2d:null 2e:["$","title",null,{"children":"GEF Drylands Portfolio Index | eastcoasting | Observable"}] 33:T437,Plot.plot({ width: 1000, marginRight: 90, marginLeft: 150, paddingBottom: 30, grid: true, x: { nice: true, label: "Index →" }, caption: `Note: Higher index values are worse, low opacity dots correspond to sub-index normalized values for a given country${ filters.region !== "All" ? ". For regional subsets, we recompute the index at a regional level." : "." }`, y: { inset: 5, label: null, grid: true }, style: { fontSize: 14, paddingTop: 0, overflow: "visible", lineWidth: 0.5 }, marks: [ Plot.frame({ fill: "#eaeaea" }), Plot.gridY({ stroke: "#fff", strokeOpacity: 1 }), Plot.gridX({ stroke: "#fff", strokeOpacity: 1 }), Plot.dot(normalizedData, { x: "averageIndex", y: "country_name", sort: { y: "x", reverse: true } }), ...lowOpacityDotMarks.map((variable) => createLowOpacityDotMark(variable.name, variable.fill) ) ], color: { type: "categorical", scheme: "dark2", style: { fontSize: "20px" }, legend: false, swatchSize: 15 } })34:T556,Plot.plot({ color: { type: "ordinal", scheme: "dark2", style: { fontSize: "16px" }, legend: true, swatchSize: 20 }, y: { grid: true }, marks: [ Plot.axisX({ lineWidth: 8, marginBottom: 0, label: `${chartInputs.xAxis} →` }), Plot.axisY({ tickSize: 0, ticks: 6, lineAnchor: "middle", label: `↑ ${chartInputs.yAxis}` }), Plot.frame({ fill: "#eaeaea" }), Plot.gridY({ stroke: "#fff", strokeOpacity: 1 }), Plot.gridX({ stroke: "#fff", strokeOpacity: 1 }), Plot.dot(normalizedDataFull, { x: `normalized_${mappedVariableInputs[chartInputs.xAxis]}`, y: `normalized_${mappedVariableInputs[chartInputs.yAxis]}`, stroke: chartInputs.group === "Region" ? "region" : "black", r: 4, symbol: "circle" }), Plot.linearRegressionY(normalizedDataFull, { x: `normalized_${mappedVariableInputs[chartInputs.xAxis]}`, y: `normalized_${mappedVariableInputs[chartInputs.yAxis]}`, stroke: "red" }) ], width: 1100, height: 500, inset: 10, marginLeft: 75, marginTop: 30, marginBottom: 50, style: { fontSize: 20, paddingTop: 0, lineWidth: 0.5 }, caption: "Note: These figures are agnostic to other filters on the page (i.e., regional or sub-indicator filters do not apply)." })35:T1d9c,

Index Indicators

Note: Shaded rows are primary variables for the index, alternate scenarios (e.g., alternate RCP/SSP variants of the INFORM Climate Change Risk Index) are presented for comparison

Variable	Period	Methodological Notes	Source
INFORM Risk Index	2023	No adjustments	European Commission Disaster Risk Management Knowledge Centre/INFORM
INFORM Baseline Climate Change Risk Index	2023	No adjustments	European Commission Disaster Risk Management Knowledge Centre/INFORM
INFORM Climate Change Risk Index (RCP 4.5 & SSP 1)	2050	No adjustments. See figure below for further detail on RCP/SSPs.	European Commission Disaster Risk Management Knowledge Centre/INFORM
INFORM Climate Change Risk Index (RCP 8.5 & SSP 3)	2050	No adjustments. See figure below for further detail on RCP/SSPs.	European Commission Disaster Risk Management Knowledge Centre/INFORM
INFORM Climate Change Risk Index (RCP 4.5 & SSP 1)	2080	No adjustments. See figure below for further detail on RCP/SSPs.	European Commission Disaster Risk Management Knowledge Centre/INFORM
INFORM Climate Change Risk Index (RCP 8.5 & SSP 3)	2080	No adjustments. See figure below for further detail on RCP/SSPs.	European Commission Disaster Risk Management Knowledge Centre/INFORM
Share of Key Biodiversity Areas Covered by Protected Areas	2022/2023	Calculated as the share of KBAs that are covered by protected areas in terrestrial drylands. Terrestrial drylands are defined as any region which has an aridity <0.65 and less than one month of permanent seasonal water defined by the JRC Global Surface Water product.	Key Biodiversity Areas Programme World Database on Protected Areas JRC Global Surface Water
