2b:null 2c:["$","title",null,{"children":"Adaptation Atlas Hackathon Notebook Starter Template | earth-observation | Observable"}] 31:T423,Track 5: Rainfall Reliability Explorer. In this track, the aim is to build a decision-support tool designed to help users assess how the start and duration of the rainy season have changed across Kenyan counties, a critical factor for agricultural planning. By analysing historical and projected climate data, the tool offers insights into seasonal variability, highlighting counties facing shifts in rainfall onset or growing period length. These insights are especially valuable for smallholder farmers and stakeholders planning climate-smart agriculture, insurance schemes, and adaptation investments. Tailored for county planners, researchers, and proposal developers, the tool should allow users to filter by county, time period, and climate scenario, and to export maps, graphs, and key data summaries. Optional features include county comparisons and crop-specific overlays. With a user-friendly interface and decision-ready outputs, the notebook will support evidence-based planning to improve resilience in Kenya’s rain-fed agricultural systems.32:T11cc,// ----------------------------- // Kenya Hazard Map // ----------------------------- viewof kenya_hazard_map = (async () => { const hazard = hazardSelector; const scenario = scenarioSelector; // Query hazard for Kenya, single timeframe const geoHazard = await hazard_db.query(` SELECT admin1_name, gaul1_code, value FROM hazard_mean WHERE admin0_name = 'Kenya' AND hazard = '${hazard}' AND scenario = '${scenario}' AND timeframe = '2021-2040' `); // Build GeoJSON for Kenya const geojson = buildKenyaGeojson(geoHazard); // Map container const container = htl.html`

`; const map = L.map(container).setView([-1.3, 37.0], 6); // Add OpenStreetMap tiles L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { attribution: "© OpenStreetMap contributors" }).addTo(map); // Compute min/max values for color scale const values = geojson.features.map(f => f.properties.hazard_value || 0); const minV = Math.min(...values); const maxV = Math.max(...values); // Color scale const colorScale = d3.scaleLinear() .domain([minV, maxV]) .range([0, 1]); // normalized function getColor(v) { if (v == null || v === 0) return "#ccc"; return d3.interpolateCool(colorScale(v)); } // Floating div for time series const floatingDiv = htl.html`

`; container.appendChild(floatingDiv); // Function to update time series when a county is clicked function updateTimeSeries(admin1_name) { hazard_db.query(` SELECT timeframe, value FROM hazard_mean WHERE admin0_name = 'Kenya' AND admin1_name = '${admin1_name}' AND hazard = '${hazard}' AND scenario = '${scenario}' ORDER BY timeframe `).then(rows => { floatingDiv.innerHTML = ""; const svg = d3.select(floatingDiv) .append("svg") .attr("width", 360) .attr("height", 200); const x = d3.scalePoint() .domain(rows.map(d => d.timeframe)) .range([40, 340]); const y = d3.scaleLinear() .domain(d3.extent(rows, d => d.value)) .nice() .range([160, 20]); const line = d3.line() .x(d => x(d.timeframe)) .y(d => y(d.value)); svg.append("g") .attr("transform", "translate(0,160)") .call(d3.axisBottom(x)); svg.append("g") .attr("transform", "translate(40,0)") .call(d3.axisLeft(y)); svg.append("path") .datum(rows) .attr("fill", "none") .attr("stroke", "steelblue") .attr("stroke-width", 2) .attr("d", line); svg.append("text") .attr("x", 180) .attr("y", 15) .attr("text-anchor", "middle") .text(`${admin1_name} - ${hazard} (${scenario})`); }); } // Draw Kenya counties L.geoJSON(geojson, { style: f => ({ fillColor: getColor(f.properties.hazard_value), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
${hazard}: ${f.properties.hazard_value?.toFixed(2)}`); layer.on("click", () => updateTimeSeries(f.properties.admin1_name)); } }).addTo(map); // Cool color legend const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(255, 255, 255, 0.8)"; div.style.padding = "6px 10px"; div.style.borderRadius = "6px"; div.style.boxShadow = "0 0 15px rgba(0,0,0,0.2)"; div.style.fontSize = "12px"; const grades = [0, 0.2, 0.4, 0.6, 0.8]; const labels = []; for (let i = 0; i < grades.length; i++) { const from = (minV + grades[i]*(maxV - minV)).toFixed(2); const to = i < grades.length - 1 ? (minV + grades[i+1]*(maxV - minV)).toFixed(2) : maxV.toFixed(2); const colorValue = d3.interpolateCool(grades[i]); labels.push( ` ${from} – ${to}` ); } div.innerHTML = `${hazard} (${scenario})
` + labels.join("
"); return div; }; legend.addTo(map); return container; })(); 33:T522,// // Query SOS and EOS using DER method for season 1 // viewof lgpSummary = phenology_db.query(` // SELECT admin1_name, season_yr, "DER.sos" AS SOS, "DER.eos" AS EOS // FROM kenya_phenology // WHERE season_num = 1 // `).then(rows => { // // Convert SOS/EOS to day-of-year and compute LGP // const cleanRows = rows.map(d => { // const sosDate = new Date(d.SOS); // const eosDate = new Date(d.EOS); // const startOfYear = new Date(sosDate.getFullYear(), 0, 1); // return { // ...d, // SOS_day: (sosDate - startOfYear) / (1000*60*60*24), // SOS in day-of-year // EOS_day: (eosDate - startOfYear) / (1000*60*60*24), // EOS in day-of-year // LGP_days: (eosDate - sosDate) / (1000*60*60*24) // Length of Growing Period // }; // }); // // Group by county // const byCounty = d3.group(cleanRows, d => d.admin1_name); // // Aggregate per county // return Array.from(byCounty, ([county, data]) => { // const lgpValues = data.map(d => d.LGP_days); // const avgLGP = d3.mean(lgpValues); // const sdLGP = d3.deviation(lgpValues); // return { // county, // avgLGP_days: +avgLGP.toFixed(1), // avgLGP_months: +(avgLGP/30).toFixed(1), // convert to months // sdLGP_days: +sdLGP.toFixed(1) // }; // }); // }); 34:T5bc,viewof lgpData = (async () => { const rows = await phenology_db.query(` SELECT admin1_name, season_yr, origin, "DER.sos" AS SOS, "DER.eos" AS EOS FROM kenya_phenology WHERE season_num = 1 `); // Convert dates to day-of-year and compute LGP in days const processed = rows.map(d => { const origin = new Date(d.origin); const sos = new Date(d.SOS); const eos = new Date(d.EOS); const SOS_day = (sos - origin) / (1000 * 60 * 60 * 24); let EOS_day = (eos - origin) / (1000 * 60 * 60 * 24); // wrap if EOS spills into next year // if (EOS_day < SOS_day) EOS_day += 365; let LGP_days = EOS_day - SOS_day; return { county: d.admin1_name, year: +d.season_yr, sos_day: SOS_day, LGP_days }; }); // Aggregate by county const byCounty = d3.group(processed, d => d.county); const summary = Array.from(byCounty, ([county, data]) => { const lgpValues = data.map(d => d.LGP_days); const sosValues = data.map(d => d.sos_day); const avgLGP = d3.mean(lgpValues); const sdLGP = d3.deviation(lgpValues); const avgSOS = d3.mean(sosValues); return { county, avgSOS_day: +avgSOS.toFixed(1), avgSOS_date: new Date(new Date(2000,0,1).getTime() + avgSOS*24*60*60*1000).toISOString().slice(5,10), avgLGP_days: +avgLGP.toFixed(1), avgLGP_months: +(avgLGP/30).toFixed(1), sdLGP_days: +sdLGP.toFixed(1) }; }); return summary; })(); 35:T624, viewof kenyaLGPMap = (async () => { const geojson = kenya_boundaries.map(f => { const match = viewof lgpData.find(d => d.county === f.properties.admin1_name); f.properties.avgLGP_days = match ? match.avgLGP_days : 0; return f; }); const container = html`

`; const map = L.map(container).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { attribution: "© OpenStreetMap contributors" }).addTo(map); // function getColor(d) { // if (d == null || d === 0) return "#ccc"; // const t = d / 180; // normalize assuming max LGP ~180 days // return d3.interpolateCool(t); // } // Compute min/max values for color scale // Extract avgLGP_days values const lgpValues = geojson.map(f => f.properties.avgLGP_days).filter(v => v > 0); // Compute min and max const minV = Math.min(...lgpValues); const maxV = Math.max(...lgpValues); // Color scale const colorScale = d3.scaleLinear() .domain([minV, maxV]) .range([0, 1]); // normalized function getColor(v) { if (v == null || v === 0) return "#ccc"; return d3.interpolateCool(colorScale(v)); } L.geoJSON(geojson, { style: f => ({ fillColor: getColor(f.properties.avgLGP_days), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP: ${f.properties.avgLGP_days.toFixed(1)} days`); } }).addTo(map); return container; })(); 36:T1f50,viewof phenologyMap2 = { const container = html`

`; const map = L.map(container).setView([0.1, 37.9], 6.5); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); const layerGroup = L.layerGroup().addTo(map); // Year display const yearDisplay = L.control({position: "topright"}); yearDisplay.onAdd = () => { const div = L.DomUtil.create("div", "year-display"); div.style.background = "rgba(0,0,0,0.5)"; div.style.color = "white"; div.style.padding = "5px 10px"; div.style.borderRadius = "6px"; div.style.fontSize = "18px"; div.style.fontWeight = "bold"; div.textContent = ""; return div; }; yearDisplay.addTo(map); // Legend control (with gradient) const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; return div; }; legend.addTo(map); // (async () => { // const raw = await phenology_db.query(` // SELECT admin1_name, season_yr, "DER.sos" AS SOS, "DER.eos" AS EOS // FROM kenya_phenology // WHERE season_num = 1 // `); // const data = raw.map(d => { // const sosDate = new Date(d.SOS); // const eosDate = new Date(d.EOS); // const startOfYear = new Date(sosDate.getFullYear(), 0, 1); // return { // admin1_name: d.admin1_name, // season_yr: +d.season_yr, // SOS_day: (sosDate - startOfYear) / (1000*60*60*24), // EOS_day: (eosDate - startOfYear) / (1000*60*60*24), // LGP: (eosDate - sosDate) / (1000*60*60*24) // }; // }); (async () => { const raw = await phenology_db.query(` SELECT admin1_name, season_yr, origin, "DER.sos" AS SOS, "DER.eos" AS EOS FROM kenya_phenology WHERE season_num = 1 `); const data = raw.map(d => { const origin = new Date(d.origin); const sos = new Date(d.SOS); const eos = new Date(d.EOS); const SOS_day = (sos - origin) / (1000 * 60 * 60 * 24); let EOS_day = (eos - origin) / (1000 * 60 * 60 * 24); if (EOS_day < SOS_day) EOS_day += 365; return { admin1_name: d.admin1_name, season_yr: +d.season_yr, SOS_day, EOS_day, LGP: EOS_day - SOS_day }; }); const grouped = d3.rollups( data, v => ({ SOS_day: d3.mean(v, d => d.SOS_day), EOS_day: d3.mean(v, d => d.EOS_day), LGP: d3.mean(v, d => d.LGP), LGP_sd: d3.deviation(v, d => d.LGP) }), d => d.admin1_name, d => d.season_yr ); const aggregated = []; for (const [county, byYear] of grouped) { for (const [year, stats] of byYear) { aggregated.push({ county, season_yr: year, ...stats }); } } const lgpExtent = d3.extent(aggregated, d => d.LGP); const radiusScale = d3.scaleLinear().domain(lgpExtent).range([5, 25]); const haloScale = d3.scaleLinear() .domain([0, d3.max(aggregated, d => d.LGP_sd || 0)]) .range([0, 20]); const years = Array.from(new Set(aggregated.map(d => d.season_yr))).sort((a,b)=>a-b); let idx = years.indexOf(year.value); let playing = false; // Store previous circle markers for smooth transition const countyMarkers = {}; function update(targetYear, t=1) { // Filter and optionally interpolate const prevYear = Math.floor(targetYear); const nextYear = Math.ceil(targetYear); const filteredPrev = aggregated.filter(d=>d.season_yr===prevYear); const filteredNext = aggregated.filter(d=>d.season_yr===nextYear); const interpolated = filteredPrev.map(d => { const nextData = filteredNext.find(n=>n.county===d.county) || d; return { county: d.county, SOS_day: d.SOS_day*(1-t) + nextData.SOS_day*t, EOS_day: d.EOS_day*(1-t) + nextData.EOS_day*t, LGP: d.LGP*(1-t) + nextData.LGP*t, LGP_sd: d.LGP_sd*(1-t) + (nextData.LGP_sd || 0)*t }; }); // Update year display yearDisplay.getContainer().textContent = `Year: ${targetYear.toFixed(1)}`; // Dynamic color scale const sosExtent = d3.extent(interpolated, d => d.SOS_day); const colorScale = d3.scaleSequential(d3.interpolateCool).domain(sosExtent.reverse()); // Update legend with gradient const legendDiv = legend.getContainer(); legendDiv.innerHTML = ` Legend

Late SOSEarly SOS

◯ White halo = higher variability (σ) `; interpolated.forEach(d => { const feature = kenya_boundaries.find(f => f.properties.admin1_name===d.county); if(!feature) return; const centroid = turf.centroid(feature).geometry.coordinates; if(!countyMarkers[d.county]) { // Create markers countyMarkers[d.county] = { main: L.circleMarker([centroid[1], centroid[0]], { radius: radiusScale(d.LGP), fillColor: colorScale(d.SOS_day), color: "none", fillOpacity:0.8 }).bindPopup(` ${d.county} (${year})
🌱 SOS: Day ${d.SOS_day.toFixed(0)}
🌾 EOS: Day ${d.EOS_day.toFixed(0)}
📏 LGP: ${d.LGP.toFixed(0)} days (~${(d.LGP/30).toFixed(1)} months)
±σ LGP: ${d.LGP_sd ? d.LGP_sd.toFixed(1) : "n/a"} days `).addTo(layerGroup), halo: L.circleMarker([centroid[1], centroid[0]], { radius: radiusScale(d.LGP)+haloScale(d.LGP_sd), color:"white", weight:2, fill:false, opacity:0.4 }).addTo(layerGroup) }; } else { // Animate marker countyMarkers[d.county].main.setRadius(radiusScale(d.LGP)); countyMarkers[d.county].main.setStyle({fillColor: colorScale(d.SOS_day)}); countyMarkers[d.county].halo.setRadius(radiusScale(d.LGP)+haloScale(d.LGP_sd)); } }); } const playBtn = html``; container.appendChild(playBtn); playBtn.onclick = () => { playing = !playing; playBtn.textContent=playing?"⏸ Pause":"▶ Play"; if(playing) step(); }; function step() { if(!playing) return; const currentYear = years[idx]; const nextYear = years[(idx+1)%years.length]; let t=0; const animate = () => { if(!playing) return; update(currentYear + t*(nextYear-currentYear), t); t+=0.1; if(t<=1) requestAnimationFrame(animate); else { idx = (idx+1)%years.length; setTimeout(step, 100); } }; animate(); } // Manual slider viewof year.addEventListener("input", ()=>{ playing=false; playBtn.textContent="▶ Play"; idx=years.indexOf(year.value); update(year.value); }); update(year.value); })(); invalidation.then(()=>map.remove()); return container; } 37:T441, // Convert to numeric day-of-year & compute LGP // processed = data.map(d => { // const sos = new Date(d.SOS); // const eos = new Date(d.EOS); // const startOfYear = new Date(sos.getFullYear(), 0, 1);//new Date(d.season_yr, 0, 1);//new Date(d.origin);// // return { // county: d.admin1_name, // year: +d.season_yr, // SOS_day: (sos - startOfYear)/(1000*60*60*24), // EOS_day: (eos - startOfYear)/(1000*60*60*24), // LGP: (eos - sos)/(1000*60*60*24) // }; // }); // Convert to numeric day-of-year & compute LGP processed = data.map(d => { // const origin = new Date(d.origin); const origin = new Date(d.season_yr, 0, 1); // Jan 1 of the season year const sos = new Date(d.SOS); const eos = new Date(d.EOS); const SOS_day = (sos - origin) / (1000 * 60 * 60 * 24); let EOS_day = (eos - origin) / (1000 * 60 * 60 * 24); // if (EOS_day < SOS_day) EOS_day += 365; return { county: d.admin1_name, year: +d.season_yr, season_num: d.season_num, origin, sos, eos, SOS_day, EOS_day, LGP: EOS_day - SOS_day }; });38:Ta36,viewof lgpTimeseriesHover = { const container = html`

`; // Plot container const plotContainer = html`

`; container.appendChild(plotContainer); // Main plot const plot = Plot.plot({ width: 1000, height: 800, marginLeft: 25, marginRight: 0, x: { domain: [2000, 2025], label: "Year" }, y: { domain: [20, 400], label: "Mean LGP (days)" }, color: { domain: sortedCounties, legend: false, // custom legend range: sortedCounties.map(c => countyColor.get(c)) }, marks: [ Plot.areaY(statsByCountyYear, { x: "year", y1: d => d.meanLGP - (d.sdLGP || 0), y2: d => d.meanLGP + (d.sdLGP || 0), fill: d => countyColor.get(d.county), fillOpacity: 0.2, curve: "linear" }), Plot.line(statsByCountyYear, { x: "year", y: "meanLGP", stroke: "county", strokeWidth: 3, curve: "linear", title: d => `${d.county} (${d.year}): ${d.meanLGP.toFixed(1)} ± ${d.sdLGP ? d.sdLGP.toFixed(1) : "n/a"}` }), Plot.dot(statsByCountyYear, { x: "year", y: "meanLGP", fill: "county", r: 3, title: d => `${d.county} (${d.year}): ${d.meanLGP.toFixed(1)} ± ${d.sdLGP ? d.sdLGP.toFixed(1) : "n/a"}` }) ] }); plotContainer.appendChild(plot); // Vertical legend const legendBox = html`

`; sortedCounties.forEach(c => { const item = html`

${c}

`; legendBox.appendChild(item); }); container.appendChild(legendBox); // Hover effect: highlight the county line + SD band + dots requestAnimationFrame(() => { const svg = plot.querySelector("svg"); const elems = svg.querySelectorAll("[data-county]"); elems.forEach(el => { const county = el.getAttribute("data-county"); el.style.transition = "0.2s"; el.addEventListener("mouseover", () => { elems.forEach(e => { e.style.opacity = e.getAttribute("data-county") === county ? 1 : 0.01; }); }); el.addEventListener("mouseout", () => { elems.forEach(e => e.style.opacity = 1); }); }); }); return container; } 39:T52b,// statsByCountyfilteredSOS = d3.rollups( // processedFixed, // values => { // const mean = arr => d3.mean(arr); // const sd = arr => d3.deviation(arr); // return { // meanSOS: mean(values.map(d => d.SOS_wrapped)), // sdSOS: sd(values.map(d => d.SOS_wrapped)), // minSOS: d3.min(values, d => d.SOS_wrapped), // maxSOS: d3.max(values, d => d.SOS_wrapped), // meanLGP: mean(values.map(d => d.LGP)), // sdLGP: sd(values.map(d => d.LGP)), // minLGP: d3.min(values, d => d.LGP), // maxLGP: d3.max(values, d => d.LGP) // }; // }, // d => d.county // ).map(([county, stats]) => ({ county, ...stats })); // // Compute mean, SD, min, max for SOS and LGP statsByCountyfilteredSOS = d3.rollups( filteredProcessed, values => { const mean = arr => d3.mean(arr); const sd = arr => d3.deviation(arr); return { meanSOS: mean(values.map(d => d.SOS_day)), sdSOS: sd(values.map(d => d.SOS_day)), minSOS: d3.min(values, d => d.SOS_day), maxSOS: d3.max(values, d => d.SOS_day), meanLGP: mean(values.map(d => d.LGP)), sdLGP: sd(values.map(d => d.LGP)), minLGP: d3.min(values, d => d.LGP), maxLGP: d3.max(values, d => d.LGP) }; }, d => d.county ).map(([county, stats]) => ({ county, ...stats })); 3a:Tadf,viewof phenologyBars = (async () => { const container = html`

