lakkoofsa hojjettoota qonnaa sadarka Wet Bulb Globe Temperature (WBGT) nageenyaa hin qabne waggaa guutuu keessatti dhiyaatan, bu'uura dataa fi ibsaa irratti hundaa'uun dhiyeessa. Fakkeenyaaf, ibsiin namoota 16 miliyoona bin 45%-50% keessatti yoo agarsiise, kun namootni kun guyyaa isaanii qooda, waggaa 45% hanga 50% keessatti haala hojii nageenyaa hin qabne keessatti dhiyaatamu jechuudha." }, quickInsights: { overview: { en: `For over **100 days** in a year, the people at risk for the baseline were **:::baseRisk:::**; under the scenario **:::scenario:::** and year **:::year:::**, it ascends to **:::projectionRisk:::**.`, am: `ለከፍተኛ **100 ቀናት** በዓመት፣ ለመሠረታዊው አደጋ ያሉት ሰዎች **:::baseRisk:::** ነበሩ; በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ ወደ **:::projectionRisk:::** ይጨምራል።`, or: `Waggaa keessatti **guyyaa 100** ol ta'aniif, namni bu'uuraaf balaadha **:::baseRisk:::** ture; haala **:::scenario:::** fi waggaa **:::year:::** jala, **:::projectionRisk:::** geessa.`, ti: `ንዝያዳ **100 መዓልቲ** ኣብ ዓመት ፣ እቶም ንመሰረት ሓደጋ ዘለዎም ሰባት **:::baseRisk:::** ነይሮም; ኣብ ስነ-ስርዓት **:::scenario:::** እና ዓመት **:::year:::** ትሕቲ ፣ ናብ **:::projectionRisk:::** ይወሰኽ።`, so: `In ka badan **100 maalmood** sanadka, dadka ku khatarsan aasaaska ahaa waxay ahaayeen **:::baseRisk:::**; hoosteeda go'aamada **:::scenario:::** iyo sanadka **:::year:::**, waxay kor u kacaysaa **:::projectionRisk:::**.`, af: `Waggaa keessatti **guyyaa 100** ol ta'aniif, namni bu'uuraaf balaadha **:::baseRisk:::** ture; haala **:::scenario:::** fi waggaa **:::year:::** jala, **:::projectionRisk:::** geessa.` }, half: { en: `For the baseline period, **:::baseline:::** people experienced unsafe working conditions for at least **half of the days** in the year. Under the scenario **:::scenario:::** and year **:::year:::**, it's expected to rise to **:::projection:::** people.`, am: `ለመሠረታዊው ጊዜ፣ **:::baseline:::** ሰዎች በዓመቱ ቢያንስ **ግማሽ ቀናት** ለአደገኛ የሥራ ሁኔታዎች ተጋልጠዋል። በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ ወደ **:::projection:::** ሰዎች እንደሚጨምር ይጠበቃል።`, or: `Yeroo bu'uuraa, namni **:::baseline:::** guyyaa waggaa keessatti yartuufuu **walakkaa guyyaa** haala hojii nageenyaa hin qabne dhiyaatan. Haala **:::scenario:::** fi waggaa **:::year:::** jala, namoota **:::projection:::** geessuu ni eegama.`, ti: `ንመሰረት እዋን ፣ **:::baseline:::** ሰባት ኣብ ዓመት እንተኾነ ቢያንስ **ፍርቂ መዓልቲ** ንዘይክልተ ስራሕ ተጋሊጦም ኣለዉ። ኣብ ስነ-ስርዓት **:::scenario:::** እና ዓመት **:::year:::** ትሕቲ ፣ ናብ **:::projection:::** ሰባት ክውሰኽ እዩ ዝተጸበየ።`, so: `Waqtiga aasaaska, **:::baseline:::** dad ayaa la kulmay xaaladaha shaqo ee aan ammaan ahayn ugu yaraan **kala badh maalmaha** sanadka. Hoosteeda go'aamada **:::scenario:::** iyo sanadka **:::year:::**, waxaa la filayaa inay kor u kacdo **:::projection:::** dad.`, af: `Yeroo bu'uuraa, namni **:::baseline:::** guyyaa waggaa keessatti yartuufuu **walakkaa guyyaa** haala hojii nageenyaa hin qabne dhiyaatan. Haala **:::scenario:::** fi waggaa **:::year:::** jala, namoota **:::projection:::** geessuu ni eegama.` } } } } })53:Tc16b,nbTextSection2=({ sections:{ workingHours: { title: { en: "Loss of Safe Working Hours", am: "የደህንነታዊ የሥራ ሰዓቶች መጥፋት", or: "Hafura Sa'aatii Hojii Nageenyaa", ti: "ክሳራ ሰዓት ስራሕ ደህንነት", so: "Khasaaraha Saacadaha Shaqo ee Ammaan ah", af: "Hafura Sa'aatii Hojii Nageenyaa" }, intro: { en: "Importantly, it is not just the days of climate disruptions to the agricultural workforce that matter. The hours during the day and the time in the growing season, whether planting, or harvesting, all influence how these disruptions will take place and impact on the sector. Use the figure below to explore how these safe working hours will be disrupted for different populations under different climate scenarios, temperature thresholds, and different geographies on the African continent.", am: "አስፈላጊ ነገር የሚባለው የአየር ንብረት ማቋረጥ ለወቅቱ ሰራተኞች የሚጋለጡት ቀናት ብቻ አይደለም። በቀኑ ውስጥ ያሉ ሰዓቶች እና በእድገት ወቅት ያለው ጊዜ፣ መትከል ወይም ማጨድ ሲሆን፣ እነዚህ ማቋረጦች እንዴት እንደሚከሰቱ እና በስራው ላይ እንዴት እንደሚኖራቸው ተጽዕኖ ሁሉም ይኖራቸዋል። እነዚህ ደህንነታዊ የሥራ ሰዓቶች ለተለያዩ ሕዝቦች በተለያዩ የአየር ንብረት እቅዶች፣ በሙቀት ደረጃዎች እና በአፍሪካ አህጉር ላይ ባሉ የተለያዩ ጂኦግራፊዎች እንዴት እንደሚበላሹ ለማሰስ ከዚህ በታች ያለውን ስዕል ይጠቀሙ።", or: "Bakka bu'uuraa, guyyaan jijjiirama qilleensaa qaama hojii qonnaa irratti dhiyaatu qofa miti. Sa'aatii guyyaa keessaa fi yeroo ayyaana dagaagina, kuufachiisuu ykn qabsiisuu, hundinuu akkaataa jijjiiramni kun akka raawwatu fi sektara irratti akka dhiyaatu dha. Figaarii armaan gadii fayyadamtee akkaataa sa'aatii hojii nageenyaa kana ummata adda addaa haala jijjiirama qilleensaa, safartuu ho'aa fi alaa alaa kontinentii Aafrikaa keessatti akka jijjiiramu ilaali.", ti: "ኣገዳሲ እንተኾነ ፣ መዓልቲ ምቁራጽ ኩነታት ኣየር ንሓይሊ ስራሕ ግብርና ጥራይ ኣይኮነን። ሰዓት ኣብ መዓልቲ እና ግዜ ኣብ ወቕቲ ዕብየት ፣ መትከል ወይ ማጨድ ፣ ኩሎም እዞም ምቁራጽታት ብኸመይ ከም ዝከሰቱን ኣብ ክፍሊ ብኸመይ ከም ዝነብሩን ይጸልእ። ነዚ ሰዓት ስራሕ ደህንነት እዚ ንፈላስያን ህዝቢ በቲ ፈላስያን ስነ-ስርዓት ለውጢ ኩነታት ኣየር ፣ ደረጃ ሙቀትን ኣብ ሓጺር ኣፍሪቃ ፈላስያን ጂኦግራፍያዊ ከም ዝበላሽዎ ንምፍታሽ ነቲ ኣብ ታሕቲ ዘሎ ስእሊ ተጠቐም።", so: "Muhiim ah, maahan oo kaliya maalmaha kala qaybsanaanta cimilada ee shaqaalaha beeraha ee muhiimka ah. Saacadaha maalinta iyo waqtiga xilliga dhirta, mid kasta oo beerista, ama goosashada, dhammaan waxay saameeyaan sida kala qaybsanaankan u dhacayo iyo saameynta uu ku leeyahay baahida. Isticmaal jaantuska hoose si aad u baadhid sida saacadahan shaqo ee ammaan ah ay u kala qaybsamaan dad kala duwan hoos ay ka jiraan go'aamada cimilada kala duwan, xadadka heerkulka, iyo goobo kala duwan ee qaaradda Afrika.", af: "Bakka bu'uuraa, guyyaan jijjiirama qilleensaa qaama hojii qonnaa irratti dhiyaatu qofa miti. Sa'aatii guyyaa keessaa fi yeroo ayyaana dagaagina, kuufachiisuu ykn qabsiisuu, hundinuu akkaataa jijjiiramni kun akka raawwatu fi sektara irratti akka dhiyaatu dha. Figaarii armaan gadii fayyadamtee akkaataa sa'aatii hojii nageenyaa kana ummata adda addaa haala jijjiirama qilleensaa, safartuu ho'aa fi alaa alaa Afriikaa keessatti akka jijjiiramu ilaali." }, barChart: { xLabel: { en: "Hour of Day", am: "የቀን ሰዓት", or: "Sa'aatii Guyyaa", ti: "ሰዓት መዓልቲ", so: "Saacadda Maalinta", af: "Sa'aatii Guyyaa" }, yLabel: { en: "Percent Loss of Season due to Extreme Heat", am: "በከፍተኛ ሙቀት ምክንያት የወቅት መቶኛ ኪሳራ", or: "Lakkaawwii Hafura Ayyaanaa Ho'aa Guddan Waliin", ti: "ኣብ ወቕቲ መበገሲ ሚእቲ ምቕንኣት ብጽኑዕ ሙቀት", so: "Boqolkiiba khasaaraha xilliga sababtoo ah kul aad u daran", af: "Lakkaawwii Hafura Ayyaanaa Ho'aa Guddan Waliin" }, tooltip: { perc: { en: "Percent loss", am: "መቶኛ ኪሳራ", or: "Lakkaawwii hafura", ti: "መበገሲ ሚእቲ ምቕንኣት", so: "Boqolkiiba khasaaraha", af: "Lakkaawwii hafura" }, period: { en: "Period", am: "ጊዜ", or: "Yeroo", ti: "እዋን", so: "Waqti", af: "Yeroo" } } }, quickInsights: { intro: { en: `The figure illustrates the percentage of the harvesting and planting seasons during which the Wet Bulb Globe Temperature (WBGT) surpasses safe working limits, as per the selected threshold, relative to the baseline. This highlights the projected changes for the future. For example, a **15%** figure for the harvesting season at 11am indicates that, under the specified scenario and year, there will be **15% fewer days** out of the total season where the population can work safely at that time.`, am: `ስዕሉ የማጨድ እና የመትከል ወቅቶችን መቶኛ ያሳያል በዚህም ውስጥ የእርጥበት ግሎበ ሙቀት (WBGT) ከተመረጠው ደረጃ መሠረት ከደህንነታዊ የስራ ገደቦች በላይ ይሆናል፣ ከመሠረታዊው ጋር በማነፃፀር። ይህ ለወደፊቱ የተቀመጡ ለውጦችን ያብራራል። ለምሳሌ፣ በማጨድ ወቅት በጧት 11 ሰዓት **15%** የሚል ቁጥር ከሆነ፣ ይህ ማለት በተገለጸው እቅድ እና ዓመት ስር፣ ሕዝቡ በዚያን ጊዜ በደህንነት ሊሠራበት ከሚችለው አጠቃላይ ወቅት **15% ከፍለ ቀናት** ያነሱ ይሆናሉ ማለት ነው።`, or: `Figaariin lakkaawwii ayyaana qabsiisuu fi kuufachiisuu keessaa kan WBGT daanga'aa hojii nageenyaa irra geessu, safartuu filatametti, bu'uuraa wajjin walitti maku. Kun jijjiirama ibsaa fuulduraa guddisa. Fakkeenyaaf, lakkaawwii **15%** ayyaana qabsiisuuf sa'aatii 11:00 irratti, haala fi waggaa murtaa'e jala, **15% guyyaa xiqqaa** ayyaana guutuu irraa kan ummanni yeroo sana nageenyaan hojjechuu danda'u agarsiisa.`, ti: `እቲ ስእሊ ነቲ መበገሲ ሚእቲ ወቕቲ ማጨድን መትከልን ኣብ ውሽጡ የእርጥበት ግሎበ ሙቀት (WBGT) ካብ ደረጃ ምረጺ ከም ካብ ደህንነታዊ ስራሕ ዝሓልፍ ኣብ መሰረት የዘርዝር። እዚ ንዝተመርኰሰ ለውጢ ኣብ መጻኢ የድምቀስ። ንኣብነት ፣ ቁጽሪ **15%** ንወቕቲ ማጨድ ኣብ 11:00 ንግሆ እንተ ዘርኢ ፣ ኣብ ተወሰነ ስነ-ስርዓትን ዓመትን ትሕቲ ፣ **15% ካብ ጠቕላላ ወቕቲ ዝያዳ መዓልቲ** እቲ ህዝቢ ኣብቲ ግዜ ብደህንነት ክሰርሕ ዝኽእል ከም ዘሎ የሕብር።`, so: "Jaantusku wuxuu muujinayaa boqolkiiba xilliyada goosashada iyo beerista ee WBGT ka sarreeya xadadka shaqo ee ammaanka ah, iyadoo loo eegayo xadka la xushay, iyadoo la raacayo aasaaska. Tani waxay soo bandhigtaa isbeddellada la saadaalinayo mustaqbalka. Tusaale ahaan, jaantus **15%** ee xilliga goosashada ee 11ka subaxnimo wuxuu muujinayaa in, hoosteeda go'aamada la sheegay iyo sanadka, ay jiri doonaan **15% maalmood yar** oo ka mid ah wadarta xilliga ee dadku ku shaqeyn karaan ammaan ah waqtigaas.", af: `Figaariin lakkaawwii ayyaana qabsiisuu fi kuufachiisuu keessaa kan WBGT daanga'aa hojii nageenyaa irra geessu, safartuu filatametti, bu'uuraa wajjin walitti maku. Kun jijjiirama ibsaa fuulduraa guddisa. Fakkeenyaaf, lakkaawwii **15%** ayyaana qabsiisuuf sa'aatii 11:00 irratti, haala fi waggaa murtaa'e jala, **15% guyyaa xiqqaa** ayyaana guutuu irraa kan ummanni yeroo sana nageenyaan hojjechuu danda'u agarsiisa.` }, body: { en: `Under scenario **:::scenario:::** and year **:::year:::**, a policy of avoiding unsafe working hours would translate into **:::totalHours:::** working hours lost during the harvesting season, spanning **:::daysHarvest:::** days; if we consider a common working day to have 8 hours, this translates into **:::wholeWorkingDays:::** total working days lost in harvesting in a year.`, am: `በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ አደገኛ የሥራ ሰዓቶችን ለማስወገድ የሚያደርግ ፖሊሲ በማጨድ ወቅት **:::daysHarvest:::** ቀናት የሚቆይ ሲሆን **:::totalHours:::** የሥራ ሰዓቶች ኪሳራ ያስከትላል; አንድ የተለመደ የሥራ ቀን 8 ሰዓት ከያዘ ፣ ይህ በዓመት በማጨድ ወቅት ከጠቅላላው የሥራ ቀናት **:::wholeWorkingDays:::** ኪሳራ ማለት ነው።`, or: `Haala **:::scenario:::** fi waggaa **:::year:::** jala, siyaasni sa'aatii hojii nageenyaa hin qabne dhiisuu **:::totalHours:::** sa'aatii hojii ayyaana qabsiisuu keessatti hafuramuu, **:::daysHarvest:::** guyyaa ta'u; yoo guyyaa hojii waliigalaa sa'aatii 8 qabaachuu yaadanne, kun **:::wholeWorkingDays:::** guyyaa hojii guutuu waggaa keessatti qabsiisuu keessatti hafuramuu jechuudha.`, ti: `ኣብ ስነ-ስርዓት **:::scenario:::** እና ዓመት **:::year:::** ትሕቲ ፣ ፖሊሲ ንዘይክልተ ሰዓት ስራሕ ምኽራኽ **:::totalHours:::** ሰዓት ስራሕ ኣብ ወቕቲ ማጨድ ምቕንኣት ክገብር ፣ ኣብ **:::daysHarvest:::** መዓልቲ ዝዝርጋሕ; እንተ ሒዝና ልሙድ መዓልቲ ስራሕ 8 ሰዓት እንተ ወሲዱ ፣ እዚ **:::wholeWorkingDays:::** ጠቕላላ መዓልቲ ስራሕ ኣብ ማጨድ ኣብ ዓመት ምቕንኣት ክገብር ይከኣል።`, so: `Hoosteeda go'aamada **:::scenario:::** iyo sanadka **:::year:::**, siyaasadda laga reebo saacadaha shaqo ee aan ammaan ahayn waxay u rogmi doontaa **:::totalHours:::** saacadaha shaqo ee laga badiyay xilliga goosashada, oo ku fidaya **:::daysHarvest:::** maalmood; haddii aan u malaynno maalintii shaqada caadiga ah inay leedahay 8 saacadood, tan waxay u rogantaa **:::wholeWorkingDays:::** wadarta maalmaha shaqo ee laga badiyay goosashada sanadka.`, af: `Haala **:::scenario:::** fi waggaa **:::year:::** jala, siyaasni sa'aatii hojii nageenyaa hin qabne dhiisuu **:::totalHours:::** sa'aatii hojii ayyaana qabsiisuu keessatti hafuramuu, **:::daysHarvest:::** guyyaa ta'u; yoo guyyaa hojii waliigalaa sa'aatii 8 qabaachuu yaadanne, kun **:::wholeWorkingDays:::** guyyaa hojii guutuu waggaa keessatti qabsiisuu keessatti hafuramuu jechuudha.` }, lostDays: { en: `For the planting period, spanning **:::period:::** days, **:::geo:::** would lose **:::lostDays:::** whole working days.`, am: `ለመትከል ጊዜ፣ ለ**:::period:::** ቀናት የሚቆይ፣ **:::geo:::** **:::lostDays:::** ከጠቅላላ የሥራ ቀናት ያጣል።`, or: `Yeroo kuufachiisuuf, **:::period:::** guyyaa ta'u, **:::geo:::** **:::lostDays:::** guyyaa hojii guutuu hafura.`, ti: `ንግዜ መትከል ፣ ኣብ **:::period:::** መዓልቲ ዝዝርጋሕ ፣ **:::geo:::** **:::lostDays:::** ጠቕላላ መዓልቲ ስራሕ ክቕንእ ይከኣል።`, so: `Waqtiga beerista, oo ku fidaya **:::period:::** maalmood, **:::geo:::** waxay lumin lahayd **:::lostDays:::** maalmaha shaqo oo dhan.`, af: `Yeroo kuufachiisuuf, **:::period:::** guyyaa ta'u, **:::geo:::** **:::lostDays:::** guyyaa hojii guutuu hafura.` } } }, summary: { title: { en: "Summary", am: "ማጠቃለያ", or: "Xumura", ti: "መጨረሻት", so: "Koobid", af: "Xumura" }, intro: { en: "Climate change will drive disruptions to the African agricultural labour force. This has important implications for economic development, human health, gender equity, as well as food security. The size of the expected disruptions depend not only on climate stressors, such as increasing heat stress, but also on the expected change in the size of the agricultural workforce. There are a number of locations where these two factors will collide, driving increased risks, loss of safe working hours, agricultural productivity, and ultimately the right to work in a safe climate for millions. A number of solutions have been proposed to deal with this stress, including shifting working hours shifting in working hours, increased shade on farms, increased mechanisation, improved worker safety protocols, improved hydration and other heat stress coping mechanisms. Ultimately however, solutions on the scale of adaptation required have yet to be developed, and based on the size of this risk need to be addressed urgently to maintain human health and environmental rights, agricultural productivity, and food security, now and in the future.", am: "የአየር ንብረት ለውጥ ለአፍሪካ የወቅቱ ሰራተኞች ማቋረጥ ያስከትላል። ይህ ለኢኮኖሚያዊ ልማት፣ ለሰው ደህንነት፣ ለጾታ እኩልነት እንዲሁም ለምግብ ዋስትና አስፈላጊ ተጽዕኖ አለው። የሚጠበቁ ማቋረጦች መጠን በአየር ንብረት አደጋዎች ላይ ብቻ ሳይሆን በወቅቱ ሰራተኞች መጠን ላይ በሚጠበቀው ለውጥ ላይም የተመሠረተ ነው። ᥙይህ ሁለት ሁኔታዎች የሚጋጩባቸው በርካታ ቦታዎች አሉ፣ ይህም አደጋን የሚጨምር፣ ደህንነታዊ የሥራ ሰዓቶችን ኪሳራ፣ የወቅቱ ምርታማነት እና በመጨረሻም ለሚሊዮኖች በደህንነታዊ አየር ንብረት ውስጥ የመሥራት መብት ያስከትላል። ለዚህ ጫና ለመቋቋም በርካታ መፍትሄዎች ቀርበዋል፣ ከነዚህም ውስጥ የሥራ ሰዓቶችን ማዛወር፣ በግብርና ላይ ጥላ ማሳደግ፣ ሜካናይዜሽን ማሳደግ፣ የሰራተኞች ደህንነት ፕሮቶኮሎች ማሻሻል፣ የውሃ መጠቀም ማሻሻል እና ሌሎች የሙቀት ጫና መቋቋም ዘዴዎች ይገኙበታል። ሆኖም ግን፣ በሚፈለገው የማስተካከል ደረጃ ላይ ያሉ መፍትሄዎች እስካሁን አልተገኙም፣ እና በዚህ አደጋ መጠን ላይ በመመርኮዝ የሰው ደህንነት እና የአካባቢ መብቶች፣ የወቅቱ ምርታማነት እና ምግብ ዋስትና ለማቆየት አሁን እና በወደፊቱ አስቸኳይ መፍትሄ ማግኘት አለበት።", or: "Jijjiirama qilleensaa qaama hojii qonnaa Aafrikaa jijjiiramuu dhaqa. Kun dhimma guddaa fooyya'ina daldalaa, qaama namaa, walqixxummaa saalaa, akkasumas dhuunfaa nyaataa irratti qaba. Bal'ina jijjiiramaa eegamu ho'a jijjiiramaa qilleensaa, fakkeenyaaf dagaagina cimdii ho'a, malee bal'ina qaama hojii qonnaa eegamu irrattis hirkata. Bakka baay'ee kan dhimmiwwan lamaani kun wal dhaban, balaa dagaagisu, hafura sa'aatii hojii nageenyaa, fooyya'ina qonnaa, fi dhuma irratti mirga miliyoonotaaf qilleensa nageenyaa keessatti hojjechuuf dhaqa. Furmaata baay'ee cimdii kanaan wal qabsiisuuf dhiyaate, sa'aatii hojii jijjiiramuu, gaaddisa qonnaa irratti dagaagisu, meeshaalee dagaagisu, sagantaa nageenyaa hojjettoota fooyyessuu, dhangala'aa fooyyessuu fi karaa biraa cimdii ho'a dhabamsiisuu dabalata. Dhuma irratti garuu, furmaata sadarkaa guddina eegamu kan jijjiiramu hin jiru, fi bal'aasaa kana irratti hundaa'uun fooyya'ina qaama namaa fi mirga naannoo, fooyya'ina qonnaa, fi dhuunfaa nyaataa ammaafi fuulduraatti tursiisuu yeroo barbaachisaa ta'eedha.", ti: "ለውጢ ኩነታት ኣየር ንሓይሊ ስራሕ ግብርና ኣፍሪቃ ምቁራጽ ይስዕብ። እዚ ንልምምድ ስነ-ንግዲ ፣ ጥዕና ሰብ ፣ ማዕርነት ጾታ ፣ ከምኡውን ውሑስ መግቢ ኣገዳሲ ተጻሕሪ ኣለዎ። መጠን ምቁራጽ እተጸበየ ብሙቀት ጸቕጢ ምዕባይ ዝኣመሰለ ምጭዳይ ኩነታት ኣየር ጥራይ ዘይኮነስ ኣብ መጠን ሓይሊ ስራሕ ግብርና እተጸበየ ለውጥ እውን ይውከል። ብዙሕ ቦታታት ኣለዉ ኣብቲ ክልተ ኣቃውማ እዚ ዝጋገርዎ ፣ እዚ ድማ ምዕባይ ሓደጋ ፣ ምቕንኣት ሰዓት ስራሕ ደህንነት ፣ ምርት ግብርና ፣ ከምኡውን ኣብ መወዳእታ ንሚልዮናት ኣብ ደህንነታዊ ኩነታት ኣየር ዝሰርሕ መሰል የስዕብ። ብዙሕ መፍትሕታት ንዚ ጸቕጢ ምምላስ ቀሪቡ ኣሎ ፣ ከም ምልውዋጥ ሰዓት ስራሕ ፣ ምዕባይ ጽላሎት ኣብ ሕርሻ ፣ ምዕባይ ሜካናይዜሽን ፣ ምምሕራሽ ፕሮቶኮሎታት ደህንነት ሰራሕተኛታት ፣ ምምሕራሽ ምጥቃም ማይ ከምኡውን ካልእ ኣገባባት ምምላስ ጸቕጢ ሙቀት። ይኹን እምበር ፣ መፍትሕታት ኣብ ደረጃ ምምላስ እተወሰነ ክሳዕ ሕጂ ኣይተማዕበሉን ፣ ከምኡውን ብመጠን እዚ ሓደጋ እዚ ተመርኲሱ ጥዕና ሰብን መሰል ኣካባቢን ፣ ምርት ግብርናን ፣ ውሑስ መግቢን ኣሁንን ኣብ መጻኢን ንምዕቃብ ቀልጢፍ ክፍቲ ምዃኑ የድሊ።", so: "Isbeddelka cimilada waxay horseedi doontaa kala-qaybsanaanta shaqaalaha beeraha Afrikaanka. Tani waxay leedahay saamayn muhiim ah oo ku saabsan horumar dhaqaale, caafimaadka aadanaha, sinnaanta jinsiga, iyo sidoo kale aaminaadda cuntada. Baaxada kala-qaybsanaanta la filayo ma ku xiraneyn oo kaliya cayayaanka cimilada, sida koritaanka cadaadiska kulka, laakiin sidoo kale isbeddelka la filayo ee baaxada shaqaalaha beeraha. Waxaa jira meelo badan halkaasoo labadan arrimood ay isku dhegaan, taasoo keenta koritaanka khatarta, khasaaraha saacadaha shaqo ee ammaanka ah, wax soo saarka beeraha, iyo ugu dambaynta xuquuqda in lagu shaqeeyo cimilada ammaan ah ee milyanada. Waxaa soo jeediyay tixraabo badan si loo dagaallamo cadaadiskan, oo ay ku jiraan beddelida saacadaha shaqo, koritaanka hooska beeraha, koritaanka makiinadaha, hormarinta hababka ammaanka shaqaalaha, koritaanka cabitaanka biyaha iyo hababka kale ee loo dagaallamo cadaadiska kulka. Si kastaba ha ahaatee, xalka heerka is-beddelka loo baahan yahay wali lama horumarin, iyadoo lagu salaynayo baaxada khatartan waxaa loo baahan yahay in si degdeg ah loo xalliyo si loo ilaaliyo caafimaadka aadanaha iyo xuquuqda deegaanka, wax soo saarka beeraha, iyo aaminaadda cuntada, hadda iyo mustaqbalka.", af: "Jijjiirama qilleensaa qaama hojii qonnaa Afriikaa jijjiiramuu dhaqa. Kun dhimma guddaa fooyya'ina daldalaa, qaama namaa, walqixxummaa saalaa, akkasumas dhuunfaa nyaataa irratti qaba. Ballina jijjiiramaa eegamu ho'a jijjiiramaa qilleensaa, fakkeenyaaf dagaagina cimdii ho'a, malee ballina qaama hojii qonnaa eegamu irrattis hirkata. Bakka baay'ee kan dhimmiwwan lamaani kun wal dhaban, balaa dagaagisu, hafura sa'aatii hojii nageenyaa, fooyya'ina qonnaa, fi dhuma irratti mirga miliyoonotaaf qilleensa nageenyaa keessatti hojjechuuf dhaqa. Furmaata baay'ee cimdii kanaan wal qabsiisuuf dhiyaate, sa'aatii hojii jijjiiramuu, gaaddisa qonnaa irratti dagaagisu, meeshaalee dagaagisu, sagantaa nageenyaa hojjettoota fooyyessuu, dhangala'aa fooyyessuu fi karaa biraa cimdii ho'a dhabamsiisuu dabalata. Dhuma irratti garuu, furmaata sadarkaa guddina eegamu kan jijjiiramu hin jiru, fi bal'aasaa kana irratti hundaa'uun fooyya'ina qaama namaa fi mirga naannoo, fooyya'ina qonnaa, fi dhuunfaa nyaataa ammaafi fuulduraatti tursiisuu yeroo barbaachisaa ta'eedha." } }, methods: { title: { en: "Methods", am: "ዘዴዎች", or: "Karaa", ti: "ኣገባባት", so: "Hababka", af: "Karaa" }, agWorkforce: { title: { en: "Agricultural workforce", am: "የወቅቱ ሰራተኞች", or: "Qaama hojii qonnaa", ti: "ሓይሊ ስራሕ ግብርና", so: "Shaqaalaha beeraha", af: "Qaama hojii qonnaa" }, body: { en: `We generated gridded predictions for the agricultural workforce by first harmonising subnational data from census statistics from a range of government sources, with national data from the [International Labour Organisation (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) on the proportion of the population employed in agriculture. Then, using a geospatial version of the predictive framework introduced by [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y), we fit functions relating workforce size to [population statistics from Jones and O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population), [gridded gdp from Wang and Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x), and [agricultural land area by Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952). We employed stacked machine learning models (boosted regression trees, random forests) for the fitting process. Using these models and current and future gridded data we generated baseline-period (circa 2005) and future (2030, 2050) estimates of the agricultural workforce at 0.083 degrees resolution for the African continent.