Aqueduct Water Scarcity Area Weighted Index	2019/2023	Calculated as the dryland area weighted average index of five subindicators (Baseline water stress, Interannual variability, Seasonal variability, Groundwater table decline, Unimproved/no drinking water). Dryland area weighted is defined as the area of a given basin that is dryland (aridity <0.65).	World Resources Institute
Share of Tree Cover Lost in Drylands	2012 - 2021	Calculated as the share of tree cover loss in drylands from 2012 to 2021. Tree cover is based on 2000 tree cover extent and a >30% tree canopy threshold, drylands are defined as area with an aridity <0.65.	Global Land Analysis and Discovery (GLAD)
Population Weighted PM2.5 Air Pollution, Mean Annual Exposure	2017	No adjustments	The World Bank

36:Ta78,normalizedDataFull = { const baseIndexDataFull = () => { const gefSourceData = sourceData.sheet("Sheet1", { headers: true }); return gefSourceData.filter((country) => { const propertiesToCheck = [ "inform_risk", "baseline_cc_risk", "2050_pessimistic_cc_risk", "2050_optimistic_cc_risk", "2080_pessimistic_cc_risk", "2080_optimistic_cc_risk", "kba_overlap_percentage", "area_weighted_custom_aqueduct", "forest_loss_drylands_percentage", "mean_annual_exposure_pm25" ]; country.region = LSIBSet[country.country_name]; return propertiesToCheck.every((property) => { return ( typeof country[property] === "number" && !isNaN(country[property]) && !country[property].toString().includes("Error:") ); }); }); }; function normalizeDataWithoutExposure(data, propertiesConfig) { const minMaxValues = Object.keys(propertiesConfig).map((property) => { const values = data.map((item) => item[property]); return { property, min: Math.min(...values), max: Math.max(...values) }; }); const normalizedData = data.map((item) => { const newItem = {}; // Include only properties that exist in the indexItems array for (const key in item) { if ( drylandsIndex.variables.includes(key) || [ "aridity_index", "country_name", "gef_total_grant_usd", "number_of_gef_projects", "region" ].includes(key) ) { newItem[key] = item[key]; } } minMaxValues.forEach(({ property, min, max }) => { const positiveHigherSkew = propertiesConfig[property]; let normalizedValue; if (positiveHigherSkew) { normalizedValue = (max - item[property]) / (max - min); } else { normalizedValue = (item[property] - min) / (max - min); } newItem[`normalized_${property}`] = normalizedValue; }); return newItem; }); return normalizedData.sort((a, b) => b.averageIndex - a.averageIndex); } const propertiesConfig = { inform_risk: false, "2050_pessimistic_cc_risk": false, kba_overlap_percentage: true, area_weighted_custom_aqueduct: false, forest_loss_drylands_percentage: false, "2050_optimistic_cc_risk": false, "2080_pessimistic_cc_risk": false, "2080_optimistic_cc_risk": false, baseline_cc_risk: false, mean_annual_exposure_pm25: false }; return normalizeData(baseIndexDataFull(), propertiesConfig); }37:T56a,function normalizeData(data, propertiesConfig) { const minMaxValues = Object.keys(propertiesConfig).map((property) => { const values = data.map((item) => item[property]); return { property, min: Math.min(...values), max: Math.max(...values) }; }); const normalizedData = data.map((item) => { const newItem = {}; // Include only properties that exist in the indexItems array for (const key in item) { if ( drylandsIndex.variables.includes(key) || [ "aridity_index", "country_name", "gef_total_grant_usd", "number_of_gef_projects", "region" ].includes(key) ) { newItem[key] = item[key]; } } let