`; // --- SOS Bars --- const sosPlot = Plot.plot({ width: 500, height: 700, marginLeft: 120, marginBottom: 50, style: { background: "black", color: "white" }, title: "Average Start of Season (2000–2025)", x: { label: "Day of Year (SOS)", labelOffset: 35 }, y: { domain: statsByCountyfilteredSOS.sort((a,b) => d3.ascending(a.meanSOS, b.meanSOS)).map(d => d.county), label: null }, marks: [ Plot.barX(statsByCountyfilteredSOS, { x: "meanSOS", y: "county", fill: "darkgreen", title: d => `${d.county}\nMean: DOY ${d.meanSOS.toFixed(0)}\n±${d.sdSOS.toFixed(0)}\nRange: ${d.minSOS.toFixed(0)} – ${d.maxSOS.toFixed(0)}` }), Plot.barX(statsByCountyfilteredSOS, { x: d => d.meanSOS + d.sdSOS, x2: "meanSOS", y: "county", fill: "gold", opacity: 0.7, title: d => `${d.county}\nSD: ${d.sdSOS.toFixed(0)} days` }), Plot.ruleX(statsByCountyfilteredSOS, { x1: "minSOS", x2: "maxSOS", y: "county", stroke: "white", strokeWidth: 1 }) ] }); container.querySelector("#sosBarsContainer").appendChild(sosPlot); // --- LGP Bars --- const lgpPlot = Plot.plot({ width: 500, height: 700, marginLeft: 120, marginBottom: 50, style: { background: "black", color: "white" }, title: "Average Length of Growing Period (2000–2025)", x: { label: "LGP (days)", labelOffset: 35 }, y: { domain: statsByCountyfilteredSOS.sort((a,b) => d3.ascending(a.meanLGP, b.meanLGP)).map(d => d.county), label: null }, marks: [ Plot.barX(statsByCountyfilteredSOS, { x: "meanLGP", y: "county", fill: "steelblue", title: d => `${d.county}\nMean: ${d.meanLGP.toFixed(1)} days\n±${d.sdLGP.toFixed(1)}\nRange: ${d.minLGP.toFixed(1)} – ${d.maxLGP.toFixed(1)}` }), Plot.barX(statsByCountyfilteredSOS, { x: d => d.meanLGP + d.sdLGP, x2: "meanLGP", y: "county", fill: "purple", opacity: 0.7, title: d => `${d.county}\nSD: ${d.sdLGP.toFixed(1)} days` }), Plot.ruleX(statsByCountyfilteredSOS, { x1: "minLGP", x2: "maxLGP", y: "county", stroke: "white", strokeWidth: 1 }) ] }); container.querySelector("#lgpBarsContainer").appendChild(lgpPlot); return container; })(); 3b:Tb42,viewof phenologyBarsOverYears = (async () => { const container = html`

`; // --- SOS Bars --- const sosPlot = Plot.plot({ width: 500, height: 700, marginLeft: 120, marginBottom: 50, style: { background: "black", color: "white" }, title: "Average Start of Season (2000–2025)", x: { label: "Day of Year (SOS)", labelOffset: 35 }, y: { domain: sosLgpTrends.sort((a,b) => d3.ascending(a.meanSOS_overYears, b.meanSOS_overYears)).map(d => d.county), label: null }, marks: [ Plot.barX(sosLgpTrends , { x: "meanSOS_overYears", y: "county", fill: "darkgreen", title: d => `${d.county}\nMean: DOY ${d.meanSOS_overYears.toFixed(0)}\n±${d.sdSOS_overYears.toFixed(0)}\nRange: ${d.minSOS.toFixed(0)} – ${d.maxSOS.toFixed(0)}` }), Plot.barX(sosLgpTrends , { x: d => d.meanSOS_overYears + d.sdSOS_overYears, x2: "meanSOS_overYears", y: "county", fill: "gold", opacity: 0.7, title: d => `${d.county}\nSD: ${d.sdSOS_overYears.toFixed(0)} days` }), Plot.ruleX(sosLgpTrends , { x1: "minSOS", x2: "maxSOS", y: "county", stroke: "white", strokeWidth: 1 }) ] }); container.querySelector("#sosBarsContainer").appendChild(sosPlot); // --- LGP Bars --- const lgpPlot = Plot.plot({ width: 500, height: 700, marginLeft: 120, marginBottom: 50, style: { background: "black", color: "white" }, title: "Average Length of Growing Period (2000–2025)", x: { label: "LGP (days)", labelOffset: 35 }, y: { domain: sosLgpTrends.sort((a,b) => d3.ascending(a.meanLGP_overYears, b.meanLGP_overYears)).map(d => d.county), label: null }, marks: [ Plot.barX(sosLgpTrends , { x: "meanLGP_overYears", y: "county", fill: "steelblue", title: d => `${d.county}\nMean: ${d.meanLGP_overYears.toFixed(1)} days\n±${d.sdLGP_overYears.toFixed(1)}\nRange: ${d.minLGP.toFixed(1)} – ${d.maxLGP.toFixed(1)}` }), Plot.barX(sosLgpTrends , { x: d => d.meanLGP_overYears + d.sdLGP_overYears, x2: "meanLGP_overYears", y: "county", fill: "purple", opacity: 0.7, title: d => `${d.county}\nSD: ${d.sdLGP_overYears.toFixed(1)} days` }), Plot.ruleX(sosLgpTrends , { x1: "minLGP", x2: "maxLGP", y: "county", stroke: "white", strokeWidth: 1 }) ] }); container.querySelector("#lgpBarsContainer").appendChild(lgpPlot); return container; })(); 3c:T113c, viewof kenyaPhenologyMaps = (async () => { // Container for side-by-side maps const container = html`

`; // Append to DOM (Observable automatically handles this) // --- LGP Map --- const lgpGeo = kenya_boundaries.map(f => { const match = viewof lgpData.find(d => d.county === f.properties.admin1_name); f.properties.avgLGP_days = match ? match.avgLGP_days : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.avgLGP_days).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolateYlGn(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.avgLGP_days), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP: ${f.properties.avgLGP_days.toFixed(1)} days`); } }).addTo(lgpMap); // Legend for LGP const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Average LGP (days)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS Map --- const sosGeo = kenya_boundaries.map(f => { const match = viewof lgpData.find(d => d.county === f.properties.admin1_name); f.properties.avgSOS_day = match ? match.avgSOS_day : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.avgSOS_day).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolateBlues(1-sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.avgSOS_day), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
SOS: DOY ${f.properties.avgSOS_day.toFixed(1)}`); } }).addTo(sosMap); // Legend for SOS const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Average SOS (DOY)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 3d:T10ba, viewof kenyaPhenologyMapsSD = (async () => { const container = html`

`; // --- LGP SD Map --- const lgpGeo = kenya_boundaries.map(f => { const match = statsByCountyfilteredSOS.find(d => d.county === f.properties.admin1_name); f.properties.sdLGP = match ? match.sdLGP : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.sdLGP).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolatePiYG(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.sdLGP), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP SD: ${f.properties.sdLGP.toFixed(1)} days`); } }).addTo(lgpMap); // Legend for LGP SD const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `LGP SD (days)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS SD Map --- const sosGeo = kenya_boundaries.map(f => { const match = statsByCountyfilteredSOS.find(d => d.county === f.properties.admin1_name); f.properties.sdSOS = match ? match.sdSOS : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.sdSOS).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolatePiYG(sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.sdSOS), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
SOS SD: ${f.properties.sdSOS.toFixed(1)} days`); } }).addTo(sosMap); // Legend for SOS SD const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `SOS SD (days)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 3e:T10bd,viewof kenyaPhenologyMapsyear = (async () => { const container = html`

`; // --- LGP Mean Map --- const lgpGeo = kenya_boundaries.map(f => { const match = sosLgpTrends.find(d => d.county === f.properties.admin1_name); f.properties.meanLGP = match ? match.meanLGP_overYears : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.meanLGP).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolateYlGn(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.meanLGP), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
Mean LGP: ${f.properties.meanLGP.toFixed(1)} days`); } }).addTo(lgpMap); // --- LGP Legend --- const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Mean LGP (days)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS Mean Map --- const sosGeo = kenya_boundaries.map(f => { const match = sosLgpTrends.find(d => d.county === f.properties.admin1_name); f.properties.meanSOS = match ? match.meanSOS_overYears : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.meanSOS).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolateBlues(1-sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.meanSOS), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
Mean SOS: DOY ${f.properties.meanSOS.toFixed(1)}`); } }).addTo(sosMap); // --- SOS Legend --- const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Mean SOS (DOY)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 3f:T10d1,viewof kenyaPhenologyMapsSDyear = (async () => { const container = html`

`; // --- LGP SD Map --- const lgpGeo = kenya_boundaries.map(f => { const match = sosLgpTrends.find(d => d.county === f.properties.admin1_name); f.properties.sdLGP = match ? match.sdLGP_overYears : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.sdLGP).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolatePiYG(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.sdLGP), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP SD (over years): ${f.properties.sdLGP.toFixed(1)} days`); } }).addTo(lgpMap); // --- LGP SD Legend --- const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `LGP SD (days, over years)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS SD Map --- const sosGeo = kenya_boundaries.map(f => { const match = sosLgpTrends.find(d => d.county === f.properties.admin1_name); f.properties.sdSOS = match ? match.sdSOS_overYears : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.sdSOS).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolatePiYG(sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.sdSOS), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
SOS SD (over years): ${f.properties.sdSOS.toFixed(1)} days`); } }).addTo(sosMap); // --- SOS SD Legend --- const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `SOS SD (days, over years)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 40:T1268,viewof kenya_hazard_map1 = (async () => { const hazard = hazardSelector; const scenario = scenarioSelector; // Query hazard for Kenya, single timeframe const geoHazard = await hazard_db.query(` SELECT admin1_name, gaul1_code, value FROM hazard_mean WHERE admin0_name = 'Kenya' AND hazard = '${hazard}' AND scenario = '${scenario}' AND timeframe = '2021-2040' `); // Build GeoJSON for Kenya const geojson = buildKenyaGeojson(geoHazard); // Map container const container = htl.html`

`; const map = L.map(container).setView([-1.3, 37.0], 6); // Dark basemap L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); // Compute min/max for color scale const values = geojson.features.map(f => f.properties.hazard_value || 0); const minV = Math.min(...values); const maxV = Math.max(...values); // Reddish color scale const colorScale = d3.scaleLinear().domain([minV, maxV]).range([0, 1]); function getColor(v) { if (v == null || v === 0) return "#333"; return d3.interpolateReds(colorScale(v)); // reddish palette } // Floating div for time series const floatingDiv = htl.html`

`; container.appendChild(floatingDiv); // Update time series: average by year for selected county async function updateTimeSeries(admin1_name) { const rows = await hazard_db.query(` SELECT SUBSTRING(timeframe,1,4) AS year, AVG(value) AS avg_value FROM hazard_mean WHERE admin0_name = 'Kenya' AND admin1_name = '${admin1_name}' AND hazard = '${hazard}' AND scenario = '${scenario}' GROUP BY year ORDER BY year `); floatingDiv.innerHTML = ""; const svg = d3.select(floatingDiv) .append("svg") .attr("width", 360) .attr("height", 200); const x = d3.scalePoint() .domain(rows.map(d => d.year)) .range([40, 340]); const y = d3.scaleLinear() .domain([0, d3.max(rows, d => d.avg_value)]) .nice() .range([160, 20]); const line = d3.line() .x(d => x(d.year)) .y(d => y(d.avg_value)); svg.append("g") .attr("transform", "translate(0,160)") .call(d3.axisBottom(x)); svg.append("g") .attr("transform", "translate(40,0)") .call(d3.axisLeft(y)); svg.append("path") .datum(rows) .attr("fill", "none") .attr("stroke", "darkred") .attr("stroke-width", 2) .attr("d", line); svg.append("text") .attr("x", 180) .attr("y", 15) .attr("text-anchor", "middle") .text(`${admin1_name} - ${hazard} (${scenario})`); } // Draw Kenya counties L.geoJSON(geojson, { style: f => ({ fillColor: getColor(f.properties.hazard_value), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.5 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
${hazard}: ${f.properties.hazard_value?.toFixed(2)}`); layer.on("click", () => updateTimeSeries(f.properties.admin1_name)); } }).addTo(map); // Update default time series to Nairobi on load updateTimeSeries("Nairobi"); // Legend const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(255, 255, 255, 0.8)"; div.style.padding = "6px 10px"; div.style.borderRadius = "6px"; div.style.boxShadow = "0 0 15px rgba(0,0,0,0.2)"; div.style.fontSize = "12px"; const grades = [0, 0.2, 0.4, 0.6, 0.8]; const labels = []; for (let i = 0; i < grades.length; i++) { const from = (minV + grades[i]*(maxV - minV)).toFixed(2); const to = i < grades.length - 1 ? (minV + grades[i+1]*(maxV - minV)).toFixed(2) : maxV.toFixed(2); const colorValue = d3.interpolateReds(grades[i]); labels.push( ` ${from} – ${to}` ); } div.innerHTML = `${hazard} (${scenario})
` + labels.join("
"); return div; }; legend.addTo(map); return container; })(); 41:T87e,viewof lgpTimeseriesHoversig = { const container = html`

`; // Restrict to first 10 counties const topCounties = significantCounties; const filteredData = statsByCountyYear.filter(d => topCounties.includes(d.county)); // Plot container const plotContainer = html`

`; container.appendChild(plotContainer); // Main plot const plot = Plot.plot({ width: 1000, height: 800, marginLeft: 25, marginRight: 0, x: { domain: [2000, 2025], label: "Year" }, y: { domain: [20, 400], label: "Mean LGP (days)" }, color: { domain: topCounties, legend: false, // custom legend range: topCounties.map(c => countyColor.get(c)) }, marks: [ Plot.areaY(filteredData, { x: "year", y1: d => d.meanLGP - (d.sdLGP || 0), y2: d => d.meanLGP + (d.sdLGP || 0), fill: d => countyColor.get(d.county), fillOpacity: 0.2, curve: "linear" }), Plot.line(filteredData, { x: "year", y: "meanLGP", stroke: "county", strokeWidth: 3, curve: "linear", title: d => `${d.county} (${d.year}): ${d.meanLGP.toFixed(1)} ± ${d.sdLGP ? d.sdLGP.toFixed(1) : "n/a"}` }), Plot.dot(filteredData, { x: "year", y: "meanLGP", fill: "county", r: 3, title: d => `${d.county} (${d.year}): ${d.meanLGP.toFixed(1)} ± ${d.sdLGP ? d.sdLGP.toFixed(1) : "n/a"}` }) ] }); plotContainer.appendChild(plot); // Vertical legend only for top 10 counties const legendBox = html`

`; topCounties.forEach(c => { const item = html`

${c}

`; legendBox.appendChild(item); }); container.appendChild(legendBox); return container; } 42:Tcdc,viewof trendMaps = { const container = html`

`; const lgpMap = L.map(container.querySelector("#lgpMap")).setView([0.1, 37.9], 6); const sosMap = L.map(container.querySelector("#sosMap")).setView([0.1, 37.9], 6); // Base tiles [lgpMap, sosMap].forEach(map => { L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); }); // Prepare color scales const lgpValues = countyTrends.map(d => d.lgpTrend); const sosValues = countyTrends.map(d => d.sosTrend); const lgpColor = d3.scaleDiverging(d3.interpolateRdBu) .domain([d3.max(lgpValues), 0, d3.min(lgpValues)]); const sosColor = d3.scaleDiverging(d3.interpolateRdBu) .domain([d3.max(sosValues), 0, d3.min(sosValues)]); // Attach trend values to boundaries const geoColored = kenya_boundaries.map(f => { const match = countyTrends.find(d => d.county === f.properties.admin1_name); f.properties.lgpTrend = match ? match.lgpTrend : 0; f.properties.sosTrend = match ? match.sosTrend : 0; return f; }); // LGP trend layer L.geoJSON(geoColored, { style: f => ({ fillColor: lgpColor(f.properties.lgpTrend), weight: 1, color: "white", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(` ${f.properties.admin1_name}
LGP trend: ${f.properties.lgpTrend.toFixed(2)} days/year
SOS trend: ${f.properties.sosTrend.toFixed(2)} days/year `); } }).addTo(lgpMap); // SOS trend layer L.geoJSON(geoColored, { style: f => ({ fillColor: sosColor(f.properties.sosTrend), weight: 1, color: "white", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(` ${f.properties.admin1_name}
SOS trend: ${f.properties.sosTrend.toFixed(2)} days/year
LGP trend: ${f.properties.lgpTrend.toFixed(2)} days/year `); } }).addTo(sosMap); // Legends function addLegend(map, values, colorScale, title) { const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length:10}, (_,i) => { const val = d3.min(values) + i*(d3.max(values)-d3.min(values))/9; return ` ${val.toFixed(2)}`; }).join("
"); div.innerHTML = `${title}
${gradient}`; return div; }; legend.addTo(map); } addLegend(lgpMap, lgpValues, lgpColor, "LGP Trend (days/year)"); addLegend(sosMap, sosValues, sosColor, "SOS Trend (days/year)"); return container; } 43:Tbec,viewof seasonalPlots = { const container = html`