`, am: `የወቅቱ ሰራተኞች ለመገመት በመጀመሪያ የተለያዩ የመንግሥት ምንጮች የሆኑ የስታቲስቲክስ የሕዝብ ቆጠራ ውሂብን ከ[ዓለም አቀፍ የሥራ ድርጅት (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) ከሚገኘው በወቅቱ ስራ ላይ የሚሰማሩ ሕዝብ መጠን ጋር በማጣመር ጀምረናል። ከዚያ [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y) የሚያስተዋውቀውን የጂኦስፕሻል ትንበያ መዋቅር በመጠቀም፣ የሰራተኞችን መጠን ከ[Jones and O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population) የሕዝብ ስታቲስቲክስ፣ ከ[Wang and Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x) የጂዲፒ መረጃ እና ከ[Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952) የወቅቱ መሬት ስፋት ጋር የሚያገናኝ ተግባር አጣመርን። ለማጣመር ሂደት የማሽን ማሰልጠን ሞዴሎችን (boosted regression trees, random forests) ተጠቀምን። እነዚህን ሞዴሎች እና የአሁኑን እና የወደፊቱን የመረጃ ማዕቀፎች በመጠቀም ለአፍሪካ አህጉር በ0.083 ዲግሪ ጥራት ያለው መሠረታዊ-ጊዜ (በ2005 ግድም) እና የወደፊት (2030፣ 2050) የወቅቱ ሰራተኞች ግምት አዘጋጅተናል።`, or: `Qaama hojii qonnaaf ibsa gidiraa uumne data naannoo aanaa irraa statistiika sensasii piroojeektii mootummaa adda addaa, data dhaabbata [Hojii Addunyaa (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) waliin wal qabsiifnee jalqabnee. Ergasii, kaartaa [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y) fayyadamuun, bal'ina qaama hojii [statistiika ummata Jones fi O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population), [GDP gidiraa Wang fi Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x), fi [baala lafa qonnaa Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952) waliin walitti hidhuu dha. Modelii mashinii barannoo (boosted regression trees, random forests) fayyadamuun waliin hidhne. Modeliiwwan fi data ammaa fi fuulduraa gidiraa fayyadamuun yeroo bu'uuraa (waggaa 2005) fi fuulduraa (2030, 2050) ibsa qaama hojii qonnaa kontinentii Aafrikaa 0.083 digirii resoluushinatti uumne.`, ti: `ንሓይሊ ስራሕ ግብርና ንትንበያታት ማዕቀፍ ኣምሊእና ብመጀመርታ ዳታ ንኡስ ሃገራዊ ካብ ስታቲስቲክስ ቆጠራ ሕዝቢ ካብ ፈላስያን ምንጪ መንግስት ፣ ምስ ሃገራዊ ዳታ ካብ [ዓለምለኻዊ ድርጅት ስራሕ (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) ብዝምልከት መጠን ሕዝቢ ኣብ ግብርና ዝሰርሕ ምስ ሓደ ኣቢልና። ድሕሪኡ ፣ ብጂኦስፕሻል ኣርእስቲ ኣቃውማ ትንበያ ብ[Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y) ተመሪኹና ፣ ንተግባራት መጠን ሓይሊ ስራሕ ኣብ ዝምልከት [ስታቲስቲክስ ሕዝቢ Jones fi O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population) ፣ [GDP ማዕቀፍ Wang fi Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x) ፣ ከምኡውን [ስፍሓት መሬት ግብርና Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952) ኣማሊእና። ንምምላእ ሂደት ሞዴል ማሽን ትምህርቲ (boosted regression trees, random forests) ተጠቒምና። ነዞም ሞዴላትን ዳታ ማዕቀፍ ሎሚን መጻኢን ብምጥቃም መሰረት-እዋን (ከም 2005) ከምኡውን መጻኢ (2030፣ 2050) ግምት ሓይሊ ስራሕ ግብርና ንሓጺር ኣፍሪቃ ብ0.083 ዲግሪ ጥራይ ኣምሊእና ኣለና።`, so: "Waxaan sameynay saadaalinta jadwalka ah ee shaqaalaha beeraha iyadoo marka hore la isku hagaajinayo xogta gobolka ee ka socota tirakoobka tirakoobka ee laga helo ilo kala duwan oo dawladeed, iyadoo la raacayo xogta qaranka ee [Ururka Shaqaalaha Caalamiga ah (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) oo ku saabsan qeybta dadka ee la shaqeeya beeraha. Kadib, adoo isticmaalaya nooca joomatari ee qorshaha saadaalinta ee soo bandhigay [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y), waxaan isku dhex dhignay howlo ku xiran baaxada shaqaalaha [tirakoobka dadka ee Jones iyo O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population), [GDP-ga jadwalka ah ee Wang iyo Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x), iyo [baaxada dhulka beeraha ee Ramankutty iyo xubnaha kale (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952). Waxaan isticmaalay hababka barashada mashruuca oo la safay (geedaha regression ee la kordhiyay, kaymaha kala duwan) for habka isku xirka. Isticmaalaya hababyadan iyo xogta jadwalka ah ee hadda iyo mustaqbalka waxaan sameynay qiyaasta waqtiga aasaasiga ah (2005kii) iyo mustaqbalka (2030, 2050) ee shaqaalaha beeraha ee heerka 0.083 darajo ee qaaradda Afrika.", af: `Qaama hojii qonnaaf ibsa gidiraa uumne data naannoo aanaa irraa statistiika sensasii piroojeektii mootummaa adda addaa, data dhaabbata [Hojii Addunyaa (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) waliin wal qabsiifnee jalqabnee. Ergasii, kaartaa [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y) fayyadamuun, ballina qaama hojii [statistiika ummata Jones fi O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population), [GDP gidiraa Wang fi Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x), fi [baala lafa qonnaa Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952) waliin walitti hidhuu dha. Modelii mashinii barannoo (boosted regression trees, random forests) fayyadamuun waliin hidhne. Modeliiwwan fi data ammaa fi fuulduraa gidiraa fayyadamuun yeroo bu'uuraa (waggaa 2005) fi fuulduraa (2030, 2050) ibsa qaama hojii qonnaa Afriikaa 0.083 digirii resoluushinatti uumne.` } }, heatStress: { title: { en: "Heat stress", am: "የሙቀት ጫና", or: "Cimdii ho'a", ti: "ጸቕጢ ሙቀት", so: "Cadaadiska kulka", af: "Cimdii ho'a" }, body: { en: `We use high-resolution, daily gridded data from the Climate Hazards Center Coupled Model Intercomparison Project Phase 6 (CHC-CMIP6) dataset [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w). The CHC-CMIP6 dataset provides future estimates for 2030 and 2050, whose delta fields are based on the CMIP6 multi-model ensemble forecasts for the SSP 245 and SSP 585 scenarios. As inputs to compute WBGT values, we take from the dataset three main variables: the daily maximum and minimum temperatures (Tmax and Tmin), as well as the maximum daily relative humidity (RHx) that occurs at the time of Tmax, for both, the observation period (circa 2005, average taken across 10 years), and the future projections under SSP 2.45 and 5.85 (years 2030 and 2050, average taken across 10 years).`, am: `ከClimate Hazards Center Coupled Model Intercomparison Project Phase 6 (CHC-CMIP6) የውሂብ ስብስብ [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w) ከፍተኛ ጥራት ያለው ዕለታዊ የመረጃ ማዕቀፍ እንጠቀማለን። የCHC-CMIP6 የውሂብ ስብስብ ለ2030 እና 2050 የወደፊት ግምቶችን ይሰጣል፣ የዴልታ መስኮቻቸው ለSSP 245 እና SSP 585 እቅዶች በCMIP6 ባለብዙ-ሞዴል ኢንሰምብል ትንበያዎች ላይ የተመሰረቱ ናቸው። የWBGT እሴቶችን ለማስላት እንደ ግብዓቶች፣ ከውሂብ ስብስቡ ሦስት ዋና ተለዋዋጮችን እንወስዳለን፡ ዕለታዊ ከፍተኛ እና ዝቅተኛ ሙቀቶች (Tmax እና Tmin)፣ እንዲሁም በTmax ጊዜ የሚከሰት ከፍተኛ ዕለታዊ አንጻራዊ እርጥበት (RHx)፣ ለሁለቱም፣ ለመመልከቻ ጊዜ (በ2005 ግድም፣ አማካይ በ10 ዓመታት የተወሰደ)፣ እና ለወደፊቱ ትንበያዎች በSSP 2.45 እና 5.85 (ዓመታት 2030 እና 2050፣ አማካይ በ10 ዓመታት የተወሰደ)።`, or: `Data gidiraa qulqullina guddaa, guyyaa guyyaa dataset CHC-CMIP6 [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w) fayyadama. Dataset CHC-CMIP6 ibsa fuulduraa 2030 fi 2050 kenni, kan dheelta isaanii CMIP6 modelii baay'ee SSP 245 fi SSP 585 irratti hundaa'u. Ibsa WBGT sirreessuuf, dataset irraa gosoota sadi: ho'a guyyaa guddaa fi xiqqaa (Tmax fi Tmin), dhangala'uu guyyaa guddaa (RHx) Tmax yeroo ta'u, kan lamaanuu, yeroo ilaaluu (waggaa 2005, giddu galeessa waggaa 10), fi ibsa fuulduraa SSP 2.45 fi 5.85 (waggaa 2030 fi 2050, giddu galeessa waggaa 10) fudhanna.`, ti: `ንጥቡቕ ጥራይ ዳታ ማዕቀፍ ዕለታዊ ካብ ዳታስት CHC-CMIP6 [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w) ንጥቀመሉ። ዳታስት CHC-CMIP6 ን2030 ከምኡውን 2050 ትንበያታት ይህት ፣ ዴልታ መስኮቻቸው ኣብ CMIP6 ብዙሕ ሞዴል SSP 245 ከምኡውን SSP 585 ኣብ ዝተመሰረቱ እዮም። ንእሴታት WBGT ንምሕሳብ ከም ኣተኩር ፣ ካብ ዳታስት ሰለስተ ቀንዲ ተለዋዋጥታት ንወስድ፡ ከፍቲ ከምኡውን ትሑት ሙቀት ዕለታዊ (Tmax ከምኡውን Tmin) ፣ ከምኡውን ከፍቲ ዕለታዊ ኣንጻራዊ ውሑጅነት (RHx) ኣብ ግዜ Tmax ዝፍጸም ፣ ንኩሎም ፣ ንግዜ ምርምር (ከም 2005 ፣ ዝማዕበለ 10 ዓመት) ፣ ከምኡውን ንትንበያታት መጻኢ SSP 2.45 ከምኡውን 5.85 (ዓመት 2030 ከምኡውን 2050 ፣ ዝማዕበለ 10 ዓመት)።`, so: "Waxaan isticmaalaynaa xogta tayo sare leh, maalin kasta oo jadwalka ah oo ka socota xog ururka CHC-CMIP6 [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w). Xog ururka CHC-CMIP6 wuxuu bixiyaa saadaalinta mustaqbalka ee 2030 iyo 2050, kuwaasoo dhexdooda ay ku salaysan yihiin saadaalinta kooxda modelka badan ee CMIP6 ee go'aamada SSP 245 iyo SSP 585. Sida gelitaanka loo xisaabiyo qiyamka WBGT, waxaan ka qaadannaa xog ururka saddexaad doorashooyin muhiim ah: heerkulka ugu sarreeya iyo ugu hooseeya maalinta (Tmax iyo Tmin), iyo sidoo kale qoynta ugu sarreeya maalinta (RHx) ee dhaca waqtiga Tmax, labadaba, waqtiga daawashada (2005kii, celcelis ahaan laga qaado 10 sano), iyo saadaalinta mustaqbalka hoos ay ka jiraan SSP 2.45 iyo 5.85 (sanooyinka 2030 iyo 2050, celcelis ahaan laga qaado 10 sano).", af: `Data gidiraa qulqullina guddaa, guyyaa guyyaa dataset CHC-CMIP6 [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w) fayyadama. Dataset CHC-CMIP6 ibsa fuulduraa 2030 fi 2050 kenni, kan dheelta isaanii CMIP6 modelii baay'ee SSP 245 fi SSP 585 irratti hundaa'u. Ibsa WBGT sirreessuuf, dataset irraa gosoota sadi: ho'a guyyaa guddaa fi xiqqaa (Tmax fi Tmin), dhangala'uu guyyaa guddaa (RHx) Tmax yeroo ta'u, kan lamaanuu, yeroo ilaaluu (waggaa 2005, giddu galeessa waggaa 10), fi ibsa fuulduraa SSP 2.45 fi 5.85 (waggaa 2030 fi 2050, giddu galeessa waggaa 10) fudhanna.` } }, hourlyExposure: { title: { en: "Hourly exposure", am: "የሰዓት ጋርታ", or: "Sa'aatii dhiyaachuu", ti: "ሰዓታዊ ተጋላጽነት", so: "Saacadaha la saameeyay", af: "Sa'aatii dhiyaachuu" }, body: { en: `We examine agricultural workers' hourly exposure to heat stress during the harvesting and planting seasons, crucial periods in the agricultural calendar, where agricultural labourers are exposed to prolonged labour-intense outdoor activity, making them more susceptible to the impacts of heat stress. We use the crop calendars from [GGCMI Phase 3](https://zenodo.org/records/5062513), which provide the planting and harvesting dates for eighteen distinct crops and show exposure histograms during these periods. The histograms quantify the percentage of days within the harvesting or planting periods that surpass WBGT safety standards for each hour of the day. Here we compute and rely on hourly WBGT exceedances in present and future, for different SSPs and years. The calculations for the hourly temperature rely on Linvill's (1990) method for predicting hourly temperatures from daily minimum and maximum measurements. To compute such temperature profiles, we rely on daylength values, mainly taken from [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html). This approach was used to create hourly temperature profiles; then, hourly heat index values relying on relative humidity values and following the [NOAA guidelines](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml); and, finally, hourly WBGT, which was evaluated at three different thresholds – 28, 30 and 32 degrees Celsius.`, am: `የወቅቱ ሰራተኞችን በማጨድ እና በመትከል ወቅቶች ውስጥ የሚጋለጡትን የሰዓት ጋርታ ለሙቀት ጫና እንመረምራለን፣ እነዚህም በወቅቱ ካለንደር ውስጥ ወሳኝ ጊዜዎች ናቸው፣ በዚህም ጊዜ የወቅቱ ሰራተኞች ለረጅም ጊዜ ጉልበት የሚጠይቅ ውጫዊ እንቅስቃሴ ይጋለጣሉ፣ ይህም ለሙቀት ጫና ተጽዕኖዎች የበለጠ ተጋላጭ ያደርጋቸዋል። የ[GGCMI Phase 3](https://zenodo.org/records/5062513) የእርሻ ካለንደሮችን እንጠቀማለን፣ እነዚህም ለአስራ ስምንት የተለያዩ እርሻዎች የመትከል እና የማጨድ ቀናትን ይሰጣሉ እና በእነዚህ ጊዜያት የጋርታ ሂስቶግራሞችን ያሳያሉ። ሂስቶግራሞቹ በማጨድ ወይም በመትከል ጊዜያት ውስጥ ለእያንዳንዱ የቀን ሰዓት የWBGT ደህንነት መስፈርቶችን የሚያልፉ ቀናትን መቶኛ ይለካሉ። እዚህ ላይ ለተለያዩ SSPs እና ዓመታት በአሁኑ እና በወደፊቱ የሰዓት WBGT ከመጠን በላይ የሆኑ እሴቶችን እናሰላለን እና እንጠቀምበታለን። ለሰዓታዊ ሙቀት ስሌቶች በLinvill (1990) ዘዴ ላይ የተመሰረቱ ናቸው ለሰዓታዊ ሙቀቶች ከዕለታዊ ዝቅተኛ እና ከፍተኛ መለኪያዎች ለማስተካከል። እንደነዚህ ያሉ የሙቀት መግለጫዎችን ለማስላት ፣ በዋነኛነት ከ[Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html) የተወሰዱ የቀን ርዝመት እሴቶችን እንጠቀማለን። ይህ አቀራረብ የሰዓት ሙቀት መግለጫዎችን ለመፍጠር ተጠቅሟል; ከዚያ ፣ የሰዓት ሙቀት ኢንዴክስ እሴቶች በአንጻራዊ እርጥበት እሴቶች ላይ በመመርኮዝ እና የ[NOAA መመሪያዎችን](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml) በመከተል; እና ፣ በመጨረሻም ፣ የሰዓት WBGT ፣ እሱም በሦስት የተለያዩ ደረጃዎች ላይ ተገምግሟል - 28, 30 እና 32 ዲግሪ ሴንቲግሬድ።`, or: `Qaama hojii qonnaa dhiyaachuu sa'aatii cimdii ho'a ayyaana qabsiisuu fi kuufachiisuu keessatti, yeroo guddaa kalendarrii qonnaa, hojjettoonni qonnaa hojii alaa yeroo dheeraa dhiyaatan, cimdii ho'aa dhaqqabu taasisa. Kalendarrii qonnaa [GGCMI Phase 3](https://zenodo.org/records/5062513) fayyadama, kan ayyaana kuufachiisuu fi qabsiisuu dhigaa sadiit adda addaa kenni fi histogram dhiyaachuu yeroo kanaa agarsiisu. Histogramni lakkaawwii guyyaa ayyaana qabsiisuu ykn kuufachiisuu keessaa WBGT daanga'aa nageenyaa sa'aatii guyyaa hunda irratti irra geessu qixa. Asii WBGT sa'aatii ammaa fi fuulduraa, SSP fi waggaa adda addaa irratti hundaa'uun qoranna. Ho'a sa'aatii Linvill (1990) fayyadamuun, ho'a guyyaa xiqqaa fi guddaa irraa ho'a sa'aatii tilmaamu. Akkasiin ho'a agarsiisuu, dheerina guyyaa [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html) irraa fudhannu. Kanaan ho'a sa'aatii uumne; ergasii, indeeksii ho'a sa'aatii dhangala'uu waliin [NOAA qajeelfama](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml) hordofuun; fi, dhuma irratti, WBGT sa'aatii, safartuu sadi - 28, 30 fi 32 digirii Selshiyasatti qoranne.`, ti: `ንሓይሊ ስራሕ ግብርና ሰዓታዊ ተጋላጽነት ንጸቕጢ ሙቀት ኣብ ወቕቲ ማጨድን መትከልን ንመርምር ፣ እቲ ኣገዳሲ ግዜ ኣብ ካለንደር ግብርና ፣ ኣብኡ ሰራሕተኛታት ግብርና ንነዊሕ ግዜ ጉልበት ዝጠይቅ ውጫዊ ንቅስቃሴ ይጋለጡ ፣ እዚ ድማ ንጸቕጢ ሙቀት ዝበለጸ ተጋላጺ ይገብሮም። ካለንደር እርሻ [GGCMI Phase 3](https://zenodo.org/records/5062513) ንጥቀመሉ ፣ እቲ ንዓሰርተ ዘጠምር ፈላስያን እርሻታት መትከልን ማጨድን ዘርኢ ከምኡውን ሂስቶግራም ተጋላጽነት ኣብዚ እዋናት ዘርኢ። ሂስቶግራማት መበገሲ ሚእቲ መዓልቲ ኣብ ውሽጢ ወቕቲ ማጨድ ወይ መትከል ንነፍሲ ወከፍ ሰዓት መዓልቲ WBGT ደህንነት ዝሓልፍ የቕመስ። ኣብዚ ሰዓታዊ WBGT ኣብ ሎሚን መጻኢን ንፈላስያን SSP ከምኡውን ዓመታት ንሕሰብን ንተመኩሮን። ስሌት ሙቀት ሰዓታዊ Linvill (1990) ኣብ ዝተመርኮሰ ኣብ ዕለታዊ ትሑትን ከፍታውን መለክዒታት ንሰዓታዊ ሙቀት ንምትንታን እዩ። ከምዚ ዝኣመሰለ መግለጺታት ሙቀት ንምሕሳብ ፣ ኣብ ዋናነት ካብ [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html) ዝወሰድናዮ ዋጋ ንዓርሰ ግዜ ንተመኩሮ። እዚ ኣገባብ እዚ መግለጺታት ሙቀት ሰዓታዊ ንምፍጣር ተጠቒሙ; ድሕሪኡ ፣ ዋጋ ሰዓታዊ ሙቀት ኢንዴክስ ኣብ ኣንጻራዊ ውሑጅነት ዋጋታት ተመኪሩ ከምኡውን [NOAA መመርዒ](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml) ተኸቲሉ; ከምኡውን ፣ ኣብ መወዳእታ ፣ WBGT ሰዓታዊ ፣ ኣብ ሰለስተ ፈላስያን ደረጃ - 28, 30 ከምኡውን 32 ዲግሪ ሴንቲግሬድ እተገምገመ።`, so: "Waxaan baadannaa saacadaha la saameeyay ee shaqaalaha beeraha ee cadaadiska kulka inta lagu jiro xilliyada goosashada iyo beerista, waqtiyada muhiimka ah ee jadwalka beeraha, halkaasoo shaqaalaha beeraha ay ku kulmaan hawlaha dibadda ee ku sii socda ee culeys weyn, taasoo ka dhigeysa inay sii u nugul yihiin saamaynta cadaadiska kulka. Waxaan isticmaalaynaa jadwalka dalagyada ee [GGCMI Phase 3](https://zenodo.org/records/5062513), kuwaasoo bixiya taariikhda beerista iyo goosashada ee siddeed iyo toban dalag oo kala duwan waxayna muujinayaan jadwalka la saameeyay inta lagu jiro waqtiyadan. Jadwalku waxay qiyaaseynaan boqolkiiba maalmaha ee xilliyada goosashada ama beerista ee ka sarreeya heerarka ammaanka ee WBGT ee saacad kasta oo maalinta. Halkan waxaan xisaabinnaa waxaana ku tiirsanahay kordhinta WBGT saacadeed ee hadda iyo mustaqbalka, ee go'aamada SSP kala duwan iyo sanooyinka. Xisaabinta heerkulka saacadeed waxay ku tiirsan tahay habka Linvill (1990) ee loogu talagalay saadaalinta heerkulka saacadeed laga bilaabo cabbirrada ugu hooseeya iyo ugu sarreeya ee maalinta. Si loo xisaabiyo qaababka heerkulka noocan ah, waxaan ku tiirsanahay qiimaha dhererka maalinta, gaar ahaan laga qaado [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html). Habkani waxaa loo isticmaalay sidii loo abuuri lahaa qaababka heerkulka saacadeed; kadib, qiimaha indekska kulka saacadeed oo ku tiirsan qiimaha qoynta oo raacaya [tilmaamaha NOAA](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml); iyo, ugu dambayntii, WBGT saacadeed, oo lagu qiimeeyay saddex xadood oo kala duwan – 28, 30 iyo 32 darajo Selsius.", af: `Qaama hojii qonnaa dhiyaachuu sa'aatii cimdii ho'a ayyaana qabsiisuu fi kuufachiisuu keessatti, yeroo guddaa kalendarrii qonnaa, hojjettoonni qonnaa hojii alaa yeroo dheeraa dhiyaatan, cimdii ho'aa dhaqqabu taasisa. Kalendarrii qonnaa [GGCMI Phase 3](https://zenodo.org/records/5062513) fayyadama, kan ayyaana kuufachiisuu fi qabsiisuu dhigaa sadiit adda addaa kenni fi histogram dhiyaachuu yeroo kanaa agarsiisu. Histogramni lakkaawwii guyyaa ayyaana qabsiisuu ykn kuufachiisuu keessaa WBGT daanga'aa nageenyaa sa'aatii guyyaa hunda irratti irra geessu qixa. Asii WBGT sa'aatii ammaa fi fuulduraa, SSP fi waggaa adda addaa irratti hundaa'uun qoranna. Ho'a sa'aatii Linvill (1990) fayyadamuun, ho'a guyyaa xiqqaa fi guddaa irraa ho'a sa'aatii tilmaamu. Akkasiin ho'a agarsiisuu, dheerina guyyaa [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html) irraa fudhannu. Kanaan ho'a sa'aatii uumne; ergasii, indeeksii ho'a sa'aatii dhangala'uu waliin [NOAA qajeelfama](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml) hordofuun; fi, dhuma irratti, WBGT sa'aatii, safartuu sadi - 28, 30 fi 32 digirii Selshiyasatti qoranne.