2e:null 2f:["$","title",null,{"children":"Estimating Populations of Refugee Settlements | eastcoasting | Observable"}] 34:Td8b,viewof map = { let container = html`

`; // Give the container dimensions. yield container; // Create the \`map\` object with the mapboxgl.Map constructor, referencing // the container div let map = new mapboxgl.Map({ container, center: [0, 0], zoom: 1.7, style: "mapbox://styles/mapbox/light-v10" }); map.addControl(new mapboxgl.NavigationControl(), "top-right"); map.addControl(new mapboxgl.FullscreenControl()); function addHoverTooltipForLayer(layer) { const popup = new mapboxgl.Popup({ closeButton: false, closeOnClick: true, className: "sn-mapbox-tooltip" }); map.on("click", layer.id, (e) => { // Change cursor style map.getCanvas().style.cursor = "pointer"; // Get feature coordinates const coordinates = e.features[0].geometry.type === "Point" ? e.features[0].geometry.coordinates.slice() : turf.centroid(e.features[0]).geometry.coordinates; // Construct the HTML content from the properties const description = layer.properties .map((prop) => { if (typeof e.features[0].properties[prop] === "number") { const formattedValue = intlNumberCompact( e.features[0].properties[prop] ); return `${prop}: ${formattedValue}`; } else { return `${prop}: ${e.features[0].properties[prop]}`; } }) .join("
"); // Handle international dateline wrapping while (Math.abs(e.lngLat.lng - coordinates[0]) > 180) { coordinates[0] += e.lngLat.lng > coordinates[0] ? 360 : -360; } popup.setLngLat(coordinates).setHTML(description).addTo(map); map.getCanvas().style.cursor = ""; }); } // Wait until the map loads. map.on("load", () => { container.value = map; // Original polygons layer map.addLayer({ id: "ee_population_estimates", type: "fill", source: { type: "geojson", data: ee_population_estimates }, paint: { "fill-color": "#d95f02", "fill-opacity": 0.4 } }); map.addLayer({ id: "bufferedTracedPolygonsLine", type: "line", source: { type: "geojson", data: bufferedTracedPolygons }, layout: { "line-join": "round", // This ensures the corners of the outline are rounded "line-cap": "round" // This ensures the ends of the line are rounded }, paint: { "line-color": "#d95f02", "line-width": 3 // You can adjust this value to set the thickness of the line } }); // Buffered polygons layer map.addLayer({ id: "unhcr_scraped_data", type: "circle", source: { type: "geojson", data: unhcr_scraped_data }, paint: { "circle-radius": 5, "circle-color": "#7570b3" } }); // Overlapping points layer map.addLayer({ id: "overlappingSettlements", type: "circle", source: { type: "geojson", data: overlappingSettlements }, paint: { "circle-radius": 5, "circle-color": "#1b9e77" } }); layers.forEach((layer) => { if (map.getLayer(layer.id)) { addHoverTooltipForLayer(layer); } }); container.dispatchEvent(new CustomEvent("input")); }); }35:T95b,{ const yVal = chartInputs.yAxis; const xVal = chartInputs.xAxis; const potentialDatasets = { intersectedFeatures: intersectedFeatures, fuzzyMatchResults: fuzzyMatchResults }; const currentDataset = potentialDatasets[chartInputs.dataset]; const correlationData = calculateCorr(yVal, xVal, currentDataset); const simpleLR = correlationData.simpleLR; const scoreLR = correlationData.scoreLR; const estimatedBias = correlationData.roundedBias; const subtitle = md` - Observations: ${currentDataset.length} - y = ${simpleLR.coefficients[1].toFixed(1)}x + ${-(-simpleLR .coefficients[0]).toFixed(1)} - R²: ${scoreLR.r2.toFixed(2)} - RMSD: ${scoreLR.rmsd.toFixed(2)} - Bias: ${estimatedBias} `; return Plot.plot({ title: `Relationship between and ${yVal} and ${xVal} estimates`, subtitle: subtitle, width: 1100, marginRight: 15, marginLeft: 100, marginBottom: 100, grid: true, style: { fontSize: 14, paddingTop: 0, overflow: "hidden", lineWidth: 0.5 }, marks: [ Plot.frame({ fill: "#eaeaea" }), Plot.gridY({ stroke: "#fff", strokeOpacity: 1 }), Plot.gridX({ stroke: "#fff", strokeOpacity: 1 }), Plot.axisX({ lineWidth: 8, label: xVal, tickRotate: -30, tickFormat: (d) => { return intlNumberCompact(d, 