`; const margin = { top: 30, right: 60, bottom: 40, left: 60 }; const width = 800; const height = 200; // --- Chronological seasons, no "annual" --- const seasonOrder = ["DJF","JFM","FMA","MAM","AMJ","MJJ","JJA","JAS","ASO","SON","OND","NDJ"]; const seasons = seasonOrder.filter(s => kenyaRainFiltered.some(d => d.season === s) || kenyaNDWSFiltered.some(d => d.season === s) || kenyatempFiltered.some(d => d.season === s) ); // --- Helper: average over years --- function avgBySeason(data, valueKey) { const grouped = d3.rollup( data.filter(d => d.season !== "annual"), v => d3.mean(v, d => d[valueKey]), d => d.season ); return seasons.map(s => ({ season: s, value: grouped.get(s) ?? null })); } const rainData = avgBySeason(kenyaRainFiltered, "seasonal_rain"); const ndwsData = avgBySeason(kenyaNDWSFiltered, "seasonal_ndws"); const tempData = avgBySeason(kenyatempFiltered, "seasonal_temp"); const allMax = d3.max(rainData.map(d => d.value).concat(ndwsData.map(d => d.value)).concat(tempData.map(d => d.value))); // --- X scale (band) --- const x = d3.scaleBand() .domain(seasons) .range([margin.left, width - margin.right]) .padding(0.2); // --- Y scales --- const yRain = d3.scaleLinear() .domain([0, d3.max(rainData, d => d.value)]).nice() .range([height - margin.bottom, margin.top]); const yNDWS = d3.scaleLinear() .domain([0, d3.max(ndwsData, d => d.value)]).nice() .range([height - margin.bottom, margin.top]); const yTemp = d3.scaleLinear() .domain([0, d3.max(tempData, d => d.value)]).nice() .range([height - margin.bottom, margin.top]); // --- Draw function --- function drawPlot(data, yScale, color, ylabel) { const svg = d3.create("svg") .attr("width", width) .attr("height", height); // X axis svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x)); // Y axis svg.append("g") .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(yScale)); // Y label svg.append("text") .attr("x", -height / 2) .attr("y", 15) .attr("transform", "rotate(-90)") .attr("text-anchor", "middle") .attr("fill", color) .text(ylabel); // Bars svg.selectAll(".bar") .data(data) .join("rect") .attr("x", d => x(d.season)) .attr("y", d => yScale(d.value)) .attr("width", x.bandwidth()) .attr("height", d => yScale(0) - yScale(d.value)) .attr("fill", color); return svg.node(); } // --- Rainfall plot --- container.append(drawPlot(rainData, yRain, "steelblue", "Rainfall (mm)")); // --- NDWS plot --- container.append(drawPlot(ndwsData, yNDWS, "orange", "NDWS")); // --- Temp plot --- container.append(drawPlot(tempData, yTemp, "red", "Temperature (°C)")); return container; } 44:Tdf3,viewof seasonalTimeSeries = { // --- 1. Aggregate data by season and year, averaged over counties --- const seasons = ["DJF","JFM","FMA","MAM","AMJ","MJJ","JJA","JAS","ASO","OND","NDJ","SON"]; const plotWidth = 900; const plotHeight = 200; const margin = {top: 30, right: 60, bottom: 30, left: 60}; // Helper: aggregate mean over counties per year & season function aggregateMean(data, valueField) { const nest = d3.rollups( data.filter(d => seasons.includes(d.season)), v => d3.mean(v, d => d[valueField]), d => d.season, d => d.year ); // return array of objects {season, year, value} let res = []; nest.forEach(([season, yearMap]) => { yearMap.forEach(([year, val]) => res.push({season, year, value: val})); }); return res; } const rainData = aggregateMean(kenyaRainFiltered, "seasonal_rain"); const ndwsData = aggregateMean(kenyaNDWSFiltered, "seasonal_ndws"); const tempData = aggregateMean(kenyatempFiltered, "seasonal_temp"); // --- 2. Prepare color scale for seasons --- const seasonColorScale = d3.scaleSequential() .domain([0, seasons.length-1]) .interpolator(d3.interpolatePlasma); // smooth spectral palette function seasonColor(season) { const idx = seasons.indexOf(season); return idx >= 0 ? seasonColorScale(idx) : "#222"; } // --- 3. Create SVG container --- const svg = d3.create("svg") .attr("width", plotWidth) .attr("height", plotHeight * 3 + margin.top + margin.bottom); const yPads = [0, plotHeight + margin.top, 2*plotHeight + 2*margin.top]; // --- 4. Function to draw each subplot --- function drawSubplot(data, yOffset, yLabel) { const g = svg.append("g").attr("transform", `translate(${margin.left},${yOffset})`); // X scale: seasons const x = d3.scalePoint() .domain(seasons) .range([0, plotWidth - margin.left - margin.right]) .padding(0.5); // Y scale: dynamic const yMin = d3.min(data, d => d.value); const yMax = d3.max(data, d => d.value); const y = d3.scaleLinear() .domain([yMin - 0.05*(yMax-yMin), yMax + 0.05*(yMax-yMin)]) .range([plotHeight, 0]); // Axes g.append("g").call(d3.axisLeft(y).ticks(5)); g.append("g").attr("transform", `translate(0,${plotHeight})`).call(d3.axisBottom(x)); g.append("text") .attr("x", -margin.left) .attr("y", -10) .text(yLabel) .attr("fill","#fff") .attr("font-size",12); // Nest data by year const nested = d3.group(data, d => d.year); // Line generator const line = d3.line() .x(d => x(d.season)) .y(d => y(d.value)); // Draw lines per year nested.forEach((values, year) => { g.append("path") .datum(values) .attr("fill","none") .attr("stroke","#fff") .attr("stroke-opacity",0.6) .attr("stroke-width",1) .attr("d", line); }); // Overlay seasonal markers for visual distinction nested.forEach((values, year) => { g.selectAll(`.dots-${year}`) .data(values) .enter() .append("circle") .attr("cx", d => x(d.season)) .attr("cy", d => y(d.value)) .attr("r", 3) .attr("fill", d => seasonColor(d.season)) .attr("stroke","#000") .attr("stroke-width",0.3); }); } // Draw subplots drawSubplot(rainData, 0, "Rainfall (mm)"); drawSubplot(ndwsData, plotHeight + margin.top, "NDWS"); drawSubplot(tempData, 2*plotHeight + 2*margin.top, "Temperature (°C)"); return svg.node(); } 45:Te63,viewof seasonalTimeSeriesAvgS = { const container = html`

`; const margin = { top: 30, right: 80, bottom: 40, left: 60 }; const width = 800; const height = 200; // Always include all 12 climatological seasons const seasonOrder = ["DJF","JFM","FMA","MAM","AMJ","MJJ","JJA","JAS","ASO","SON","OND","NDJ"]; const seasons = seasonOrder; // ✅ No filtering — show all 12 function avgBySeasonAndYear(data, valueKey) { return seasons.map(season => { const seasonData = data.filter(d => d.season === season); if (seasonData.length === 0) return { season, values: [] }; const grouped = d3.rollups( seasonData, v => d3.mean(v, d => d[valueKey]), d => d.year ).sort((a,b) => a[0] - b[0]); return { season, values: grouped.map(([year, value]) => ({ year, value })) }; }); } const rainSeries = avgBySeasonAndYear(kenyaRainFiltered, "seasonal_rain"); const ndwsSeries = avgBySeasonAndYear(kenyaNDWSFiltered, "seasonal_ndws"); const tempSeries = avgBySeasonAndYear(kenyatempFiltered, "seasonal_temp"); // 🌈 Continuous color scale from blue (DJF) → red (NDJ) const colorInterp = d3.interpolateTurbo; const colorScale = d3.scaleSequential() .domain([0, seasons.length - 1]) .interpolator(colorInterp); const seasonColor = d3.scaleOrdinal() .domain(seasons) .range(seasons.map((s, i) => colorScale(i))); function drawTimeSeries(seriesData, ylabel) { const allYears = Array.from(new Set(seriesData.flatMap(s => s.values.map(d => d.year)))); const x = d3.scaleLinear() .domain(d3.extent(allYears)) .range([margin.left, width - margin.right]); const allValues = seriesData.flatMap(s => s.values.map(d => d.value)).filter(v => v != null); const yMin = d3.min(allValues); const yMax = d3.max(allValues); const padding = (yMax - yMin) * 0.03 || 1; const y = d3.scaleLinear() .domain([yMin - padding, yMax + padding]) .range([height - margin.bottom, margin.top]); const svg = d3.create("svg").attr("width", width).attr("height", height); // Axes svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d"))); svg.append("g") .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(y)); svg.append("text") .attr("x", -height / 2) .attr("y", 15) .attr("transform", "rotate(-90)") .attr("text-anchor", "middle") .attr("fill", "black") .text(ylabel); const line = d3.line().x(d => x(d.year)).y(d => y(d.value)); seriesData.forEach(s => { if (s.values.length === 0) return; // Skip missing seasons svg.append("path") .datum(s.values) .attr("fill", "none") .attr("stroke", seasonColor(s.season)) .attr("stroke-width", 1.5) .attr("d", line); }); // ✅ Always show all 12 seasons in the legend const legend = svg.append("g").attr("transform", `translate(${width - margin.right + 10},${margin.top})`); seasons.forEach((s,i) => { const g = legend.append("g").attr("transform", `translate(0,${i*20})`); g.append("rect").attr("width", 12).attr("height", 12).attr("fill", seasonColor(s)); g.append("text").attr("x", 16).attr("y", 10).text(s).attr("font-size", 12); }); return svg.node(); } container.append(drawTimeSeries(rainSeries, "Rainfall (mm)")); container.append(drawTimeSeries(ndwsSeries, "NDWS")); container.append(drawTimeSeries(tempSeries, "Temperature (°C)")); return container; } 46:Td19,viewof seasonalTimeSeriesall = { const container = html`

`; const margin = { top: 30, right: 80, bottom: 40, left: 60 }; const width = 800; const height = 200; // --- Collect all seasons (excluding 'annual') --- const seasonOrder = ["DJF","JFM","FMA","MAM","AMJ","MJJ","JJA","JAS","ASO","SON","OND","NDJ"]; const seasons = seasonOrder.filter(s => kenyaRainFiltered.some(d => d.season === s) || kenyaNDWSFiltered.some(d => d.season === s) || kenyatempFiltered.some(d => d.season === s) ); // --- Helper: group by season, return year-value pairs --- function timeSeriesBySeason(data, valueKey) { const series = seasons.map(s => { const filtered = data.filter(d => d.season === s); return { season: s, values: filtered.map(d => ({ year: d.year, value: d[valueKey] })) }; }); return series; } const rainSeries = timeSeriesBySeason(kenyaRainFiltered, "seasonal_rain"); const ndwsSeries = timeSeriesBySeason(kenyaNDWSFiltered, "seasonal_ndws"); const tempSeries = timeSeriesBySeason(kenyatempFiltered, "seasonal_temp"); const colorScale = d3.scaleOrdinal() .domain(seasons) .range(d3.schemeTableau10); function drawTimeSeries(seriesData, ylabel) { // X scale over years const allYears = Array.from(new Set(seriesData.flatMap(s => s.values.map(d => d.year)))); const x = d3.scaleLinear() .domain(d3.extent(allYears)) .range([margin.left, width - margin.right]); // Y scale const y = d3.scaleLinear() .domain([0, d3.max(seriesData.flatMap(s => s.values.map(d => d.value)))]) .nice() .range([height - margin.bottom, margin.top]); const svg = d3.create("svg") .attr("width", width) .attr("height", height); // X axis svg.append("g") .attr("transform", `translate(0,${height - margin.bottom})`) .call(d3.axisBottom(x).tickFormat(d3.format("d"))); // Y axis svg.append("g") .attr("transform", `translate(${margin.left},0)`) .call(d3.axisLeft(y)); // Y label svg.append("text") .attr("x", -height / 2) .attr("y", 15) .attr("transform", "rotate(-90)") .attr("text-anchor", "middle") .attr("fill", "black") .text(ylabel); // Draw lines const line = d3.line() .x(d => x(d.year)) .y(d => y(d.value)); seriesData.forEach(s => { svg.append("path") .datum(s.values) .attr("fill", "none") .attr("stroke", colorScale(s.season)) .attr("stroke-width", 1.5) .attr("d", line); }); // Legend const legend = svg.append("g") .attr("transform", `translate(${width - margin.right + 10},${margin.top})`); seriesData.forEach((s, i) => { const g = legend.append("g").attr("transform", `translate(0,${i*20})`); g.append("rect").attr("width", 12).attr("height", 12).attr("fill", colorScale(s.season)); g.append("text").attr("x", 16).attr("y", 10).text(s.season).attr("font-size", 12); }); return svg.node(); } // Append plots container.append(drawTimeSeries(rainSeries, "Rainfall (mm)")); container.append(drawTimeSeries(ndwsSeries, "NDWS")); container.append(drawTimeSeries(tempSeries, "Temperature (°C)")); return container; }47:T104b, // Helper function to group and summarize with SD function summarizeExposure(rainData) { const summary = {}; rainData.forEach(d => { if (!d.admin2_name) return; const key = d.admin1_name + '_' + d.crop; if (!summary[key]) { summary[key] = { admin1_name: d.admin1_name, crop: d.crop, total_value: 0, total_rain: 0, years_count: 0, rain_values: [], value_values: [] }; } summary[key].total_value += d.value; summary[key].total_rain += d.seasonal_rain; summary[key].years_count += 1; summary[key].rain_values.push(d.seasonal_rain); summary[key].value_values.push(d.value); }); // Helper to compute standard deviation const sd = arr => { if (!arr.length) return 0; const mean = d3.mean(arr); return Math.sqrt(d3.mean(arr.map(v => (v - mean) ** 2))); }; // Convert summary object to array with averages and SD return Object.values(summary).map(d => ({ admin1_name: d.admin1_name, crop: d.crop, avg_rainfall: d.total_rain / d.years_count, rain_sd: sd(d.rain_values), avg_value: d.total_value / d.years_count, value_sd: sd(d.value_values), total_value: d.total_value })); } // // Helper function to group and summarize with SD, filtering out null admin2_name // function summarizeExposure(rainData) { // const summary = {}; // rainData.forEach(d => { // // Skip rows where admin2_name is null // if (!d.admin2_name) return; // const key = d.admin1_name + '_' + d.admin2_name + '_' + d.crop; // if (!summary[key]) { // summary[key] = { // admin1_name: d.admin1_name, // admin2_name: d.admin2_name, // crop: d.crop, // total_value: 0, // total_rain: 0, // years_count: 0, // rain_values: [], // value_values: [] // }; // } // summary[key].total_value += d.value; // summary[key].total_rain += d.seasonal_rain; // summary[key].years_count += 1; // summary[key].rain_values.push(d.seasonal_rain); // summary[key].value_values.push(d.value); // }); // // Helper to compute standard deviation // const sd = arr => { // if (!arr.length) return 0; // const mean = d3.mean(arr); // return Math.sqrt(d3.mean(arr.map(v => (v - mean) ** 2))); // }; // // Convert summary object to array with averages and SD // return Object.values(summary).map(d => ({ // admin1_name: d.admin1_name, // admin2_name: d.admin2_name, // crop: d.crop, // avg_rainfall: d.total_rain / d.years_count, // rain_sd: sd(d.rain_values), // avg_value: d.total_value / d.years_count, // value_sd: sd(d.value_values), // total_value: d.total_value // })); // } // Helper function to group and summarize with rainfall SD only // function summarizeExposure(rainData) { // const summary = {}; // rainData.forEach(d => { // // Skip rows where admin2_name is null // if (!d.admin2_name) return; // const key = d.admin1_name + '_' + d.admin2_name + '_' + d.crop; // if (!summary[key]) { // summary[key] = { // admin1_name: d.admin1_name, // admin2_name: d.admin2_name, // crop: d.crop, // total_value: 0, // total_rain: 0, // years_count: 0, // rain_values: [] // }; // } // summary[key].total_value += d.value; // summary[key].total_rain += d.seasonal_rain; // summary[key].years_count += 1; // summary[key].rain_values.push(d.seasonal_rain); // }); // // Helper to compute standard deviation // const sd = arr => { // if (!arr.length) return 0; // const mean = d3.mean(arr); // return Math.sqrt(d3.mean(arr.map(v => (v - mean) ** 2))); // }; // // Convert summary object to array with averages and rainfall SD // return Object.values(summary).map(d => ({ // admin1_name: d.admin1_name, // admin2_name: d.admin2_name, // crop: d.crop, // avg_rainfall: d.total_rain / d.years_count, // rain_sd: sd(d.rain_values), // avg_value: d.total_value / d.years_count, // total_value: d.total_value // })); // } 48:T601,/** * Summarize exposure data for any numeric field. * @param {Array} data - Array of objects with admin1_name, admin2_name, crop, value, and numericField. * @param {String} numericField - Name of the numeric field to summarize (e.g., "seasonal_rain", "NDWS", "temp"). * @returns {Array} Summary array with avg_, _sd, total_value. */ function summarizeExposureGeneral(rainData, numericField, numericField2) { const summary = {}; rainData.forEach(d => { if (!d.admin2_name) return; const key = d.admin1_name + '_' + d.crop; if (!summary[key]) { summary[key] = { admin1_name: d.admin1_name, crop: d.crop, total_value: 0, total_numeric: 0, years_count: 0, numeric_values: [], value_values: [] }; } summary[key].total_value += d.value; summary[key].total_numeric += d[numericField]; summary[key].years_count += 1; summary[key].numeric_values.push(d[numericField]); summary[key].value_values.push(d.value); }); // Helper to compute standard deviation const sd = arr => { if (!arr.length) return 0; const mean = d3.mean(arr); return Math.sqrt(d3.mean(arr.map(v => (v - mean) ** 2))); }; // Convert summary object to array with averages and SD return Object.values(summary).map(d => ({ admin1_name: d.admin1_name, crop: d.crop, [`avg_${numericField2}`]: d.total_numeric / d.years_count, [`${numericField2}_sd`]: sd(d.numeric_values), total_value: d.total_value })); }49:T4b3,function summarizeExposureSeasonal(rainData, numericField, numericField2) { const summary = {}; rainData.forEach(d => { if (!d.admin2_name || !d.season) return; // key now includes season const key = `${d.admin1_name}_${d.crop}_${d.season}`; if (!summary[key]) { summary[key] = { admin1_name: d.admin1_name, crop: d.crop, season: d.season, total_value: 0, total_numeric: 0, years_count: 0, numeric_values: [], value_values: [] }; } summary[key].total_value += d.value ?? 0; summary[key].total_numeric += d[numericField] ?? 0; summary[key].years_count += 1; summary[key].numeric_values.push(d[numericField]); summary[key].value_values.push(d.value); }); const sd = arr => { if (!arr.length) return 0; const mean = d3.mean(arr); return Math.sqrt(d3.mean(arr.map(v => (v - mean) ** 2))); }; return Object.values(summary).map(d => ({ admin1_name: d.admin1_name, crop: d.crop, season: d.season, [`avg_${numericField2}`]: d.total_numeric / d.years_count, [`${numericField2}_sd`]: sd(d.numeric_values), total_value: d.total_value })); } 4a:T46f,// 🌍 1️⃣ ALL CROP VALUE BAR PLOT (Kenya) viewof kenyaAllCropValuePlot = { // Aggregate total value per crop const cropTotals = d3.rollups( kenyaCrops1, v => d3.sum(v, d => d.value), d => d.crop ).map(([crop, total_value]) => ({ crop, total_value })) .sort((a, b) => d3.descending(a.total_value, b.total_value)); // Color palette (optional, categorical) const palette = d3.scaleOrdinal() .domain(cropTotals.map(d => d.crop)) .range(d3.schemeTableau10); // Plot setup const plot = Plot.plot({ width: 950, height: 650, style: { background: "#111", color: "#ddd" }, marginLeft: 100, marginBottom: 120, x: { label: "Crop", domain: cropTotals.map(d => d.crop), tickRotate: -45 }, y: { label: "Total Value (USD)", grid: true, tickFormat: d3.format(".2s") }, marks: [ Plot.barY(cropTotals, { x: "crop", y: "total_value", fill: d => palette(d.crop), title: d => `${d.crop}: ${d3.format(",.0f")(d.total_value)} USD` }), Plot.ruleY([0]) ] }); return plot; }4b:T478,// 🌍 1️⃣ ALL CROP VALUE BAR PLOT (Kenya) viewof kenyaAllLivestockValuePlot = { // Aggregate total value per crop const cropTotals = d3.rollups( kenyaLivestock1, v => d3.sum(v, d => d.value), d => d.crop ).map(([crop, total_value]) => ({ crop, total_value })) .sort((a, b) => d3.descending(a.total_value, b.total_value)); // Color palette (optional, categorical) const palette = d3.scaleOrdinal() .domain(cropTotals.map(d => d.crop)) .range(d3.schemeTableau10); // Plot setup const plot = Plot.plot({ width: 950, height: 650, style: { background: "#111", color: "#ddd" }, marginLeft: 100, marginBottom: 120, x: { label: "Crop", domain: cropTotals.map(d => d.crop), tickRotate: -45 }, y: { label: "Total Value (USD)", grid: true, tickFormat: d3.format(".2s") }, marks: [ Plot.barY(cropTotals, { x: "crop", y: "total_value", fill: d => palette(d.crop), title: d => `${d.crop}: ${d3.format(",.0f")(d.total_value)} USD` }), Plot.ruleY([0]) ] }); return plot; }4c:T557,viewof kenyaRainBarPlotWithSD = { const container = html`