` } }, additionalMethods: { title: { en: "Additional methods", am: "ተጨማሪ ዘዴዎች", or: "Karaa dabalataa", ti: "ተወሳኺ ኣገባባት", so: "Hababka dheeraad ah", af: "Karaa dabalataa" }, body: { en: `Full methodological details are contained in two forthcoming background papers: - Ormaza-Zulueta, N and Mehrabi, Z. *Mapping global agricultural workforce* - Ormaza-Zulueta, N and Mehrabi, Z. *Reductions in the agricultural workday in future due to climate change*`, am: `ሙሉ የዘዴ ዝርዝሮች በሁለት ቀጣይ የጀርባ ሰነዶች ውስጥ ተካትተዋል: - Ormaza-Zulueta, N and Mehrabi, Z. *የዓለም የወቅቱ ሰራተኞችን ካርታ ማውጣት* - Ormaza-Zulueta, N and Mehrabi, Z. *በወደፊቱ በአየር ንብረት ለውጥ ምክንያት በወቅቱ የሥራ ቀን መቀነስ*`, or: `Gadadoo karaa guutuun waraqaa duuba lama keessatti argama: - Ormaza-Zulueta, N and Mehrabi, Z. *Kaartaa qaama hojii qonnaa addunyaa* - Ormaza-Zulueta, N and Mehrabi, Z. *Hir'isaa guyyaa hojii qonnaa fuulduraa jijjiirama qilleensaa wajjin*`, ti: `ቅዋም ዘዴ ዝርዝር ኣብ ክልተ ናይ መጻኢ ወረቓት ጀርባ ኣሎ: - Ormaza-Zulueta, N and Mehrabi, Z. *ምንዋሕ ሓይሊ ስራሕ ግብርና ዓለም* - Ormaza-Zulueta, N and Mehrabi, Z. *ኣብ መጻኢ ምንእሽታ መዓልቲ ስራሕ ግብርና ብለውጢ ኩነታት ኣየር*`, so: `Faahfaahinta dhamaystiran ee hababka waxaa ku jira laba waraaqo oo dabaaldag ah oo soo socda: - Ormaza-Zulueta, N and Mehrabi, Z. *Jadwalka shaqaalaha beeraha adduunka* - Ormaza-Zulueta, N and Mehrabi, Z. *Hoos u dhigga maalinta shaqada beeraha mustaqbalka sababtoo ah isbeddelka cimilada*`, af: `Gadadoo karaa guutuun waraqaa duuba lama keessatti argama: - Ormaza-Zulueta, N and Mehrabi, Z. *Kaartaa qaama hojii qonnaa addunyaa* - Ormaza-Zulueta, N and Mehrabi, Z. *Hir'isaa guyyaa hojii qonnaa fuulduraa jijjiirama qilleensaa wajjin*` }, linkNote: { en: "Links will be updated when published.", am: "አገናኞች በሚታተሙበት ጊዜ ይታደሳሉ።", or: "Linkiin yeroo maxxanfaman haaromsamu.", ti: "ሊንክታት ምስ እተሓትመ ክሕደስ እየን።", so: "Isku xirka waxaa la cusbooneysiin doonaa marka la daabacayo.", af: "Linkiin yeroo maxxanfaman haaromsamu." } }, citation: { title: { en: "Citation", am: "ጥቅስ", or: "Faaruu", ti: "ምጥቃስ", so: "Tixraac", af: "Faaruu" }, body: { en: "If the above links to background papers are not live, please use the following citation for this analysis: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR.", am: "ከላይ ያሉት ወደ ጀርባ ሰነዶች አገናኞች ከማይሰሩ ከሆነ ፣ እባክዎ ለዚህ ትንታኔ የሚከተለውን ጥቅስ ይጠቀሙ: Ormaza-Zulueta, N and Mehrabi, Z. 2024. በሰፊው የሰራተኞች ማቋረጥ. የአፍሪካ የወቅቱ አስተካከል አትላስ. CGIAR.", or: "Yoo linkiin waraqaa duuba ol ta'e jiraachuu baate, faaruuf kana fayyadami: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR.", ti: "እቲ ኣብ ላዕሊ ዘሎ ሊንክታት ናብ ወረቓት ጀርባ እንተዘይሰርሑ ፣ ነዚ ትንተና እዚ ነዚ ዝስዕብ ጥቅሲ ተጠቐም: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR.", so: "Haddii isku xirka kor ku xusan ee waraaqaha dabaaldaga aanu shaqayn, fadlan isticmaal tixraacan soo socda ee dhigan: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR.", af: "Yoo linkiin waraqaa duuba ol ta'e jiraachuu baate, faaruuf kana fayyadami: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR." } } } } })54:T4427,nbText=({ legends:{ ...nbTextLegend.legends, }, sections: { ...nbTextSection1.sections, ...nbTextSection2.sections }, export: { geoImpact: { title: { en: "Export Geographic Impact Map:", am: "የጂኦግራፊ ተጽዕኖ ካርታ ላክ:", or: "Maappii Tarkaanfii Jiyoogiraafii Ba'aa:", ti: "ውጻኢ ካርታ ተጽዕኖ ጂኦግራፊ:", so: "Soo deji khariidadda saameynta joogiraafiga:", af: "Maappii Tarkaanfii Jiyoogiraafii Ba'aa:" }, chooseFormat: { en: "Choose format...", am: "የፎርማት ምርጫ...", or: "Qormaata filadhaa...", ti: "ሓይሊ ምረጹ...", so: "Dooro nooca...", af: "Qormaata filadhaa..." }, png: { en: "PNG (High Quality)", am: "PNG (ከፍተኛ ጥራት)", or: "PNG (Qulqullina Ol)", ti: "PNG (ጥራት ህያው)", so: "PNG (Tayada sarreysa)", af: "PNG (Qulqullina Ol)" }, jpeg: { en: "JPEG (Smaller File)", am: "JPEG (ትንሽ ፋይል)", or: "JPEG (Faayili Xinnacha)", ti: "JPEG (ፋይል ንኣሽቱ)", so: "JPEG (Faylka yar)", af: "JPEG (Faayili Xinnacha)" }, svg: { en: "SVG (Vector)", am: "SVG (ቬክተር)", or: "SVG (Veektora)", ti: "SVG (ቬክተር)", so: "SVG (Vector)", af: "SVG (Veektora)" }, csv: { en: "CSV (Data)", am: "CSV (ውሂብ)", or: "CSV (Daata)", ti: "CSV (ውሂብ)", so: "CSV (Xogta)", af: "CSV (Daata)" }, json: { en: "JSON (Data)", am: "JSON (ውሂብ)", or: "JSON (Daata)", ti: "JSON (ውሂብ)", so: "JSON (Xogta)", af: "JSON (Daata)" }, geojson: { en: "GeoJSON (Geographic Data)", am: "GeoJSON (የጂኦግራፊ ውሂብ)", or: "GeoJSON (Daata Jiyoogiraafii)", ti: "GeoJSON (ውሂብ ጂኦግራፊ)", so: "GeoJSON (Xogta joogiraafiga)", af: "GeoJSON (Daata Jiyoogiraafii)" }, shp: { en: "Shapefile (via GeoJSON conversion)", am: "Shapefile (በ GeoJSON በመቀየር)", or: "Shapefile (Jijjiirraa GeoJSON)", ti: "Shapefile (ብምርዕድም GeoJSON)", so: "Shapefile (Beddelka GeoJSON)", af: "Shapefile (Jijjiirraa GeoJSON)" }, mapNotFound: { en: "Map not found. Please make sure the map is rendered above this export cell.", am: "ካርታ አልተገኘም። ካርታው ከዚህ ላክ ሴል በላይ መገናኛ እንደሆነ ያረጋግጡ።", or: "Maappii hin argamne. Maappii kutaa ba'aa kana olitti mul'ata jiraachuu hubadhaa.", ti: "ካርታ ኣይተረኽበን። ካርታ ኣብ ላይ ናይዚ ውጻኢ ሴል ምስላ ከም ዝወሃብ ኣረጋግጹ።", so: "Khariidadda ma la helin. Hubi in khariidaddu ka sarreysa xigta dhoofintan lagu muujiyay.", af: "Maappii hin argamne. Maappii kutaa ba'aa kana olitti mul'ata jiraachuu hubadhaa." }, noDataAvailable: { en: "No data available for CSV export", am: "ለ CSV ላክ ውሂብ የለም", or: "Daataan ba'uu CSV irratti hin jiru", ti: "ንውጻኢ CSV ውሂብ የለን", so: "Xogta dhoofinta CSV ma jirto", af: "Daataan ba'uu CSV irratti hin jiru" }, csvFailed: { en: "CSV export failed:", am: "የ CSV ላክ አልተሳካም:", or: "Ba'e CSV didde:", ti: "ውጻኢ CSV ኣይዓወተን:", so: "Dhoofinta CSV way fashilantay:", af: "Ba'e CSV didde:" }, jsonFailed: { en: "JSON export failed:", am: "የ JSON ላክ አልተሳካም:", or: "Ba'e JSON didde:", ti: "ውጻኢ JSON ኣይዓወተን:", so: "Dhoofinta JSON way fashilantay:", af: "Ba'e JSON didde:" }, geojsonFailed: { en: "GeoJSON export failed:", am: "የ GeoJSON ላክ አልተሳካም:", or: "Ba'e GeoJSON didde:", ti: "ውጻኢ GeoJSON ኣይዓወተን:", so: "Dhoofinta GeoJSON way fashilantay:", af: "Ba'e GeoJSON didde:" }, exportFailed: { en: "Export failed:", am: "ላክ አልተሳካም:", or: "Ba'e didde:", ti: "ውጻኢ ኣይዓወተን:", so: "Dhoofinta way fashilantay:", af: "Ba'e didde:" }, downloadFailed: { en: "Download failed:", am: "ማውረድ አልተሳካም:", or: "Laadachuu didde:", ti: "ምውራድ ኣይዓወተን:", so: "Soo dejinta way fashilantay:", af: "Laadachuu didde:" }, exportSuccess: { en: "Export successful", am: "ላክ ተሳክቷል", or: "Ba'e milkoofe", ti: "ውጻኢ ዓወተ", so: "Dhoofinta way ku guulaysatay", af: "Ba'e milkoofe" }, csvSuccess: { en: "CSV export successful", am: "የ CSV ላክ ተሳክቷል", or: "Ba'e CSV milkoofe", ti: "ውጻኢ CSV ዓወተ", so: "Dhoofinta CSV way ku guulaysatay", af: "Ba'e CSV milkoofe" }, jsonSuccess: { en: "JSON export successful", am: "የ JSON ላክ ተሳክቷል", or: "Ba'e JSON milkoofe", ti: "ውጻኢ JSON ዓወተ", so: "Dhoofinta JSON way ku guulaysatay", af: "Ba'e JSON milkoofe" }, geojsonSuccess: { en: "GeoJSON export successful", am: "የ GeoJSON ላክ ተሳክቷል", or: "Ba'e GeoJSON milkoofe", ti: "ውጻኢ GeoJSON ዓወተ", so: "Dhoofinta GeoJSON way ku guulaysatay", af: "Ba'e GeoJSON milkoofe" }, geojsonDownloadedInstructions: { en: "GeoJSON downloaded. See conversion instructions.", am: "GeoJSON ተሸክሟል። የመቀየር መመሪያዎችን ይመልከቱ።", or: "GeoJSON laadamtee. Qajeelfama jijjiirraa ilaali.", ti: "GeoJSON ተሸኪሙ። መምርሒ ምርዕድም ርአ።", so: "GeoJSON waa la soo dejiyay. Fiiri tilmaamaha beddelka.", af: "GeoJSON laadamtee. Qajeelfama jijjiirraa ilaali." }, hintPng: { en: "PNG: High quality raster image", am: "PNG: ከፍተኛ ጥራት ያለው ራስተር ምስል", or: "PNG: Suura raasterii qulqullina ol", ti: "PNG: ስእሊ ራስተር ጥራት ህያው", so: "PNG: Sawir raster ah oo tayo leh", af: "PNG: Suura raasterii qulqullina ol" }, hintJpeg: { en: "JPEG: Smaller file size", am: "JPEG: ትንሽ ፋይል መጠን", or: "JPEG: Guddina faayilaa xinnacha", ti: "JPEG: መጠን ፋይል ንኣሽቱ", so: "JPEG: Cabbir fayl yar", af: "JPEG: Guddina faayilaa xinnacha" }, hintSvg: { en: "SVG: Vector format (best quality, scalable)", am: "SVG: ቬክተር ቅርጸት (ምርጡ ጥራት፣ በመለካት ሊጨምር)", or: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)", ti: "SVG: መልክዕ ቬክተር (ጥራት ህያው፣ ብጽንድር ዝወሰኽ)", so: "SVG: Qaab vector (tayada ugu fiican, cabbir la beddeli karo)", af: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)" }, hintCsv: { en: "CSV: Tabular data export", am: "CSV: የሠንጠረዥ ውሂብ ላክ", or: "CSV: Ba'aa daataa taabulaa", ti: "CSV: ውጻኢ ዳታ ጠረጴዛ", so: "CSV: Dhoofinta xogta miisaaniyadda", af: "CSV: Ba'aa daataa taabulaa" }, hintJson: { en: "JSON: Structured data export", am: "JSON: የተዋቀረ ውሂብ ላክ", or: "JSON: Ba'aa daataa qindaa'e", ti: "JSON: ውጻኢ ዳታ ተዋህዲ", so: "JSON: Dhoofinta xogta qaabaysan", af: "JSON: Ba'aa daataa qindaa'e" }, hintGeojson: { en: "GeoJSON: Geographic data with geometry (for GIS)", am: "GeoJSON: ከጂኦግራፊ ውሂብ ከጂኦሜትሪ ጋር (ለ GIS)", or: "GeoJSON: Daata jiyoogiraafii fi jiioomeetirii (GIS f)", ti: "GeoJSON: ዳታ ጂኦግራፊ ምስ ጂኦሜትሪ (ን GIS)", so: "GeoJSON: Xogta joogiraafiga leh geometry (GIS)", af: "GeoJSON: Daata jiyoogiraafii fi jiioomeetirii (GIS f)" }, hintShp: { en: "Shapefile: Downloads GeoJSON with conversion instructions", am: "Shapefile: GeoJSON ከመቀየር መመሪያዎች ጋር ያወርዳል", or: "Shapefile: GeoJSON laaduu fi qajeelfama jijjiirraa", ti: "Shapefile: GeoJSON ይሸይጥ ምስ መምርሒ ምርዕድም", so: "Shapefile: Soo dejinta GeoJSON iyo tilmaamaha beddelka", af: "Shapefile: GeoJSON laaduu fi qajeelfama jijjiirraa" } }, heatStressChart: { title: { en: "Export Heat Stress Chart:", am: "የሙቀት ጭንቀት ቻርት ላክ:", or: "Chaartii Cimdii Ho'a Ba'aa:", ti: "ውጻኢ ቻርት ጽዕነት ሙቐት:", so: "Soo deji chartaanka cimriga kulaylka:", af: "Chaartii Cimdii Ho'a Ba'aa:" }, chooseFormat: { en: "Choose format...", am: "የፎርማት ምርጫ...", or: "Qormaata filadhaa...", ti: "ሓይሊ ምረጹ...", so: "Dooro nooca...", af: "Qormaata filadhaa..." }, png: { en: "PNG (High Quality)", am: "PNG (ከፍተኛ ጥራት)", or: "PNG (Qulqullina Ol)", ti: "PNG (ጥራት ህያው)", so: "PNG (Tayada sarreysa)", af: "PNG (Qulqullina Ol)" }, jpeg: { en: "JPEG (Smaller File)", am: "JPEG (ትንሽ ፋይል)", or: "JPEG (Faayili Xinnacha)", ti: "JPEG (ፋይል ንኣሽቱ)", so: "JPEG (Faylka yar)", af: "JPEG (Faayili Xinnacha)" }, svg: { en: "SVG (Vector)", am: "SVG (ቬክተር)", or: "SVG (Veektora)", ti: "SVG (ቬክተር)", so: "SVG (Vector)", af: "SVG (Veektora)" }, csv: { en: "CSV (Data)", am: "CSV (ውሂብ)", or: "CSV (Daata)", ti: "CSV (ውሂብ)", so: "CSV (Xogta)", af: "CSV (Daata)" }, json: { en: "JSON (Data)", am: "JSON (ውሂብ)", or: "JSON (Daata)", ti: "JSON (ውሂብ)", so: "JSON (Xogta)", af: "JSON (Daata)" }, chartNotFound: { en: "Chart not found. Please make sure the heat stress chart is rendered above this export cell.", am: "ቻርት አልተገኘም። የሙቀት ጭንቀት ቻርት ከዚህ ላክ ሴል በላይ መገናኛ እንደሆነ ያረጋግጡ።", or: "Chaartii hin argamne. Chaartii cimdii ho'a kutaa ba'aa kana olitti mul'ata jiraachuu hubadhaa.", ti: "ቻርት ኣይተረኽበን። ቻርት ጽዕነት ሙቐት ኣብ ላይ ናይዚ ውጻኢ ሴል ምስላ ከም ዝወሃብ ኣረጋግጹ።", so: "Chartaanka ma la helin. Hubi in chartaanka cimriga kulaylka ka sarreysa xigta dhoofintan lagu muujiyay.", af: "Chaartii hin argamne. Chaartii cimdii ho'a kutaa ba'aa kana olitti mul'ata jiraachuu hubadhaa." }, noDataAvailable: { en: "No data available for CSV export", am: "ለ CSV ላክ ውሂብ የለም", or: "Daataan ba'uu CSV irratti hin jiru", ti: "ንውጻኢ CSV ውሂብ የለን", so: "Xogta dhoofinta CSV ma jirto", af: "Daataan ba'uu CSV irratti hin jiru" }, csvFailed: { en: "CSV export failed:", am: "የ CSV ላክ አልተሳካም:", or: "Ba'e CSV didde:", ti: "ውጻኢ CSV ኣይዓወተን:", so: "Dhoofinta CSV way fashilantay:", af: "Ba'e CSV didde:" }, jsonFailed: { en: "JSON export failed:", am: "የ JSON ላክ አልተሳካም:", or: "Ba'e JSON didde:", ti: "ውጻኢ JSON ኣይዓወተን:", so: "Dhoofinta JSON way fashilantay:", af: "Ba'e JSON didde:" }, downloadFailed: { en: "Download failed:", am: "ማውረድ አልተሳካም:", or: "Laadachuu didde:", ti: "ምውራድ ኣይዓወተን:", so: "Soo dejinta way fashilantay:", af: "Laadachuu didde:" }, exportSuccess: { en: "Export successful", am: "ላክ ተሳክቷል", or: "Ba'e milkoofe", ti: "ውጻኢ ዓወተ", so: "Dhoofinta way ku guulaysatay", af: "Ba'e milkoofe" }, csvSuccess: { en: "CSV export successful", am: "የ CSV ላክ ተሳክቷል", or: "Ba'e CSV milkoofe", ti: "ውጻኢ CSV ዓወተ", so: "Dhoofinta CSV way ku guulaysatay", af: "Ba'e CSV milkoofe" }, jsonSuccess: { en: "JSON export successful", am: "የ JSON ላክ ተሳክቷል", or: "Ba'e JSON milkoofe", ti: "ውጻኢ JSON ዓወተ", so: "Dhoofinta JSON way ku guulaysatay", af: "Ba'e JSON milkoofe" }, hintPng: { en: "PNG: High quality raster image", am: "PNG: ከፍተኛ ጥራት ያለው ራስተር ምስል", or: "PNG: Suura raasterii qulqullina ol", ti: "PNG: ስእሊ ራስተር ጥራት ህያው", so: "PNG: Sawir raster ah oo tayo leh", af: "PNG: Suura raasterii qulqullina ol" }, hintJpeg: { en: "JPEG: Smaller file size", am: "JPEG: ትንሽ ፋይል መጠን", or: "JPEG: Guddina faayilaa xinnacha", ti: "JPEG: መጠን ፋይል ንኣሽቱ", so: "JPEG: Cabbir fayl yar", af: "JPEG: Guddina faayilaa xinnacha" }, hintSvg: { en: "SVG: Vector format (best quality, scalable)", am: "SVG: ቬክተር ቅርጸት (ምርጡ ጥራት፣ በመለካት ሊጨምር)", or: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)", ti: "SVG: መልክዕ ቬክተር (ጥራት ህያው፣ ብጽንድር ዝወሰኽ)", so: "SVG: Qaab vector (tayada ugu fiican, cabbir la beddeli karo)", af: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)" }, hintCsv: { en: "CSV: Data export (crop range, hazard, vop, and admin details)", am: "CSV: የውሂብ ላክ (የተከስት ክልል፣ አደጋ፣ vop እና የአስተዳዳር ዝርዝሮች)", or: "CSV: Ba'aa daataa (bal'ina midhaan, haazaara, vop fi ibsaa bulchiinsa)", ti: "CSV: ውጻኢ ዳታ (ኣድማ፣ haazaara፣ vop ንኣስተዳዳሪ)", so: "CSV: Dhoofinta xogta (koonka beeraha, khatar, vop iyo faahfaahinta maamulka)", af: "CSV: Ba'aa daataa (bal'ina midhaan, haazaara, vop fi ibsaa bulchiinsa)" }, hintJson: { en: "JSON: Structured data export (same data as CSV)", am: "JSON: የተዋቀረ ውሂብ ላክ (እንደ CSV ተመሳሳይ ውሂብ)", or: "JSON: Ba'aa daataa qindaa'e (daata CSV wajjiin walqixa)", ti: "JSON: ውጻኢ ዳታ ተዋህዲ (ከ CSV ዝኸውን ዳታ)", so: "JSON: Dhoofinta xogta qaabaysan (xogta isla CSV)", af: "JSON: Ba'aa daataa qindaa'e (daata CSV wajjiin walqixa)" } }, hourSeasonLoss: { title: { en: "Export Hour Season Loss Chart:", am: "የሰዓት ወቅት ማጣት ቻርት ላክ:", or: "Chaartii Buraalli Sa'aa Ba'aa:", ti: "ውጻኢ ቻርት ምስሓት ወቅቲ:", so: "Soo deji chartaanka dhimashada saacada:", af: "Chaartii Buraalli Sa'aa Ba'aa:" }, chooseFormat: { en: "Choose format...", am: "የፎርማት ምርጫ...", or: "Qormaata filadhaa...", ti: "ሓይሊ ምረጹ...", so: "Dooro nooca...", af: "Qormaata filadhaa..." }, png: { en: "PNG (High Quality)", am: "PNG (ከፍተኛ ጥራት)", or: "PNG (Qulqullina Ol)", ti: "PNG (ጥራት ህያው)", so: "PNG (Tayada sarreysa)", af: "PNG (Qulqullina Ol)" }, jpeg: { en: "JPEG (Smaller File)", am: "JPEG (ትንሽ ፋይል)", or: "JPEG (Faayili Xinnacha)", ti: "JPEG (ፋይል ንኣሽቱ)", so: "JPEG (Faylka yar)", af: "JPEG (Faayili Xinnacha)" }, svg: { en: "SVG (Vector)", am: "SVG (ቬክተር)", or: "SVG (Veektora)", ti: "SVG (ቬክተር)", so: "SVG (Vector)", af: "SVG (Veektora)" }, csv: { en: "CSV (Data)", am: "CSV (ውሂብ)", or: "CSV (Daata)", ti: "CSV (ውሂብ)", so: "CSV (Xogta)", af: "CSV (Daata)" }, chartNotFound: { en: "Chart not found. Please make sure the hour season loss chart is rendered.", am: "ቻርት አልተገኘም። የሰዓት ወቅት ማጣት ቻርት መገናኛ እንደሆነ ያረጋግጡ።", or: "Chaartii hin argamne. Chaartii buraalli sa'aa kutaa ba'aa kana keessatti mul'ata jiraachuu hubadhaa.", ti: "ቻርት ኣይተረኽበን። ቻርት ምስሓት ወቅቲ ምስላ ከም ዝወሃብ ኣረጋግጹ።", so: "Chartaanka ma la helin. Hubi in chartaanka dhimashada saacada lagu muujiyay.", af: "Chaartii hin argamne. Chaartii buraalli sa'aa kutaa ba'aa kana keessatti mul'ata jiraachuu hubadhaa." }, noDataAvailable: { en: "No data available for CSV export", am: "ለ CSV ላክ ውሂብ የለም", or: "Daataan ba'uu CSV irratti hin jiru", ti: "ንውጻኢ CSV ውሂብ የለን", so: "Xogta dhoofinta CSV ma jirto", af: "Daataan ba'uu CSV irratti hin jiru" }, csvFailed: { en: "CSV export failed:", am: "የ CSV ላክ አልተሳካም:", or: "Ba'e CSV didde:", ti: "ውጻኢ CSV ኣይዓወተን:", so: "Dhoofinta CSV way fashilantay:", af: "Ba'e CSV didde:" }, downloadFailed: { en: "Download failed:", am: "ማውረድ አልተሳካም:", or: "Laadachuu didde:", ti: "ምውራድ ኣይዓወተን:", so: "Soo dejinta way fashilantay:", af: "Laadachuu didde:" }, exportSuccess: { en: "Export successful", am: "ላክ ተሳክቷል", or: "Ba'e milkoofe", ti: "ውጻኢ ዓወተ", so: "Dhoofinta way ku guulaysatay", af: "Ba'e milkoofe" }, hintPng: { en: "PNG: High quality raster image", am: "PNG: ከፍተኛ ጥራት ያለው ራስተር ምስል", or: "PNG: Suura raasterii qulqullina ol", ti: "PNG: ስእሊ ራስተር ጥራት ህያው", so: "PNG: Sawir raster ah oo tayo leh", af: "PNG: Suura raasterii qulqullina ol" }, hintJpeg: { en: "JPEG: Smaller file size", am: "JPEG: ትንሽ ፋይል መጠን", or: "JPEG: Guddina faayilaa xinnacha", ti: "JPEG: መጠን ፋይል ንኣሽቱ", so: "JPEG: Cabbir fayl yar", af: "JPEG: Guddina faayilaa xinnacha" }, hintSvg: { en: "SVG: Vector format (best quality, scalable)", am: "SVG: ቬክተር ቅርጸት (ምርጡ ጥራት፣ በመለካት ሊጨምር)", or: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)", ti: "SVG: መልክዕ ቬክተር (ጥራት ህያው፣ ብጽንድር ዝወሰኽ)", so: "SVG: Qaab vector (tayada ugu fiican, cabbir la beddeli karo)", af: "SVG: Qormaata veektora (qulqullina gaariitiin, bal'inaan)" }, hintCsv: { en: "CSV: Data export (hour, hazard, delta, and admin details)", am: "CSV: የውሂብ ላክ (ሰዓት፣ አደጋ፣ ዴልታ እና የአስተዳዳር ዝርዝሮች)", or: "CSV: Ba'aa daataa (sa'aa, haazaara, delta fi ibsaa bulchiinsa)", ti: "CSV: ውጻኢ ዳታ (ሰዓት፣ haazaara፣ delta ንኣስተዳዳሪ)", so: "CSV: Dhoofinta xogta (saac, khatar, delta iyo faahfaahinta maamulka)", af: "CSV: Ba'aa daataa (sa'aa, haazaara, delta fi ibsaa bulchiinsa)" } } } })55:T8871,nbTextOriginal = { return { legends: { language: { en: "language", am: "ቋንቋ" }, toc: { en: "in this notebook", am: "በዚህ ኖትቡክ ውስጥ" }, adminSelections: { country: { en: "country", am: "አገር" }, region: { en: "region", am: "ክልል" }, subregion: { en: "subregion", am: "ንዑስ ክልል" }, woreda: { en: "woreda", am: "ወረዳ" } }, scenario: { en: "scenario", am: "እቅድ" }, threshold: { en: "threshold", am: "ደረጃ" }, season: { en: "season", am: "ወቅት" }, seasonTypes: { planting: { en: "planting", am: "መትከል" }, harvesting: { en: "harvesting", am: "ማጨድ" } } }, sections: { quickInsightsGeneral: { header: { en: "Quick Insights for :::selection:::", am: "ፈጣን ግንዛቤዎች ለ :::selection:::" }, noDataBlurb: { en: "Currently, there is no available data for this country. Please, select another country.", am: "በአሁኑ ጊዜ ለዚህ አገር የሚገኝ ውሂብ የለም። እባክዎ ሌላ አገር ይምረጡ።" } }, overview: { title: { en: "Overview", am: "አጠቃላይ እይታ" }, intro: { en: "African food systems depend on manual labour for production, distribution, and processing. Economic development is leading to changes in the structure and size of the labour force. This change, in the number and size of the agricultural workforce, is happening in parallel to climate change. The intersection of these two drivers together pose a range of risks to the African continent's food systems. This notebook aims to communicate the size of the agricultural workforce at risk to extreme heat today, and in the future. It presents a new insight into this risk leveraging a range of new datasets and highlights how the workday for different labourers will be disrupted by climate change in the future. You can follow the narrative for the continent as a whole, or select individual countries for geography-specific statistics.", am: "የአፍሪካ የምግብ ስርዓቶች ለማምረት፣ ለማከፋፈል እና ለማቀነባበር በእጅ ጉልበት ላይ የተመሰረቱ ናቸው። ኢኮኖሚያዊ ልማት የሰው ኃይል መዋቅር እና መጠን ለውጥ እያምጣ ነው። ይህ ለውጥ፣ በየወቅቱ ሰራተኞች ቁጥር እና መጠን፣ ከአየር ንብረት ለውጥ ጋር ትይዩ እየተከሰተ ነው። የእነዚህ ሁለት አንገብጋቢ ሁኔታዎች መገናኛ ለአፍሪካ አህጉር የምግብ ስርዓቶች ተለያይ ተጽዕኖዎችን ያስከትላል። ይህ ኖትቡክ በአሁኑ ጊዜ እና በወደፊቱ ለከፍተኛ ሙቀት በሚጋልጡ የወቅቱ ሰራተኞች መጠን ለመግለጽ የተዘጋጀ ነው። አዳዲስ የውሂብ ስብስቦችን በመጠቀም ለዚህ አደጋ አዳዲስ ግንዛቤዎችን ያቀርባል እንዲሁም የተለያዩ ሠራተኞች የሥራ ቀን በወደፊቱ በአየር ንብረት ለውጥ እንዴት እንደሚበላሽ ያብራራል። ለአህጉሩ በሙሉ ያለውን ዘገባ ሊከተሉ ወይም ለተወሰኑ አገራት የተለየ ስታቲስቲክስ ሊመርጡ ይችላሉ።" } }, futureWorkforce: { title: { en: "The Future of the Agricultural Workforce", am: "የወደፊቱ የወቅቱ ሰራተኞች" }, intro: { en: "The agricultural workforce covers the entire employed portion of the employable population involved in the agriculture sector. In the African continent, the vast majority of these are farmers. However, the number of farmers and the size of the workforce is not static, and depends on structural factors which drive rural population growth, push or pull the rural population towards urban areas over time. Here, you can explore how the size of the agricultural workforce today and how it will change as a result of these factors in the future.", am: "የወቅቱ ሰራተኞች በወቅቱ ስራ ውስጥ የሚሳተፉ የሥራ ክህሎት ያላቸው ሕዝብ ሙሉ ክፍል ይጨምራሉ። በአፍሪካ አህጉር አብዛኞቻቸው አርሶ አደሮች ናቸው። ሆኖም የአርሶ አደሮች ቁጥር እና የሰራተኞች መጠን የማይለወጥ አይደለም፣ እና የገጠር ሕዝብ እድገትን የሚያጋጥሙ፣ የገጠር ሕዝብን በጊዜ ሂደት ወደ ከተማ አካባቢዎች የሚጎትቱ ወይም የሚጎትቱ መዋቅራዊ ሁኔታዎች ላይ የተመሰረተ ነው። እዚህ የወቅቱ ሰራተኞች መጠን አሁን እንዴት እንዳለ እና በወደፊቱ በእነዚህ ሁኔታዎች ምክንያት እንዴት እንደሚቀየር ማሰስ ይችላሉ።" }, map: { caption: { en: `Grey regions signal lack of :::selector::: data`, am: `ግራጫ ክልሎች የ :::selector::: ውሂብ እጥረትን ያመለክታሉ` }, mapLayer: { title: { en: "Map Layer", am: "የካርታ ንብርብር" }, values: { ag_workforce_data_2: { label: { en: "Ag population baseline (circa 2005)", am: "የወቅቱ ሕዝብ መሠረት (በ2005 ግድም)" }, labelTip: { en: "Ag population", am: "የወቅቱ ሕዝብ" }, labelLegend: { en: "Ag population", am: "የወቅቱ ሕዝብ" } }, ag_workforce_data_1: { label: { en: "Percentage change by 2030", am: "እስከ 2030 የሚከሰት መቶኛ ለውጥ" }, labelTip: { en: "Population change by 2030", am: "እስከ 2030 የሚከሰት የሕዝብ ለውጥ" }, labelLegend: { en: "Population change by 2030 (%)", am: "እስከ 2030 የሚከሰት የሕዝብ ለውጥ (%)" } }, ag_workforce_data: { label: { en: "Percentage change by 2050", am: "እስከ 2050 የሚከሰት መቶኛ ለውጥ" }, labelTip: { en: "Population change by 2050", am: "እስከ 2050 የሚከሰት የሕዝብ ለውጥ" }, labelLegend: { en: "Population change by 2050 (%)", am: "እስከ 2050 የሚከሰት የሕዝብ ለውጥ (%)" } }, ag_workforce_data_15: { label: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030" }, labelTip: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030" }, labelLegend: { en: "Ag population in 2030", am: "የወቅቱ ሕዝብ በ2030" } }, ag_workforce_data_10: { label: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050" }, labelTip: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050" }, labelLegend: { en: "Ag population in 2050", am: "የወቅቱ ሕዝብ በ2050" } } } } }, quickInsights: { direction: { inc: { en: "an increase", am: "ጭማሪ" }, dec: { en: "a decrease", am: "ቅነሳ" } }, basePop: { en: `**:::geo:::**'s total agricultural population is **:::total:::** for the baseline year, circa 2005.