sum = 0; const propertyCount = minMaxValues.length; minMaxValues.forEach(({ property, min, max }) => { const positiveHigherSkew = propertiesConfig[property]; let normalizedValue; if (positiveHigherSkew) { normalizedValue = (max - item[property]) / (max - min); } else { normalizedValue = (item[property] - min) / (max - min); } newItem[`normalized_${property}`] = normalizedValue; sum += normalizedValue; }); newItem.averageIndex = sum / propertyCount; return newItem; }); return normalizedData.sort((a, b) => b.averageIndex - a.averageIndex); }38:T528,renameCountriesMap = new Map([ ["Antigua & Barbuda", "Antigua and Barb."], ["Bolivia (Plurinational State of)", "Bolivia"], ["Bosnia and Herzegovina", "Bosnia and Herz."], ["Central African Republic", "Central African Rep."], ["Cook Islands", "Cook Is."], ["Democratic People's Republic of Korea", "North Korea"], ["Democratic Republic of the Congo", "Dem. Rep. Congo"], ["Dominican Republic", "Dominican Rep."], ["Equatorial Guinea", "Eq. Guinea"], ["Eswatini", "eSwatini"], // formerly known as Swaziland ["Iran (Islamic Republic of)", "Iran"], ["Lao People's Democratic Republic", "Laos"], ["Marshall Islands", "Marshall Is."], ["Republic of Korea", "South Korea"], ["Russian Federation", "Russia"], ["Saint Kitts and Nevis", "St. Kitts and Nevis"], ["Saint Vincent and the Grenadines", "St. Vin. and Gren."], ["Sao Tome and Principe", "São Tomé and Principe"], ["Solomon Islands", "Solomon Is."], ["South Sudan", "S. Sudan"], ["Syrian Arab Republic", "Syria"], ["Türkiye", "Turkey"], ["United Kingdom of Great Britain and Northern Ireland", "United Kingdom"], ["United Republic of Tanzania", "Tanzania"], ["Venezuela (Bolivarian Republic of)", "Venezuela"], ["Viet Nam", "Vietnam"], ["occupied Palestinian territory, including east Jerusalem", "Palestine"] ])39:[{"id":"0b99b625b7d9600c5b377d563879a84b855705706992608b6d30b7b0f8ae827a1eb7d958f2347e7b3e0b135e2e74f341e07fd69a7c91e1566786e96a27247c1d","href":"https://static.observableusercontent.com/files/0b99b625b7d9600c5b377d563879a84b855705706992608b6d30b7b0f8ae827a1eb7d958f2347e7b3e0b135e2e74f341e07fd69a7c91e1566786e96a27247c1d","name":"carbonbriefrcpssp.png","type":"image/png","lastModified":1683307821659.44,"size":317858},{"id":"80ce2602e2a3c4f678a1793c4e22a0524e09fcfb90b16d15264872b6b80649e0945a85e5db46aed3dc0cea9357110452c03ffe9bcbdf0db4c3fa560780f96383","href":"https://static.observableusercontent.com/files/80ce2602e2a3c4f678a1793c4e22a0524e09fcfb90b16d15264872b6b80649e0945a85e5db46aed3dc0cea9357110452c03ffe9bcbdf0db4c3fa560780f96383","name":"lsib_polygons.csv","type":"text/csv","lastModified":1683312952174.384,"size":9930},{"id":"312d2de2730356c4ade0a253083add8162708cf367f7a6ed5d20cacfe47d35cd4a5b2e75f630903169686008fab2cb201a1a6da3ef52ff48b196c4f608dd1729","href":"https://static.observableusercontent.com/files/312d2de2730356c4ade0a253083add8162708cf367f7a6ed5d20cacfe47d35cd4a5b2e75f630903169686008fab2cb201a1a6da3ef52ff48b196c4f608dd1729","name":"gef_index_source_data@1.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1683251393648.141,"size":24675}] 31:{"id":"dab00fe4329f07d6","slug":"gef-drylands-index","title":"GEF Drylands Portfolio Index","thumbnail":"dd363cd1338490e3c1287251f3d13d600959600a97f888d09a5667cc74a9dab3","access_level":"public","create_time":"$D2023-05-05T00:44:27.080Z","update_time":"$D2023-05-08T20:03:56.392Z","view_time":null,"first_public_time":"$D2023-05-05T00:44:27.080Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"eeff2c5459868c71","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":"starter_2024"},"version":1194,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"64bdb7d8ac477035","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":null}],"members":[{"type":"individual","id":"64bdb7d8ac477035","login":"eastcoasting","name":"","avatar_url":"https://avatars-next.observableusercontent.com/0bd996e681cf4368cc7b26ea220c43fedfda41be2b9473bc75d19acdb83ef3f1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"GEF