`; const background = "#1e1e1e"; const textColor = "#ffffff"; // Aggregate avg rainfall and sd per county const countyData = d3.rollups( kenyaRainFiltered, v => ({ avg_rainfall: d3.mean(v, d => d.seasonal_rain), rain_sd: d3.deviation(v, d => d.seasonal_rain) }), d => d.admin1_name ).map(([county, stats]) => ({ county, ...stats })) .sort((a,b) => b.avg_rainfall - a.avg_rainfall); const plot = Plot.plot({ width: 900, height: 700, marginLeft: 100, style: `background:${background}; color:${textColor}`, x: { label: "Rainfall (mm)", grid: true }, y: { label: "County", domain: countyData.map(d => d.county) }, marks: [ // Main bars: avg rainfall Plot.barX(countyData, { x: d => d.avg_rainfall, y: d => d.county, fill: "#1f77b4", fillOpacity: 0.7, title: d => `${d.county}\nAvg Rainfall: ${d.avg_rainfall.toFixed(1)} mm` }), // Overlay bars: SD Plot.barX(countyData, { x: d => d.rain_sd, y: d => d.county, fill: "#ff7f0e", fillOpacity: 0.6, title: d => `${d.county}\nRainfall SD: ${d.rain_sd.toFixed(1)} mm` }) ] }); container.appendChild(plot); return container; } 4d:T400, kenyaCropTrendMerge = kenyaCropSummary.map(d => { const rain = rainfallTrends.find(t => t.admin1_name === d.admin1_name); const ndws = NDWSTrends.find(t => t.admin1_name === d.admin1_name); const temp = tempTrends.find(t => t.admin1_name === d.admin1_name); const ndwsSummary = kenyaCropNDWSSummary.find(s => s.admin1_name === d.admin1_name && s.crop === d.crop); const tempSummary = kenyaCroptempSummary.find(s => s.admin1_name === d.admin1_name && s.crop === d.crop); return { ...d, rainfall_slope: rain ? rain.slope : null, rainfall_intercept: rain ? rain.intercept : null, ndws_slope: ndws ? ndws.slope : null, ndws_intercept: ndws ? ndws.intercept : null, temp_slope: temp ? temp.slope : null, temp_intercept: temp ? temp.intercept : null, avg_ndws: ndwsSummary ? ndwsSummary.avg_ndws : null, avg_temp: tempSummary ? tempSummary.avg_temp : null, ndws_sd: ndwsSummary ? ndwsSummary.ndws_sd : null, temp_sd: tempSummary ? tempSummary.temp_sd : null }; }); 4e:T507,// === STEP 1: Merge seasonal crop summaries with seasonal trends === kenyaCropTrendMergeSeasonal = kenyaCropSummary.map(d => { const rain = rainfallTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const ndws = NDWSTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const temp = tempTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const ndwsSummary = kenyaCropNDWSSummary.find( s => s.admin1_name === d.admin1_name && s.crop === d.crop && s.season === d.season ); const tempSummary = kenyaCroptempSummary.find( s => s.admin1_name === d.admin1_name && s.crop === d.crop && s.season === d.season ); return { ...d, rainfall_slope: rain ? rain.slope : null, rainfall_intercept: rain ? rain.intercept : null, ndws_slope: ndws ? ndws.slope : null, ndws_intercept: ndws ? ndws.intercept : null, temp_slope: temp ? temp.slope : null, temp_intercept: temp ? temp.intercept : null, avg_ndws: ndwsSummary ? ndwsSummary.avg_ndws : null, avg_temp: tempSummary ? tempSummary.avg_temp : null, ndws_sd: ndwsSummary ? ndwsSummary.ndws_sd : null, temp_sd: tempSummary ? tempSummary.temp_sd : null }; }); 4f:T442,// === STEP 1: Merge livestock summaries with trends === kenyaLivestockMerge = kenyaLivestockSummary.map(d => { const rain = rainfallTrends.find(t => t.admin1_name === d.admin1_name); const ndws = NDWSTrends.find(t => t.admin1_name === d.admin1_name); const temp = tempTrends.find(t => t.admin1_name === d.admin1_name); const ndwsSummary = kenyaLivestockNDWSSummary.find(s => s.admin1_name === d.admin1_name && s.crop === d.crop); const tempSummary = kenyaLivestocktempSummary.find(s => s.admin1_name === d.admin1_name && s.crop === d.crop); return { ...d, rainfall_slope: rain ? rain.slope : null, rainfall_intercept: rain ? rain.intercept : null, ndws_slope: ndws ? ndws.slope : null, ndws_intercept: ndws ? ndws.intercept : null, temp_slope: temp ? temp.slope : null, temp_intercept: temp ? temp.intercept : null, avg_ndws: ndwsSummary ? ndwsSummary.avg_ndws : null, avg_temp: tempSummary ? tempSummary.avg_temp : null, ndws_sd: ndwsSummary ? ndwsSummary.ndws_sd : null, temp_sd: tempSummary ? tempSummary.temp_sd : null }; });50:T51b, // === STEP 2: Merge seasonal livestock summaries with seasonal trends === kenyaLivestockMergeSeasonal = kenyaLivestockSummary.map(d => { const rain = rainfallTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const ndws = NDWSTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const temp = tempTrendsSeasonal.find( t => t.admin1_name === d.admin1_name && t.season === d.season ); const ndwsSummary = kenyaLivestockNDWSSummary.find( s => s.admin1_name === d.admin1_name && s.crop === d.crop && s.season === d.season ); const tempSummary = kenyaLivestocktempSummary.find( s => s.admin1_name === d.admin1_name && s.crop === d.crop && s.season === d.season ); return { ...d, rainfall_slope: rain ? rain.slope : null, rainfall_intercept: rain ? rain.intercept : null, ndws_slope: ndws ? ndws.slope : null, ndws_intercept: ndws ? ndws.intercept : null, temp_slope: temp ? temp.slope : null, temp_intercept: temp ? temp.intercept : null, avg_ndws: ndwsSummary ? ndwsSummary.avg_ndws : null, avg_temp: tempSummary ? tempSummary.avg_temp : null, ndws_sd: ndwsSummary ? ndwsSummary.ndws_sd : null, temp_sd: tempSummary ? tempSummary.temp_sd : null }; }); 51:T722,viewof kenyaCropTrendPlot = { const colorSchemes = { maize: "rgb(0,180,255)", sorghum: "rgb(50,205,50)", "small millet": "rgb(255,100,100)" }; const scatter = Plot.plot({ width: 900, height: 600, style: { background: "#111", color: "#ddd" }, x: { label: "Rainfall Trend (mm/year)", grid: true }, y: { label: "Total Value (USD)", grid: true, tickFormat: d => d3.format(".2s")(d) }, marks: [ Plot.dot(kenyaCropTrendMerge, { x: "rainfall_slope", y: "total_value", fill: d => colorSchemes[d.crop] || "gray", fillOpacity: d => Math.min(1, d.avg_rainfall / 500), r: d => d.avg_rainfall, title: d => `${d.admin1_name}\n${d.crop}\nRainfall Trend: ${d.rainfall_slope?.toFixed(2)} mm/yr\nAvg Rain: ${d.avg_rainfall?.toFixed(1)} mm\nValue: ${d.total_value?.toLocaleString()}` }) ] }); const legend = html`

Legend

● Maize
● Sorghum
● Small Millet

Dot size: Avg rainfall (PTOT)
Dot opacity: Rainfall intensity
X-axis: Rainfall trend (mm/year)
Y-axis: Total crop value (USD)

`; const container = html`

${scatter}${legend}

`; return container; } 52:Tc54,viewof kenyaCropTrendPlot_MAM = { // === Step 1: Define color scheme === const colorSchemes = { maize: "rgb(0,180,255)", sorghum: "rgb(50,205,50)", "small millet": "rgb(255,100,100)" }; // === Step 2: Filter for MAM (long rains) season === const mamData = kenyaCropTrendMergeSeasonal.filter(d => d.season === "MAM"); // === Step 3: Create plot === const scatter = Plot.plot({ width: 900, height: 600, style: { background: "#111", color: "#ddd" }, x: { label: "Rainfall Trend (mm/year)", grid: true }, y: { label: "Total Value (USD)", grid: true, tickFormat: d => d3.format(".2s")(d) }, marks: [ Plot.dot(kenyaCropTrendMergeSeasonal.filter(d => d.season === "MAM"), { x: "rainfall_slope", y: "total_value", fill: d => colorSchemes[d.crop] || "gray", fillOpacity: d => Math.min(1, d.avg_rainfall / 500), r: d => d.avg_rainfall, title: d => `${d.admin1_name}\n${d.crop}\nSeason: ${d.season}\nRainfall Trend: ${d.rainfall_slope?.toFixed(2)} mm/yr\nAvg Rain: ${d.avg_rainfall?.toFixed(1)} mm\nValue: ${d.total_value?.toLocaleString()}` }) ] }); // const scatter = Plot.plot({ // width: 900, // height: 600, // style: { background: "#111", color: "#ddd" }, // x: { label: "Seasonal Rainfall Trend (mm/year)", grid: true }, // y: { label: "Total Value (USD)", grid: true, tickFormat: d => d3.format(".2s")(d) }, // marks: [ // Plot.dot(kenyaCropTrendMergeSeasonal.filter(d => d.season === "MAM"), { // x: "rainfall_slope_seasonal", // seasonal analog of rainfall_slope // y: "total_value", // fill: d => colorSchemes[d.crop] || "gray", // fillOpacity: d => Math.min(1, d.avg_rainfall ), // r: d => d.avg_rainfall , // scale radius to avoid oversized dots // title: d => // `${d.admin1_name}\n${d.crop}\nSeason: ${d.season}\nRainfall Trend: ${d.rainfall_slope?.toFixed(2)} mm/yr\nAvg Rain: ${d.avg_rainfall?.toFixed(1)} mm\nValue: ${d.total_value?.toLocaleString()}` // }) // ] // }); // === Step 4: Legend === const legend = html`

Legend — MAM (Mar–May, Long Rains)

● Maize
● Sorghum
● Small Millet

Dot size: Avg rainfall (MAM)
Dot opacity: Rainfall intensity
X-axis: Seasonal rainfall trend (mm/year)
Y-axis: Total crop value (USD)

`; // === Step 5: Combine plot and legend === const container = html`

${scatter}${legend}

`; return container; } 53:Td5a, viewof cropTrendMaps = { const crops = ["maize", "sorghum", "small millet"]; const container = html`

`; crops.forEach(crop => { const mapDiv = html`

`; const crops = d3.union(cropRiskSummary.map(d => d.crop_name)); crops.forEach(crop => { // 👇 Filter by crop and selected season const cropRisk = cropRiskSummary.filter( d => d.crop_name === crop && d.season === selectedSeason ); // Compute min/max for the selected risk type const riskValues = cropRisk.map(d => d[selectedRisk]).filter(v => v != null && !isNaN(v)); const riskMin = d3.min(riskValues); const riskMax = d3.max(riskValues); const exp = 0.35; const colorInterpolate = d3.interpolateInferno; const colorScale = v => { if (v == null || isNaN(v)) return "#222"; if (riskMax === riskMin) return colorInterpolate(0.5); const t = (v - riskMin) / (riskMax - riskMin); return colorInterpolate(Math.pow(Math.max(0, Math.min(1, t)), exp)); }; // === Map container for each crop === const mapDiv = html`

${crop} — ${selectedRisk.toUpperCase()} (${selectedSeason})

`; container.append(mapDiv); // === Attach county-level risk data === const features = kenya_boundaries.map(f => { const match = cropRisk.find(d => d.admin1_name === f.properties.admin1_name); return { ...f, properties: { ...f.properties, risk_value: match ? match[selectedRisk] : null } }; }); // === Leaflet map setup === const map = L.map(mapDiv.querySelector("div"), { attributionControl: false, zoomControl: false }).setView([0, 37], 6.3); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { opacity: 0.2 }).addTo(map); L.geoJson(features, { style: f => ({ color: "#000", weight: 0.5, fillOpacity: 0.85, fillColor: colorScale(f.properties.risk_value) }), onEachFeature: (f, layer) => { const name = f.properties.admin1_name; const val = f.properties.risk_value; layer.bindTooltip(`${name}: ${val != null ? val.toFixed(3) : "N/A"}`); } }).addTo(map); // === Top-10 county table === const top = cropRisk .filter(d => d[selectedRisk] != null && !isNaN(d[selectedRisk])) .sort((a, b) => b[selectedRisk] - a[selectedRisk]) .slice(0, 10); const tbl = html` ${top.map( (d, i) => html`` )}

Rank	County	${selectedRisk.toUpperCase()}
${i + 1}	${d.admin1_name}	${d[selectedRisk].toFixed(3)}

`; mapDiv.append(tbl); }); return container; };58:Td01,// === Step 2: Reactive Livestock Risk Maps (2x2 layout) === viewof livestockRiskMaps = { const selectedRisk = livestockRiskType; const selectedSeason = livestockSeason; const container = html`

`; const livestockTypes = d3.union(livestockRiskSummary.map(d => d.crop_name)); livestockTypes.forEach(type => { // 👇 Filter by both livestock type and season const livestockRisk = livestockRiskSummary.filter( d => d.crop_name === type && d.season === selectedSeason ); // Compute min/max for selected risk type const riskValues = livestockRisk.map(d => d[selectedRisk]).filter(v => v != null && !isNaN(v)); const riskMin = d3.min(riskValues); const riskMax = d3.max(riskValues); const exp = 0.35; const colorInterpolate = d3.interpolateInferno; const colorScale = v => { if (v == null || isNaN(v)) return "#222"; if (riskMax === riskMin) return colorInterpolate(0.5); const t = (v - riskMin) / (riskMax - riskMin); return colorInterpolate(Math.pow(Math.max(0, Math.min(1, t)), exp)); }; // Create card for each livestock type const mapDiv = html`

${type} — ${selectedRisk.toUpperCase()} (${selectedSeason})

`; container.append(mapDiv); // Join county data const features = kenya_boundaries.map(f => { const match = livestockRisk.find(d => d.admin1_name === f.properties.admin1_name); return { ...f, properties: { ...f.properties, risk_value: match ? match[selectedRisk] : null } }; }); // Initialize map const map = L.map(mapDiv.querySelector("div"), { attributionControl: false, zoomControl: false }).setView([0, 37], 6.3); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { opacity: 0.2 }).addTo(map); // Apply color-coded geojson L.geoJson(features, { style: f => ({ color: "#000", weight: 0.5, fillOpacity: 0.85, fillColor: colorScale(f.properties.risk_value) }), onEachFeature: (f, layer) => { const name = f.properties.admin1_name; const val = f.properties.risk_value; layer.bindTooltip(`${name}: ${val != null ? val.toFixed(3) : "N/A"}`); } }).addTo(map); // Top 10 counties const top = livestockRisk .filter(d => d[selectedRisk] != null && !isNaN(d[selectedRisk])) .sort((a, b) => b[selectedRisk] - a[selectedRisk]) .slice(0, 10); const tbl = html` ${top.map( (d, i) => html`` )}

Rank	County	${selectedRisk.toUpperCase()}
${i + 1}	${d.admin1_name}	${d[selectedRisk].toFixed(3)}

`; mapDiv.append(tbl); }); return container; }; 59:Tf81,viewof cropScenarioRiskMaps = { const container = html`

`; // --- Crop selector --- const allCrops = Array.from(d3.union(cropsWithScenarioRisk.map(d => d.crop))); const cropSelector = html``; // --- Hazard selector --- const allHazards = Array.from(d3.union(cropsWithScenarioRisk.map(d => d.hazard))); const hazardSelector = html``; const selectorsDiv = html`

`; selectorsDiv.append(cropSelector, hazardSelector); container.appendChild(selectorsDiv); // --- Map container --- const mapsContainer = html`

`; container.appendChild(mapsContainer); const scenarios = ["ssp126", "ssp245", "ssp370", "ssp585"]; function drawMaps(selectedCrop, selectedHazard) { mapsContainer.innerHTML = ""; // clear previous maps scenarios.forEach(scenario => { // Filter by crop and hazard const filtered = cropsWithScenarioRisk.filter( d => d.crop === selectedCrop && d.hazard === selectedHazard && d.scenario === scenario ); const mapDiv = html`

${scenario} — ${selectedCrop} — ${selectedHazard}

`; mapsContainer.appendChild(mapDiv); const map = L.map(mapDiv.querySelector("div")).setView([0, 37], 6); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { opacity: 0.3 }).addTo(map); const features = kenya_boundaries.map(f => { const match = filtered.find(d => d.county === f.properties.admin1_name); return { ...f, properties: { ...f.properties, risk_value: match ? match.scenario_risk : null } }; }); const riskValues = features.map(f => f.properties.risk_value).filter(v => v != null); const minV = riskValues.length ? d3.min(riskValues) : 0; const maxV = riskValues.length ? d3.max(riskValues) : 1; const colorScale = d3.scaleSequential(d3.interpolateReds).domain([minV, maxV]); L.geoJSON(features, { style: f => ({ color: "#000", weight: 0.5, fillOpacity: 0.85, fillColor: f.properties.risk_value != null ? colorScale(f.properties.risk_value) : "#ccc" }), onEachFeature: (f, layer) => { layer.bindTooltip(`${f.properties.admin1_name}: ${f.properties.risk_value != null ? f.properties.risk_value.toFixed(3) : "N/A"}`); } }).addTo(map); // Add continuous legend const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); const gradient = document.createElement("div"); gradient.style.height = "15px"; gradient.style.width = "150px"; gradient.style.background = `linear-gradient(to right, ${colorScale(minV)}, ${colorScale(maxV)})`; div.appendChild(gradient); const labels = html`

${minV.toFixed(3)}${maxV.toFixed(3)}

`; div.appendChild(labels); return div; }; legend.addTo(map); }); } // Initial draw drawMaps(allCrops[0], allHazards[0]); // Update maps when crop or hazard changes cropSelector.addEventListener("change", () => drawMaps(cropSelector.value, hazardSelector.value)); hazardSelector.addEventListener("change", () => drawMaps(cropSelector.value, hazardSelector.value)); return container; }; 5a:T1032, viewof livestockScenarioRiskMaps = { const container = html`

`; // --- Livestock selector --- const allLivestock = Array.from(d3.union(livestockWithScenarioRisk.map(d => d.crop))); const livestockSelector = html``; // --- Hazard selector --- const allHazards = Array.from(d3.union(livestockWithScenarioRisk.map(d => d.hazard))); const hazardSelector = html``; const selectorsDiv = html`

`; selectorsDiv.append(livestockSelector, hazardSelector); container.appendChild(selectorsDiv); // --- Maps container (2x2) --- const mapsContainer = html`

`; container.appendChild(mapsContainer); const scenarios = ["ssp126", "ssp245", "ssp370", "ssp585"]; function drawMaps(selectedLivestock, selectedHazard) { mapsContainer.innerHTML = ""; // clear previous maps // Compute global min/max for consistent color scale across all scenarios const filteredAll = livestockWithScenarioRisk.filter( d => d.crop === selectedLivestock && d.hazard === selectedHazard ); const allRiskValues = filteredAll.map(d => d.scenario_risk).filter(v => v != null); const minV = allRiskValues.length ? d3.min(allRiskValues) : 0; const maxV = allRiskValues.length ? d3.max(allRiskValues) : 1; const colorScale = d3.scaleSequential(d3.interpolateReds).domain([minV, maxV]); scenarios.forEach(scenario => { const filtered = filteredAll.filter(d => d.scenario === scenario); const mapDiv = html`