`, am: `የ**:::geo:::** አጠቃላይ የወቅቱ ሕዝብ ለመሠረታዊው ዓመት፣ በ2005 ግድም **:::total:::** ነው።` }, popChange: { en: `The total agricultural population is expected to change to **:::change2030:::** by 2030, and to **:::change2050:::** by 2050, under SSP245. These represent **:::direction2030:::** of **:::perc2030:::%** and **:::direction2050:::** of **:::perc2050:::%**, respectively.`, am: `አጠቃላይ የወቅቱ ሕዝብ በSSP245 ስር እስከ 2030 ዓ.ም **:::change2030:::** እና እስከ 2050 ዓ.ም **:::change2050:::** እንደሚለወጥ ይጠበቃል። እነዚህ በቅደም ተከተል **:::direction2030:::** **:::perc2030:::%** እና **:::direction2050:::** **:::perc2050:::%** ይወክላሉ።` }, popScenario: { en: `Under SSP585, the total agricultural population will be **:::pop2030:::** by 2030 and **:::pop2050:::** by 2050. These represent **:::direction:::** of **:::perc2030:::%** and **:::perc2050:::%**, respectively.`, am: `በSSP585 ስር፣ አጠቃላይ የወቅቱ ሕዝብ እስከ 2030 ዓ.ም **:::pop2030:::** እና እስከ 2050 ዓ.ም **:::pop2050:::** ይሆናል። እነዚህ በቅደም ተከተል **:::direction:::** **:::perc2030:::%** እና **:::perc2050:::%** ይወክላሉ።` } } }, heatStress: { title: { en: "Heat Stress in Numbers", am: "የሙቀት ጫና በቁጥር" }, intro: { en: "An important question is how the increases in agricultural workforce on the African continent will be affected by climate change. A key indicator of climate change impacts on human well being is heat stress. The level of heat stress experienced by a person depends not only on temperature but also on humidity, which are together often represented in terms of Wet Bulb Globe Temperature, where thresholds exceeding 28°C are considered harmful, and those exceeding 32°C extremely harmful. Use the dropdowns below to explore how many days in the year the agricultural workforce will be exposed to harmful heat stress for specific geographies, temperature thresholds and climate change scenarios.", am: "አስፈላጊ ጥያቄ በአፍሪካ አህጉር ላይ ያለው የወቅቱ ሰራተኞች ጭማሪ በአየር ንብረት ለውጥ እንዴት እንደሚጎዳ ነው። የአየር ንብረት ለውጥ በሰው ደህንነት ላይ ያለው ዋና አመላካች የሙቀት ጫና ነው። የሙቀት ጫና ደረጃ በአንድ ሰው ላይ በሚኖረው ብቻ ሳይሆን በእርጥበት ላይም የተመሠረተ ነው፣ እነዚህም በአብዛኛው አንጻራዊ ሙቀት እንደ የሚወከሉ ሲሆን ከ28°ሴ በላይ የሆኑ ደረጃዎች ጎጂ እንደሆኑ እና ከ32°ሴ በላይ የሆኑት እጅግ ጎጂ እንደሆኑ ይቆጠራሉ። በዓመቱ ውስጥ የወቅቱ ሰራተኞች ለተወሰኑ ጂኦግራፊዎች፣ ለሙቀት ደረጃዎች እና ለአየር ንብረት ለውጥ እቅዶች ለጎጂ የሙቀት ጫና ለምን ያህል ቀናት እንደሚጋለጡ ለማሰስ ከዚህ በታች ያሉትን የዝርዝር ምረጥ ይጠቀሙ።" }, barChart: { caption: { en: "To ensure that the details across all bins are visible without being dominated by the initial range, the 0-5% bin has been intentionally excluded from this visualization.", am: "በሁሉም መደርደሪያዎች ላይ ያሉ ዝርዝሮች በመጀመሪያው ክልል ሳይቆጣጠሩ እንዲታዩ ለማድረግ ፣ 0-5% መደርደሪያ በማሰብ ከዚህ ምስላዊ አቀራረብ ውጭ ተወግዷል።" }, group: { baseline: { en: "Baseline", am: "መሠረት" }, projection: { en: "Projection", am: "ትንበያ" } }, xLabel: { en: "Percent of Year Exceeding Safe Working Conditions", am: "የሚጣሱ ደህንነታዊ የሥራ ሁኔታዎች የዓመት መቶኛ" }, yLabel: { en: "People Exposed", am: "ተጋላጭ ሰዎች" }, tooltip: { exposed: { en: "Ag pop exposed", am: "ተጋላጭ የወቅቱ ሕዝብ" }, period: { en: "Period", am: "ጊዜ" } } }, figureBody: { en: "The figure presents the count of agricultural workers subject to unsafe Wet Bulb Globe Temperature (WBGT) levels across the year, based on both baseline data and projections. For instance, if a projection displays 16 million people within the 45%-50% bin, it means that these individuals are exposed to unsafe working conditions for a portion of their day, 45% to 50% of the days in the year.", am: "ስዕሉ በመሠረታዊ ውሂብ እና በትንበያዎች ላይ በመመርኮዝ በዓመቱ ውስጥ ለአደገኛ የእርጥበት ግሎበ ሙቀት (WBGT) ደረጃዎች የተጋለጡ የወቅቱ ሰራተኞችን ቁጥር ያቀርባል። ለምሳሌ፣ አንድ ትንበያ 16 ሚሊዮን ሰዎችን በ45%-50% መደርደሪያ ውስጥ ከሚያሳይ ከሆነ፣ ይህ ማለት እነዚህ ሰዎች ለቀናቸው አንድ ክፍል፣ በዓመቱ 45% እስከ 50% ያሉ ቀናት ለአደገኛ የሥራ ሁኔታዎች ይጋለጣሉ ማለት ነው።" }, quickInsights: { overview: { en: `For over **100 days** in a year, the people at risk for the baseline were **:::baseRisk:::**; under the scenario **:::scenario:::** and year **:::year:::**, it ascends to **:::projectionRisk:::**.`, am: `ለከፍተኛ **100 ቀናት** በዓመት፣ ለመሠረታዊው አደጋ ያሉት ሰዎች **:::baseRisk:::** ነበሩ; በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ ወደ **:::projectionRisk:::** ይጨምራል።` }, half: { en: `For the baseline period, **:::baseline:::** people experienced unsafe working conditions for at least **half of the days** in the year. Under the scenario **:::scenario:::** and year **:::year:::**, it's expected to rise to **:::projection:::** people.`, am: `ለመሠረታዊው ጊዜ፣ **:::baseline:::** ሰዎች በዓመቱ ቢያንስ **ግማሽ ቀናት** ለአደገኛ የሥራ ሁኔታዎች ተጋልጠዋል። በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ ወደ **:::projection:::** ሰዎች እንደሚጨምር ይጠበቃል።` } } }, workingHours: { title: { en: "Loss of Safe Working Hours", am: "የደህንነታዊ የሥራ ሰዓቶች መጥፋት" }, intro: { en: "Importantly, it is not just the days of climate disruptions to the agricultural workforce that matter. The hours during the day and the time in the growing season, whether planting, or harvesting, all influence how these disruptions will take place and impact on the sector. Use the figure below to explore how these safe working hours will be disrupted for different populations under different climate scenarios, temperature thresholds, and different geographies on the African continent.", am: "አስፈላጊ ነገር የሚባለው የአየር ንብረት ማቋረጥ ለወቅቱ ሰራተኞች የሚጋለጡት ቀናት ብቻ አይደለም። በቀኑ ውስጥ ያሉ ሰዓቶች እና በእድገት ወቅት ያለው ጊዜ፣ መትከል ወይም ማጨድ ሲሆን፣ እነዚህ ማቋረጦች እንዴት እንደሚከሰቱ እና በስራው ላይ እንዴት እንደሚኖራቸው ተጽዕኖ ሁሉም ይኖራቸዋል። እነዚህ ደህንነታዊ የሥራ ሰዓቶች ለተለያዩ ሕዝቦች በተለያዩ የአየር ንብረት እቅዶች፣ በሙቀት ደረጃዎች እና በአፍሪካ አህጉር ላይ ባሉ የተለያዩ ጂኦግራፊዎች እንዴት እንደሚበላሹ ለማሰስ ከዚህ በታች ያለውን ስዕል ይጠቀሙ።" }, barChart: { xLabel: { en: "Hour of Day", am: "የቀን ሰዓት" }, yLabel: { en: "Percent Loss of Season due to Extreme Heat", am: "በከፍተኛ ሙቀት ምክንያት የወቅት መቶኛ ኪሳራ" }, tooltip: { perc: { en: "Percent loss", am: "መቶኛ ኪሳራ" }, period: { en: "Period", am: "ጊዜ" } } }, quickInsights: { intro: { en: `The figure illustrates the percentage of the harvesting and planting seasons during which the Wet Bulb Globe Temperature (WBGT) surpasses safe working limits, as per the selected threshold, relative to the baseline. This highlights the projected changes for the future. For example, a **15%** figure for the harvesting season at 11am indicates that, under the specified scenario and year, there will be **15% fewer days** out of the total season where the population can work safely at that time.`, am: `ስዕሉ የማጨድ እና የመትከል ወቅቶችን መቶኛ ያሳያል በዚህም ውስጥ የእርጥበት ግሎበ ሙቀት (WBGT) ከተመረጠው ደረጃ መሠረት ከደህንነታዊ የሥራ ገደቦች በላይ ይሆናል፣ ከመሠረታዊው ጋር በማነፃፀር። ይህ ለወደፊቱ የተቀመጡ ለውጦችን ያብራራል። ለምሳሌ፣ በማጨድ ወቅት በጧት 11 ሰዓት **15%** የሚል ቁጥር ከሆነ፣ ይህ ማለት በተገለጸው እቅድ እና ዓመት ስር፣ ሕዝቡ በዚያን ጊዜ በደህንነት ሊሠራበት ከሚችለው አጠቃላይ ወቅት **15% ከፍለ ቀናት** ያነሱ ይሆናሉ ማለት ነው።` }, body: { en: `Under scenario **:::scenario:::** and year **:::year:::**, a policy of avoiding unsafe working hours would translate into **:::totalHours:::** working hours lost during the harvesting season, spanning **:::daysHarvest:::** days; if we consider a common working day to have 8 hours, this translates into **:::wholeWorkingDays:::** total working days lost in harvesting in a year.`, am: `በ**:::scenario:::** እቅድ እና በ**:::year:::** ዓመት ስር፣ አደገኛ የሥራ ሰዓቶችን ለማስወገድ የሚያደርግ ፖሊሲ በማጨድ ወቅት **:::daysHarvest:::** ቀናት የሚቆይ ሲሆን **:::totalHours:::** የሥራ ሰዓቶች ኪሳራ ያስከትላል; አንድ የተለመደ የሥራ ቀን 8 ሰዓት ከያዘ ፣ ይህ በዓመት በማጨድ ወቅት ከጠቅላላው የሥራ ቀናት **:::wholeWorkingDays:::** ኪሳራ ማለት ነው።` }, lostDays: { en: `For the planting period, spanning **:::period:::** days, **:::geo:::** would lose **:::lostDays:::** whole working days.`, am: `ለመትከል ጊዜ፣ ለ**:::period:::** ቀናት የሚቆይ፣ **:::geo:::** **:::lostDays:::** ከጠቅላላ የሥራ ቀናት ያጣል።` } } }, summary: { title: { en: "Summary", am: "ማጠቃለያ" }, intro: { en: "Climate change will drive disruptions to the African agricultural labour force. This has important implications for economic development, human health, gender equity, as well as food security. The size of the expected disruptions depend not only on climate stressors, such as increasing heat stress, but also on the expected change in the size of the agricultural workforce. There are a number of locations where these two factors will collide, driving increased risks, loss of safe working hours, agricultural productivity, and ultimately the right to work in a safe climate for millions. A number of solutions have been proposed to deal with this stress, including shifting working hours shifting in working hours, increased shade on farms, increased mechanisation, improved worker safety protocols, improved hydration and other heat stress coping mechanisms. Ultimately however, solutions on the scale of adaptation required have yet to be developed, and based on the size of this risk need to be addressed urgently to maintain human health and environmental rights, agricultural productivity, and food security, now and in the future.", am: "የአየር ንብረት ለውጥ ለአፍሪካ የወቅቱ ሰራተኞች ማቋረጥ ያስከትላል። ይህ ለኢኮኖሚያዊ ልማት፣ ለሰው ደህንነት፣ ለጾታ እኩልነት እንዲሁም ለምግብ ዋስትና አስፈላጊ ተጽዕኖ አለው። የሚጠበቁ ማቋረጦች መጠን በአየር ንብረት አደጋዎች ላይ ብቻ ሳይሆን በወቅቱ ሰራተኞች መጠን ላይ በሚጠበቀው ለውጥ ላይም የተመሠረተ ነው። እነዚህ ሁለት ሁኔታዎች የሚጋጩባቸው በርካታ ቦታዎች አሉ፣ ይህም አደጋን የሚጨምር፣ ደህንነታዊ የሥራ ሰዓቶችን ኪሳራ፣ የወቅቱ ምርታማነት እና በመጨረሻም ለሚሊዮኖች በደህንነታዊ አየር ንብረት ውስጥ የመሥራት መብት ያስከትላል። ለዚህ ጫና ለመቋቋም በርካታ መፍትሄዎች ቀርበዋል፣ ከነዚህም ውስጥ የሥራ ሰዓቶችን ማዛወር፣ በግብርና ላይ ጥላ ማሳደግ፣ ሜካናይዜሽን ማሳደግ፣ የሰራተኞች ደህንነት ፕሮቶኮሎች ማሻሻል፣ የውሃ መጠቀም ማሻሻል እና ሌሎች የሙቀት ጫና መቋቋም ዘዴዎች ይገኙበታል። ሆኖም ግን፣ በሚፈለገው የማስተካከል ደረጃ ላይ ያሉ መፍትሄዎች እስካሁን አልተገኙም፣ እና በዚህ አደጋ መጠን ላይ በመመርኮዝ የሰው ደህንነት እና የአካባቢ መብቶች፣ የወቅቱ ምርታማነት እና ምግብ ዋስትና ለማቆየት አሁን እና በወደፊቱ አስቸኳይ መፍትሄ ማግኘት አለበት።" } }, methods: { title: { en: "Methods", am: "ዘዴዎች" }, agWorkforce: { title: { en: "Agricultural workforce", am: "የወቅቱ ሰራተኞች" }, body: { en: `We generated gridded predictions for the agricultural workforce by first harmonising subnational data from census statistics from a range of government sources, with national data from the [International Labour Organisation (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) on the proportion of the population employed in agriculture. Then, using a geospatial version of the predictive framework introduced by [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y), we fit functions relating workforce size to [population statistics from Jones and O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population), [gridded gdp from Wang and Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x), and [agricultural land area by Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952). We employed stacked machine learning models (boosted regression trees, random forests) for the fitting process. Using these models and current and future gridded data we generated baseline-period (circa 2005) and future (2030, 2050) estimates of the agricultural workforce at 0.083 degrees resolution for the African continent.`, am: `የወቅቱ ሰራተኞች ለመገመት በመጀመሪያ የተለያዩ የመንግሥት ምንጮች የሆኑ የስታቲስቲክስ የሕዝብ ቆጠራ ውሂብን ከ[ዓለም አቀፍ የሥራ ድርጅት (ILO)](https://rshiny.ilo.org/dataexplorer36/?lang=en&segment=indicator&id=EAP_2WAP_SEX_AGE_RT_A) ከሚገኘው በወቅቱ ስራ ላይ የሚሰማሩ ሕዝብ መጠን ጋር በማጣመር ጀምረናል። ከዚያ [Mehrabi, Z (2023) Nat Sust](https://doi.org/10.1038/s41893-023-01110-y) የሚያስተዋውቀውን የጂኦስፕሻል ትንበያ መዋቅር በመጠቀም፣ የሰራተኞችን መጠን ከ[Jones and O'Neill (2016)](https://www.cgd.ucar.edu/sections/iam/modeling/spatial-population) የሕዝብ ስታቲስቲክስ፣ ከ[Wang and Sun (2022)](https://www.nature.com/articles/s41597-022-01300-x) የጂዲፒ መረጃ እና ከ[Ramankutty et al. (2008)](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2007GB002952) የወቅቱ መሬት ስፋት ጋር የሚያገናኝ ተግባር አጣመርን። ለማጣመር ሂደት የማሽን ማሰልጠን ሞዴሎችን (boosted regression trees, random forests) ተጠቀምን። እነዚህን ሞዴሎች እና የአሁኑን እና የወደፊቱን የመረጃ ማዕቀፎች በመጠቀም ለአፍሪካ አህጉር በ0.083 ዲግሪ ጥራት ያለው መሠረታዊ-ጊዜ (በ2005 ግድም) እና የወደፊት (2030፣ 2050) የወቅቱ ሰራተኞች ግምት አዘጋጅተናል።` } }, heatStress: { title: { en: "Heat stress", am: "የሙቀት ጫና" }, body: { en: `We use high-resolution, daily gridded data from the Climate Hazards Center Coupled Model Intercomparison Project Phase 6 (CHC-CMIP6) dataset [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w). The CHC-CMIP6 dataset provides future estimates for 2030 and 2050, whose delta fields are based on the CMIP6 multi-model ensemble forecasts for the SSP 245 and SSP 585 scenarios. As inputs to compute WBGT values, we take from the dataset three main variables: the daily maximum and minimum temperatures (Tmax and Tmin), as well as the maximum daily relative humidity (RHx) that occurs at the time of Tmax, for both, the observation period (circa 2005, average taken across 10 years), and the future projections under SSP 2.45 and 5.85 (years 2030 and 2050, average taken across 10 years).`, am: `ከClimate Hazards Center Coupled Model Intercomparison Project Phase 6 (CHC-CMIP6) የውሂብ ስብስብ [Williams et al. (2024)](https://www.nature.com/articles/s41597-024-03074-w) ከፍተኛ ጥራት ያለው ዕለታዊ የመረጃ ማዕቀፍ እንጠቀማለን። የCHC-CMIP6 የውሂብ ስብስብ ለ2030 እና 2050 የወደፊት ግምቶችን ይሰጣል፣ የዴልታ መስኮቻቸው ለSSP 245 እና SSP 585 እቅዶች በCMIP6 ባለብዙ-ሞዴል ኢንሰምብል ትንበያዎች ላይ የተመሰረቱ ናቸው። የWBGT እሴቶችን ለማስላት እንደ ግብዓቶች፣ ከውሂብ ስብስቡ ሦስት ዋና ተለዋዋጮችን እንወስዳለን፡ ዕለታዊ ከፍተኛ እና ዝቅተኛ ሙቀቶች (Tmax እና Tmin)፣ እንዲሁም በTmax ጊዜ የሚከሰት ከፍተኛ ዕለታዊ አንጻራዊ እርጥበት (RHx)፣ ለሁለቱም፣ ለመመልከቻ ጊዜ (በ2005 ግድም፣ አማካይ በ10 ዓመታት የተወሰደ)፣ እና ለወደፊቱ ትንበያዎች በSSP 2.45 እና 5.85 (ዓመታት 2030 እና 2050፣ አማካይ በ10 ዓመታት የተወሰደ)።` } }, hourlyExposure: { title: { en: "Hourly exposure", am: "የሰዓት ጋርታ" }, body: { en: `We examine agricultural workers' hourly exposure to heat stress during the harvesting and planting seasons, crucial periods in the agricultural calendar, where agricultural labourers are exposed to prolonged labour-intense outdoor activity, making them more susceptible to the impacts of heat stress. We use the crop calendars from [GGCMI Phase 3](https://zenodo.org/records/5062513), which provide the planting and harvesting dates for eighteen distinct crops and show exposure histograms during these periods. The histograms quantify the percentage of days within the harvesting or planting periods that surpass WBGT safety standards for each hour of the day. Here we compute and rely on hourly WBGT exceedances in present and future, for different SSPs and years. The calculations for the hourly temperature rely on Linvill's (1990) method for predicting hourly temperatures from daily minimum and maximum measurements. To compute such temperature profiles, we rely on daylength values, mainly taken from [Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html). This approach was used to create hourly temperature profiles; then, hourly heat index values relying on relative humidity values and following the [NOAA guidelines](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml); and, finally, hourly WBGT, which was evaluated at three different thresholds – 28, 30 and 32 degrees Celsius.`, am: `የወቅቱ ሰራተኞችን በማጨድ እና በመትከል ወቅቶች ውስጥ የሚጋለጡትን የሰዓት ጋርታ ለሙቀት ጫና እንመረምራለን፣ እነዚህም በወቅቱ ካለንደር ውስጥ ወሳኝ ጊዜዎች ናቸው፣ በዚህም ጊዜ የወቅቱ ሰራተኞች ለረጅም ጊዜ ጉልበት የሚጠይቅ ውጫዊ እንቅስቃሴ ይጋለጣሉ፣ ይህም ለሙቀት ጫና ተጽዕኖዎች የበለጠ ተጋላጭ ያደርጋቸዋል። የ[GGCMI Phase 3](https://zenodo.org/records/5062513) የእርሻ ካለንደሮችን እንጠቀማለን፣ እነዚህም ለአስራ ስምንት የተለያዩ እርሻዎች የመትከል እና የማጨድ ቀናትን ይሰጣሉ እና በእነዚህ ጊዜያት የጋርታ ሂስቶግራሞችን ያሳያሉ። ሂስቶግራሞቹ በማጨድ ወይም በመትከል ጊዜያት ውስጥ ለእያንዳንዱ የቀን ሰዓት የWBGT ደህንነት መስፈርቶችን የሚያልፉ ቀናትን መቶኛ ይለካሉ። እዚህ ላይ ለተለያዩ SSPs እና ዓመታት በአሁኑ እና በወደፊቱ የሰዓት WBGT ከመጠን በላይ የሆኑ እሴቶችን እናሰላለን እና እንጠቀምበታለን። ለሰዓታዊ ሙቀት ስሌቶች በLinvill (1990) ዘዴ ላይ የተመሰረቱ ናቸው ለሰዓታዊ ሙቀቶች ከዕለታዊ ዝቅተኛ እና ከፍተኛ መለኪያዎች ለማስተካከል። እንደነዚህ ያሉ የሙቀት መግለጫዎችን ለማስላት ፣ በዋነኛነት ከ[Spencer (1971)](https://www.mail-archive.com/sundial@uni-koeln.de/msg01050.html) የተወሰዱ የቀን ርዝመት እሴቶችን እንጠቀማለን። ይህ አቀራረብ የሰዓት ሙቀት መግለጫዎችን ለመፍጠር ተጠቅሟል; ከዚያ ፣ የሰዓት ሙቀት ኢንዴክስ እሴቶች በአንጻራዊ እርጥበት እሴቶች ላይ በመመርኮዝ እና የ[NOAA መመሪያዎችን](https://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml) በመከተል; እና ፣ በመጨረሻም ፣ የሰዓት WBGT ፣ እሱም በሦስት የተለያዩ ደረጃዎች ላይ ተገምግሟል - 28, 30 እና 32 ዲግሪ ሴንቲግሬድ።` } }, additionalMethods: { title: { en: "Additional methods", am: "ተጨማሪ ዘዴዎች" }, body: { en: `Full methodological details are contained in two forthcoming background papers: - Ormaza-Zulueta, N and Mehrabi, Z. *Mapping global agricultural workforce* - Ormaza-Zulueta, N and Mehrabi, Z. *Reductions in the agricultural workday in future due to climate change*`, am: `ሙሉ የዘዴ ዝርዝሮች በሁለት ቀጣይ የጀርባ ሰነዶች ውስጥ ተካትተዋል: - Ormaza-Zulueta, N and Mehrabi, Z. *የዓለም የወቅቱ ሰራተኞችን ካርታ ማውጣት* - Ormaza-Zulueta, N and Mehrabi, Z. *በወደፊቱ በአየር ንብረት ለውጥ ምክንያት በወቅቱ የሥራ ቀን መቀነስ*` }, linkNote: { en: "Links will be updated when published.", am: "አገናኞች በሚታተሙበት ጊዜ ይታደሳሉ።" } }, citation: { title: { en: "Citation", am: "ጥቅስ" }, body: { en: "If the above links to background papers are not live, please use the following citation for this analysis: Ormaza-Zulueta, N and Mehrabi, Z. 2024. Widespread Workforce Disruptions. Africa Agriculture Adaptation Atlas. CGIAR.", am: "ከላይ ያሉት ወደ ጀርባ ሰነዶች አገናኞች ከማይሰሩ ከሆነ ፣ እባክዎ ለዚህ ትንታኔ የሚከተለውን ጥቅስ ይጠቀሙ: Ormaza-Zulueta, N and Mehrabi, Z. 2024. በሰፊው የሰራተኞች ማቋረጥ. የአፍሪካ የወቅቱ አስተካከል አትላስ. CGIAR." } } } } }; }56:T621,styling = { return !toggleCustomStyling ? "no custom styling" : htl.html`` }57:T4cf,function tocOneliner({ selector = "h1", // selectors to include (can comma-separate, like "h1,h2,h3") heading = "In this notebook", // heading for ToC delim = ' | ', // delimiter between links skip = [], // text content for headers to exclude } = {}) { // return a one-liner table of contents // allow for skips // modified from https://observablehq.com/@mbostock/toc return Generators.observe(notify => { let headings = []; function observed() { let h = Array.from(document.querySelectorAll(selector)); h = h.filter((d) => !skip.includes(d.textContent)) if (h.length !== headings.length || h.some((h, i) => headings[i] !== h)) { notify(html`${heading}