1); } }), Plot.axisY({ tickSize: 0, ticks: 6, tickFormat: (d) => { return intlNumberCompact(d, 1); }, lineAnchor: "middle", label: yVal }), Plot.dot(currentDataset, { x: xVal, y: yVal, r: 4, symbol: "circle" }), Plot.linearRegressionY(currentDataset, { x: xVal, y: yVal, stroke: "red" }), Plot.tip( currentDataset, Plot.pointer({ x: xVal, y: yVal, title: (d) => [ generateTooltip("UNHCR Name", d.unhcrName), generateTooltip("Digitized Name", d.digitizedName), generateTooltip("World Pop Estimate", d.worldPop2020), generateTooltip("GHSL Estimate", d.ghslPop2020), generateTooltip("UNHCR Population", d.unhcrPopulation), generateTooltip("Country", d.country) ].join("\n") }) ) ] }); }36:T831,{ const yVal = "ghslPop2020"; const xVal = "worldPop2020"; const currentDataset = digitizedFeatures; const correlationData = calculateCorr(yVal, xVal, currentDataset); const simpleLR = correlationData.simpleLR; const scoreLR = correlationData.scoreLR; const estimatedBias = correlationData.roundedBias; const subtitle = md` - Observations: ${currentDataset.length} - y = ${simpleLR.coefficients[1].toFixed(1)}x + ${-(-simpleLR .coefficients[0]).toFixed(1)} - R²: ${scoreLR.r2.toFixed(2)} - RMSD: ${scoreLR.rmsd.toFixed(2)} - Bias: ${estimatedBias} `; return Plot.plot({ title: `Relationship between and UNHCR and World Pop estimates for all digitized camp boundaries`, subtitle: subtitle, width: 1100, marginRight: 15, marginLeft: 100, marginBottom: 100, grid: true, style: { fontSize: 14, paddingTop: 0, overflow: "hidden", lineWidth: 0.5 }, marks: [ Plot.frame({ fill: "#eaeaea" }), Plot.gridY({ stroke: "#fff", strokeOpacity: 1 }), Plot.gridX({ stroke: "#fff", strokeOpacity: 1 }), Plot.axisX({ lineWidth: 8, label: xVal, tickRotate: -30, tickFormat: (d) => { return intlNumberCompact(d, 1); } }), Plot.axisY({ tickSize: 0, ticks: 6, tickFormat: (d) => { return intlNumberCompact(d, 1); }, lineAnchor: "middle", label: yVal }), Plot.dot(currentDataset, { x: xVal, y: yVal, r: 4, symbol: "circle" }), Plot.linearRegressionY(currentDataset, { x: xVal, y: yVal, stroke: "red" }), Plot.tip( currentDataset, Plot.pointer({ x: xVal, y: yVal, title: (d) => [ generateTooltip("Digitized Name", d.digitizedName), generateTooltip("World Pop Estimate", d.worldPop2020), generateTooltip("GHSL Estimate", d.ghslPop2020) ].join("\n") }) ) ] }); }37:T87e,// Copyright 2021, Observable Inc. // Released under the ISC license. // https://observablehq.com/@d3/color-legend function Swatches( color, { columns = null, format, unknown: formatUnknown, swatchSize = 15, swatchWidth = swatchSize, swatchHeight = swatchSize, marginLeft = 0 } = {} ) { const id = `-swatches-${Math.random().toString(16).slice(2)}`; const unknown = formatUnknown == null ? undefined : color.unknown(); const unknowns = unknown == null || unknown === d3.scaleImplicit ? [] : [unknown]; const domain = color.domain().concat(unknowns); if (format === undefined) format = (x) => (x === unknown ? formatUnknown : x); function entity(character) { return `&#${character.charCodeAt(0).toString()};`; } if (columns !== null) return htl.html`

${domain.map((value) => { const label = `${format(value)}`; return htl.html`

${label}

`; })}

`; return htl.html`

${domain.map( (value) => htl.html`${format( value )}` )}

`; }38:[{"id":"87b459e096a32ffd573f21afce1b9493b2a6bab829e2b1843d89045172f5ae1b0310d8968a8ed71b025c4c8dde4da3ffbdd20ab9c535745f3ab9e042a49e453c","href":"https://static.observableusercontent.com/files/87b459e096a32ffd573f21afce1b9493b2a6bab829e2b1843d89045172f5ae1b0310d8968a8ed71b025c4c8dde4da3ffbdd20ab9c535745f3ab9e042a49e453c","name":"ee_population_estimates.geojson","type":"application/geo+json","lastModified":1696454660892.405,"size":1225174},{"id":"fe0698f1d135d382c4abbfa0fc3e31bdf8bcc3d23d32cb9c33eeba7d9c5c8f813ff13384a30b1d2ae06cdb899485f0a9392abcc36cccfda2d858196c430e3b55","href":"https://static.observableusercontent.com/files/fe0698f1d135d382c4abbfa0fc3e31bdf8bcc3d23d32cb9c33eeba7d9c5c8f813ff13384a30b1d2ae06cdb899485f0a9392abcc36cccfda2d858196c430e3b55","name":"unhcr_scraped_data.geojson","type":"application/geo+json","lastModified":1696455360635.312,"size":1243423}] 