${scenario} — ${selectedLivestock} — ${selectedHazard}

`; mapsContainer.appendChild(mapDiv); const map = L.map(mapDiv.querySelector("div")).setView([0, 37], 6); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { opacity: 0.3 }).addTo(map); const features = kenya_boundaries.map(f => { const match = filtered.find(d => d.county === f.properties.admin1_name); return { ...f, properties: { ...f.properties, risk_value: match ? match.scenario_risk : null } }; }); L.geoJSON(features, { style: f => ({ color: "#000", weight: 0.5, fillOpacity: 0.85, fillColor: f.properties.risk_value != null ? colorScale(f.properties.risk_value) : "#ccc" }), onEachFeature: (f, layer) => { layer.bindTooltip(`${f.properties.admin1_name}: ${f.properties.risk_value != null ? f.properties.risk_value.toFixed(3) : "N/A"}`); } }).addTo(map); // Add continuous legend const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); const gradient = document.createElement("div"); gradient.style.height = "15px"; gradient.style.width = "150px"; gradient.style.background = `linear-gradient(to right, ${colorScale(minV)}, ${colorScale(maxV)})`; div.appendChild(gradient); const labels = html`

${minV.toFixed(3)}${maxV.toFixed(3)}

`; div.appendChild(labels); return div; }; legend.addTo(map); }); } // Initial draw drawMaps(allLivestock[0], allHazards[0]); // Update maps when livestock type or hazard changes livestockSelector.addEventListener("change", () => drawMaps(livestockSelector.value, hazardSelector.value)); hazardSelector.addEventListener("change", () => drawMaps(livestockSelector.value, hazardSelector.value)); return container; }; 5b:T1216,viewof scenarioRiskMaps = { const container = html`

`; // --- Type selector --- const typeSelector = html``; // --- Item selector (crop or livestock) --- const itemSelector = html``; // --- Hazard selector --- const hazardSelector = html``; const selectorsDiv = html`

`; selectorsDiv.append(typeSelector, itemSelector, hazardSelector); container.appendChild(selectorsDiv); // --- Maps container (2x2) --- const mapsContainer = html`

`; container.appendChild(mapsContainer); const scenarios = ["ssp126", "ssp245", "ssp370", "ssp585"]; function updateSelectors() { const type = typeSelector.value; const data = type === "crops" ? cropsWithScenarioRisk : livestockWithScenarioRisk; const allItems = Array.from(d3.union(data.map(d => d.crop))); itemSelector.innerHTML = ""; allItems.forEach(i => itemSelector.append(html``)); const allHazards = Array.from(d3.union(data.map(d => d.hazard))); hazardSelector.innerHTML = ""; allHazards.forEach(h => hazardSelector.append(html``)); } function drawMaps() { mapsContainer.innerHTML = ""; // clear previous maps const type = typeSelector.value; const selectedItem = itemSelector.value; const selectedHazard = hazardSelector.value; const data = type === "crops" ? cropsWithScenarioRisk : livestockWithScenarioRisk; const filteredAll = data.filter(d => d.crop === selectedItem && d.hazard === selectedHazard); // Compute min/max across all scenarios for consistent color const allRiskValues = filteredAll.map(d => d.scenario_risk).filter(v => v != null); const minV = allRiskValues.length ? d3.min(allRiskValues) : 0; const maxV = allRiskValues.length ? d3.max(allRiskValues) : 1; const colorScale = d3.scaleSequential(d3.interpolateReds).domain([minV, maxV]); scenarios.forEach(scenario => { const filtered = filteredAll.filter(d => d.scenario === scenario); const mapDiv = html`

${scenario} — ${selectedItem} — ${selectedHazard}

`; mapsContainer.appendChild(mapDiv); const map = L.map(mapDiv.querySelector("div")).setView([0, 37], 6); L.tileLayer("https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png", { opacity: 0.3 }).addTo(map); const features = kenya_boundaries.map(f => { const match = filtered.find(d => d.county === f.properties.admin1_name); return { ...f, properties: { ...f.properties, risk_value: match ? match.scenario_risk : null } }; }); L.geoJSON(features, { style: f => ({ color: "#000", weight: 0.5, fillOpacity: 0.85, fillColor: f.properties.risk_value != null ? colorScale(f.properties.risk_value) : "#ccc" }), onEachFeature: (f, layer) => { layer.bindTooltip(`${f.properties.admin1_name}: ${f.properties.risk_value != null ? f.properties.risk_value.toFixed(3) : "N/A"}`); } }).addTo(map); // Add continuous legend const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); const gradient = document.createElement("div"); gradient.style.height = "15px"; gradient.style.width = "150px"; gradient.style.background = `linear-gradient(to right, ${colorScale(minV)}, ${colorScale(maxV)})`; div.appendChild(gradient); const labels = html`

${minV.toFixed(3)}${maxV.toFixed(3)}

`; div.appendChild(labels); return div; }; legend.addTo(map); }); } // Initial setup updateSelectors(); drawMaps(); // Update maps when selectors change typeSelector.addEventListener("change", () => { updateSelectors(); drawMaps(); }); itemSelector.addEventListener("change", drawMaps); hazardSelector.addEventListener("change", drawMaps); return container; }; 5c:T9f2, // viewof cropTrendMaps = { // const crops = ["maize", "sorghum", "small millet"]; // const container = html`

`; // crops.forEach(crop => { // const mapDiv = html`

`; // container.appendChild(mapDiv); // const map = L.map(mapDiv).setView([0.1, 37.9], 6); // L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { // attribution: '© OSM contributors & CARTO' // }).addTo(map); // const data = kenyaCropTrendMerge.filter(d => d.crop === crop); // // Correct color scale over slope // const slopeExtent = d3.extent(data, d => d.slope); // const colorScale = d3.scaleSequential(d3.interpolatePlasma).domain(slopeExtent); // L.geoJSON(kenya_boundaries, { // style: f => { // const d = data.find(x => x.admin1_name === f.properties.admin1_name); // return { // fillColor: d ? colorScale(d.slope) : "#222", // directly use slope // color: "#333", // weight: 0.5, // fillOpacity: 0.8 // }; // }, // onEachFeature: (f, layer) => { // const d = data.find(x => x.admin1_name === f.properties.admin1_name); // if (d) { // layer.bindPopup(` // ${d.admin1_name}
// Rainfall Trend: ${d.slope?.toFixed(2)} mm/yr
// Rainfall SD: ${d.rain_sd?.toFixed(1)} mm
// Avg Value: ${d.avg_value?.toLocaleString()} USD
// Value SD: ${d.value_sd?.toLocaleString()} USD
// Total Value: ${d.total_value?.toLocaleString()} USD // `); // } // } // }).addTo(map); // // Legend // const legend = L.control({position: "bottomright"}); // legend.onAdd = () => { // const div = L.DomUtil.create("div", "info legend"); // div.style.background = "rgba(30,30,30,0.85)"; // div.style.padding = "8px"; // div.style.borderRadius = "6px"; // div.style.color = "white"; // div.innerHTML = ` // ${crop.charAt(0).toUpperCase() + crop.slice(1)} Rainfall Trend (mm/yr) //
Min: ${slopeExtent[0].toFixed(2)}, Max: ${slopeExtent[1].toFixed(2)} // `; // return div; // }; // legend.addTo(map); // invalidation.then(() => map.remove()); // }); // return container; // } 5d:T113c,viewof kenyaPhenologyMaps2 = (async () => { // Container for side-by-side maps const container = html`

`; // Append to DOM (Observable automatically handles this) // --- LGP Map --- const lgpGeo = kenya_boundaries.map(f => { const match = viewof lgpData.find(d => d.county === f.properties.admin1_name); f.properties.avgLGP_days = match ? match.avgLGP_days : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.avgLGP_days).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolateYlGn(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.avgLGP_days), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP: ${f.properties.avgLGP_days.toFixed(1)} days`); } }).addTo(lgpMap); // Legend for LGP const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Average LGP (days)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS Map --- const sosGeo = kenya_boundaries.map(f => { const match = viewof lgpData.find(d => d.county === f.properties.admin1_name); f.properties.avgSOS_day = match ? match.avgSOS_day : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.avgSOS_day).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolateBlues(1-sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.avgSOS_day), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
SOS: DOY ${f.properties.avgSOS_day.toFixed(1)}`); } }).addTo(sosMap); // Legend for SOS const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${Math.round(val)}`; }).join("
"); div.innerHTML = `Average SOS (DOY)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 5e:T10a4,viewof kenyaPhenologyMapsSD2 = (async () => { const container = html`

`; // --- LGP SD Map --- const lgpGeo = kenya_boundaries.map(f => { const match = statsByCounty.find(d => d.county === f.properties.admin1_name); f.properties.sdLGP = match ? match.sdLGP : 0; return f; }); const lgpMap = L.map(container.querySelector("#lgpMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(lgpMap); const lgpValues = lgpGeo.map(f => f.properties.sdLGP).filter(v => v > 0); const minLGP = Math.min(...lgpValues); const maxLGP = Math.max(...lgpValues); const lgpColor = d3.scaleLinear().domain([minLGP, maxLGP]).range([0,1]); function getLGPColor(v) { return v ? d3.interpolatePiYG(lgpColor(v)) : "#333"; } L.geoJSON(lgpGeo, { style: f => ({ fillColor: getLGPColor(f.properties.sdLGP), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
LGP SD: ${f.properties.sdLGP.toFixed(1)} days`); } }).addTo(lgpMap); // Legend for LGP SD const lgpLegend = L.control({position: "bottomright"}); lgpLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minLGP + i*(maxLGP-minLGP)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `LGP SD (days)
${gradient}`; return div; }; lgpLegend.addTo(lgpMap); // --- SOS SD Map --- const sosGeo = kenya_boundaries.map(f => { const match = statsByCounty.find(d => d.county === f.properties.admin1_name); f.properties.sdSOS = match ? match.sdSOS : 0; return f; }); const sosMap = L.map(container.querySelector("#sosMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(sosMap); const sosValues = sosGeo.map(f => f.properties.sdSOS).filter(v => v > 0); const minSOS = Math.min(...sosValues); const maxSOS = Math.max(...sosValues); const sosColor = d3.scaleLinear().domain([minSOS, maxSOS]).range([0,1]); function getSOSColor(v) { return v ? d3.interpolatePiYG(sosColor(v)) : "#333"; } L.geoJSON(sosGeo, { style: f => ({ fillColor: getSOSColor(f.properties.sdSOS), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
SOS SD: ${f.properties.sdSOS.toFixed(1)} days`); } }).addTo(sosMap); // Legend for SOS SD const sosLegend = L.control({position: "bottomright"}); sosLegend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minSOS + i*(maxSOS-minSOS)/9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `SOS SD (days)
${gradient}`; return div; }; sosLegend.addTo(sosMap); return container; })(); 5f:Tca6,viewof kenyaPopulationMaps0 = (async () => { const container = html`

`; // --- 1️⃣ Aggregate subcounty populations to county level --- const popByCounty = Array.from( d3.rollups( kenyaPopulation.filter(d => d.admin1_name && d.value != null), v => d3.sum(v, d => d.value), d => `${d.admin1_name}_${d.type}` // key = county + type (rural/urban) ), ([key, value]) => [key, value] ); const popMap = new Map(popByCounty); // --- 2️⃣ Helper to create color-coded maps for each type --- function makeMap(type, divId, title) { const map = L.map(container.querySelector(divId)).setView([-0.5, 38.5], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: "abcd", maxZoom: 19, opacity: 0.6 }).addTo(map); // Join aggregated data with county boundaries const geo = kenya_boundaries.map(f => { const key = `${f.properties.admin1_name}_${type}`; f.properties.pop_value = popMap.get(key) ?? 0; return f; }); // Compute min/max population const popValues = geo.map(f => f.properties.pop_value).filter(v => v > 0); const minP = d3.min(popValues); const maxP = d3.max(popValues); // Smooth color gradient const colorScale = d3.scaleSequential(d3.interpolateInferno).domain([minP, maxP]); // Draw polygons L.geoJSON(geo, { style: f => ({ fillColor: colorScale(f.properties.pop_value), weight: 0.5, color: "white", fillOpacity: 0.8 }), onEachFeature: (f, layer) => { const val = f.properties.pop_value; layer.bindPopup( `${f.properties.admin1_name}
${title} Population: ${ val ? val.toLocaleString("en-US") : "N/A" }` ); } }).addTo(map); // Add legend with population ranges const legend = L.control({ position: "bottomright" }); legend.onAdd = function () { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const n = 7; const breaks = d3.range(n).map(i => minP + (i * (maxP - minP)) / (n - 1)); const items = breaks .map( b => `${d3.format(".2s")(b)}` ) .join("
"); div.innerHTML = `${title} Population
${items}`; return div; }; legend.addTo(map); } // --- 3️⃣ Create Rural and Urban maps side by side --- makeMap("rural", "#ruralMap", "Rural"); makeMap("urban", "#urbanMap", "Urban"); return container; })(); 60:Tea5, viewof kenyaCompoundRiskMap = (async () => { const container = html`

`; // --- Preprocessing --- // Normalize functions const sdLGPValues = statsByCounty.map(d => d.meanLGP); const minLGP = Math.min(...sdLGPValues); const maxLGP = Math.max(...sdLGPValues); const normLGP = v => (v - minLGP) / (maxLGP - minLGP); // Crop risk per county const cropRiskByCounty = Object.fromEntries( d3.rollups( cropRiskSummary, v => d3.mean(v, d => d.risk_ptot), d => d.admin1_name ) ); // Livestock risk per county const livestockRiskByCounty = Object.fromEntries( d3.rollups( livestockRiskSummary, v => d3.mean(v, d => d.risk_ptot), d => d.admin1_name ) ); // Population (rural only) normalization const popValues = kenyaPopulation.filter(d => d.type === "rural").map(d => d.value); const minPop = Math.min(...popValues); const maxPop = Math.max(...popValues); const normPop = v => (v - minPop) / (maxPop - minPop); // --- Prepare GeoJSON with compound risk --- const geoData = kenya_boundaries.map(f => { const county = f.properties.admin1_name; const sdLGP = statsByCounty.find(d => d.county === county)?.meanLGP ?? 0; const cropRisk = cropRiskByCounty[county] ?? 0; const livestockRisk = livestockRiskByCounty[county] ?? 0; const population = kenyaPopulation.find(d => d.admin1_name === county && d.type === "rural")?.value ?? 0; // Compound risk: equal weighting const compoundRisk = 0.25 * normLGP(sdLGP) + 0.25 * cropRisk + 0.25 * livestockRisk + 0.25 * normPop(population); f.properties.compoundRisk = compoundRisk; return f; }); // --- Create Leaflet map --- const riskMap = L.map(container.querySelector("#riskMap")).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(riskMap); const riskValues = geoData.map(f => f.properties.compoundRisk).filter(v => v > 0); const minRisk = Math.min(...riskValues); const maxRisk = Math.max(...riskValues); const riskColorScale = d3.scaleLinear().domain([minRisk, maxRisk]).range([0,1]); const getRiskColor = v => v ? d3.interpolateYlOrRd(riskColorScale(v)) : "#333"; // --- Add counties --- L.geoJSON(geoData, { style: f => ({ fillColor: getRiskColor(f.properties.compoundRisk), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
Compound Risk: ${f.properties.compoundRisk.toFixed(2)}`); } }).addTo(riskMap); // --- Legend --- const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = minRisk + i*(maxRisk-minRisk)/9; return ` ${val.toFixed(2)}`; }).join("
"); div.innerHTML = `Compound Risk
${gradient}`; return div; }; legend.addTo(riskMap); return container; })(); 61:Tbf8, viewof kenyaCompoundedRiskMap = (async () => { const container = html`

`; // --- Aggregate mean values by county --- const meanByCounty = (data) => { const agg = {}; data.forEach(d => { const county = d.admin1_name; if (!agg[county]) agg[county] = []; agg[county].push(d.value ?? 0); }); const meanVals = {}; Object.keys(agg).forEach(k => { meanVals[k] = d3.mean(agg[k]); }); return meanVals; }; const cropByCounty = meanByCounty(kenyaCrops); const livestockByCounty = meanByCounty(kenyaLivestock ?? []); const ruralPop = meanByCounty(kenyaPopulation.filter(d => d.type === "rural")); // --- Compute compounded risk --- let compoundedRiskByCounty = {}; statsByCounty.forEach(d => { const county = d.county; const cropVal = cropByCounty[county] ?? 1; const livestockVal = livestockByCounty[county] ?? 1; const popVal = ruralPop[county] ?? 1; compoundedRiskByCounty[county] = (d.meanLGP ?? 1) * cropVal * livestockVal * popVal; }); // --- Normalize 0-1 --- const values = Object.values(compoundedRiskByCounty); const minVal = Math.min(...values); const maxVal = Math.max(...values); Object.keys(compoundedRiskByCounty).forEach(k => { compoundedRiskByCounty[k] = (compoundedRiskByCounty[k] - minVal) / (maxVal - minVal); }); // --- Create map --- const map = L.map(container).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); const geoData = kenya_boundaries.map(f => ({ ...f, properties: {...f.properties, compoundedRisk: compoundedRiskByCounty[f.properties.admin1_name] ?? 0} })); const getColor = v => v ? d3.interpolateYlOrRd(v) : "#333"; L.geoJSON(geoData, { style: f => ({ fillColor: getColor(f.properties.compoundedRisk), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
Compounded Risk: ${f.properties.compoundedRisk.toFixed(2)}`); } }).addTo(map); // Legend const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = i / 9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `Normalized Compounded Risk
(Relative, 0 = min, 1 = max)
${gradient}`; return div; }; legend.addTo(map); return container; })(); 62:Tf38,viewof kenyaCompoundedRiskComparison = (async () => { const container = html`

`; // --- Helper: compute mean by county --- const meanByCounty = (data) => { const agg = {}; data.forEach(d => { const county = d.admin1_name; if (!agg[county]) agg[county] = []; agg[county].push(d.value ?? 0); }); const meanVals = {}; Object.keys(agg).forEach(k => { meanVals[k] = d3.mean(agg[k]); }); return meanVals; }; const cropByCounty = meanByCounty(kenyaCrops); const livestockByCounty = meanByCounty(kenyaLivestock ?? []); const ruralPop = meanByCounty(kenyaPopulation.filter(d => d.type === "rural")); // --- Compute compounded risk using mean values + epsilon --- const computeCompounded = (baseValues) => { const eps = 1; // small value to avoid zeros let compounded = {}; statsByCounty.forEach(d => { const county = d.county; const base = (baseValues[county] ?? 0) + eps; const cropVal = (cropByCounty[county] ?? 0) + eps; const livestockVal = (livestockByCounty[county] ?? 0) + eps; const popVal = (ruralPop[county] ?? 0) + eps; compounded[county] = base * cropVal * livestockVal * popVal; }); // normalize 0–1 const values = Object.values(compounded); const minVal = Math.min(...values); const maxVal = Math.max(...values); Object.keys(compounded).forEach(k => { compounded[k] = (compounded[k] - minVal) / (maxVal - minVal); }); return compounded; }; const lgpCompounded = computeCompounded(statsByCounty.reduce((acc,d) => { acc[d.county]=d.meanLGP; return acc; }, {})); const sosCompounded = computeCompounded(statsByCounty.reduce((acc,d) => { acc[d.county]=d.meanSOS; return acc; }, {})); // --- Function to create a Leaflet map --- const createMap = (containerId, dataByCounty, title) => { const map = L.map(container.querySelector(containerId)).setView([-0.5, 39.0], 6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); const geoData = kenya_boundaries.map(f => ({ ...f, properties: {...f.properties, compoundedRisk: dataByCounty[f.properties.admin1_name] ?? 0} })); const getColor = v => d3.interpolateYlOrRd(v); L.geoJSON(geoData, { style: f => ({ fillColor: getColor(f.properties.compoundedRisk), weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
Compounded Risk: ${f.properties.compoundedRisk.toFixed(2)}`); } }).addTo(map); const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length: 10}, (_,i) => { const val = i / 9; return ` ${val.toFixed(1)}`; }).join("
"); div.innerHTML = `${title}
(Relative, 0 = min, 1 = max)
${gradient}`; return div; }; legend.addTo(map); }; createMap("#lgpRiskMap", lgpCompounded, "LGP-based Compounded Risk"); createMap("#sosRiskMap", sosCompounded, "SOS-based Compounded Risk"); return container; })(); 63:Te8b, viewof kenyaCompoundedRiskMaps = (async () => { const container = html`