${Array.from(headings = h, (h, i) => { const end = i === headings.length - 1 return Object.assign( html`${DOM.text(h.textContent)}${end ? '' : delim}`, {onclick: e => (e.preventDefault(), h.scrollIntoView())} ); })}`); } } const observer = new MutationObserver(observed); observer.observe(document.body, {childList: true, subtree: true}); observed(); return () => observer.disconnect(); }); }58:T17f7,// grab tabular data for choropleth dataGeoImpact = { // Check if we need to use mock woreda data (admin2 selected, admin3 not selected, Ethiopia only) if (adminSelections.selectAdmin2.value && !adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { const dataColumn = selectGeoDataType.dataColumn; const a2_name = adminSelections.selectAdmin2.value; const a1_name = adminSelections.selectAdmin1.value; // Use mockWoredaData for admin3 level const mockData = mockWoredaData .filter(d => d.admin0_name === adminSelections.selectAdmin0.value && d.admin1_name === a1_name && d.admin2_name === a2_name && d.admin3_name // Only admin3 rows have admin3_name populated ) .map(d => { // Map all the required columns based on selectGeoDataType const result = { admin0_name: d.admin0_name, admin1_name: d.admin1_name, admin2_name: d.admin2_name, admin3_name: d.admin3_name, }; // Map CSV columns to the expected dataColumn values if (selectGeoDataType.key == "ag_workforce_data_2") { result.corrected_ag_pop = d.corrected_ag_pop || 0; result[dataColumn] = d.corrected_ag_pop || 0; } else if (selectGeoDataType.key == "ag_workforce_data_1") { result.agw_total_mid = d.percentage_change_SSP2_2030_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2030_ag || 0; } else if (selectGeoDataType.key == "ag_workforce_data") { result.agw_total = d.percentage_change_SSP2_2050_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2050_ag || 0; } else if (selectGeoDataType.key == "ag_workforce_data_15") { result.agw_total_mid_abs = d.corrected_index_ag_pop_SSP2_2030 || 0; result[dataColumn] = d.corrected_index_ag_pop_SSP2_2030 || 0; } else if (selectGeoDataType.key == "ag_workforce_data_10") { result.agw_total_abs = d.corrected_index_ag_pop_SSP2_2050 || 0; result[dataColumn] = d.corrected_index_ag_pop_SSP2_2050 || 0; } else { result.agw_total_mid = d.percentage_change_SSP2_2030_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2030_ag || 0; } return result; }); return mockData; } // Check if admin3 is selected (single woreda) if (adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { const dataColumn = selectGeoDataType.dataColumn; const a3_name = adminSelections.selectAdmin3.value; const a2_name = adminSelections.selectAdmin2.value; const a1_name = adminSelections.selectAdmin1.value; // Use mockWoredaData for specific admin3 const mockData = mockWoredaData .filter(d => d.admin0_name === adminSelections.selectAdmin0.value && d.admin1_name === a1_name && d.admin2_name === a2_name && d.admin3_name === a3_name ) .map(d => { const result = { admin0_name: d.admin0_name, admin1_name: d.admin1_name, admin2_name: d.admin2_name, admin3_name: d.admin3_name, }; // Map CSV columns to the expected dataColumn values if (selectGeoDataType.key == "ag_workforce_data_2") { result.corrected_ag_pop = d.corrected_ag_pop || 0; result[dataColumn] = d.corrected_ag_pop || 0; } else if (selectGeoDataType.key == "ag_workforce_data_1") { result.agw_total_mid = d.percentage_change_SSP2_2030_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2030_ag || 0; } else if (selectGeoDataType.key == "ag_workforce_data") { result.agw_total = d.percentage_change_SSP2_2050_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2050_ag || 0; } else if (selectGeoDataType.key == "ag_workforce_data_15") { result.agw_total_mid_abs = d.corrected_index_ag_pop_SSP2_2030 || 0; result[dataColumn] = d.corrected_index_ag_pop_SSP2_2030 || 0; } else if (selectGeoDataType.key == "ag_workforce_data_10") { result.agw_total_abs = d.corrected_index_ag_pop_SSP2_2050 || 0; result[dataColumn] = d.corrected_index_ag_pop_SSP2_2050 || 0; } else { result.agw_total_mid = d.percentage_change_SSP2_2030_ag || 0; result[dataColumn] = d.percentage_change_SSP2_2030_ag || 0; } return result; }); return mockData; } // get data for choropleth map based on choice let dataSource; if (selectGeoDataType.key == "ag_workforce_data_2") { dataSource = dataPopulation; } else if (selectGeoDataType.key == "ag_workforce_data") { dataSource = dataTotalAgW; } else if (selectGeoDataType.key == "ag_workforce_data_15") { dataSource = dataTotalAgWMid_abs; } else if (selectGeoDataType.key == "ag_workforce_data_10") { dataSource = dataTotalAgW_abs; } else { dataSource = dataTotalAgWMid; } // select different data based on admin selections if (adminSelections.selectAdmin1.value) { // admin1 or 2 is selected, show all admin2's for selected admin1 return T.tidy( dataSource, T.filter((d) => { return d.admin0_name == adminSelections.selectAdmin0.value && d.admin1_name == adminSelections.selectAdmin1.value && d.admin2_name // always non-null }) ); } else if (adminSelections.selectAdmin0.value) { // admin0 is selected, with no admin1 // get all admin1 data for selected admin0 return T.tidy( dataSource, T.filter((d) => { return d.admin0_name == adminSelections.selectAdmin0.value && d.admin1_name // always non-null && !d.admin2_name // always null }) ) } else { // end case: admin0 is not selected // get all admin0 data return T.tidy( dataSource, T.filter((d) => { return !d.admin1_name // always null && !d.admin2_name // always null }) ) } } 59:Teda,// bind geojson to tabular data mapDataGeoImpact = { // admin3 selected - show specific woreda (Ethiopia only) if (adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { const data = dataGeoImpact.map(d => ({ ...d, a3_a2_a1_a0: [d.admin3_name, d.admin2_name, d.admin1_name, d.admin0_name].join("_") })); const geoData = { ...boundaries.admin3, features: boundaries.admin3.features.filter((d) => { return ( d.properties.admin3_name == adminSelections.selectAdmin3.value && d.properties.admin2_name == adminSelections.selectAdmin2.value && d.properties.admin1_name == adminSelections.selectAdmin1.value && d.properties.admin0_name == adminSelections.selectAdmin0.value ); }).map(f => ({ ...f, properties: { ...f.properties, a3_a2_a1_a0: [f.properties.admin3_name, f.properties.admin2_name, f.properties.admin1_name, f.properties.admin0_name].join("_") } })) }; return bindTabularToGeo({ data: data, dataBindColumn: "a3_a2_a1_a0", geoData: geoData, geoDataBindColumn: "a3_a2_a1_a0" }); } // admin2 selected - show all admin3 woredas (Ethiopia only) else if (adminSelections.selectAdmin2.value && !adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { const data = dataGeoImpact.map(d => ({ ...d, a3_a2_a1_a0: [d.admin3_name, d.admin2_name, d.admin1_name, d.admin0_name].join("_") })); const geoData = { ...boundaries.admin3, features: boundaries.admin3.features.filter((d) => { return ( d.properties.admin2_name == adminSelections.selectAdmin2.value && d.properties.admin1_name == adminSelections.selectAdmin1.value && d.properties.admin0_name == adminSelections.selectAdmin0.value ); }).map(f => ({ ...f, properties: { ...f.properties, a3_a2_a1_a0: [f.properties.admin3_name, f.properties.admin2_name, f.properties.admin1_name, f.properties.admin0_name].join("_") } })) }; return bindTabularToGeo({ data: data, dataBindColumn: "a3_a2_a1_a0", geoData: geoData, geoDataBindColumn: "a3_a2_a1_a0" }); } // no selections if (!adminSelections.selectAdmin0.value) { return bindTabularToGeo({ data: dataGeoImpact, dataBindColumn: "admin0_name", geoData: boundaries.admin0, geoDataBindColumn: "admin0_name" }); } // admin0 selected only else if (!adminSelections.selectAdmin1.value) { const data = T.tidy( dataGeoImpact, T.mutate({ a1_a0: (d) => [d.admin1_name, d.admin0_name].join("_") }) ); const geoData = { ...boundaries.admin1, features: boundaries.admin1.features.filter( (d) => d.properties.admin0_name == adminSelections.selectAdmin0.value ) }; return bindTabularToGeo({ data: data, dataBindColumn: "a1_a0", geoData: geoData, geoDataBindColumn: "a1_a0" }); } // admin1 is selected // show all admin2 regions within admin1 else { const data = T.tidy( dataGeoImpact, T.mutate({ a2_a1_a0: (d) => [d.admin2_name, d.admin1_name, d.admin0_name].join("_") }) ); const geoData = { ...boundaries.admin2, features: boundaries.admin2.features.filter( (d) => d.properties.admin1_name == adminSelections.selectAdmin1.value && d.properties.admin0_name == adminSelections.selectAdmin0.value ) }; return bindTabularToGeo({ data: data, dataBindColumn: "a2_a1_a0", geoData: geoData, geoDataBindColumn: "a2_a1_a0" }); } } 5a:Tf58,// dynamic insights for an Admin Selection dynamicInsights_geoImpact = { // Check if admin3 (woreda) is selected - use mock data let population; if (adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { // Use mock woreda data for population const woredaData = mockWoredaData.find(d => d.admin0_name === adminSelections.selectAdmin0.value && d.admin1_name === adminSelections.selectAdmin1.value && d.admin2_name === adminSelections.selectAdmin2.value && d.admin3_name === adminSelections.selectAdmin3.value ); if (woredaData) { // Map mock data to population structure population = [{ admin0_name: woredaData.admin0_name, admin1_name: woredaData.admin1_name, admin2_name: woredaData.admin2_name, total_pop: woredaData.total || 0, rural_pop: woredaData.rural || 0, ag_pop: woredaData.corrected_ag_pop || 0, ag_pop_2_30: woredaData.corrected_index_ag_pop_SSP2_2030 || 0, ag_pop_2_50: woredaData.corrected_index_ag_pop_SSP2_2050 || 0, ag_pop_5_30: woredaData.corrected_index_ag_pop_SSP5_2030 || 0, ag_pop_5_50: woredaData.corrected_index_ag_pop_SSP5_2050 || 0 }]; } else { // Fallback to empty data if woreda not found population = [{ total_pop: 0, rural_pop: 0, ag_pop: 0, ag_pop_2_30: 0, ag_pop_2_50: 0, ag_pop_5_30: 0, ag_pop_5_50: 0 }]; } } else { // Use regular database query for higher admin levels population = filterByAdminNames(dynInsightGeo_population); } // define data const totalVop = filterByAdminNames(dynInsightGeo_vopTotals); const perc30 = filterByAdminNames(dynInsightGeo_percentTotals_mid); const perc50 = filterByAdminNames(dynInsightGeo_percentTotals); // format functions const _formatTopNVop = formatUSD; const _formatNumber = formatNumCompactLong({locale: language.locale}); function getTopValue({ d, i } = {}) { return { data: d?.[i]?.agw_total, value: formatWithDefault({ value: d?.[i]?.agw_total, format: _formatNumber }), label: formatWithDefault({ value: d?.[i]?.crop }) }; } // make insights ------------------------------------ return { data: { totalVop, population }, insight: { totalVop: formatWithDefault({ value: totalVop?.[0]?.agw_total, format: _formatNumber }), totalPop: formatWithDefault({ value: population?.[0]?.total_pop, format: _formatNumber }), totalRuralPop: formatWithDefault({ value: population?.[0]?.rural_pop, format: _formatNumber }), totalAgPop: formatWithDefault({ value: population?.[0]?.ag_pop, format: _formatNumber }), totalAgPop_noformat: formatWithDefault({ value: population?.[0]?.ag_pop }), totalAgPop_2_30: formatWithDefault({ value: population?.[0]?.ag_pop_2_30, format: _formatNumber }), totalAgPop_2_30_noformat: formatWithDefault({ value: population?.[0]?.ag_pop_2_30 }), totalAgPop_2_50: formatWithDefault({ value: population?.[0]?.ag_pop_2_50, format: _formatNumber }), totalAgPop_2_50_noformat: formatWithDefault({ value: population?.[0]?.ag_pop_2_50 }), totalAgPop_5_30: formatWithDefault({ value: population?.[0]?.ag_pop_5_30, format: _formatNumber }), totalAgPop_5_30_noformat: formatWithDefault({ value: population?.[0]?.ag_pop_5_30 }), totalAgPop_5_50: formatWithDefault({ value: population?.[0]?.ag_pop_5_50, format: _formatNumber }), totalAgPop_5_50_noformat: formatWithDefault({ value: population?.[0]?.ag_pop_5_50 }), } }; } 5b:T450,dynInsightGeo_population = db.query(` with tbl as ( -- ag workforce population data select admin0_name , admin1_name , admin2_name , total , rural , corrected_ag_pop , corrected_index_ag_pop_SSP2_2050 , corrected_index_ag_pop_SSP2_2030 , corrected_index_ag_pop_SSP5_2050 , corrected_index_ag_pop_SSP5_2030 from ag_workforce_data order by admin0_name , admin1_name , admin2_name ) -- QUERY ------------------------------------------ ( -- total for all regions select null as admin0_name , null as admin1_name , null as admin2_name , * from ( select sum(total) as total_pop , sum(rural) as rural_pop , sum(corrected_ag_pop) as ag_pop , sum(corrected_index_ag_pop_SSP2_2030) as ag_pop_2_30 , sum(corrected_index_ag_pop_SSP2_2050) as ag_pop_2_50 , sum(corrected_index_ag_pop_SSP5_2050) as ag_pop_5_50 , sum(corrected_index_ag_pop_SSP5_2030) as ag_pop_5_30 from tbl where admin1_name is null ) union select * from tbl order by 1, 2, 3 ) `)5c:T475,// make the bar chart for top crops // approach is passing in data for selected hazard and crop type function makeTopCropBarChart(data) { // if no data is available for selected hazard, show a message if (data.length == 0) { return md`*No VoP exposure*`; } const topN = 10; return Plot.plot({ width, marginLeft: 100, caption: monetaryVoPNote, x: { tickFormat: formatUSD, label: "VoP at risk ($)", grid: true }, y: { label: null, tickSize: 0, }, color: { domain: hazardPlotLookup.color.domain, range: hazardPlotLookup.color.range }, marks: [ Plot.barX(data, { x: "vop", y: (d) => hazardPlotLookup.crops[d.crop], fill: (d) => hazardPlotLookup.names[d.hazard], sort: { y: "x", reverse: true, limit: 10 }, channels: { vop: { label: "VoP at risk", value: "vop" } }, tip: { format: { x: false, y: false, fill: false, vop: (d) => formatUSD(d) } } }) ] }); }5d:T7a9,dataHazardAdmin3 = { // Use mock woreda data for admin3 level (Ethiopia only) if (!adminSelections.selectAdmin3.value || adminSelections.selectAdmin0.value !== "Ethiopia") { return []; } // Normalize values for comparison (handle string/number mismatches) const expectedScenario = String(selectScenarioAndTimeframe.scenario); const expectedTimeframe = Number(selectScenarioAndTimeframe.timeframe); const expectedThreshold = Number(selectThreshold.threshold); // First, let's see what we have in the data const sampleRow = mockWoredaHeatStressData[0]; const filtered = mockWoredaHeatStressData .filter(d => { // Normalize CSV values for comparison const csvAdmin0 = String(d.admin0_name || "").trim(); const csvAdmin1 = String(d.admin1_name || "").trim(); const csvAdmin2 = String(d.admin2_name || "").trim(); const csvAdmin3 = String(d.admin3_name || "").trim(); const csvScenario = String(d.scenario || "").trim(); const csvTimeframe = Number(d.timeframe); const csvThreshold = Number(d.threshold); const matches = csvAdmin0 === adminSelections.selectAdmin0.value && csvAdmin1 === adminSelections.selectAdmin1.value && csvAdmin2 === adminSelections.selectAdmin2.value && csvAdmin3 === adminSelections.selectAdmin3.value && csvScenario === expectedScenario && csvTimeframe === expectedTimeframe && csvThreshold === expectedThreshold; return matches; }); return filtered.map(d => ({ admin0_name: d.admin0_name, admin1_name: d.admin1_name, admin2_name: d.admin2_name, admin3_name: d.admin3_name, scenario: d.scenario, timeframe: d.timeframe, crop: String(d.crop), // Ensure crop is string to match other queries threshold: d.threshold, hazard: d.hazard, vop: d.value || 0, total_pop: d.total_pop || 0, rural_pop: d.rural_pop || 0 })); }5e:T480,// make the bar chart for top crops // approach is passing in data for selected hazard and crop type function makeHourlyBarChart(data) { // if no data is available for selected hazard, show a message if (data.length == 0) { return md`*No VoP exposure*`; } const topN = 10; return Plot.plot({ width, marginLeft: 100, caption: monetaryVoPNote, x: { tickFormat: formatUSD, label: "VoP at risk ($)", grid: true }, y: { label: null, tickSize: 0, }, color: { domain: hourlyPlotLookup.color.domain, range: hourlyPlotLookup.color.range }, marks: [ Plot.barX(data, { x: "delta", y: (d) => hourlyPlotLookup.hours[d.hour_day], fill: (d) => hourlyPlotLookup.names[d.hazard], sort: { y: "x", reverse: true, limit: 10 }, channels: { delta: { label: "VoP at risk", value: "delta" } }, tip: { format: { x: false, y: false, fill: false, delta: (d) => formatUSD(d) } } }) ] }); }5f:T131c,dataHourlyAdmin3 = { // Use mock woreda data for admin3 level (Ethiopia only) if (!adminSelections.selectAdmin3.value || adminSelections.selectAdmin0.value !