32:{"id":"686ffe313e50dd73","slug":"estimating-populations-of-refugee-settlements","title":"Estimating Populations of Refugee Settlements","thumbnail":"a765e761b4e211df558cdb5d3df8c7dcb39e4fa49a9b8bb9b005a4d83b2a9717","access_level":"public","create_time":"$D2023-10-04T21:22:33.589Z","update_time":"$D2023-10-05T19:30:14.205Z","view_time":null,"first_public_time":"$D2023-10-04T21:22:33.589Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"eeff2c5459868c71","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":"starter_2024"},"version":872,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"64bdb7d8ac477035","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":null}],"members":[{"type":"individual","id":"64bdb7d8ac477035","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Estimating Populations of Refugee Settlements","cells":[{"id":0,"value":"# Estimating Populations of Refugee Settlements","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":79,"value":"Processing steps:\n\n1. Build polygons for settlements from imagery\n2. Estimate population using GHSL and WorldPop in earth engine: https://code.earthengine.google.com/3c906cd238bb54455dab258726d73ab4\n3. Scrape data from UNHCR API\n\nAnalysis\n1. Use Fuse to find fuzzy matches between two datasets\n2. Use Turf to find spatial matches between two datasets\n3. Plot linear regression between both datasets (spatial, fuzzy string, combined)\n4. Estimate the RMSD and degree of bias for each\n\nFollow-up\n1. Account for bias when we measure WorldPop or GHSL pops in the boundaries at other camps we are currently drawing\n2. Right now biases are close to 0 in most cases - need to make sure calculations are correct here ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":95,"value":"### Analysis\nFind fuzzy matches between scraped and hand drawn data\n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":748,"value":"viewof fuzzyThreshold = Inputs.range([0.02, 1], {\n label: \"Fuzzy Match Threshold\",\n step: 0.01,\n value: 0.55\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"eeNames = ee_population_estimates.features.map(f => f.properties.name);\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"unhcrNames = unhcr_scraped_data.features.map((f) => f.properties.geomaster_name)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"options = [\n {\n includeScore: true,\n threshold: fuzzyThreshold,\n keys: [\"properties.name\"]\n }\n][0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":39,"value":"fuse = new Fuse(ee_population_estimates.features, options)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":42,"value":"fuzzyMatchResults = unhcr_scraped_data.features\n .map((feature) => {\n const searchResults = fuse.search(feature.properties.geomaster_name);\n\n if (\n searchResults.length > 0 &&\n searchResults[0].score <= options.threshold\n ) {\n const bestMatch = searchResults[0];\n\n return {\n unhcrName: feature.properties.geomaster_name,\n digitizedName: bestMatch.item.properties.name,\n bitapScore: bestMatch.score,\n\n unhcrPopulation: feature.properties[\"total_population\"],\n worldPop2020: bestMatch.item.properties[\"worldPop_2020\"],\n ghslPop2020: bestMatch.item.properties[\"ghslPop_2020\"],\n country: feature.properties[\"country\"],\n polygonGISName: bestMatch.item.properties[\"gis_name\"],\n geomasterID: feature.properties[\"geomaster_id\"],\n polygonNameAlt: bestMatch.item.properties[\"name_alt\"]\n };\n }\n })\n .filter(Boolean)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":107,"value":"---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":101,"value":"#### Text-based matching\n- 32 exact matches\n- 100 additional likely matches","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"fuzzyMatchResults","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"---\n\n\nFind spatial matches between data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":137,"value":"bufferedData = {\n