`; // Helper: compute compounded risk per county const counties = [...new Set(kenyaPopulation.map(d => d.admin1_name))]; const compoundedData = counties.map(county => { const pop = kenyaPopulation .filter(d => d.admin1_name === county && d.type === "rural") .reduce((s,d)=>s+d.value,0); const crop = kenyaCrops .filter(d => d.admin1_name === county) .reduce((s,d)=>s+d.value,0); const livestock = kenyaLivestock .filter(d => d.admin1_name === county) .reduce((s,d)=>s+d.value,0); const stats = statsByCounty.find(d => d.county === county); const meanLGP = stats ? stats.meanLGP : 0; const meanSOS = stats ? stats.meanSOS : 0; return {county, meanLGP, meanSOS, pop, crop, livestock}; }); // Normalize function function normalize(values) { const min = Math.min(...values); const max = Math.max(...values); return values.map(v => (v-min)/(max-min)); } // Prepare normalized components const lgpNorm = normalize(compoundedData.map(d=>d.meanLGP)); const sosNorm = normalize(compoundedData.map(d=>d.meanSOS)); const popNorm = normalize(compoundedData.map(d=>d.pop)); const cropNorm = normalize(compoundedData.map(d=>d.crop)); const livestockNorm = normalize(compoundedData.map(d=>d.livestock)); // Compute compounded risk (0-1) const lgpCompounded = compoundedData.map((d,i)=>lgpNorm[i]*popNorm[i]*cropNorm[i]*livestockNorm[i]); const sosCompounded = compoundedData.map((d,i)=>sosNorm[i]*popNorm[i]*cropNorm[i]*livestockNorm[i]); // Function to create map function createMap(containerId, values, title, colorInterp) { const map = L.map(container.querySelector(containerId)).setView([-0.5,39],6.2); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors © CARTO', subdomains: 'abcd', maxZoom: 19 }).addTo(map); const geo = kenya_boundaries.map((f,i) => { f.properties.compounded = values[i] || 0; return f; }); L.geoJSON(geo, { style: f => ({ fillColor: f.properties.compounded ? colorInterp(f.properties.compounded) : "#333", weight: 1, color: "white", dashArray: "3", fillOpacity: 0.7 }), onEachFeature: (f, layer) => { layer.bindPopup(`${f.properties.admin1_name}
${title} Compounded Risk: ${f.properties.compounded.toFixed(3)}`); } }).addTo(map); // Legend const legend = L.control({position:"bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.8)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; const gradient = Array.from({length:10},(_,i)=>{ const val = i/9; return ` ${val.toFixed(2)}`; }).join("
"); div.innerHTML = `${title} Compounded Risk (relative 0-1)
${gradient}`; return div; }; legend.addTo(map); return map; } // Create LGP & SOS compounded risk maps createMap("#lgpMap", lgpCompounded, "LGP", d3.interpolateYlOrRd); createMap("#sosMap", sosCompounded, "SOS", d3.interpolateBlues); return container; })(); 64:T1597, // viewof kenyaPopulationMaps = (async () => { // const container = html` //

// `; // // --- Preprocess population data --- // const popByCounty = new Map(); // kenyaPopulation.forEach(d => { // if (d.admin1_name && d.type) { // popByCounty.set(`${d.admin1_name}_${d.type}`, d.value); // } // }); // // // --- Aggregate subcounty populations to county level --- // // const popByCounty = Array.from( // // d3.rollups( // // kenyaPopulation.filter(d => d.admin1_name && d.value != null), // // v => d3.sum(v, d => d.value), // // d => `${d.admin1_name}_${d.type}` // // ), // // ([key, value]) => [key, value] // // ); // // const popMap = new Map(popByCounty); // function getColorScale(values) { // const minV = Math.min(...values); // const maxV = Math.max(...values); // const scale = d3.scaleSequential() // .domain([minV, maxV]) // .interpolator(d3.interpolateViridis); // return { scale, minV, maxV }; // } // // --- RURAL MAP --- // const ruralGeo = kenya_boundaries.map(f => { // f.properties.pop = popByCounty.get(`${f.properties.admin1_name}_rural`) ?? 0; // return f; // }); // const ruralValues = ruralGeo.map(f => f.properties.pop).filter(v => v > 0); // const { scale: ruralColor, minV: minR, maxV: maxR } = getColorScale(ruralValues); // const ruralMap = L.map(container.querySelector("#ruralMap")).setView([-0.5, 37.5], 6); // L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { // attribution: '© OSM contributors', // subdomains: 'abcd', maxZoom: 19 // }).addTo(ruralMap); // L.geoJSON(ruralGeo, { // style: f => ({ // fillColor: ruralColor(f.properties.pop), // weight: 1, // color: "white", // dashArray: "3", // fillOpacity: 0.75 // }), // onEachFeature: (f, layer) => { // layer.bindPopup(`${f.properties.admin1_name}
Rural Population: ${f.properties.pop.toLocaleString()}`); // } // }).addTo(ruralMap); // // --- RURAL LEGEND (with ranges) --- // const ruralLegend = L.control({ position: "bottomright" }); // ruralLegend.onAdd = () => { // const div = L.DomUtil.create("div", "info legend"); // div.style.background = "rgba(30,30,30,0.85)"; // div.style.padding = "8px"; // div.style.borderRadius = "6px"; // div.style.color = "white"; // const breaks = [0, 5e5, 1e6, 2e6, 5e6, maxR]; // const labels = []; // for (let i = 0; i < breaks.length - 1; i++) { // const from = breaks[i]; // const to = breaks[i + 1]; // labels.push( // ` ${ // i === breaks.length - 2 ? `>${(from/1e6).toFixed(1)}M` : `${(from/1e6).toFixed(1)}–${(to/1e6).toFixed(1)}M` // }` // ); // } // div.innerHTML = `Rural Population
${labels.join("
")}`; // return div; // }; // ruralLegend.addTo(ruralMap); // // --- URBAN MAP --- // const urbanGeo = kenya_boundaries.map(f => { // f.properties.pop = popByCounty.get(`${f.properties.admin1_name}_urban`) ?? 0; // return f; // }); // const urbanValues = urbanGeo.map(f => f.properties.pop).filter(v => v > 0); // const { scale: urbanColor, minV: minU, maxV: maxU } = getColorScale(urbanValues); // const urbanMap = L.map(container.querySelector("#urbanMap")).setView([-0.5, 37.5], 6); // L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { // attribution: '© OSM contributors', // subdomains: 'abcd', maxZoom: 19 // }).addTo(urbanMap); // L.geoJSON(urbanGeo, { // style: f => ({ // fillColor: urbanColor(f.properties.pop), // weight: 1, // color: "white", // dashArray: "3", // fillOpacity: 0.75 // }), // onEachFeature: (f, layer) => { // layer.bindPopup(`${f.properties.admin1_name}
Urban Population: ${f.properties.pop.toLocaleString()}`); // } // }).addTo(urbanMap); // // --- URBAN LEGEND (with ranges) --- // const urbanLegend = L.control({ position: "bottomright" }); // urbanLegend.onAdd = () => { // const div = L.DomUtil.create("div", "info legend"); // div.style.background = "rgba(30,30,30,0.85)"; // div.style.padding = "8px"; // div.style.borderRadius = "6px"; // div.style.color = "white"; // const breaks = [0, 1e5, 5e5, 1e6, 2e6, maxU]; // const labels = []; // for (let i = 0; i < breaks.length - 1; i++) { // const from = breaks[i]; // const to = breaks[i + 1]; // labels.push( // ` ${ // i === breaks.length - 2 ? `>${(from/1e6).toFixed(1)}M` : `${(from/1e6).toFixed(1)}–${(to/1e6).toFixed(1)}M` // }` // ); // } // div.innerHTML = `Urban Population
${labels.join("
")}`; // return div; // }; // urbanLegend.addTo(urbanMap); // return container; // })(); 65:T708,viewof kenyaLivestockTrendPlot = { const colorSchemes = { cattle_tropical: "rgb(255,180,50)", goats_tropical: "rgb(255,120,120)", sheep_tropical: "rgb(150,200,255)", poultry_tropical: "rgb(180,255,150)" }; const scatter = Plot.plot({ width: 900, height: 600, style: { background: "#111", color: "#ddd" }, x: { label: "Rainfall Trend (mm/year)", grid: true }, y: { label: "Total Livestock Value (USD)", grid: true, tickFormat: d3.format(".2s") }, marks: [ Plot.dot(kenyaLivestockTrendMerge, { x: "slope", y: "total_value", fill: d => colorSchemes[d.crop], fillOpacity: 0.7, r: d => Math.sqrt(d.avg_value) / 50, title: d => `${d.admin1_name}\n${d.crop}\nRainfall Trend: ${d.slope?.toFixed(2)} mm/yr\nRain SD: ${d.rain_sd?.toFixed(1)} mm\nValue SD: ${d.value_sd?.toLocaleString()}` }) ] }); const legend = html`

Legend
● Cattle
● Goats
● Sheep
● Poultry

• X-axis: Rainfall trend (mm/year)
• Y-axis: Total livestock value (USD)
• Size: Avg value per county
• Color: Livestock type

`; const container = html`

${scatter}${legend}

`; return container; };66:Tbed,viewof livestockTrendMaps = { const livestock = ["cattle_tropical", "sheep_tropical", "goats_tropical", "poultry_tropical"]; const container = html`

