2f:T893,fstPlotPop = { const plotData = fst_plotData.population; const popTranslated = [ _lang(nbText.sections.keyFacts.plots.pop.population.rural), _lang(nbText.sections.keyFacts.plots.pop.population.urban), ] const popRename = d3 .scaleOrdinal() .domain(["rural_pop", "urban_pop"]) .range(popTranslated); const formatPerc = d3.format(".1f"); const formatPop = formatNumCompactSigFigs() return Plot.plot({ style: { overflow: "visible" }, caption: _lang(nbText.sections.keyFacts.plots.pop.fCaption)({year: plotData?.[0].year}), title: addHtmlPlotTitle(_lang(nbText.sections.keyFacts.plots.pop.title)), width, height: 80, x: { axis: null }, y: { axis: null }, color: { domain: popTranslated, range: ["#79A1B7", "#195B83"], legend: true }, marks: [ Plot.barX( plotData, Plot.stackX({ x: "percentage", y: "admin0_name", fill: (d) => popRename(d.type), channels: { type: { label: _lang(nbText.sections.keyFacts.plots.tooltips.type), value: (d) => popRename(d.type) }, pop: { label: _lang(nbText.sections.keyFacts.plots.tooltips.population), value: (d) => formatPop(d.population) }, country: { label: _lang(nbText.sections.keyFacts.plots.tooltips.country), value: (d) => _lang(country_translations[d.iso3]?.general) } }, tip: { dy: 6, format: { country: true, type: true, pop: true, fill: false, x: false, y: false } } }) ), Plot.textX( plotData, Plot.stackX({ x: "percentage", y: "admin0_name", text: (d) => showTextMark({ text: `${formatPerc(d.percentage)}%`, perc: d.percentage, cutoff: 5 }), fill: "#ffffff", fontWeight: "bold" }) ) ] }); }30:T8b3,fstPlotPoverty = { // get reverse of each percentage const plotData = [ ...fst_plotData.poverty.map((d) => ({ ...d, group: "poverty" })), ...fst_plotData.poverty.map((d) => { return { ...d, group: "no_poverty", percentage: 100 - d.percentage }; }) ]; const povertyRange = [ _lang(nbText.sections.keyFacts.plots.pov.poverty.poverty), _lang(nbText.sections.keyFacts.plots.pov.poverty.notPoverty) ]; const poverty = d3 .scaleOrdinal() .domain(["poverty", "no_poverty"]) .range(povertyRange); const povertyString = "$" + plotData[0].type .match(/\d+$/)[0] .match(/(\d+)(\d{2})/) .slice(1) .join("."); const formatPerc = d3.format(".1f"); return Plot.plot({ style: { overflow: "visible" }, title: addHtmlPlotTitle(_lang(nbText.sections.keyFacts.plots.pov.title)), caption: _lang(nbText.sections.keyFacts.plots.pov.fCaption)({ year: plotData?.[0].year, threshold: povertyString }), width, y: { axis: null }, x: { axis: null }, color: { legend: true, domain: povertyRange, range: ["#EC5A47", "#F4BB21"] }, marks: [ Plot.barX(plotData, { x: "percentage", y: (d) => d.type, fill: (d) => poverty(d.group), channels: { type: { label: _lang(nbText.sections.keyFacts.plots.tooltips.type), value: (d) => poverty(d.group) }, country: { label: _lang(nbText.sections.keyFacts.plots.tooltips.country), value: (d) => _lang(country_translations[d.iso3]?.general) } }, tip: { dy: 6, format: { country: true, type: true, fill: false, x: false, y: false } } }), Plot.textX( plotData, Plot.stackX({ x: "percentage", y: (d) => d.type, text: (d) => showTextMark({ text: `${formatPerc(d.percentage)}%`, perc: d.percentage, cutoff: 5 }), fontWeight: "bold" }) ) ] }); }31:Tde7,fstPlotGdp = { // filter non-null const plotData = fst_plotData.gdp.filter((d) => d.gdp && d.percentage); const fillOptions = ["agriculture", "industry", "manufacturing", "services"]; const fillRange = [ _lang(nbText.sections.keyFacts.plots.gdp.gdp.ag), _lang(nbText.sections.keyFacts.plots.gdp.gdp.ind), _lang(nbText.sections.keyFacts.plots.gdp.gdp.man), _lang(nbText.sections.keyFacts.plots.gdp.gdp.serve) ]; const sectorRename = d3 .scaleOrdinal() .domain(fillOptions) .range(fillRange); const fillColors = ["#74B95A", "#8C3E5F", "#B34E65", "#523D4E"]; const formatPerc = d3.format(".1f"); const formatGdp = (d) => `$${formatNumCompactSigFigs()(d)}`; const plotDataNull = [{ percentage: 100 }]; const textNull = _lang(nbText.sections.keyFacts.plots.gdp.missing); const plotNull = Plot.plot({ title: addHtmlPlotTitle(_lang(nbText.sections.keyFacts.plots.gdp.title)), width, height: 80, x: { axis: null }, y: { axis: null }, marks: [ Plot.barX( plotDataNull, Plot.stackX({ x: "percentage", y: "admin0_name", fill: "#e4e4e4" }) ), Plot.textX( plotDataNull, Plot.stackX({ x: "percentage", y: "admin0_name", text: (d) => textNull, fontWeight: "bold" }) ) ] }); if (plotData.length <= 0) return plotNull; const plotMain = Plot.plot({ style: { overflow: "visible" }, title: addHtmlPlotTitle(_lang(nbText.sections.keyFacts.plots.gdp.title)), caption: _lang(nbText.sections.keyFacts.plots.gdp.fCaption)({ year: plotData?.