const bufferedEstimatedPolygons = turf.buffer(\n ee_population_estimates,\n distanceBuffer,\n {\n units: \"kilometers\"\n }\n );\n\n // Find overlapping points\n const overlappingPoints = turf.pointsWithinPolygon(\n unhcr_scraped_data,\n bufferedEstimatedPolygons\n );\n\n overlappingPoints.features.forEach((point) => {\n bufferedEstimatedPolygons.features.forEach((polygon) => {\n if (turf.booleanPointInPolygon(point, polygon)) {\n point.properties.polygonMetadata = polygon.properties; // Append the metadata to the point's properties\n }\n });\n });\n\n return { overlappingPoints, bufferedEstimatedPolygons };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"overlappingSettlements = bufferedData.overlappingPoints","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":383,"value":"bufferedTracedPolygons = bufferedData.bufferedEstimatedPolygons","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":292,"value":"intersectedFeatures = overlappingSettlements.features.map((settlement) => {\n settlement = settlement.properties;\n\n let settlementPolygonMetadata = settlement.polygonMetadata;\n return {\n unhcrName: settlement[\"geomaster_name\"],\n digitizedName: settlementPolygonMetadata[\"name\"],\n\n unhcrPopulation: settlement[\"total_population\"],\n\n worldPop2020: settlementPolygonMetadata[\"worldPop_2020\"],\n ghslPop2020: settlementPolygonMetadata[\"ghslPop_2020\"],\n\n country: settlement[\"country\"],\n\n polygonGISName: settlementPolygonMetadata[\"gis_name\"],\n geomasterID: settlement[\"geomaster_id\"],\n polygonNameAlt: settlementPolygonMetadata[\"name_alt\"]\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"digitizedFeatures = bufferedTracedPolygons.features.map((settlement) => {\n settlement = settlement.properties;\n\n return {\n digitizedName: settlement[\"name\"],\n worldPop2020: settlement[\"worldPop_2020\"],\n ghslPop2020: settlement[\"ghslPop_2020\"],\n polygonGISName: settlement[\"gis_name\"],\n polygonNameAlt: settlement[\"name_alt\"]\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"viewof distanceBuffer = Inputs.range([0, 100], {\n label: \"KM Buffer from traced polygons\",\n step: 0.1,\n value: 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":847,"value":"Below we plot the UNHCR scraped point data, the digitized data with EE-derived estimates of population using GHSL and WorldPop, and the intersection of these two datasets using a calculated buffer. The intersected data matches to some extent with the text-based matching, but there are some instances where these conflict. \n\nFor example - the UNHCR data are more specific in Kutupalong (showing multiple camps within the delinated bounds e.g., Camp 8E, not just Kutupalong). This over indexing on high population settlements will need to be filtered out in the final iteration to a single data point per settlement.\n\nThe buffer estimates are reactive, modifying this variable will recalculate the intersections and update the map with new data.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":432,"value":"key = Swatches(\n d3.scaleOrdinal(\n [\n \"Intersection of EE and UNHCR\",\n \"Digitized EE derived estimates\",\n \"UNHCR scraped data\"\n ],\n d3.schemeDark2\n ),\n {\n swatchSize: 20\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":290,"value":"intersectedFeatures","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":862,"value":"\n---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":856,"value":"These are high level relationships between the three variables:\n- UNHCR provided scraped data\n- World Pop 2020 earth engine calculations\n- GHSL earth engine calculations\n\nand three datasets (two underlying) we have:\n- Intersection of data based on spatial matching\n- Intersection of data based on fuzzy matching\n- Unfiltered earth engine calculations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":662,"value":"viewof chartInputs = Inputs.form({\n xAxis: Inputs.select(potentialVariables, {\n value: \"unhcrPopulation\",\n label: \"X Axis\"\n }),\n yAxis: Inputs.select(potentialVariables, {\n value: \"ghslPop2020\",\n label: \"Y Axis\"\n }),\n dataset: Inputs.select([\"intersectedFeatures\", \"fuzzyMatchResults\"], {\n value: \"intersectedFeatures\",\n label: \"Dataset\"\n })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":483,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":756,"value":"\n---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":760,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":15,"value":"### Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":147,"value":"#### Utils","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":194,"value":"unhcr_scraped_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"layers = [\n {\n id: \"ee_population_estimates\",\n name: \"Earth engine derived estimates\",\n properties: [\"name\", \"worldPop_2020\", \"ghslPop_2020\"],\n color: \"#d95f02\"\n },\n {\n id: \"unhcr_scraped_data\",\n name: \"UNHCR scraped data\",\n properties: [\"geomaster_name\", \"total_population\"],\n color: \"#7570b3\"\n },\n {\n id: \"overlappingSettlements\",\n name: \"Intersection of EE and UNHCR\",\n properties: [\"geomaster_name\", \"total_population\"],\n color: \"#1b9e77\"\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":502,"value":"intlNumberCompact = (d, minFraction, maxFraction) => {\n return Intl.NumberFormat(\"en\", {\n notation: \"compact\",\n minimumFractionDigits: minFraction ? minFraction : 0,\n maximumFractionDigits: maxFraction ? maxFraction : 1\n }).format(d);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":520,"value":"function generateTooltip(title, property) {\n if (typeof property === \"number\") {\n return `${title}: ${intlNumberCompact(property)}`;\n } else {\n return `${title}: ${property}`;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"potentialVariables = Object.entries(intersectedFeatures[0])\n .map((val) => {\n if (typeof val.at(1) === \"number\" && val.at(0) !== \"geomasterID\") {\n return val.at(0);\n }\n })\n .filter(Boolean)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":587,"value":"function calculateCorr(yVal, xVal, dataset) {\n const filteredData = dataset.filter(\n (d) => !isNaN(d[xVal]) && !isNaN(d[yVal])\n );\n\n const simpleLR = new ML.SimpleLinearRegression(\n filteredData.map((d) => d[xVal]),\n filteredData.map((d) => d[yVal])\n );\n\n const scoreLR = simpleLR.score(\n filteredData.map((d) => d[xVal]),\n filteredData.map((d) => d[yVal])\n );\n\n const predictedValues = filteredData.map((d) => simpleLR.predict(d[xVal]));\n\n const differences = predictedValues.map(\n (predicted, index) => predicted - filteredData[index][yVal]\n );\n\n const bias =\n differences.reduce((acc, curr) => acc + curr, 0) / differences.length;\n\n const roundedBias = bias.toFixed(2);\n\n return { simpleLR, scoreLR, roundedBias };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"#### libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3,"value":"Fuse = require(\"fuse.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"turf = require(\"https://cdn.jsdelivr.net/npm/@turf/turf@latest/turf.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"mapboxgl = {\n const gl = await require(\"mapbox-gl@0.49\");\n if (!gl.accessToken) {\n gl.accessToken =\n \"pk.eyJ1IjoibWljaGFlbG93ZW4iLCJhIjoiY2xuY2g4M2F2MGtwZjJqb2FlYzk5ZnJ2NSJ9.0t8KUPr1xEEFHGj0H05a_g\";\n const href = await require.resolve(\"mapbox-gl@0.49/dist/mapbox-gl.css\");\n document.head.appendChild(html``);\n }\n return gl;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"mapboxgl_css = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":560,"value":"ML = require(\"https://www.lactame.com/lib/ml/6.0.0/ml.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"---\n\n#### files","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":21,"value":"ee_population_estimates = FileAttachment(\"ee_population_estimates.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"unhcr_scraped_data = FileAttachment(\"unhcr_scraped_data.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 33:"$32:body"