`; livestock.forEach(type => { const mapDiv = html`

`; container.appendChild(mapDiv); const map = L.map(mapDiv).setView([0.1, 37.9], 6); L.tileLayer("https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png", { attribution: '© OSM contributors & CARTO' }).addTo(map); const data = kenyaLivestockTrendMerge.filter(d => d.crop === type); // Color scale for slope const slopeExtent = d3.extent(data, d => d.slope); const colorScale = d3.scaleSequential(d3.interpolateViridis).domain(slopeExtent); // Radius scale for avg value const radiusScale = d3.scaleSqrt() .domain([0, d3.max(data, d => d.avg_value)]) .range([5, 30]); // min/max circle radius L.geoJSON(kenya_boundaries, { style: f => { const d = data.find(x => x.admin1_name === f.properties.admin1_name); return { fillColor: d ? colorScale(d.slope) : "#222", color: "#333", weight: 0.5, fillOpacity: 0.8 }; }, onEachFeature: (f, layer) => { const d = data.find(x => x.admin1_name === f.properties.admin1_name); if (d) { layer.bindPopup(` ${d.admin1_name}
Rainfall Trend: ${d.slope?.toFixed(2)} mm/yr
Rainfall SD: ${d.rain_sd?.toFixed(1)} mm
Avg Value: ${d.avg_value?.toLocaleString()} USD
Value SD: ${d.value_sd?.toLocaleString()} USD
Total Value: ${d.total_value?.toLocaleString()} USD `); // Overlay circle marker at centroid const centroid = turf.centroid(f).geometry.coordinates; L.circleMarker([centroid[1], centroid[0]], { radius: radiusScale(d.avg_value), fillColor: "orange", color: "#fff", fillOpacity: 0.6, weight: 1 }).addTo(map); } } }).addTo(map); // Legend const legend = L.control({position: "bottomright"}); legend.onAdd = () => { const div = L.DomUtil.create("div", "info legend"); div.style.background = "rgba(30,30,30,0.85)"; div.style.padding = "8px"; div.style.borderRadius = "6px"; div.style.color = "white"; div.innerHTML = ` ${type.replace("_tropical", "").charAt(0).toUpperCase() + type.replace("_tropical", "").slice(1)} Rainfall Trend (mm/yr)
Color = slope (min: ${slopeExtent[0].toFixed(2)}, max: ${slopeExtent[1].toFixed(2)})
Circle size = avg livestock value `; return div; }; legend.addTo(map); invalidation.then(() => map.remove()); }); return container; }67:[{"id":"d27ca85467029add40b5e8ee77a57fb1bdb2dd59476341a450d04cd1c6d9a64644f43cf2192ed9dad6c588642d75785ee21559869336c9ba55c3a34534890ebc","href":"https://static.observableusercontent.com/files/d27ca85467029add40b5e8ee77a57fb1bdb2dd59476341a450d04cd1c6d9a64644f43cf2192ed9dad6c588642d75785ee21559869336c9ba55c3a34534890ebc","name":"atlas_gaul24_a0_africa_simple-vlowres@1.topojson","type":"application/octet-stream","lastModified":1760373824852.333,"size":57073},{"id":"92ddf9c810aa8aa53fd1d970db5a698e85dba22f86f3942f94bd61675a1b873d333926032e71774dd25fd4655085ebf431855e6c0fb269bd262c89f31ed32f4f","href":"https://static.observableusercontent.com/files/92ddf9c810aa8aa53fd1d970db5a698e85dba22f86f3942f94bd61675a1b873d333926032e71774dd25fd4655085ebf431855e6c0fb269bd262c89f31ed32f4f","name":"atlas_gaul24_a1_africa_simple-vlowres.topojson","type":"application/octet-stream","lastModified":1760373824852.333,"size":381496},{"id":"3ae800f8b9cbd5af4bd8204dec9f9d888b61d6745df9f954fdc2e0b99989a478be2d0b65819d4906dc087d1694d9de8b26a6df1d8c2ad073e4a81c0443b397f1","href":"https://static.observableusercontent.com/files/3ae800f8b9cbd5af4bd8204dec9f9d888b61d6745df9f954fdc2e0b99989a478be2d0b65819d4906dc087d1694d9de8b26a6df1d8c2ad073e4a81c0443b397f1","name":"atlas_gaul24_a2_africa_simple-lowres.topojson","type":"application/octet-stream","lastModified":1760373824852.333,"size":3339623},{"id":"72ebc92b938121c093956d0a085908e12e4268af900fb3edf669b31f66703ff16420d9e8690250097768543c42254d5a1ee724690aef9ced627672ad3264a1a9","href":"https://static.observableusercontent.com/files/72ebc92b938121c093956d0a085908e12e4268af900fb3edf669b31f66703ff16420d9e8690250097768543c42254d5a1ee724690aef9ced627672ad3264a1a9","name":"africa_wmobb_basins.topojson","type":"application/octet-stream","lastModified":1760373824852.333,"size":222044}] 2f:{"id":"c360c1a8d90db91e","slug":null,"title":"Adaptation Atlas Hackathon Notebook Starter Template","thumbnail":"1586b941c25ff6c8dc01aff75482a6f1bcac92a01f14cceb8fd08c095b34be95","access_level":"unlisted","create_time":"$D2025-10-13T16:43:44.852Z","update_time":"$D2025-10-13T16:43:44.852Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"2b74b884341f171b","login":"earth-observation","name":"earth-observation","avatar_url":"https://avatars-next.observableusercontent.com/879615ccef14eeec2ccc264f5f904d6d3eab3f9e28a9cd3452de06ae6ee0a9b0","tier":"starter_2024","bio":""},"version":3044,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"7adb8c3373fb7d93","login":"prachi-bisht","name":"Prachi Bisht","avatar_url":"https://avatars-next.observableusercontent.com/9f122fae171cfb03f94aa6b274825eea5bf417a26b9d92037ab9db8f51036070","tier":null,"bio":""}],"members":[{"type":"individual","id":"7adb8c3373fb7d93","login":"prachi-bisht","name":"Prachi Bisht","avatar_url":"https://avatars-next.observableusercontent.com/9f122fae171cfb03f94aa6b274825eea5bf417a26b9d92037ab9db8f51036070","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Adaptation Atlas Hackathon Notebook Starter Template","cells":[{"id":0,"value":"# ${nbTitle}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"tocOneliner({\n selector: \"h1,h2\", // selectors to include (can comma-separate, like \"h1,h2,h3\")\n heading :\"In this notebook\", // heading for ToC\n delim : ' | ', // delimiter between links\n skip: [nbTitle, \"Imports\"], // text content for headers to exclude\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":115,"value":"# Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":120,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":113,"value":"# Question 1","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":142,"value":"When does the rainy season start, and what is the typical length and year-to-year variability of the length of growing period (LGP) across counties?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":199,"value":"viewof adminLevel = {\n let opts = [\n {value: \"adm0\", label:\"Country\", adminName0: \"admin0_name\", adminName1: null },\n {value: \"adm1\", label:\"Admin 1\", adminName0: \"admin1_name\", adminName1: \"admin0_name\"},\n {value: \"adm2\", label:\"Admin 2\", adminName0: \"admin2_name\", adminName1: \"admin0_name\"},\n {value: \"watershed\", label:\"Watershed\", adminName0: \"WMOBB_DESC\", adminName1: \"WMOBB_NAME\"}\n ]\n \n return Inputs.radio(opts, {value: \"Country\", label: \"Boundary Type\", format: x => x.label, value: opts[0]})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1419,"value":"// boundary_geojson = {\n// let bounds = admin_boundaries[adminLevel.value] || watershed_boundaries[adminLevel.value];\n// return bounds\n// }","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"Plot.plot({\n height: 550,\n projection: {\n type: \"azimuthal-equal-area\",\n domain: boundary_geojson\n },\n marks: [\n Plot.geo(boundary_geojson),\n Plot.geo(boundary_geojson, Plot.pointer(\n Plot.centroid({\n stroke: \"#2E7636\",\n strokeWidth: 1.5,\n }))),\n Plot.tip(\n boundary_geojson.features,\n Plot.pointer(\n Plot.centroid({\n channels: {\n name: {\n label: \"Admin Name:\", \n value: (d) => {\n console.log(d)\n return d.properties[adminLevel.adminName0]\n } \n },\n name1: {\n label: \"Higher Admin:\", \n value: (d) => d.properties[adminLevel.adminName1]\n },\n },\n // format: {\n // name: true,\n // data: (d) => selector.formatFunc(d)\n // }\n })\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":310,"value":"import {hazard_db} from \"b2a8f20822affaca\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"import {exposure_db} from \"b2a8f20822affaca\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":944,"value":"import {phenology_db} from \"b2a8f20822affaca\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":946,"value":"import {pest_db} from \"b2a8f20822affaca\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"kenya_boundaries = admin_boundaries.adm1.features.filter(\n d => d.properties.iso3 === \"KEN\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":456,"value":"viewof scenarioSelector = Inputs.select(\n [\"ssp126\", \"ssp245\", \"ssp585\"], \n { label: \"Select SSP Scenario\", value: \"ssp245\" }\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":478,"value":"viewof hazardSelector = Inputs.select(\n [\"NDD-mean\", \"TMAX-max\", \"NDWS-mean\", \"FireRisk\"], \n { label: \"Select Hazard\", value: \"NDD-mean\" }\n)\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":473,"value":"function buildKenyaGeojson(hazard_data) {\n const features = kenya_boundaries.map(f => JSON.parse(JSON.stringify(f)));\n\n features.forEach(f => {\n const code = f.properties.gaul1_code;\n let match = null;\n\n if (code != null) {\n match = hazard_data.find(r => String(r.gaul1_code) === String(code));\n }\n\n if (!match) {\n const name = (f.properties.admin1_name || \"\").trim().toLowerCase();\n match = hazard_data.find(r => (r.admin1_name || \"\").trim().toLowerCase() === name);\n }\n\n f.properties.hazard_value = match ? match.value : 0; // use 0 if not found\n });\n\n return { type: \"FeatureCollection\", features };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":903,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":977,"value":"// Example Use:\nphenology_db.query(`\nSELECT *\nFROM kenya_phenology\nWHERE season_num = 1\nLIMIT 6\n`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":979,"value":"$33","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":992,"value":"$34","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":994,"value":"$35","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1012,"value":"phenology_db.query(`SELECT *\n FROM kenya_phenology\n WHERE admin1_name= 'Embu'\nAND season_num = 1\nAND season_yr='2000'\nAND meth = 'Zhang'`)\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1005,"value":"turf = require(\"@turf/turf@5\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3035,"value":"The white halo around each county’s circle shows how much the LGP varies within that county in that year — bigger halo means more variability, smaller halo means the LGP is more uniform across that county.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1009,"value":"viewof year = Inputs.range([2000, 2024], {\n step: 1,\n value: 2000,\n label: \"Season Year\"\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1100,"value":"$36","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1128,"value":"\n//time series\ndata = await phenology_db.query(`\n SELECT admin1_name, season_yr, origin, \"DER.sos\" AS SOS, \"DER.eos\" AS EOS, season_num\n FROM kenya_phenology\n WHERE season_num = 1\n`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2969,"value":"await phenology_db.query(`\n SELECT season_num, COUNT(*) AS n_records\n FROM kenya_phenology\n GROUP BY season_num\n ORDER BY season_num\n`);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1131,"value":"$37","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1467,"value":"negativeSOS = processed.filter(d => d.SOS_day < 0);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2923,"value":"negativelgp = processed.filter(d => d.LGP < 0);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1134,"value":"// Aggregate per county\nstatsByCounty0 = d3.rollups(\n processed,\n v => ({\n // meanSOS: d3.mean(v, d => d.SOS_day),\n meanLGP: d3.mean(v, d => d.LGP),\n sdLGP: d3.deviation(v, d => d.LGP),\n years: v.map(d => d.year)\n }),\n d => d.county\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1181,"value":"// Aggregate per county & year\nstatsByCountyYear = Array.from(\n d3.rollups(\n processed,\n v => ({\n meanLGP: d3.mean(v, d => d.LGP),\n sdLGP: d3.deviation(v, d => d.LGP)\n }),\n d => d.county,\n d => d.year\n ),\n ([county, yearMap]) => {\n return Array.from(yearMap, ([year, stats]) => ({\n county,\n year,\n meanLGP: stats.meanLGP,\n sdLGP: stats.sdLGP\n }));\n }\n).flat();\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1380,"value":"statsByCountyYear.sort((a, b) => a.year - b.year);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1278,"value":"// Compute average LGP per county (to sort by color)\n countyMeans = d3.rollups(\n statsByCountyYear,\n v => d3.mean(v, d => d.meanLGP),\n d => d.county\n).map(([county, meanLGP]) => ({ county, meanLGP }));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1281,"value":"// Sort counties by mean LGP\ncountyMeans.sort((a, b) => a.meanLGP - b.meanLGP);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1283,"value":"sortedCounties = countyMeans.map(d => d.county);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1285,"value":"// Color scale: light to dark blue\ncolorScale = d3.scaleSequential()\n .domain([0, sortedCounties.length - 1])\n .interpolator(d3.interpolateBlues);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1295,"value":"countyColor = new Map(sortedCounties.map((c, i) => [c, colorScale(i)]));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1356,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1456,"value":"//truly gibberish like 1970 eos for origin 1999\nfilteredProcessed = processed.filter(d => d.SOS_day > -200);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3002,"value":"processed.filter(d => d.SOS_day < 0);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2961,"value":"// Fix SOS values that are negative (belong to previous year)\nprocessedFixed = filteredProcessed.map(d => {\n // Adjust for leap years if you want precision\n const isLeap = (d.year % 4 === 0 && (d.year % 100 !== 0 || d.year % 400 === 0));\n const yearDays = isLeap ? 366 : 365;\n\n // Wrap negative SOS to previous year scale\n const sosWrapped = d.SOS_day < 0 ? d.SOS_day + yearDays : d.SOS_day;\n\n return { ...d, SOS_wrapped: sosWrapped };\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1382,"value":"$39","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1450,"value":"// processed","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2973,"value":"// statsByCountyYearFiltered = d3.rollups(\n// processedFixed,\n// values => ({\n// meanSOS: d3.mean(values, d => d.SOS_wrapped),\n// sdSOS: d3.deviation(values, d => d.SOS_wrapped),\n// meanLGP: d3.mean(values, d => d.LGP),\n// sdLGP: d3.deviation(values, d => d.LGP)\n// }),\n// d => d.county,\n// d => d.year\n// )\n// .map(([county, yearStats]) => \n// yearStats.map(([year, stats]) => ({ county, year, ...stats }))\n// )\n// .flat();\n\n\nstatsByCountyYearFiltered = d3.rollups(\n processedFixed,\n values => ({\n meanSOS: d3.mean(values, d => d.SOS_day),\n sdSOS: d3.deviation(values, d => d.SOS_day),\n meanLGP: d3.mean(values, d => d.LGP),\n sdLGP: d3.deviation(values, d => d.LGP)\n }),\n d => d.county,\n d => d.year\n)\n.map(([county, yearStats]) => \n yearStats.map(([year, stats]) => ({ county, year, ...stats }))\n)\n.flat();\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2975,"value":"sosLgpTrends = d3.rollups(\n statsByCountyYearFiltered,\n v => ({\n meanSOS_overYears: d3.mean(v, d => d.meanSOS),\n sdSOS_overYears: d3.deviation(v, d => d.meanSOS),\n meanLGP_overYears: d3.mean(v, d => d.meanLGP),\n sdLGP_overYears: d3.deviation(v, d => d.meanLGP),\n minLGP: d3.min(v, d => d.meanLGP),\n maxLGP: d3.max(v, d => d.meanLGP),\n minSOS: d3.min(v, d => d.meanSOS),\n maxSOS: d3.max(v, d => d.meanSOS),\n \n }),\n d => d.county\n).map(([county, stats]) => ({ county, ...stats }));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1517,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2980,"value":"$3b","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1514,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1604,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2990,"value":"year wise","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2992,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2995,"value":"$3f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1521,"value":"$40","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3023,"value":"# Question 2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":3025,"value":"*Which counties are experiencing significant shifts in onset or\nshortening of the rainy season?*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1545,"value":"// processedPhenology = rows.map(d => {\n// const greenup = new Date(d.Greenup);\n// const senescence = new Date(d.Senescence);\n// const origin = new Date(greenup.getFullYear(), 0, 1);//new Date(d.origin);\n\n// const SOS_day = (greenup - origin) / (1000 * 60 * 60 * 24); // day of year\n// const EOS_day = (senescence - origin) / (1000 * 60 * 60 * 24);\n// const LGP = EOS_day - SOS_day;\n\n// return {\n// county: d.admin1_name,\n// year: +d.season_yr,\n// SOS_day,\n// EOS_day,\n// LGP\n// };\n// });\n\nprocessedPhenology = data.map(d => {\n const sos = new Date(d.SOS);\n const eos = new Date(d.EOS);\n const startOfYear = new Date(sos.getFullYear(), 0, 1);//new Date(d.season_yr, 0, 1);//new Date(d.origin);//\n return {\n county: d.admin1_name,\n year: +d.season_yr,\n SOS_day: (sos - startOfYear)/(1000*60*60*24),\n EOS_day: (eos - startOfYear)/(1000*60*60*24),\n LGP: (eos - sos)/(1000*60*60*24)\n };\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1563,"value":"dataByCounty = d3.group(processed, d => d.county);\n// dataByCounty = d3.group(sosLgpTrends, d => d.county);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1565,"value":"linearTrend = function linearTrend(x, y) {\n const n = x.length;\n const x_mean = d3.mean(x);\n const y_mean = d3.mean(y);\n const num = d3.sum(x.map((xi,i) => (xi-x_mean)*(y[i]-y_mean)));\n const den = d3.sum(x.map(xi => (xi-x_mean)**2));\n return num / den; // slope\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1567,"value":"countyTrends = Array.from(dataByCounty, ([county, rows]) => {\n const years = rows.map(d => d.year);\n const sos = rows.map(d => d.SOS_day);\n const lgp = rows.map(d => d.LGP);\n\n return {\n county,\n sosTrend: linearTrend(years, sos), // +ve → delayed onset\n lgpTrend: linearTrend(years, lgp) // -ve → shortening\n };\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1571,"value":" lgpThreshold = -1; // days per decade","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1569,"value":" sosThreshold = 1; // days per decade","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3028,"value":" // significantCounties = countyTrends.filter(d => d.sosTrend > sosThreshold || d.lgpTrend < lgpThreshold).map(d => d.county);\n significantCounties = statsByCountyYearFiltered.filter(d => d.sdLGP > 100).map(d => d.county);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1595,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3038,"value":"$42","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"*${lipsum({\n count:1, units: \"paragraphs\", sentenceLowerBound:3, sentenceUpperBound: 15, paragraphLowerBound: 4, paragraphUpperBound: 4})\n}*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":152,"value":"# Question 3","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":167,"value":"*What do these rainfall trends imply for key rain-fed crops (maize,\nsorghum, fodder) and for livestock populations (cattle, goats,\nsheep, poultry)?*\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1616,"value":"exposure_db.describeTables()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1620,"value":"hazard_db.describeTables()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1776,"value":"\n await exposure_db.query(`\n SELECT *\n FROM livestock_vop\n WHERE admin0_name = 'Kenya'\n`)\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2148,"value":"kenya0 = await hazard_db.query(`\n SELECT *\n FROM hazard_mean\n WHERE admin0_name = 'Kenya'\n AND admin1_name = 'Nairobi'\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2134,"value":"kenya = await hazard_db.query(`\n SELECT hazard, \n FROM hazard_monthly_historic\n WHERE admin0_name = 'Kenya'\n AND admin1_name = 'Nairobi'\n AND year = '2000'\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1638,"value":"// 1️⃣ Get rainfall data\nkenyaRain = await hazard_db.query(`\n SELECT admin1_name, season, year, mean AS seasonal_rain\n FROM hazard_monthly_historic\n WHERE admin0_name = 'Kenya'\n AND hazard IN ('PTOT')\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2129,"value":"// Get historical seasonal NDD for Kenya\nkenyaNDWS = await hazard_db.query(`\n SELECT admin1_name, season, year, mean AS seasonal_ndws\n FROM hazard_monthly_historic\n WHERE admin0_name = 'Kenya'\n AND hazard = 'NDWS'\n`);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2272,"value":"// Get historical seasonal NDD for Kenya\nkenyatemp = await hazard_db.query(`\n SELECT admin1_name, season, year, mean AS seasonal_temp\n FROM hazard_monthly_historic\n WHERE admin0_name = 'Kenya'\n AND hazard = 'TAVG'\n`);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1711,"value":"kenyaRainFiltered = kenyaRain.filter(r => r.admin1_name !== null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2131,"value":"kenyaNDWSFiltered = kenyaNDWS.filter(r => r.admin1_name !== null);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2288,"value":"kenyatempFiltered = kenyatemp.filter(r => r.admin1_name !== null);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2199,"value":"exposure_db.query(`\n SELECT *\n FROM crop_vop\n WHERE admin0_name = 'Kenya'\n AND crop IN ('maize', 'sorghum', 'small millet')\n`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1649,"value":"\nkenyaCrops0 = await exposure_db.query(`\n SELECT admin1_name, admin2_name, crop, \"value\"\n FROM crop_vop\n WHERE admin0_name = 'Kenya'\n AND crop IN ('maize', 'sorghum', 'small millet')\n`);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2184,"value":"kenyaCrops = kenyaCrops0.filter(r => r.admin1_name !== null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2203,"value":"allcropslist = await exposure_db.query(`\n SELECT crop, COUNT(*) AS count\n FROM crop_vop\n WHERE admin0_name = 'Kenya'\n GROUP BY crop\n ORDER BY count DESC;\n`);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2023,"value":" allColumns = await exposure_db.query(`\n SELECT *\n FROM crop_vop\n WHERE admin0_name = 'Kenya'\n LIMIT 1\n`);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1742,"value":"// 2️⃣ Get livestock exposure data\nkenyaLivestock0 = await exposure_db.query(`\n SELECT admin1_name,admin2_name, crop, \"value\"\n FROM livestock_vop\n WHERE admin0_name = 'Kenya'\n AND crop IN ('cattle_tropical','goats_tropical','sheep_tropical','poultry_tropical')\n`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2806,"value":"kenyaLivestock = kenyaLivestock0.filter(r => r.admin1_name !