== "Ethiopia") { return []; } // Normalize values for comparison (handle string/number mismatches) const expectedScenario = String(selectScenarioAndTimeframe2.scenario); const expectedTimeframe = Number(selectScenarioAndTimeframe2.timeframe); const expectedThreshold = Number(selectThreshold2.threshold); // First, let's see what we have in the data const sampleRow = mockWoredaHourlyData[0]; console.log("DEBUG _dataHourlyAdmin3 - Sample CSV row:", sampleRow); console.log("DEBUG _dataHourlyAdmin3 - Expected values:", { admin0: adminSelections.selectAdmin0.value, admin1: adminSelections.selectAdmin1.value, admin2: adminSelections.selectAdmin2.value, admin3: adminSelections.selectAdmin3.value, scenario: expectedScenario, timeframe: expectedTimeframe, threshold: expectedThreshold }); const filtered = mockWoredaHourlyData .filter(d => { // Normalize CSV values for comparison const csvAdmin0 = String(d.admin0_name || "").trim(); const csvAdmin1 = String(d.admin1_name || "").trim(); const csvAdmin2 = String(d.admin2_name || "").trim(); const csvAdmin3 = String(d.admin3_name || "").trim(); const csvScenario = String(d.scenario || "").trim(); const csvTimeframe = Number(d.timeframe); const csvThreshold = Number(d.threshold); const adminMatch = csvAdmin0 === adminSelections.selectAdmin0.value && csvAdmin1 === adminSelections.selectAdmin1.value && csvAdmin2 === adminSelections.selectAdmin2.value && csvAdmin3 === adminSelections.selectAdmin3.value; const scenarioMatch = csvScenario === expectedScenario; const timeframeMatch = csvTimeframe === expectedTimeframe; const thresholdMatch = csvThreshold === expectedThreshold; // Debug first row that matches admin3 to see what's failing if (adminMatch && !scenarioMatch) { console.log("DEBUG - Admin matches but scenario doesn't:", { csvScenario, expectedScenario, csvScenarioType: typeof csvScenario, expectedScenarioType: typeof expectedScenario }); } if (adminMatch && scenarioMatch && !timeframeMatch) { console.log("DEBUG - Admin and scenario match but timeframe doesn't:", { csvTimeframe, expectedTimeframe, csvTimeframeType: typeof csvTimeframe, expectedTimeframeType: typeof expectedTimeframe, rawTimeframe: d.timeframe, rawTimeframeType: typeof d.timeframe }); } if (adminMatch && scenarioMatch && timeframeMatch && !thresholdMatch) { console.log("DEBUG - Admin, scenario, timeframe match but threshold doesn't:", { csvThreshold, expectedThreshold, csvThresholdType: typeof csvThreshold, expectedThresholdType: typeof expectedThreshold }); } return adminMatch && scenarioMatch && timeframeMatch && thresholdMatch; }); // Get rows that match admin3 to see what we have const admin3Matches = mockWoredaHourlyData.filter(d => String(d.admin3_name || "").trim() === adminSelections.selectAdmin3.value ); // Check what timeframes are available const availableTimeframes = [...new Set(admin3Matches.map(d => Number(d.timeframe)))]; const availableScenarios = [...new Set(admin3Matches.map(d => String(d.scenario || "").trim()))]; console.log("DEBUG _dataHourlyAdmin3 - Filter results:", { admin3: adminSelections.selectAdmin3.value, scenario: expectedScenario, timeframe: expectedTimeframe, threshold: expectedThreshold, mockDataLength: mockWoredaHourlyData.length, filteredLength: filtered.length, sampleFiltered: filtered[0], admin3MatchesCount: admin3Matches.length, availableTimeframes: availableTimeframes, availableScenarios: availableScenarios, // Show first few rows that match admin3 admin3Matches: admin3Matches.slice(0, 5).map(d => ({ scenario: String(d.scenario || "").trim(), timeframe: Number(d.timeframe), threshold: Number(d.threshold), hour_day: d.hour_day, hazard: d.hazard })) }); return filtered.map(d => ({ admin0_name: d.admin0_name, admin1_name: d.admin1_name, admin2_name: d.admin2_name, admin3_name: d.admin3_name, scenario: d.scenario, timeframe: d.timeframe, hour_day: String(d.hour_day), // Ensure hour_day is string to match other queries threshold: d.threshold, hazard: d.hazard, period: d.period, corrected_pop_sum: d.corrected_pop || 0, delta_pop_mean: d.delta_pop || 0, days_period: d.total_days || 0, delta: d.avg_count || 0 })); }60:T436,// pick the admin dataset based on admin selection dataHourlySelected = { // Check for admin3 (woreda) selection first (Ethiopia only) if (adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === "Ethiopia") { console.log("DEBUG _dataHourlySelected: Using dataHourlyAdmin3, length:", dataHourlyAdmin3.length); return dataHourlyAdmin3; } if (adminSelections.selectAdmin2.value) { console.log("DEBUG _dataHourlySelected: Using dataHourlyAdmin2, length:", dataHourlyAdmin2.length); return dataHourlyAdmin2; } else if (adminSelections.selectAdmin1.value) { console.log("DEBUG _dataHourlySelected: Using dataHourlyAdmin1, length:", dataHourlyAdmin1.length); return dataHourlyAdmin1; } else if (adminSelections.selectAdmin0.value) { console.log("DEBUG _dataHourlySelected: Using dataHourlyAdmin0, length:", dataHourlyAdmin0.length); return dataHourlyAdmin0; } else { console.log("DEBUG _dataHourlySelected: Using dataHourlyAdminAll, length:", dataHourlyAdminAll.length); return dataHourlyAdminAll; } } 61:T4f1,-- admin0, combine for Sub-Saharan Africa with data_tbl as ( with tbl as ( -- total for all admin0 regions with base as ( -- chosen scenario and timeframe, VoP select admin0_name , admin1_name , admin2_name , scenario , timeframe , hour_day , threshold , hazard , corrected_pop as corrected_pop_sum , delta_pop as delta_pop_mean , total_days as days_period , avg_count as delta from hourly_data where timeframe = ${selectScenarioAndTimeframe2.timeframe} and scenario = ${selectScenarioAndTimeframe2.scenario} and threshold = ${selectThreshold2.threshold} ) select * from base where admin1_name is null ) -- total VoP for each crop select threshold , scenario , timeframe , hour_day , hazard , SUM(corrected_pop_sum) as corrected_pop_sum , MEAN(delta_pop_mean) as delta_pop_mean , MEAN(delta) as delta , MEAN(days_period) as days_period from tbl group by scenario , timeframe , threshold , hazard , hour_day ) select null as admin0_name -- add admin values as nulls , null as admin1_name , null as admin2_name , * from data_tbl62:T4bc,function buildHierarchy(csv) { // Helper function that transforms the given CSV into a hierarchical format. const root = { name: "root", children: [] }; for (let i = 0; i < csv.length; i++) { const sequence = csv[i][0]; const size = +csv[i][1]; if (isNaN(size)) { // e.g. if this is a header row continue; } const parts = sequence.split("_"); let currentNode = root; for (let j = 0; j < parts.length; j++) { const children = currentNode["children"]; const nodeName = parts[j]; let childNode = null; let foundChild = false; // Search for existing child with the same name for (let k = 0; k < children.length; k++) { if (children[k]["name"] === nodeName) { childNode = children[k]; foundChild = true; break; } } // If not found, create a new child node if (!foundChild) { childNode = { name: nodeName, children: [] }; children.push(childNode); } currentNode = childNode; // If it's the last part of the sequence, create a leaf node if (j === parts.length - 1) { childNode.value = size; } } } return root; }63:T648,boundaries = { const input0 = await FileAttachment("atlas-region_admin0_harmonized.json").json() const input1 = await FileAttachment("atlas-region_admin1_harmonizedV2@1.json").json() const input2 = await FileAttachment("atlas-region_admin2_harmonizedV2.json").json() const input3 = await FileAttachment("eth_admbnda_adm3_csa_bofedb_2021_simplified (2).json").json() // Normalize admin3 properties to match expected names const admin3Normalized = { ...input3, features: input3.features.map(f => ({ ...f, properties: { admin3_name: f.properties.ADM31_EN || f.properties.admin3_name || f.properties.ADM3_EN, admin3_pcode: f.properties.ADM3_PCODE || f.properties.admin3_pcode, admin2_name: f.properties.ADM2_EN || f.properties.admin2_name, admin2_pcode: f.properties.ADM2_PCODE || f.properties.admin2_pcode, admin1_name: f.properties.ADM1_EN || f.properties.admin1_name, admin1_pcode: f.properties.ADM1_PCODE || f.properties.admin1_pcode, admin0_name: f.properties.ADM0_EN || f.properties.admin0_name, admin0_pcode: f.properties.ADM0_PCODE || f.properties.admin0_pcode, ...f.properties // keep everything else } })) }; const geo = { admin0: topojson.feature(input0, input0.objects["atlas-region_admin0_harmonized"]), admin1: topojson.feature(input1, input1.objects["atlas-region_admin1_harmonizedV2"]), admin2: topojson.feature(input2, input2.objects["atlas-region_admin2_harmonizedV2"]), admin3: admin3Normalized, // admin3 is already GeoJSON, not TopoJSON } return geo 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});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3669,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3870,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3574,"value":"$${_lang(nbText.sections.heatStress.figureBody)}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2743,"value":"headerQuickInsightsBars = {\n const adminArticle = _lang(getAdminSelection()?.data?.article1);\n const adminSelection = adminArticle ?? getAdminSelection().label;\n const text = Lang.reduceReplaceTemplateItems(\n _lang(nbText.sections.quickInsightsGeneral.header),\n [{ name: \"selection\", value: adminSelection }]\n );\n\n return md`### ${text}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2755,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2260,"value":"# ${_lang(nbText.sections.workingHours.title)}\n\n${_lang(nbText.sections.workingHours.intro)}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2882,"value":"viewBindAdminSelectors = // format admin selections\n// bind to appendix inputs, add template to format\nInputs.form(\n [\n Inputs.bind(\n Inputs.select(dataAdmin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof selectAdmin0\n ),\n Inputs.bind(\n Inputs.select(dataAdmin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n viewof selectAdmin1\n ),\n Inputs.bind(\n Inputs.select(dataAdmin2, { label: adminRegions.labels.admin2, format: x => x.label }),\n viewof selectAdmin2\n ),\n Inputs.bind(\n Inputs.select(dataAdmin3, { label: adminRegions.labels.admin2, format: x => x.label }),\n viewof selectAdmin3\n )\n ],\n {\n template: adminFormTemplate\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2885,"value":"viewof selectScenarioAndTimeframe2 = {\n // scenario and timeframe\n\n const data = [\n // {label: 'SSP245: 2030', scenario: 'SSP2', timeframe: '2030'},\n {label: 'SSP245: 2050', scenario: 'SSP245', timeframe: '2050'},\n // {label: 'SSP585: 2030', scenario: 'SSP5', timeframe: '2030'},\n {label: 'SSP585: 2050', scenario: 'SSP585', timeframe: '2050'},\n ]\n \n return Inputs.select(data, {\n label: _lang(nbText.legends.scenario),\n format: x => x.label,\n value: data.find(d => d.label === 'SSP245: 2050')\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2889,"value":"viewof selectThreshold2 = {\n // threshold\n\n const data = [\n {label: '28°C', threshold: '28'},\n {label: '30°C', threshold: '30'},\n {label: '32°C', threshold: '32'},\n ]\n \n return Inputs.select(data, {\n label: _lang(nbText.legends.threshold),\n format: x => x.label,\n value: data.find(d => d.label === '30')\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3570,"value":"viewof radios = {\n const data = [\n {\n label: Lang.toTitleCase(_lang(nbText.legends.seasonTypes.planting)),\n value: \"planting\"\n },\n {\n label: Lang.toTitleCase(_lang(nbText.legends.seasonTypes.harvesting)),\n value: \"harvesting\"\n }\n ];\n return Inputs.radio(data, {\n label: _lang(nbText.legends.season),\n format: (x) => x.label,\n value: data.find((x) => x.value == \"planting\")\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3569,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3876,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3065,"value":"headerQuickInsightsHourly = {\n const adminArticle = _lang(getAdminSelection()?.data?.article1);\n const adminSelection = adminArticle ?? getAdminSelection().label;\n const text = Lang.reduceReplaceTemplateItems(\n _lang(nbText.sections.quickInsightsGeneral.header),\n [{ name: \"selection\", value: adminSelection }]\n );\n\n return md`### ${text}\n${styling}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3117,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2615,"value":"# ${_lang(nbText.sections.summary.title)}\n\n${_lang(nbText.sections.summary.intro)}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3657,"value":"# ${_lang(nbText.sections.methods.title)}\n\n## ${_lang(nbText.sections.methods.agWorkforce.title)}\n${_lang(nbText.sections.methods.agWorkforce.body)}\n\n## ${_lang(nbText.sections.methods.heatStress.title)}\n${_lang(nbText.sections.methods.heatStress.body)}\n\n## ${_lang(nbText.sections.methods.hourlyExposure.title)}\n${_lang(nbText.sections.methods.hourlyExposure.body)}\n\n# ${_lang(nbText.sections.methods.additionalMethods.title)}\n${_lang(nbText.sections.methods.additionalMethods.body)}\n\n${_lang(nbText.sections.methods.additionalMethods.linkNote)}\n\n## ${_lang(nbText.sections.methods.citation.title)}\n${_lang(nbText.sections.methods.citation.body)}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2306,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2244,"value":"toc()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1750,"value":"---\n### Support notebook\n[Support Notebook](https://observablehq.com/@periscopic/ab-atlas-spotlight-support): used for imports across spotlights.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3655,"value":"---\n### Translation\n_Notebook language handling and main text_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3654,"value":"import { Lang } from \"7d46679f7d0d917e\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3653,"value":"languages = [\n { key: \"en\", label: \"English\", locale: 'en-US' },\n { key: \"am\", label: \"አማርኛ\", locale: 'am-ET' },\n {key: \"or\", label: \"Afaan Oromoo\", locale: 'or-ET'},\n {key: \"ti\", label: \"ትግርኛ\", locale: 'ti-ET'},\n {key: \"so\", label: \"Soomaali\", locale: 'so-ET'},\n {key: \"af\", label: \"Qafar af\", locale: 'af-ET'}\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3652,"value":"// get the default language key from parameter, or use default\ndefaultLangKey = {\n const name = \"lang\";\n const list = languages.map((d) => d.key);\n const defaultKey = \"en\";\n\n const queryParam = await Lang.getParamFromList({ name, list });\n\n return queryParam ?? defaultKey;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3651,"value":"// helper shorthand for Lang text-getting\n_lang = Lang.lg(language.key)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3650,"value":"// main toggle of language key\nviewof language = Inputs.radio(languages, {\n label: \"Main language toggle\",\n format: (d) => d.key,\n value: languages.find((x) => x.key === defaultLangKey),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3919,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3922,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3925,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3936,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3649,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3648,"value":"// data translation values\ntd = {\n let data = await FileAttachment(\"abAtlas-td-translation-data-v1-1.json\").json()\n\n // update SSA default\n data.admin0_name.values.SSA.en = \"Sub-Saharan Africa\"\n // cote d'ivoire, comma version duplicate\n data.admin0_name.values[\"Côte d’Ivoire\"] = data.admin0_name.values[\"Côte d'Ivoire\"]\n \n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1747,"value":"---\n### Notebook Variables\n_Main variables across notebook_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1746,"value":"// main title of the notebook\nnotebookTitle = \"Understand Heat Stress on Producers\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1745,"value":"// info for the admin selectors\nadminRegions = {\n return {\n labels: {\n admin0: _lang(nbText.legends.adminSelections.country),\n admin1: _lang(nbText.legends.adminSelections.region),\n admin2: _lang(nbText.legends.adminSelections.subregion),\n admin3: _lang(nbText.legends.adminSelections.woreda)\n },\n allLabels: {\n admin1: _lang(nbText.legends.adminSelections.allRegions),\n admin2: _lang(nbText.legends.adminSelections.allSubregions),\n admin3: _lang(nbText.legends.adminSelections.allWoredas)\n }\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2110,"value":"function getLowerLevelAdminLabel(selections=adminSelections) {\n // based on admin selection, get the lower level label \n if (selections.selectAdmin3.value) return adminRegions.labels.admin3\n if (selections.selectAdmin2.value) return adminRegions.labels.admin2\n if (selections.selectAdmin1.value) return adminRegions.labels.admin2\n if (selections.selectAdmin0.value) return adminRegions.labels.admin1\n else return adminRegions.labels.admin0\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2551,"value":"monetaryVoPNote = `Monetary VoP values are represented in 2005 international dollars.` // note about VoP source","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2180,"value":"// width of the maps\nmapWidth = 625","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1644,"value":"---\n### Notebook Styling\n*styling helpers*\nStylesheet is a named cell, which is necessary to include for embeds.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1813,"value":"**CSS styling**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1643,"value":"viewof toggleCustomStyling = Inputs.toggle({label: \"Use custom styling\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1642,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2175,"value":"**Table of Contents**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2174,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1812,"value":"**Color Scales**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1811,"value":"// color scale info\ncolorScales = {\n return {\n range: {\n green: ['#E4F5D0', '#015023'],\n blue: ['#E8F2FF', '#003E6B'],\n brown: ['#FFFDE5', '#A87B00'],\n yellowGreen: ['#F7D732', '#216729'],\n orangeRed: ['#F4BB21', '#EC5A47'],\n blueWhiteRed: ['#003E6B', '#FFFFFF', '#EC5A47'],\n redYellowGreen: [\"#EC5A47\", \"#F4BB21\", '#216729']\n },\n unknown: \"#ccc\"\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1257,"value":"---\n### Admin Selection\n*Global admin selector using dropdowns*\n\nThis is the global selector for the selected region\n- The completed data is contained within `adminSelections` to be used in queries\n- Dropdowns are defined below then bound for each section\n - Defined as separate cells since they dynamically respond to different values\n - In narrative, they are simply views of the data \n - Dropdowns are dynamic based on higher-level choices\n - Options are defined from the geographic data\n - `null` value means unselected region\n- Dropdowns are added to each section, using `Inputs.bind` to create synchronized inputs (see `viewof` for input definitions). A form input is used to consolidate and apply a template to format.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1256,"value":"// get admin0 options from geo\ndataAdmin0 = {\n const data = boundaries.admin0.features.map((d) => d.properties);\n // add a blank value\n return [null, ...data.map((d) => d.admin_name)].map((d) => {\n return {\n label:\n d == null\n ? globalSelection.label\n : _lang(td.admin0_name.values?.[d].general),\n value: d,\n data: d == null ? globalSelection.data : td.admin0_name.values?.[d],\n };\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1255,"value":"// get admin1 options based on admin0 selection\n// (the admin1 regions within selected admin0)\ndataAdmin1 = {\n // admin 1, filter by 0\n const data = boundaries.admin1.features.map(d => d.properties)\n .filter(d => d.admin0_name == selectAdmin0.value)\n const allLabel = { label: adminRegions.allLabels.admin1, value: null };\n return [allLabel, ...data.map(d => ({ label: d.admin1_name, value: d.admin1_name }))];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1254,"value":"// get admin2 options based on admin1 selection\n// (the admin2 regions within selected admin1)\ndataAdmin2 = {\n const data = boundaries.admin2.features.map(d => d.properties)\n .filter(d => d.admin0_name == selectAdmin0.value && d.admin1_name == selectAdmin1.value)\n const allLabel = { label: adminRegions.allLabels.admin2, value: null };\n return [allLabel, ...data.map(d => ({ label: d.admin2_name, value: d.admin2_name }))];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3793,"value":"dataAdmin3 =\n{\n if (!selectAdmin2.value || !selectAdmin0.value || selectAdmin0.value !== \"Ethiopia\") {\n return [{ label: adminRegions.allLabels.admin3, value: null }];\n }\n\n if (!boundaries.admin3 || !boundaries.admin3.features) {\n console.warn(\"Admin3 boundaries not loaded\");\n return [{ label: adminRegions.allLabels.admin3, value: null }];\n }\n \n const data = boundaries.admin3.features\n .filter(d => {\n const props = d.properties || {};\n return (\n props.admin0_name === \"Ethiopia\" &&\n props.admin1_name === selectAdmin1.value &&\n props.admin2_name === selectAdmin2.value &&\n props.admin3_name // Only include features with admin3_name\n );\n })\n .map(d => d.properties.admin3_name)\n .filter(name => name); // Remove any null/undefined names\n \n const uniqueWoredas = [...new Set(data)].sort();\n const allLabel = { label: adminRegions.allLabels.admin3, value: null };\n return [allLabel, ...uniqueWoredas.map(d => ({ label: d, value: d }))];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1265,"value":"viewof selectAdmin0 = Inputs.select(dataAdmin0, {label: adminRegions.labels.admin0, format: x => x.label,value: dataAdmin0.find(x => x.label === \"Ethiopia\")})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1264,"value":"viewof selectAdmin1 = Inputs.select(dataAdmin1, {label: adminRegions.labels.admin1, format: x => x.label})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1263,"value":"viewof selectAdmin2 = Inputs.select(dataAdmin2, {label: adminRegions.labels.admin2, format: x => x.label})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3796,"value":"viewof selectAdmin3 =\n{return(\nInputs.select(dataAdmin3, {label: adminRegions.labels.admin3, format: x => x.label})\n)}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1253,"value":"// selected admin regions from dropdowns\nadminSelections = ({\n selectAdmin0: selectAdmin0,\n selectAdmin1: selectAdmin1,\n selectAdmin2: selectAdmin2,\n selectAdmin3: selectAdmin3,\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1955,"value":"// template to format admin selection form\nadminFormTemplate = (inputs) => htl.html`