[0].year }), width, height: 80, x: { axis: null }, y: { axis: null }, color: { domain: sectorRename.range(), range: fillColors, legend: true }, marks: [ Plot.barX( plotData, Plot.stackX({ x: "percentage", y: "admin0_name", fill: (d) => sectorRename(d.sector), channels: { sector: { label: _lang(nbText.sections.keyFacts.plots.tooltips.sector), value: (d) => sectorRename(d.sector) }, gdp: { label: _lang(nbText.sections.keyFacts.plots.tooltips.gdp), value: (d) => formatGdp(d.gdp) }, perc: { label: "Percentage", value: (d) => showTextMark({ text: `${formatPerc(d.percentage)}%`, perc: d.percentage * -1, cutoff: -5, invalid: null }) }, country: { label: _lang(nbText.sections.keyFacts.plots.tooltips.country), value: (d) => _lang(country_translations[d.iso3]?.general) } }, tip: { dy: 6, format: { country: true, perc: true, sector: true, fill: false, x: false, y: false } } }) ), Plot.textX( plotData, Plot.stackX({ x: "percentage", y: "admin0_name", text: (d) => showTextMark({ text: `${formatPerc(d.percentage)}%`, perc: d.percentage, cutoff: 5 }), fill: "#fff", fontWeight: "bold" }) ) ] }); return plotMain; }32:T852,fstPlotLand = { const plotData = fst_plotData.land const fillRange = [ _lang(nbText.sections.keyFacts.plots.land.type.ag), _lang(nbText.sections.keyFacts.plots.land.type.forest), _lang(nbText.sections.keyFacts.plots.land.type.other), ] const fill = d3.scaleOrdinal() .domain(["Agriculture", "Forest land", "Other land"]) .range(fillRange) const formatPerc = d3.format(".1f"); const formatLand = formatNumCompactSigFigs(); return Plot.plot({ style: { overflow: "visible" }, title: addHtmlPlotTitle(_lang(nbText.sections.keyFacts.plots.land.title)), caption: _lang(nbText.sections.keyFacts.plots.pop.fCaption)({ year: plotData?.[0].year }), height: 80, width, x: { axis: null }, y: { axis: null }, color: { domain: fillRange, range: ["#74B95A", "#216729", "#4FB5B7"], legend: true }, marks: [ Plot.barX(plotData, { x: "percentage", y: "admin0_name", fill: (d) => fill(d.group), channels: { type: { label: _lang(nbText.sections.keyFacts.plots.tooltips.type), value: (d) => fill(d.group) }, land: { label: _lang(nbText.sections.keyFacts.plots.tooltips.landUse), value: (d) => `${formatLand(d.ha)} km²` }, country: { label: _lang(nbText.sections.keyFacts.plots.tooltips.country), value: (d) => _lang(country_translations[d.iso3]?.general) } }, tip: { dy: 6, format: { country: true, type: true, fill: false, x: false, y: false } } }), Plot.textX( plotData, Plot.stackX({ x: "percentage", y: "admin0_name", text: (d) => showTextMark({ text: `${formatPerc(d.percentage)}%`, perc: d.percentage, cutoff: 5 }), fill: "#fff", fontWeight: "bold" }) ) ] }); }33:T442,fstInsight2 = { const country = fst_selections.admin0.admin0_name; const iso3 = fst_selections.admin0.iso3; const countryTranslated = _lang(country_translations[iso3].article3); const data = fst_data_pov_wide[0]; const povertyLine = "GSAP_poor365"; const povertyString = "$" + povertyLine .match(/\d+$/)[0] .match(/(\d+)(\d{2})/) .slice(1) .join("."); const percent = data[povertyLine] / 100; const formatPerc = d3.format(".1%"); const i = { percPov: formatPerc(percent), country: countryTranslated, povertyString: povertyString, percNonPov: formatPerc(1 - percent) }; const insight = _lang(nbText.sections.keyFacts.insights.pov)({ percPov: i.percPov, country: i.country, povertyString: i.povertyString, percNonPov: i.percNonPov }); return insight; // return md`An estimated **${formatPerc(percent)}** of **${country}**’s population currently lives under **${povertyString} USD/day**, whereas **${formatPerc(1 - percent)}** of the population is considered to be outside of poverty. // ` }34:T408,fstInsight5Table = { const iso3 = fst_selections.admin0.iso3; const data = fst_data_land_subgroup.filter((d) => d.iso3 == iso3); const formatHa = formatNumCompactSigFigs(); const formatPerc = d3.format(".1%") const table = Inputs.table(data, { columns: ['group', 'sub_group', 'ha', 'percentage'], sort: "percentage", reverse: true, format: { group: x => _lang(td.land_group.values?.[x]), sub_group: x => _lang(td.land_subgroup.values?.