Drylands Portfolio Index","cells":[{"id":0,"value":"# GEF Drylands Portfolio Index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":572,"value":"This notebook presents preliminary findings of constructing a prioritization index for countries in the GEF drylands portfolio. The index summary figure is presented below, with the methodology and sources presented below. The sub-index calculations are outlined in a set of jupyter notebooks linked below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":274,"value":"#### Select a set of indicators to modify the subcomponents of the index","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":261,"value":"viewof drylandsIndex = Inputs.form({\n variables: Inputs.checkbox(Object.keys(mappedVariableInputs), {\n label: \"Index Variables\",\n value: [\n \"Disaster Risk Index\",\n \"Climate Change Risk Optimistic (2050)\",\n \"Share of KBAs Protected\",\n \"Dryland Water Scarcity\",\n \"Share of Tree Cover Lost in Drylands\",\n \"Air Pollution\"\n ]\n })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":648,"value":"Plot.plot({\n height: 0,\n color: {\n type: \"ordinal\",\n style: {\n fontSize: \"16px\"\n },\n legend: true,\n swatchSize: 20,\n columns: 3,\n domain: Object.keys(legendMap),\n range: Object.values(legendMap)\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":802,"value":"viewof filters = Inputs.form({\n region: Inputs.select(\n [\n \"All\",\n \"Africa\",\n \"Caribbean\",\n \"Central Asia\",\n \"Europe\",\n \"S Asia\",\n \"South America\",\n \"SW Asia\"\n ],\n {\n value: \"All\",\n label: \"Filter by Region\"\n }\n )\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1005,"value":"Plot.plot({\n width: 1200,\n projection: {\n type: \"equal-earth\",\n rotate: [-20, 0],\n inset: filters.region !== \"All\" ? 75 : 0,\n domain:\n filters.region !== \"All\"\n ? { type: \"FeatureCollection\", features: currentBoundingBox }\n : null\n },\n color: {\n scheme: \"BuRd\",\n unknown: \"#ddd\",\n type: \"linear\",\n legend: true,\n // percent: true,\n unknown: \"#DDDDDD\",\n label: \"Index →\",\n width: 300,\n style: {\n fontSize: \"16px\"\n }\n },\n style: {\n fontSize: 20,\n paddingTop: 0,\n paddingBottom: 0,\n overflow: \"visible\",\n lineWidth: 0.5\n },\n marks: [\n Plot.geo(countriesSubset, {\n fill: (d) => indexCountryMap.get(d.properties.name),\n stroke: \"white\",\n strokeWidth: 1,\n title: (d) => d.properties.name\n })\n ],\n caption: `${\n filters.region !== \"All\"\n ? \"Note: For regional subsets, we recompute the index at a regional level.\"\n : \"\"\n }`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":879,"value":"viewof chartInputs = Inputs.form({\n xAxis: Inputs.select([...Object.keys(mappedVariableInputs)], {\n value: \"Disaster Risk Index\",\n label: \"X Axis\"\n }),\n yAxis: Inputs.select([...Object.keys(mappedVariableInputs)], {\n value: \"Baseline Climate Change Risk Index\",\n label: \"Y Axis\"\n }),\n group: Inputs.select([\"Region\", \"None\"], { value: \"Region\", label: \"Group\" })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"#### Select a set of indicators to compare their normalized relationship","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":872,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":496,"value":"$35","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"data = normalizedData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"data","database":"$undefined"},{"id":379,"value":"#### Source for calculations (private workbooks): \n- National level: https://github.com/InduforNorthAmerica/gef_drylands/blob/main/index_assembly.ipynb\n- Sub-national: https://github.com/InduforNorthAmerica/gef_drylands/blob/main/index_assembly_sub_admin.ipynb","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":993,"value":"#### SSP and RCP Overview","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":588,"value":"image = FileAttachment(\"carbonbriefrcpssp.png\").image({\n width: 1000,\n style: \"border: 1px solid gray\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":598,"value":"

Source: https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change/

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":406,"value":"

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":408,"value":"### Input and calculations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":684,"value":"legendMap = {\n const invertedMappedVariableInputs = invert(mappedVariableInputs);\n\n return lowOpacityDotMarks.reduce((accumulator, domain) => {\n let originalVariableName = domain.name.split(\"normalized_\")[1];\n let unifiedVariableName =\n invertedMappedVariableInputs[originalVariableName];\n if (drylandsIndex.variables.includes(unifiedVariableName)) {\n accumulator[unifiedVariableName] = domain.fill;\n }\n return accumulator;\n }, {});\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":688,"value":"function invert(obj) {\n const invertedObj = {};\n\n for (const key in obj) {\n if (obj.hasOwnProperty(key)) {\n invertedObj[obj[key]] = key;\n }\n }\n\n return invertedObj;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":617,"value":"mappedVariableInputs = [\n {\n \"Disaster Risk Index\": \"inform_risk\",\n \"Baseline Climate Change Risk Index\": \"baseline_cc_risk\",\n \"Climate Change Risk Pessimistic (2050) \": \"2050_pessimistic_cc_risk\",\n \"Climate Change Risk Optimistic (2050)\": \"2050_optimistic_cc_risk\",\n \"Climate Change Risk Pessimistic (2080)\": \"2080_pessimistic_cc_risk\",\n \"Climate Change Risk Index Optimistic (2080)\": \"2080_optimistic_cc_risk\",\n \"Share of KBAs Protected\": \"kba_overlap_percentage\",\n \"Dryland Water Scarcity\": \"area_weighted_custom_aqueduct\",\n \"Share of Tree Cover Lost in Drylands\": \"forest_loss_drylands_percentage\",\n \"Air Pollution\": \"mean_annual_exposure_pm25\"\n }\n][0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":447,"value":"function createLowOpacityDotMark(variable, fill) {\n return Plot.dot(normalizedData, {\n x: variable,\n y: \"country_name\",\n opacity: 0.5,\n fill: fill,\n symbol: \"circle\",\n sort: { y: \"x\", reverse: true }\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":450,"value":"lowOpacityDotMarks = [\n { name: \"normalized_inform_risk\" },\n { name: \"normalized_baseline_cc_risk\" },\n { name: \"normalized_2050_pessimistic_cc_risk\" },\n { name: \"normalized_2050_optimistic_cc_risk\" },\n { name: \"normalized_2080_pessimistic_cc_risk\" },\n { name: \"normalized_2080_optimistic_cc_risk\" },\n { name: \"normalized_kba_overlap_percentage\" },\n { name: \"normalized_area_weighted_custom_aqueduct\" },\n { name: \"normalized_forest_loss_drylands_percentage\" },\n { name: \"normalized_mean_annual_exposure_pm25\" }\n].map((variable, idx) => {\n return {\n ...variable,\n fill: d3.schemePaired[idx]\n };\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":31,"value":"normalizedData = {\n const propertiesConfig = {\n inform_risk: false,\n \"2050_pessimistic_cc_risk\": false,\n kba_overlap_percentage: true,\n area_weighted_custom_aqueduct: false,\n forest_loss_drylands_percentage: false,\n \"2050_optimistic_cc_risk\": false,\n \"2080_pessimistic_cc_risk\": false,\n \"2080_optimistic_cc_risk\": false,\n baseline_cc_risk: false,\n mean_annual_exposure_pm25: false\n };\n\n const filteredPropertiesConfig = {};\n\n drylandsIndex.variables.forEach((variable) => {\n let indicatorFullVariable = mappedVariableInputs[variable];\n\n if (propertiesConfig.hasOwnProperty(indicatorFullVariable)) {\n filteredPropertiesConfig[indicatorFullVariable] =\n propertiesConfig[indicatorFullVariable];\n }\n });\n\n return normalizeData(baseIndexData, filteredPropertiesConfig);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":940,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"baseIndexData = {\n const gefSourceData = sourceData.sheet(\"Sheet1\", { headers: true });\n\n return gefSourceData.filter((country) => {\n const propertiesToCheck = [\n \"inform_risk\",\n \"baseline_cc_risk\",\n \"2050_pessimistic_cc_risk\",\n \"2050_optimistic_cc_risk\",\n \"2080_pessimistic_cc_risk\",\n \"2080_optimistic_cc_risk\",\n \"kba_overlap_percentage\",\n \"area_weighted_custom_aqueduct\",\n \"forest_loss_drylands_percentage\",\n \"mean_annual_exposure_pm25\"\n ];\n\n country.region = LSIBSet[country.country_name];\n\n return propertiesToCheck.every((property) => {\n return (\n typeof country[property] === \"number\" &&\n !isNaN(country[property]) &&\n !country[property].toString().includes(\"Error:\") &&\n filterRegion(country.region)\n );\n });\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"sourceData = FileAttachment(\"gef_index_source_data@1.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":814,"value":"LSIBPolygonSource = FileAttachment(\"lsib_polygons.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":830,"value":"LSIBSet = LSIBPolygonSource.reduce((accumulator, country) => {\n const countryName = country.country_na;\n const region = country.usdos_region;\n accumulator[countryName] = region;\n return accumulator;\n}, {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":850,"value":"function filterRegion(region) {\n if (filters.region === \"All\") return true;\n if (filters.region === region) return true;\n return false;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1127,"value":"currentBoundingBox = {\n const filteredCountriesSet = normalizedData.map((country) => {\n return country.country_name;\n });\n\n const filteredRegionalSubset = countriesSubset.features.filter((feature) => {\n console.log(filteredCountriesSet);\n return indexCountryMap.has(feature.properties.name);\n });\n\n return filteredRegionalSubset;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":793,"value":"","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":499,"value":"html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"import { land50m as land, countries50m as countries } from \"@visionscarto/geo\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1021,"value":"indexCountryMap = new Map(\n normalizedData.map(({ country_name, averageIndex }) => [\n renameCountriesMap.has(country_name)\n ? renameCountriesMap.get(country_name)\n : country_name,\n averageIndex\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1168,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1054,"value":"countriesSubset = [\n {\n type: \"FeatureCollection\",\n features: countries.features.filter((country) => {\n if (country.properties.name !== \"Antarctica\") {\n for (const indexCountry of normalizedData) {\n if (country.properties.name === indexCountry.country_name) {\n country.properties.index = indexCountry.averageIndex;\n }\n }\n console.log(country);\n return country;\n }\n })\n }\n][0]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 32:"$31:body"