== null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1660,"value":"kenyaRainCrop = kenyaRainFiltered.flatMap(r => {\n // Get all crop rows for this county\n const cropsInCounty = kenyaCrops.filter(l => l.admin1_name === r.admin1_name);\n\n // For each crop, pair with rainfall\n return cropsInCounty.map(cropRow => ({\n admin1_name: r.admin1_name,\n admin2_name: cropRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_rain: r.seasonal_rain,\n crop: cropRow.crop,\n value: cropRow.value\n }));\n});\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2281,"value":"kenyaNDWSCrop = kenyaNDWSFiltered.flatMap(r => {\n // Get all crop rows for this county\n const cropsInCounty = kenyaCrops.filter(l => l.admin1_name === r.admin1_name);\n\n // For each crop, pair with rainfall\n return cropsInCounty.map(cropRow => ({\n admin1_name: r.admin1_name,\n admin2_name: cropRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_ndws: r.seasonal_ndws,\n crop: cropRow.crop,\n value: cropRow.value\n }));\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2285,"value":"kenyatempCrop = kenyatempFiltered.flatMap(r => {\n // Get all crop rows for this county\n const cropsInCounty = kenyaCrops.filter(l => l.admin1_name === r.admin1_name);\n\n // For each crop, pair with rainfall\n return cropsInCounty.map(cropRow => ({\n admin1_name: r.admin1_name,\n admin2_name: cropRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_temp: r.seasonal_temp,\n crop: cropRow.crop,\n value: cropRow.value\n }));\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2717,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2735,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2737,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2720,"value":"$46","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1716,"value":"kenyaRainLivestock = kenyaRainFiltered.flatMap(r => {\n // Get all livestock records for this county\n const livestockInCounty = kenyaLivestock.filter(l => l.admin1_name === r.admin1_name);\n\n // Combine rainfall data with each livestock type for that county\n return livestockInCounty.map(livestockRow => ({\n admin1_name: r.admin1_name,\n admin2_name: livestockRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_rain: r.seasonal_rain,\n crop: livestockRow.crop, // livestock type (e.g. cattle_tropical)\n value: livestockRow.value\n }));\n});\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2294,"value":"kenyaNDWSLivestock = kenyaNDWSFiltered.flatMap(r => {\n // Get all livestock records for this county\n const livestockInCounty = kenyaLivestock.filter(l => l.admin1_name === r.admin1_name);\n\n // Combine rainfall data with each livestock type for that county\n return livestockInCounty.map(livestockRow => ({\n admin1_name: r.admin1_name,\n admin2_name: livestockRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_ndws: r.seasonal_ndws,\n crop: livestockRow.crop, // livestock type (e.g. cattle_tropical)\n value: livestockRow.value\n }));\n});\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2298,"value":"kenyatempLivestock = kenyatempFiltered.flatMap(r => {\n // Get all livestock records for this county\n const livestockInCounty = kenyaLivestock.filter(l => l.admin1_name === r.admin1_name);\n\n // Combine rainfall data with each livestock type for that county\n return livestockInCounty.map(livestockRow => ({\n admin1_name: r.admin1_name,\n admin2_name: livestockRow.admin2_name,\n season: r.season,\n year: r.year,\n seasonal_temp: r.seasonal_temp,\n crop: livestockRow.crop, // livestock type (e.g. cattle_tropical)\n value: livestockRow.value\n }));\n});\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1705,"value":"$47","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2375,"value":"$48","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2628,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1694,"value":"kenyaCropSummary = summarizeExposureSeasonal(kenyaRainCrop, \"seasonal_rain\", 'rainfall');","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1790,"value":"kenyaLivestockSummary = summarizeExposureSeasonal(kenyaRainLivestock, \"seasonal_rain\",'rainfall');","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2308,"value":"kenyaCropNDWSSummary = summarizeExposureSeasonal(kenyaNDWSCrop, \"seasonal_ndws\", 'ndws')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2310,"value":"kenyaCroptempSummary = summarizeExposureSeasonal(kenyatempCrop, \"seasonal_temp\", 'temp')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2312,"value":"kenyaLivestockNDWSSummary = summarizeExposureSeasonal(kenyaNDWSLivestock, \"seasonal_ndws\", 'ndws')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2314,"value":"kenyaLivestocktempSummary = summarizeExposureSeasonal(kenyatempLivestock, \"seasonal_temp\", 'temp')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1794,"value":"Inputs.table(kenyaCropSummary)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2316,"value":"Inputs.table(kenyaCroptempSummary)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1799,"value":"Inputs.table(kenyaLivestockSummary)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2035,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2796,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2014,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1892,"value":"// Group by county & year\nrainfallTrend = Array.from(\n d3.rollup(\n kenyaRainFiltered,\n v => d3.mean(v, d => d.seasonal_rain),\n d => d.admin1_name,\n d => d.year\n ),\n ([admin1_name, yearMap]) => ({\n admin1_name,\n values: Array.from(yearMap, ([year, avgRain]) => ({ year, avgRain }))\n })\n);\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2637,"value":"// === Group by county, year, and season ===\nrainfallTrendSeasonal = Array.from(\n d3.rollup(\n kenyaRainFiltered,\n v => d3.mean(v, d => d.seasonal_rain),\n d => d.admin1_name,\n d => d.season,\n d => d.year\n ),\n ([admin1_name, seasonMap]) => ({\n admin1_name,\n seasons: Array.from(seasonMap, ([season, yearMap]) => ({\n season,\n values: Array.from(yearMap, ([year, avgRain]) => ({ year, avgRain }))\n }))\n })\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2646,"value":"NDWSTrendSeasonal = Array.from(\n d3.rollup(\n kenyaNDWSFiltered,\n v => d3.mean(v, d => d.seasonal_ndws),\n d => d.admin1_name,\n d => d.season,\n d => d.year\n ),\n ([admin1_name, seasonMap]) => ({\n admin1_name,\n seasons: Array.from(seasonMap, ([season, yearMap]) => ({\n season,\n values: Array.from(yearMap, ([year, avgNDWS]) => ({ year, avgNDWS }))\n }))\n })\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2644,"value":"tempTrendSeasonal = Array.from(\n d3.rollup(\n kenyatempFiltered,\n v => d3.mean(v, d => d.seasonal_temp),\n d => d.admin1_name,\n d => d.season,\n d => d.year\n ),\n ([admin1_name, seasonMap]) => ({\n admin1_name,\n seasons: Array.from(seasonMap, ([season, yearMap]) => ({\n season,\n values: Array.from(yearMap, ([year, avgtemp]) => ({ year, avgtemp }))\n }))\n })\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2327,"value":"NDWSTrend = Array.from(\n d3.rollup(\n kenyaNDWSFiltered,\n v => d3.mean(v, d => d.seasonal_ndws),\n d => d.admin1_name,\n d => d.year\n ),\n ([admin1_name, yearMap]) => ({\n admin1_name,\n values: Array.from(yearMap, ([year, avgNDWS]) => ({ year, avgNDWS }))\n })\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2329,"value":"tempTrend = Array.from(\n d3.rollup(\n kenyatempFiltered,\n v => d3.mean(v, d => d.seasonal_temp),\n d => d.admin1_name,\n d => d.year\n ),\n ([admin1_name, yearMap]) => ({\n admin1_name,\n values: Array.from(yearMap, ([year, avgtemp]) => ({ year, avgtemp }))\n })\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1894,"value":"// General linear trend function\nfunction linearTrend1(points, yAccessor) {\n const n = points.length;\n if (n === 0) return { slope: null, intercept: null };\n\n const sumX = d3.sum(points, d => d.year);\n const sumY = d3.sum(points, d => yAccessor(d));\n const sumXY = d3.sum(points, d => d.year * yAccessor(d));\n const sumX2 = d3.sum(points, d => d.year * d.year);\n\n const denominator = n * sumX2 - sumX * sumX;\n if (denominator === 0) return { slope: 0, intercept: sumY / n };\n\n const slope = (n * sumXY - sumX * sumY) / denominator;\n const intercept = (sumY - slope * sumX) / n;\n\n return { slope, intercept };\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1897,"value":"// Rainfall\nrainfallTrends = rainfallTrend.map(({ admin1_name, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgRain);\n return { admin1_name, slope, intercept };\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2648,"value":"// === Compute seasonal rainfall trends ===\nrainfallTrendsSeasonal = rainfallTrendSeasonal.flatMap(({ admin1_name, seasons }) =>\n seasons.map(({ season, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgRain);\n return { admin1_name, season, slope, intercept };\n })\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2333,"value":"// NDWS\nNDWSTrends = NDWSTrend.map(({ admin1_name, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgNDWS);\n return { admin1_name, slope, intercept };\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2650,"value":"\n// === 2️⃣ Seasonal NDWS trends ===\nNDWSTrendsSeasonal = NDWSTrendSeasonal.flatMap(({ admin1_name, seasons }) =>\n seasons.map(({ season, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgNDWS);\n return { admin1_name, season, slope, intercept };\n })\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2336,"value":"// Temperature\ntempTrends = tempTrend.map(({ admin1_name, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgtemp);\n return { admin1_name, slope, intercept };\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2652,"value":"\n// === 3️⃣ Seasonal temperature trends ===\ntempTrendsSeasonal = tempTrendSeasonal.flatMap(({ admin1_name, seasons }) =>\n seasons.map(({ season, values }) => {\n const { slope, intercept } = linearTrend1(values, d => d.avgtemp);\n return { admin1_name, season, slope, intercept };\n })\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1899,"value":"$4d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2655,"value":"$4e","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2440,"value":"$4f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2657,"value":"$50","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2659,"value":"//","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1901,"value":"$51","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2669,"value":"mamData = kenyaCropTrendMergeSeasonal.filter(d => d.season === \"MAM\");","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2663,"value":"$52","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2355,"value":"$53","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2675,"value":"$54","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2301,"value":"// Compute robust (5th–95th percentile) value ranges per crop\nvalueRange = d3.rollup(\n kenyaCropTrendMergeSeasonal.filter(d => d.total_value != null),\n v => {\n const vals = v.map(d => d.total_value).sort(d3.ascending);\n const min = d3.quantile(vals, 0.05);\n const max = d3.quantile(vals, 0.95);\n return { min, max };\n },\n d => d.crop\n);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2442,"value":"\n// === STEP 2: Compute robust (5th–95th percentile) value ranges per livestock crop/type ===\nlivestockValueRange = d3.rollup(\n kenyaLivestockMergeSeasonal.filter(d => d.total_value != null),\n v => {\n const vals = v.map(d => d.total_value).sort(d3.ascending);\n const min = d3.quantile(vals, 0.05);\n const max = d3.quantile(vals, 0.95);\n return { min, max };\n },\n d => d.crop\n);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2444,"value":"$55","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2176,"value":"$56","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2276,"value":"// kkk","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2178,"value":"\n// // === STEP 2: Prepare summary table for mapping ===\n// cropRiskSummary = kenyaCropTrendMerge.map(d => ({\n// admin1_name: d.admin1_name,\n// crop_name: d.crop,\n// risk_ptot: d.risk_ptot,\n// avg_value: d.avg_value,\n// rain_mean: d.avg_rainfall,\n// rain_sd: d.rain_sd,\n// value_sd: d.value_sd\n// }));\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2424,"value":"\n// STEP 3: Prepare summary table for mapping\ncropRiskSummary = kenyaCropTrendMergeSeasonal.map(d => ({\n admin1_name: d.admin1_name,\n crop_name: d.crop,\n risk_ptot: d.risk_ptot,\n risk_ndws: d.risk_ndws,\n risk_temp: d.risk_temp,\n season: d.season,\n rain_mean: d.avg_rainfall,\n rain_sd: d.rainfall_sd,\n ndws_mean: d.avg_ndws,\n ndws_sd: d.ndws_sd,\n temp_mean: d.avg_temp,\n temp_sd: d.temp_sd,\n}));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2446,"value":"\n// === STEP 4: Prepare livestock summary table for mapping ===\nlivestockRiskSummary = kenyaLivestockMergeSeasonal.map(d => ({\n admin1_name: d.admin1_name,\n crop_name: d.crop,\n risk_ptot: d.risk_ptot,\n risk_ndws: d.risk_ndwsl,\n risk_temp: d.risk_temp,\n season: d.season,\n rain_mean: d.avg_rainfall,\n rain_sd: d.rainfall_sd,\n ndws_mean: d.avg_ndws,\n ndws_sd: d.ndws_sd,\n temp_mean: d.avg_temp,\n temp_sd: d.temp_sd,\n}));","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2474,"value":"// Step 1: Dropdown selector for risk type\nviewof cropRiskType = Inputs.select(\n [\"risk_ptot\", \"risk_ndws\", \"risk_temp\"],\n { label: \"Select Crop Risk Type\", value: \"risk_ptot\" }\n);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2700,"value":"// === Step 1b: Dropdown for season selection ===\nviewof cropSeason = Inputs.select(\n [\"MAM\", \"JJAS\", \"OND\", \"DJF\"],\n { label: \"Select Season\", value: \"MAM\" }\n);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2170,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2469,"value":"// === Step 1: Dropdown selector ===\nviewof livestockRiskType = Inputs.select(\n [\"risk_ptot\", \"risk_ndws\", \"risk_temp\"],\n { label: \"Select Livestock Risk Type\", value: \"risk_ptot\" }\n);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2706,"value":"// === Step 1b: Dropdown for season selection ===\nviewof livestockSeason = Inputs.select(\n [\"MAM\", \"JJAS\", \"OND\", \"DJF\"],\n { label: \"Select Season\", value: \"MAM\" }\n);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2448,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2682,"value":"Scenario building","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2459,"value":"geoHazard = await hazard_db.query(`\n SELECT *\n FROM hazard_mean\n WHERE admin0_name = 'Kenya'\n AND admin1_name = 'Nairobi'\n AND scenario = 'ssp245'\n `);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2503,"value":"// Example for crops\n scenarioHazard = await hazard_db.query(`\n SELECT *\n FROM hazard_mean\n WHERE admin0_name = 'Kenya'\n AND hazard = 'PTOT-sum'\n`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2508,"value":"hazards = [\"PTOT-sum\", \"NDWS-mean\", \"TAVG-mean\"];","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2521,"value":"allHazards = await hazard_db.query(`\n SELECT admin1_name, value, scenario, hazard, timeframe\n FROM hazard_mean\n WHERE admin0_name = 'Kenya'\n AND admin1_name IS NOT NULL\n AND value IS NOT NULL\n AND hazard IN (${hazards.map(h => `'${h}'`).join(\",\")})\n AND stat = 'mean'\n`);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2613,"value":"await hazard_db.query(`\n SELECT hazard\n FROM hazard_mean\n WHERE admin0_name = 'Kenya'\n AND stat = 'mean'\n`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2523,"value":" grouped = d3.group(allHazards, d => d.scenario, d => d.hazard, d => d.admin1_name);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2525,"value":"filteredRows = scenarioHazard.filter(d => d.admin1_name);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2527,"value":"\n// ----------------------------\n// 2️⃣ Summarize: mean + SD per county × hazard × scenario\n// ----------------------------\n summaryHazards = d3.rollups(\n allHazards,\n rows => {\n const values = rows.map(d => d.value);\n const mean = d3.mean(values);\n const sd = Math.sqrt(d3.mean(values.map(v => (v - mean) ** 2)));\n return { mean, sd };\n },\n d => d.scenario,\n d => d.hazard,\n d => d.admin1_name\n)\n.flatMap(([scenario, hazardMap]) =>\n hazardMap.flatMap(([hazard, countyMap]) =>\n countyMap.map(([county, stats]) => ({\n scenario,\n hazard,\n county,\n mean: stats.mean,\n sd: stats.sd\n }))\n )\n);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2535,"value":"//","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2539,"value":"//","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2550,"value":"function mergeCropWithHazard(summaryHazards, cropData) {\n // Map county → array of crops\n const cropMap = new Map();\n cropData.forEach(d => {\n if (!cropMap.has(d.admin1_name)) cropMap.set(d.admin1_name, []);\n cropMap.get(d.admin1_name).push({ crop: d.crop, value: d.total_value ?? d.avg_value });\n });\n\n // For each hazard row, create multiple rows if county has multiple crops\n return summaryHazards.flatMap(h => {\n const crops = cropMap.get(h.county) || [{ crop: null, value: null }];\n return crops.map(cropInfo => ({\n ...h,\n crop: cropInfo.crop,\n crop_value: cropInfo.value\n }));\n });\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2566,"value":"//","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2552,"value":"cropsWithRisks = mergeCropWithHazard(summaryHazards,kenyaCropTrendMerge); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2576,"value":"livestockWithRisks = mergeCropWithHazard(summaryHazards,kenyaLivestockTrendMerge); ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2564,"value":"// Step 1: Calculate scenario risk using your robust valueRange\n cropsWithScenarioRisk = cropsWithRisks.map(d => {\n if (!d.crop || d.crop_value == null || !valueRange.has(d.crop)) {\n return { ...d, scenario_risk: null }; // skip invalid rows\n }\n\n const { min, max } = valueRange.get(d.crop);\n\n // Normalize crop value between 0-1 using 5th–95th percentile\n let valueNorm = (d.crop_value - min) / (max - min + 1e-6);\n valueNorm = Math.max(0, Math.min(1, valueNorm));\n\n // Hazard variability term (sd / mean), capped at 2\n const hazardVar = Math.min(d.sd / Math.max(d.mean, 1e-3), 2);\n\n // Scenario risk\n const scenarioRisk = valueNorm * hazardVar;\n\n return {\n ...d,\n scenario_risk: scenarioRisk\n };\n});\n\n// console.log(cropsWithScenarioRisk);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2578,"value":"// Step 1: Calculate scenario risk using your robust valueRange\n livestockWithScenarioRisk = livestockWithRisks.map(d => {\n if (!d.crop || d.crop_value == null || !livestockValueRange.has(d.crop)) {\n return { ...d, scenario_risk: null }; // skip invalid rows\n }\n\n const { min, max } = livestockValueRange.get(d.crop);\n\n // Normalize crop value between 0-1 using 5th–95th percentile\n let valueNorm = (d.crop_value - min) / (max - min + 1e-6);\n valueNorm = Math.max(0, Math.min(1, valueNorm));\n\n // Hazard variability term (sd / mean), capped at 2\n const hazardVar = Math.min(d.sd / Math.max(d.mean, 1e-3), 2);\n\n // Scenario risk\n const scenarioRisk = valueNorm * hazardVar;\n\n return {\n ...d,\n scenario_risk: scenarioRisk\n };\n});\n\n// console.log(cropsWithScenarioRisk);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2606,"value":"Array.from(d3.union(cropsWithScenarioRisk.map(d => d.crop)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2597,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2619,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2623,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2086,"value":"$5c","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2749,"value":"Population","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2763,"value":"$5d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2761,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2840,"value":"await exposure_db.query(`\n SELECT value\n FROM population\n WHERE admin0_name = 'Kenya'\nAND admin1_name ='Nairobi'\nAND exposure = 'hpop'\n`)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2843,"value":"await exposure_db.query(`\n SELECT SUM(value) AS total_population\n FROM population\n WHERE admin0_name = 'Kenya'\n AND admin1_name = 'Nairobi'\nAND type = 'total'\n AND exposure = 'hpop'\n`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2800,"value":"kenyaPopulation0 = await exposure_db.query(`\n SELECT *\n FROM population\n WHERE admin0_name = 'Kenya'\nAND exposure = 'hpop'\n`)\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2814,"value":"kenyaPopulation = kenyaPopulation0.filter(r => r.admin1_name !== null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2837,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2857,"value":"$60","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2862,"value":"$61","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2873,"value":"$62","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2880,"value":"$63","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2831,"value":"$64","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2810,"value":"//","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2812,"value":"Scraps","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2102,"value":"kenyaLivestockTrendMerge = kenyaLivestockSummary.map(d => {\n const ref = kenyaCropTrendMerge.find(x => x.admin1_name === d.admin1_name);\n return { ...d, slope: ref ? ref.slope : null };\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2115,"value":"kenyaLivestockTrendMerge.slice(0, 5)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2083,"value":"$65","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2110,"value":"$66","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2168,"value":"const hazardExtent = d3.extent(kenyaCropTrendMerge, d => d.ptot_value); // or seasonal NDWS\nkenyaCropTrendMerge.forEach(d => {\n d.hazard_index = 1 - (d.ptot_value - hazardExtent[0]) / (hazardExtent[1] - hazardExtent[0]);\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2127,"value":"kenyaNDWSCrop1 = kenyaNDWSFiltered.flatMap(r => {\n const cropsInCounty = kenyaCropTrendMerge.filter(c => c.admin1_name === r.admin1_name);\n return cropsInCounty.map(cropRow => ({\n admin1_name: r.admin1_name,\n season: r.season,\n year: r.year,\n seasonal_ndws: r.seasonal_ndws,\n crop: cropRow.crop,\n total_value: cropRow.total_value,\n avg_value: cropRow.avg_value\n }));\n});","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2163,"value":"// Compute normalized hazard index\nndwsExtent = d3.extent(kenyaNDWSCrop, d => d.seasonal_ndws);\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2166,"value":"\nkenyaNDWSCrop.forEach(d => {\n d.ndws_risk = 1 - (d.seasonal_ndws - ndwsExtent[0]) / (ndwsExtent[1] - ndwsExtent[0]);\n});\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1919,"value":"kenyaCrops1 = await exposure_db.query(`\n SELECT admin1_name, crop, \"value\"\n FROM crop_vop\n WHERE admin0_name = 'Kenya'\n`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2790,"value":"\nkenyaLivestock1 = await exposure_db.query(`\n SELECT admin1_name, crop, \"value\"\n FROM livestock_vop\n WHERE admin0_name = 'Kenya'\n`);","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2078,"value":"function pearsonCorrelation(xArr, yArr) {\n const meanX = d3.mean(xArr);\n const meanY = d3.mean(yArr);\n const cov = d3.mean(xArr.map((x,i) => (x-meanX)*(yArr[i]-meanY)));\n const stdX = d3.deviation(xArr);\n const stdY = d3.deviation(yArr);\n return cov / (stdX * stdY);\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1905,"value":"viewof kenyaLivestockTrendPlotw = {\n const colorSchemes = {\n cattle_tropical: \"rgb(255,200,0)\",\n goats_tropical: \"rgb(200,255,0)\",\n sheep_tropical: \"rgb(255,120,0)\",\n poultry_tropical: \"rgb(255,255,255)\"\n };\n\n const plot = Plot.plot({\n width: 950,\n height: 650,\n style: { background: \"#111\", color: \"#ddd\" },\n x: { label: \"Rainfall Trend (mm/year)\", grid: true },\n y: { label: \"Total Value (USD)\", grid: true, tickFormat: d => d3.format(\".2s\")(d) },\n marks: [\n Plot.dot(kenyaLivestockTrendMerge, {\n x: \"slope\",\n y: \"total_value\",\n fill: d => colorSchemes[d.crop] || \"gray\",\n fillOpacity: d => Math.min(1, d.avg_rainfall / 500),\n r: 8,\n title: d =>\n `${d.admin1_name}\\n${d.crop}\\nRainfall Trend: ${d.slope.toFixed(2)} mm/yr\\nAvg Rain: ${d.avg_rainfall.toFixed(1)} mm\\nValue: ${d.total_value.toLocaleString()}`\n })\n ]\n });\n\n return plot;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":189,"value":"# Conclusion","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":193,"value":"*${lipsum({\n count:1, units: \"paragraphs\", sentenceLowerBound:3, sentenceUpperBound: 15, paragraphLowerBound: 4, paragraphUpperBound: 4})\n}*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"# Appendix\nThis section is all the underlying code and variables to power the notebook","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":65,"value":"nbTitle = \"Rainfall Reliability 3\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":816,"value":"GFW_API_KEY = \"a6662433-c910-48bf-a327-1d71daab55cb\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":52,"value":"import {tocOneliner} from \"8a953dc1cde63a86\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"lipsum = require('https://bundle.run/lorem-ipsum@1.0.6')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"### Default Adaptation Atlas Styling\nThis can be found [here](https://observablehq.com/d/8a953dc1cde63a86) to be modified and adjusted","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":10,"value":"import {atlas_stylesheet} from \"8a953dc1cde63a86\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"atlas_stylesheet","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":74,"value":"### Adaptation Atlas Boundaries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":81,"value":"admin_boundaries = {\n let a0_topoj = await FileAttachment(\"atlas_gaul24_a0_africa_simple-vlowres@1.topojson\").json();\n let a1_topoj = await FileAttachment(\"atlas_gaul24_a1_africa_simple-vlowres.topojson\").json();\n let a2_topoj = await FileAttachment(\"atlas_gaul24_a2_africa_simple-lowres.topojson\").json();\n\n let a0_geojson = topojson.feature(\n a0_topoj,\n a0_topoj.objects[\"atlas_gaul24_a0_africa\"]\n );\n let a1_geojson = topojson.feature(\n a1_topoj,\n a1_topoj.objects[\"atlas_gaul24_a1_africa\"]\n );\n let a2_geojson = topojson.feature(\n a2_topoj,\n a2_topoj.objects[\"atlas_gaul24_a2_africa\"]\n );\n\n return {\n adm0: a0_geojson,\n adm1: a1_geojson,\n adm2: a2_geojson,\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"watershed_boundaries = {\n let ws_topoj = await FileAttachment(\"africa_wmobb_basins.topojson\").json()\n let ws_geoj = topojson.feature(\n ws_topoj,\n ws_topoj.objects[\"africa_wmobb_basins\"]\n );\n return {\n watershed: ws_geoj\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"## Additional Resources\n- [**Notebook Style and Developer Guide**](https://observablehq.com/d/8a953dc1cde63a86)\n- [**Existing Adaptation Atlas Notebooks**](https://observablehq.com/collection/@adaptationatlas/data-explorations)\n- [**Example:** Use Python and Google Earth Engine to process soil data](https://colab.research.google.com/drive/1Cz8G1uUn6pdvadVjq9ikppyFeU36uE-3?usp=sharing)\n- [Adaptation Atlas Data Guide](https://adaptationatlas.github.io/data-management/structure.html)\n- [Atlas Data FAQ](https://adaptationatlas.github.io/data-management/faq.html)\n- [Observable JS Documentation](https://observablehq.com/documentation/cells/observable-javascript)\n- [Observable JS vs vanilla Javascript](https://observablehq.com/@observablehq/observable-javascript)\n- [Data wrangling in JS vs R, Python, and SQL](https://observablehq.com/@observablehq/data-wrangling-translations)\n- [Adaptation Atlas ObservableHQ](https://observablehq.com/@adaptationatlas)\n- [Zindi Challenge Link]()","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W67","stdlib":"1"}} 30:"$2f:body"