${inputs}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1339,"value":"---\n### Admin Selection - Display\n*Showing admin selections*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1338,"value":"// options for the global admin selection\n// used when no admin selection is made\nglobalSelection = {\n return {\n label: _lang(td.admin0_name.values.SSA.general),\n value: null,\n data: td.admin0_name.values.SSA\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1336,"value":"function getAdminSelection(selections=adminSelections) {\n // get the most granular admin selection made\n const a0 = selections.selectAdmin0\n const a1 = selections.selectAdmin1\n const a2 = selections.selectAdmin2\n const a3 = selections.selectAdmin3\n const global = globalSelection\n\n // Prioritize admin3 (woreda) if selected (Ethiopia only)\n return a3.value && a0.value === \"Ethiopia\"\n ? a3\n : a2.value\n ? a2\n : a1.value\n ? a1\n : a0.value\n ? a0\n : global\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3603,"value":"function getAdminSelection0(selections=adminSelections) {\n // get the most granular admin selection made\n const a0 = selections.selectAdmin0\n\n return a0\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1629,"value":"function getAdminSelectionAdmin0(selections=adminSelections) {\n // get the selected admin0 or global value\n const a0 = selections.selectAdmin0\n const global = globalSelection\n\n return a0.value\n ? a0\n : global\n} ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1335,"value":"function getAdminSelectionMarkdownPath(selections=adminSelections) {\n // function to show selected admin region\n // return path showing nested admin selection\n \n const delim = ' → '\n const path = [\n selections.selectAdmin0.label,\n selections.selectAdmin1.label,\n selections.selectAdmin2.label\n ].filter(d => d)\n \n const formatted = path\n .filter((d) => d)\n .map((d, i) => {\n return (i == path.length - 1)\n ? `**${d}**`\n : `*${d}*`\n })\n \n return formatted.length == 0\n ? `**${globalSelection.label}**` // no selection, all of Africa\n : `${formatted.join(delim)}`\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3637,"value":"getAdmin0WithAdminSubParens = ({\n articleField = \"general\",\n showParens = true\n} = {}) => {\n // getting admin0, with admin1 in parens\n // can specify admin0article lookup\n const a0 = adminSelections.selectAdmin0;\n const a1 = adminSelections.selectAdmin1;\n const a2 = adminSelections.selectAdmin2;\n const a3 = adminSelections.selectAdmin3;\n\n const a0WithArticle =\n a0.value == null\n ? _lang(td.admin0_name.values?.['SSA']?.[articleField])\n : _lang(td.admin0_name.values?.[a0.value]?.[articleField]);\n if (a1.value == null) return a0WithArticle;\n if (!showParens) return a0WithArticle;\n // If admin3 (woreda) is selected, show it; otherwise show admin2 or admin1\n const subLabel = a3.value ? a3.label : (a2.value == null ? a1.label : a2.label);\n return `${a0WithArticle} (${subLabel})`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1252,"value":"---\n### Choropleth map - Geographic Impact\n*Showing context for admin selection*\n\nThere is some hoop-jumping to get the right data based on the Admin Selection. The tabular data is then bound to the applicable geojson, and visualized with Plot.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1293,"value":"// population data\ndataPopulation = db.query(`\nselect * \nfrom ag_workforce_data\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1292,"value":"// total VoP data\ndataTotalAgW = db.query(`\nselect admin0_name\n , admin1_name\n , admin2_name\n , MEAN(percentage_change_SSP2_2050_ag) as agw_total\n from ag_workforce_data\n group by 1, 2, 3\n order by 1, 2, 3\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3337,"value":"// total VoP data\ndataTotalAgW_abs = db.query(`\nselect admin0_name\n , admin1_name\n , admin2_name\n , SUM(corrected_index_ag_pop_SSP2_2050) as agw_total_abs\n from ag_workforce_data\n group by 1, 2, 3\n order by 1, 2, 3\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2624,"value":"// total Ag_SSP2_2030 data\ndataTotalAgWMid = db.query(`\nselect admin0_name\n , admin1_name\n , admin2_name\n , MEAN(percentage_change_SSP2_2030_ag) as agw_total_mid\n from ag_workforce_data\n group by 1, 2, 3\n order by 1, 2, 3\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3332,"value":"// total VoP data\ndataTotalAgWMid_abs = db.query(`\nselect admin0_name\n , admin1_name\n , admin2_name\n , SUM(corrected_index_ag_pop_SSP2_2030) as agw_total_mid_abs\n from ag_workforce_data\n group by 1, 2, 3\n order by 1, 2, 3\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1251,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1250,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3800,"value":"mockWoredaData =\n{\n try {\n return await FileAttachment(\"mock_woreda_data@1.csv\").csv({ typed: true });\n } catch (error) {\n console.warn(\"Could not load mock woreda data:\", error);\n return [];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3844,"value":"mockWoredaHourlyData =\n{\n try {\n return await FileAttachment(\"mock_woreda_hourly_data@1.csv\").csv({ typed: true });\n } catch (error) {\n console.warn(\"Could not load mock woreda hourly data:\", error);\n return [];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1334,"value":"---\n### Dynamic Insights - Geographic Impact\n*quick insights for given admin selection*\n\n- Sql cells add a row that is the combined value for all admin regions, where all admin names are `null`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1453,"value":"Here are the combined data and insights for creating the dynamic narrative. Data tables are below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1333,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1332,"value":"---\n#### Data cells for insights","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1452,"value":"// total harvested area\ndynInsightGeo_percentTotals_mid = db.query(`\nwith tbl as (\n -- total percentages\n SELECT admin0_name, admin1_name, admin2_name, MEAN(percentage_change_SSP2_2030_ag) AS percent_change_30\n FROM ag_workforce_data\n GROUP BY 1, 2, 3\n ORDER BY 1, 2, 3\n)\n\n-- QUERY ------------------------------------------\n(\n -- total for all regions\n select null as admin0_name\n , null as admin1_name\n , null as admin2_name\n , percent_change_30\n from (\n select mean(percent_change_30) as percent_change_30\n from tbl\n where admin1_name is null\n )\n \n union\n \n select * \n from tbl\n \n order by 1, 2, 3\n)\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3425,"value":"// total harvested area\ndynInsightGeo_percentTotals = db.query(`\nwith tbl as (\n -- total percentages\n SELECT admin0_name, admin1_name, admin2_name, MEAN(percentage_change_SSP2_2050_ag) AS percent_change_50\n FROM ag_workforce_data\n GROUP BY 1, 2, 3\n ORDER BY 1, 2, 3\n)\n\n-- QUERY ------------------------------------------\n(\n -- total for all regions\n select null as admin0_name\n , null as admin1_name\n , null as admin2_name\n , percent_change_50\n from (\n select mean(percent_change_50) as percent_change_50\n from tbl\n where admin1_name is null\n )\n \n union\n \n select * \n from tbl\n \n order by 1, 2, 3\n)\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1451,"value":"dynInsightGeo_vopTotals = db.query(`\n-- total ag workforce\nwith tbl as (\n -- total VoP and harvested area\n SELECT admin0_name, admin1_name, admin2_name, sum(corrected_index_ag_pop_SSP2_2050) AS agw_total\n FROM ag_workforce_data\n GROUP BY 1, 2, 3\n ORDER BY 1, 2, 3\n)\n\n-- QUERY ------------------------------------------\n(\n -- total for all regions\n select null as admin0_name\n , null as admin1_name\n , null as admin2_name\n , agw_total\n from (\n select mean(agw_total) as agw_total\n from tbl\n where admin1_name is null\n )\n \n union\n \n select * \n from tbl\n \n order by 1, 2, 3\n)\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1694,"value":"---\n### Bar Charts - Hazards and Exposures\n*getting data based on admin selection and hazard options*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2243,"value":"#### Plot Function\nMaking the top VoP bar charts","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2242,"value":"$5c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2241,"value":"#### Hazard Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2240,"value":"➡️ The hazard data includes a `generic` crop, which we don't have total value of production data for in the other table. We exclude this from the chart.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2238,"value":"// data for selected hazard scenario, with total vop\ndata_hazardVoP = {return(\nT.tidy(\n dataHazardSelected,\n T.leftJoin(\n dataHazardVoPTotalSelected, \n {by: adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === \"Ethiopia\"\n ? ['admin0_name', 'admin1_name', 'admin2_name', 'admin3_name', 'crop']\n : ['admin0_name', 'admin1_name', 'admin2_name', 'crop']\n }\n )\n)\n)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2237,"value":"dataHazardVoPTotalSelected = {return(\nT.tidy(\n dataHazardSelected,\n T.groupBy(\n adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === \"Ethiopia\"\n ? [\"admin0_name\", \"admin1_name\", \"admin2_name\", \"admin3_name\", \"threshold\", \"scenario\", \"timeframe\", \"crop\"]\n : [\"admin0_name\", \"admin1_name\", \"admin2_name\", \"threshold\", \"scenario\", \"timeframe\", \"crop\"],\n [\n T.summarize({\n vop_total: T.sum(\"vop\")\n })\n ]\n )\n)\n)}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2236,"value":"// unique crops in the hazard data\nhazardCrops = {\n const query = await db.query(`select crop\n , count(*) as count\n from heat_stress_data\n group by 1\n order by 1`);\n\n return query\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2544,"value":"// formatting crop names\ncropNames = T.tidy(\n hazardCrops,\n T.distinct(\"crop\"),\n T.select('crop'),\n T.mutate({\n // Convert crop to number for sorting\n cropNum: d => parseInt(d.crop, 10),\n // Determine the type based on the original string format (adjust as needed)\n type: d => /_/.test(d.crop) ? 'livestock' : 'crop',\n // Format the crop name\n cropName: d => {\n let cropName = d.crop.charAt(0).toUpperCase() + d.crop.slice(1)\n if (/_/.test(d.crop)) cropName = cropName.replace('_', \" (\") + ')'\n return cropName\n }\n }),\n // Use the numeric version of crop for sorting, then remove it from the final result as it's no longer needed\n T.arrange(['type', 'cropNum']),\n T.select(['type', 'crop', 'cropName']) // Assuming you want to keep these fields in the final output\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2234,"value":"// lookups for hazard plot\nhazardPlotLookup = {\n return {\n names: {\n baseline: _lang(nbText.sections.heatStress.barChart.group.baseline),\n projection: _lang(nbText.sections.heatStress.barChart.group.projection)\n },\n color: {\n domain: [\n _lang(nbText.sections.heatStress.barChart.group.baseline),\n _lang(nbText.sections.heatStress.barChart.group.projection)\n ],\n range: [\"#B1B1B1\", \"#EC5A47\"]\n },\n crops: cropNames.reduce((acc, obj) => {\n acc[obj.crop] = obj.cropName;\n return acc;\n }, {})\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3822,"value":"mockWoredaHeatStressData =\n{\n try {\n return await FileAttachment(\"mock_woreda_heat_stress_data.csv\").csv({ typed: true });\n } catch (error) {\n console.warn(\"Could not load mock woreda heat stress data:\", error);\n return [];\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1691,"value":"#### Selecting Heat Stress data by scenario\n- The data is queried as separate components for each admin region to reduce query size and allow for dynamic null handling. A table is chosen based on the selected admin region.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3826,"value":"$5d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1690,"value":"// pick the admin dataset based on admin selection\ndataHazardSelected = {\n // Check for admin3 (woreda) selection first (Ethiopia only)\n if (adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === \"Ethiopia\") {\n return dataHazardAdmin3;\n }\n if (adminSelections.selectAdmin2.value) {\n return dataHazardAdmin2;\n }\n else if (adminSelections.selectAdmin1.value) {\n return dataHazardAdmin1;\n }\n else if (adminSelections.selectAdmin0.value) {\n return dataHazardAdmin0;\n }\n else {\n return dataHazardAdminAll;\n }\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2082,"value":"-- admin0, combine for Sub-Saharan Africa\nwith data_tbl as (\n with tbl as (\n -- total for all admin0 regions\n with base as (\n -- chosen scenario and timeframe, VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , crop\n , threshold\n , hazard\n , value as vop\n from heat_stress_data\n where timeframe = ${selectScenarioAndTimeframe.timeframe}\n and scenario = ${selectScenarioAndTimeframe.scenario}\n and threshold = ${selectThreshold.threshold}\n )\n \n select *\n from base\n where admin1_name is null\n )\n\n -- total VoP for each crop\n select threshold\n , scenario\n , timeframe\n , hazard\n , crop\n , SUM(vop) as vop\n from tbl\n group by threshold\n , scenario\n , timeframe\n , hazard\n , crop\n)\n\nselect null as admin0_name -- add admin values as nulls\n , null as admin1_name\n , null as admin2_name\n , *\n from data_tbl","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHazardAdminAll","database":"var:db"},{"id":2006,"value":"-- admin0 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , crop\n , threshold\n , hazard\n , value as vop\n from heat_stress_data\n where timeframe = ${selectScenarioAndTimeframe.timeframe}\n and scenario = ${selectScenarioAndTimeframe.scenario}\n and threshold = ${selectThreshold.threshold}\n)\n\nselect *\n from base\n where admin1_name is null\n and admin0_name = ${adminSelections.selectAdmin0.value}","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHazardAdmin0","database":"var:db"},{"id":2005,"value":"-- admin1 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , crop\n , threshold\n , hazard\n , value as vop\n from heat_stress_data\n where timeframe = ${selectScenarioAndTimeframe.timeframe}\n and scenario = ${selectScenarioAndTimeframe.scenario}\n and threshold = ${selectThreshold.threshold}\n)\n\nselect *\n from base\n where admin0_name = ${adminSelections.selectAdmin0.value}\n and admin1_name = ${adminSelections.selectAdmin1.value}\n and admin2_name is null","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHazardAdmin1","database":"var:db"},{"id":2004,"value":"-- admin2 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , crop\n , threshold\n , hazard\n , value as vop\n from heat_stress_data\n where timeframe = ${selectScenarioAndTimeframe.timeframe}\n and scenario = ${selectScenarioAndTimeframe.scenario}\n and threshold = ${selectThreshold.threshold}\n)\n\nselect *\n from base\n where admin0_name = ${adminSelections.selectAdmin0.value}\n and admin1_name = ${adminSelections.selectAdmin1.value}\n and admin2_name = ${adminSelections.selectAdmin2.value}","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHazardAdmin2","database":"var:db"},{"id":1693,"value":"---\n#### Helpers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1692,"value":"helpersHazard = {\n return {\n colorDomain: ['crop', 'livestock'],\n colorRange: ['seagreen', 'sienna']\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2231,"value":"---\n### Dynamic Insights - Heat Stress in Numbers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2230,"value":"data_selectedHazardCropType = T.tidy(\n data_hazardVoP,\n T.filter(d => \n d.scenario === selectScenarioAndTimeframe.scenario && \n d.timeframe === selectScenarioAndTimeframe.timeframe &&\n d.threshold == selectThreshold.threshold\n ),\n T.mutate({\n cropType: d => {\n if (riskDataTypes.crop.crop.includes(d.crop)) return 'crop';\n return 'other';\n },\n cropValue: d => parseFloat(d.crop) // Ensure cropValue is numeric\n }),\n T.arrange(['cropType', T.desc('vop')])\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3720,"value":"db.query(`SELECT * FROM heat_stress_data where admin0_name = 'Togo' AND admin1_name = 'Savanes' AND admin2_name IS NULL AND scenario = 'SSP2' AND threshold = 28 ORDER BY \"crop\"`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2229,"value":"// combined crop and livestock exposed for selection\ninsight_totalExposedVop = T.tidy(\n data_selectedHazardCropType,\n T.filter(d => d.cropType === \"crop\"),\n T.groupBy(['cropType', 'hazard'], [\n T.summarize({\n totalExposedVop: T.sum('vop'),\n totalExposedVopOver30: T.sum(d => d.cropValue >= 30 ? d.vop : 0), // Sum for crops over 30%\n totalExposedVopOver50: T.sum(d => d.cropValue >= 55 ? d.vop : 0) // Sum for crops over 50%\n })\n ])\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2228,"value":"insight_topExp = {\n const cropsByVop = T.tidy(\n data_selectedHazardCropType,\n T.select([\"cropType\", \"crop\", \"vop\", \"hazard\"]),\n T.filter(d => d.cropType == \"crop\")\n );\n \n // Find the top crop by vop\n const topCrop = T.tidy(\n cropsByVop,\n T.sliceMax(1, \"vop\")\n )[0];\n \n // Return the combination, including topCrop only if it's not '30' or '50'\n return [topCrop].filter(d => d !== undefined);\n};","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2227,"value":"// total vop exposed by each hazard\ninsight_rankedHazardVopTotals = T.tidy(\n data_hazardVoP,\n T.groupBy(['hazard'], [\n T.summarize({\n totalVop: T.sum('vop')\n })\n ]),\n T.filter((d) => d.hazard !== 'any'), // remove combined hazard\n T.arrange(T.desc('totalVop'))\n \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2897,"value":"---\n## BarChart - Loss of Safe Working Hours","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2999,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3854,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2903,"value":"// data for selected hazard scenario, with total vop\ndata_hourlyStress =\nT.tidy(\n dataHourlySelected,\n T.leftJoin(\n dataHazardHourlyTotalSelected, \n {by: adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === \"Ethiopia\"\n ? ['admin0_name', 'admin1_name', 'admin2_name', 'admin3_name', 'hour_day']\n : ['admin0_name', 'admin1_name', 'admin2_name', 'hour_day']\n }\n )\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2970,"value":"dataHazardHourlyTotalSelected = T.tidy(\n dataHourlySelected,\n T.groupBy(\n adminSelections.selectAdmin3.value && adminSelections.selectAdmin0.value === \"Ethiopia\"\n ? [\"admin0_name\", \"admin1_name\", \"admin2_name\", \"admin3_name\", \"threshold\", \"scenario\", \"timeframe\", \"hour_day\"]\n : [\"admin0_name\", \"admin1_name\", \"admin2_name\", \"threshold\", \"scenario\", \"timeframe\", \"hour_day\"],\n [\n T.summarize({\n delta_total: T.mean(\"delta\")\n })\n ]\n )\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2905,"value":"$60","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2907,"value":"-- admin0 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , hour_day\n , threshold\n , hazard\n , period\n , corrected_pop as corrected_pop_sum\n , delta_pop as delta_pop_mean\n , total_days as days_period\n , avg_count as delta\n from hourly_data\n where timeframe = ${selectScenarioAndTimeframe2.timeframe}\n and scenario = ${selectScenarioAndTimeframe2.scenario}\n and threshold = ${selectThreshold2.threshold}\n)\n\nselect *\n from base\n where admin1_name is null\n and admin0_name = ${adminSelections.selectAdmin0.value}","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHourlyAdmin0","database":"var:db"},{"id":2952,"value":"-- admin1 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , hour_day\n , threshold\n , hazard\n , period\n , corrected_pop as corrected_pop_sum\n , delta_pop as delta_pop_mean\n , total_days as days_period\n , avg_count as delta\n from hourly_data\n where timeframe = ${selectScenarioAndTimeframe2.timeframe}\n and scenario = ${selectScenarioAndTimeframe2.scenario}\n and threshold = ${selectThreshold2.threshold}\n)\n\nselect *\n from base\n where admin0_name = ${adminSelections.selectAdmin0.value}\n and admin1_name = ${adminSelections.selectAdmin1.value}\n and admin2_name is null","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHourlyAdmin1","database":"var:db"},{"id":2941,"value":"-- admin2 level hazard data\nwith base as (\n -- chosen scenario and timeframe VoP\n select admin0_name\n , admin1_name\n , admin2_name\n , scenario\n , timeframe\n , hour_day\n , threshold\n , hazard\n , period\n , corrected_pop as corrected_pop_sum\n , delta_pop as delta_pop_mean\n , total_days as days_period\n , avg_count as delta\n from hourly_data\n where timeframe = ${selectScenarioAndTimeframe2.timeframe}\n and scenario = ${selectScenarioAndTimeframe2.scenario}\n and threshold = ${selectThreshold2.threshold}\n)\n\nselect *\n from base\n where admin0_name = ${adminSelections.selectAdmin0.value}\n and admin1_name = ${adminSelections.selectAdmin1.value}\n and admin2_name = ${adminSelections.selectAdmin2.value}","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHourlyAdmin2","database":"var:db"},{"id":2958,"value":"$61","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"dataHourlyAdminAll","database":"var:db"},{"id":2932,"value":"// lookups for hazard plot\nhourlyPlotLookup = {\n return {\n names: {\n planting: Lang.toTitleCase(_lang(nbText.legends.seasonTypes.planting)),\n harvesting: Lang.toTitleCase(_lang(nbText.legends.seasonTypes.harvesting))\n },\n color: {\n domain: [\n Lang.toTitleCase(_lang(nbText.legends.seasonTypes.planting)),\n Lang.toTitleCase(_lang(nbText.legends.seasonTypes.harvesting)),\n ],\n range: [\n // red for both\n \"#4FB5B7\", \n \"#FCC42C\",\n ],\n // range: [\n // \"#808000\", // Olive Green\n // \"#B5651D\", // Light Brown\n // ],\n },\n hours: hourlyNames.reduce((acc, obj) => {\n acc[obj.hour_day] = obj.hourlyName;\n return acc;\n }, {})\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2936,"value":"// unique crops in the hazard data\nhazardHours = {\n const query = await db.query(`select hour_day\n , count(*) as count\n from hourly_data\n group by 1\n order by 1`);\n\n return query\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2934,"value":"// formatting crop names\nhourlyNames = T.tidy(\n hazardHours,\n T.distinct(\"hour_day\"),\n T.select('hour_day'),\n T.mutate({\n // Convert to number for sorting\n hourlyNum: d => parseInt(d.hour_day, 10),\n // Determine the type based on the original string format (adjust as needed)\n type: d => /_/.test(d.hour_day) ? 'livestock' : 'hour_day',\n // Format the name\n hourlyName: d => {\n let hourlyName = d.hour_day\n if (/_/.test(d.hour_day)) hourlyName = hourlyName.replace('_', \" (\") + ')'\n return hourlyName\n }\n }),\n // Use the numeric version of hour for sorting, then remove it from the final result as it's no longer needed\n T.arrange(['type', 'hourlyNum']),\n T.select(['type', 'hour_day', 'hourlyName']) \n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3123,"value":"### Dynamic Insights - Loss of Safe Working Hours","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3127,"value":"data_selectedHourly = T.tidy(\n data_hourlyStress\n ,\n T.filter(d => \n d.scenario === selectScenarioAndTimeframe2.scenario && \n d.timeframe === selectScenarioAndTimeframe2.timeframe \n ),\n T.mutate({\n hour_dayType: d => {\n if (hourlyDataTypes.hour_day.hour_day.includes(d.hour_day)) return 'hour_day';\n return 'other';\n },\n hour_dayValue: d => parseFloat(d.hour_day) // Ensure hour_dayValue is numeric\n }),\n T.select([\"hour_dayType\", \"hazard\", \"hour_day\", \"delta\", \"period\", \"days_period\", 'corrected_pop_sum', 'delta_pop_sum']),\n T.arrange(['hour_dayType', T.desc('delta')])\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3125,"value":"// hourly exposition data\ninsight_totalExposedHourly = T.tidy(\n data_selectedHourly,\n T.filter(d => d.hour_dayType === \"hour_day\"),\n T.groupBy(['hour_dayType', 'hazard'], [\n T.summarize({\n totalExposedHourly: T.sum('delta'),\n period: T.first('period'),\n days_period: T.first('days_period'),\n corrected_pop_sum: T.sum('corrected_pop_sum'),\n delta_pop_sum: T.mean('delta_pop_sum')\n })\n ])\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3151,"value":"insight_topHourly = {\n const hoursByDelta = T.tidy(\n data_selectedHourly,\n T.select([\"hour_dayType\", \"hour_day\", \"delta\"]),\n T.filter(d => d.hour_dayType == \"hour_day\")\n );\n \n // Find the top hour by delta\n const topHour_day = T.tidy(\n hoursByDelta,\n T.sliceMax(1, \"delta\")\n )[0];\n \n // Return the combination, including topCrop only if it's not '30' or '50'\n return [topHour_day].filter(d => d !== undefined);\n};","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1496,"value":"#### Helpers","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2221,"value":"icicle_alias = {\n const nameMap = {\n population: \"Total Population\",\n poverty0: \"Low Poverty\",\n poverty1: \"Moderate Poverty\",\n poverty2: \"High Poverty\",\n education0: \"0-1 Years of Education\",\n education1: \"1-5 Years of Education\",\n education2: \"5+ Years of Education\",\n gender0: \"Low Female Empowerment\",\n gender1: \"Moderate Female Empowerment\",\n gender2: \"High Female Empowerment\"\n };\n return nameMap;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2220,"value":"partition = (data) =>\n d3\n .partition()\n .padding(1)\n .size(narrow ? [height, width] : [width, height])(\n d3\n .hierarchy(data)\n .sum((d) => d.value)\n .sort((a, b) => b.value - a.value)\n )","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2219,"value":"icicle_color = d3\n .scaleOrdinal()\n .domain([\n \"population\",\n \"poverty2\",\n \"poverty1\",\n \"poverty0\",\n \"education2\",\n \"education1\",\n \"education0\",\n \"gender2\",\n \"gender1\",\n \"gender0\"\n ])\n .range([\"#efefef\", \"#ec5a47\", \"#fc8a34\", \"#f4bb21\", \"#f4bb21\", \"#fc8a34\", \"#ec5a47\", \"#f4bb21\", \"#fc8a34\", \"#ec5a47\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2218,"value":"targetHeight = 350","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2217,"value":"narrowHeight = 600","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2216,"value":"height = narrow ? narrowHeight : targetHeight","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2215,"value":"narrow = width <= 0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2214,"value":"segmentX = d => (narrow ? d.y0 : d.x0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2213,"value":"segmentY = d => (narrow ? d.x0 : d.y0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2212,"value":"segmentWidth = d => (narrow ? d.y1 - d.y0 : d.x1 - d.x0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2211,"value":"segmentHeight = d => (narrow ? d.x1 - d.x0 : d.y1 - d.y0)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2210,"value":"$62","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2209,"value":"breadcrumbWidth = 200","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2208,"value":"breadcrumbHeight = 35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2207,"value":"// Generate a string that describes the points of a breadcrumb SVG polygon.\nfunction breadcrumbPoints(d, i) {\n const tipWidth = 10;\n const points = [];\n points.push(\"0,0\");\n points.push(`${breadcrumbWidth},0`);\n points.push(`${breadcrumbWidth + tipWidth},${breadcrumbHeight / 2}`);\n points.push(`${breadcrumbWidth},${breadcrumbHeight}`);\n points.push(`0,${breadcrumbHeight}`);\n if (i > 0) {\n // Leftmost breadcrumb; don't include 6th vertex.\n points.push(`${tipWidth},${breadcrumbHeight / 2}`);\n }\n return points.join(\" \");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1282,"value":"### Tabular data\n- **ag_workforce_data**: ag workforce data showing current and projected ag populations under different scenarios and for different years. File: baseline_df_data.parquet.\n- **heat_stress_data**: number of ag workders facing different sums of days on a year where WBGT values exceed 30 degrees. File: heat_stress_data.parquet.\n- **hourly_data**: percent of hours across seasons (harvesting, planting) that exceed safe WBGT thresholds. File: hourly_data.parquet.\n- **population**: population data by admin levels\n - `population/population_long.parquet`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":8,"value":"db = {\n return DuckDBClient.of({\n population: FileAttachment(\"population_long.parquet\"),\n vop_ha: FileAttachment(\"exposure_adm_sum.parquet\"),\n ag_workforce_data: FileAttachment(\"baseline_df_data@16.parquet\"),\n heat_stress_data: FileAttachment(\"heat_stress_data@7.parquet\"),\n hourly_data: FileAttachment(\"hourly_data@20.parquet\"),\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":548,"value":"### Geographic data\nGeo files were converted to TopoJSON and reduced in complexity before uploading to the notebook, see [Simplify large spatial files](https://observablehq.com/d/258c68ee969e8ed5?collection=@periscopic/ab-atlas) notebook for method.\n- **admin0**: broadest level, country boundaries\n - `boundaries/atlas-region_admin0_harmonized.geojson`\n- **admin1**: one level down from `admin0`, regions within each `admin0` region\n - `boundaries/atlas-region_admin1_harmonized.geojson`\n- **admin2**: lowest level, subregions of every `admin1` region\n - `boundaries/atlas-region_admin2_harmonized.geojson`","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":557,"value":"$63","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1476,"value":"---\n### Risk data grouping\n*Lookups for risk data binning, such as hazards and crop types*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1475,"value":"// lookup for risk data type groupings\n// used by inputs and for grouping up data\nriskDataTypes = ({\n crop: {\n crop: [\n \"10\",\n \"15\",\n \"20\",\n \"25\",\n \"30\",\n \"35\",\n \"40\",\n \"45\",\n \"50\",\n \"55\",\n \"60\",\n \"65\",\n \"70\",\n \"75\",\n \"80\",\n \"85\",\n \"90\",\n \"95\"\n ],\n livestock: [\n \"cattle_highland\",\n \"cattle_tropical\",\n \"goats_highland\",\n \"goats_tropical\",\n \"pigs_highland\",\n \"pigs_tropical\",\n \"poultry_highland\",\n \"poultry_tropical\",\n \"sheep_highland\",\n \"sheep_tropical\"\n ]\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3129,"value":"// lookup for hourly data type groupings\n// used by inputs and for grouping up data\nhourlyDataTypes = ({\n hour_day: {\n hour_day: [\n 0,\n 1,\n 2,\n 3,\n 4,\n 5,\n 6,\n 7,\n 8,\n 9,\n 10,\n 11,\n 12,\n 13,\n 14,\n 15,\n 16,\n 17,\n 18,\n 19,\n 20,\n 21,\n 22,\n 23\n ]\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1327,"value":"---\n### Dynamic insights\n*functions for formatting results*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1326,"value":"function filterByAdminNames(data) {\n // filter down data by combined selected admin0/admin1/admin2/admin3 Admin Selections\n // pass data from query, use tidyjs to filter by admin selection\n const result = T.tidy(\n data,\n T.filter((d) => {\n const match = d.admin0_name == adminSelections.selectAdmin0.value\n && d.admin1_name == adminSelections.selectAdmin1.value\n && d.admin2_name == adminSelections.selectAdmin2.value;\n \n // If admin3 is selected, also filter by admin3\n if (adminSelections.selectAdmin3.value) {\n return match && d.admin3_name == adminSelections.selectAdmin3.value;\n }\n \n return match;\n })\n )\n return result\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1325,"value":"// wrapper for format functions, show chosen default\nfunction formatWithDefault({\n value=1000, \n format=d=>d, \n defaultValue=\"---\", \n debug=false\n}={}) {\n // apply a formatting function to a value\n // return default value if undefined or null\n if (!(value === null || value === undefined)) {\n return format(value)\n } else {\n return defaultValue\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1324,"value":"---\n### Data functions: format and transform\n*apply formatting to/transform a value*\n\n- Reference: [Intl.NumberFormat()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Intl/NumberFormat/NumberFormat)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1799,"value":"**Transforms**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1798,"value":"// transform non-number to percent value\ntransformPercent = (d) => {\n if (d === undefined || d === null) return d\n else return d * 100\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1797,"value":"**Formatting**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1796,"value":"// format non-number as percent\nformatPercentTenth = (d) => {\n if (d === undefined || d === null) return d\n else return d3.format('.1%')(d)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2167,"value":"// format non-number as percent\nformatPercentWhole = (d) => {\n if (d === undefined || d === null) return d\n else return d3.format('.0%')(d)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":449,"value":"// format US currency\nformatUSD = new Intl.NumberFormat(\"en-US\", {\n notation: \"compact\",\n compactDisplay: \"short\",\n style: \"currency\",\n currency: \"usd\"\n}).format","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1322,"value":"// format number, long notation\nformatNumCompactLong = ({ locale = \"en-US\" } = {}) =>\n new Intl.NumberFormat(locale, {\n notation: \"compact\",\n compactDisplay: \"long\",\n unitDisplay: \"long\"\n }).format","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1794,"value":"// format number, short notation\nformatNumCompactShort = ({ locale = \"en-US\" } = {}) =>\n new Intl.NumberFormat(locale, {\n notation: \"compact\",\n compactDisplay: \"short\"\n }).format","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1321,"value":"---\n### Querying data\n*functions for querying data*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":693,"value":"function getUniqueValues(col, tbl, db) {\n // return unique values of from db tbl as a list\n return db.query(`select distinct ${col} from ${tbl}`).then(x => x.map(d => d[col]))\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1242,"value":"function bindTabularToGeo({\n data = [],\n dataBindColumn = 'dataBindColumn',\n geoData = [],\n geoDataBindColumn = 'geoDataBindColumn'\n}) {\n // bind data to geojson\n const index = new Map(data.map(d => [d[dataBindColumn], d])) // map data by dataBindColumn\n const geojson = JSON.parse(JSON.stringify(geoData)); // do a copy, rather than mutate\n // join up data to geojson\n for (const f of geojson.features) {\n f.properties.data = index.get(f.properties[geoDataBindColumn]);\n }\n return geojson;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":264,"value":"***\n## Imports\n*External imports*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2500,"value":"import {toc} from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2498,"value":"import {T} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3742,"value":"selectGeoDataType","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W64","stdlib":"1"}} 46:"$45:body"