[x]), ha: x => formatHa(x), percentage: x => formatPerc(x / 100) }, header: { group: _lang(nbText.sections.keyFacts.insights.landTable.table.group), sub_group: _lang(nbText.sections.keyFacts.insights.landTable.table.subgroup), ha: _lang(nbText.sections.keyFacts.insights.landTable.table.land), percentage: _lang(nbText.sections.keyFacts.insights.landTable.table.perc) } }); // remove checkboxes table.append(htl.html``53:T494,/* Insight on the crop yield change - Using 10A.a (GYGA data) - Filter by admin selectors (admin0/admin1) - Horizon set to 2050 - Have yield_irr_abs_change and yield_rfd_abs_change (absolute, irrigated and rainfed) - Non-null crops for both values */ cc_insight_cropYieldChangeBase = dbGYGA .query( ` with base as ( select admin0_name , admin1_name , ssp , horizon , crop , yield_irr_abs_change , yield_rfd_abs_change from yield_change where 1=1 -- admin ${sqlWhereNull({ field: "admin0_name", value: cleanAdminInput_SQL(cc_selections.admin0.admin0_name) })} ${sqlWhereNull({ field: "admin1_name", value: cleanAdminInput_SQL(cc_selections.admin1.admin1_name) })} -- horizon and horizon = 2050 -- non-null abs fields and yield_irr_abs_change is not null and yield_rfd_abs_change is not null -- formatting order by ssp ) -- find average for non-null values, each scenario and yield type select admin0_name , admin1_name , ssp , crop , yield_irr_abs_change as yield_irr , yield_rfd_abs_change as yield_rfd from base ` ) .then((x) => x.toArray())54:T440,cc_getCCInsight_cattleProd = () => { const findValue = (data, {variable="meat", scenario="ssp126"}={}) => { return data.filter(d => d?.variable ==variable && d?.scenario == scenario)?.[0] } const template = ({meat126, meat585, milk126, milk585}={}) => { const func = _lang(nbText.sections.climateChangeImpacts.insights.cattleProd.fCattleProd)({ meat1: meat126, milk1: milk126, meat5: meat585, milk5: milk585, }) return func } const format = d3.format(".2s") const makeInsight = (data) => { const meat126 = findValue(data, {variable: "meat", scenario: "ssp126"}) const meat585 = findValue(data, {variable: "meat", scenario: "ssp585"}) const milk126 = findValue(data, {variable: "milk", scenario: "ssp126"}) const milk585 = findValue(data, {variable: "milk", scenario: "ssp585"}) return template({ meat126: format(meat126?.value), meat585: format(meat585?.value), milk126: format(milk126?.value), milk585: format(milk585?.value), }) } return makeInsight(cc_insight_meatMilk_data) }55:T480,summary_climateProjTempIncreases = { // extent of temperature increases // const table = dataTable.projectionLineChart.name // const _iso3 = as_i_admin0.iso3; // const _gaul0 = iso3ToGaul0[_iso3] // const _gaul0_name = iso3ToGaul0Name[_iso3] // const _admin1 = as_i_admin1.admin1_name // const query = await db2.query(` // select * // from ${await ts_data_url("2041-2060")} // where 1=1 // -- geo // ${sqlWhereNull({ field: "admin0_name", value: cleanAdminInput_SQL(as_i_admin0.admin0_name) })} // ${sqlWhereNull({ field: "admin1_name", value: cleanAdminInput_SQL(_admin1) })} // -- fixed values // and season = 'annual' // and hazard = 'TAVG' // and timeframe = '2041-2060' // and year = 2050 // and mean_anomaly > 0 // `) // let data // if (!_admin1 && disputed_area_weights.map((d) => d.claimant_gaul0).includes(_gaul0)) { // data = calculateWeightedClimateData(query, disputed_area_weights) // } else { // data = query // } let data = plc_insights.filter((d) => d.hazard === 'TAVG' && d.mean_anomaly > 0) const tempExtent = d3.extent(data, d => d.mean_anomaly) return tempExtent }56:T8f2,styling = { const ns = Inputs.text().classList?.[0] // inputs class return !toggleCustomStyling ? "no custom styling" : htl.html``; }57:T4cf,function tocOneliner({ selector = "h1", // selectors to include (can comma-separate, like "h1,h2,h3") heading = "In this notebook", // heading for ToC delim = ' | ', // delimiter between links skip = [], // text content for headers to exclude } = {}) { // return a one-liner table of contents // allow for skips // modified from https://observablehq.com/@mbostock/toc return Generators.observe(notify => { let headings = []; function observed() { let h = Array.from(document.querySelectorAll(selector)); h = h.filter((d) => !skip.includes(d.textContent)) if (h.length !== headings.length || h.some((h, i) => headings[i] !== h)) { notify(html`${heading}
${Array.from(headings = h, (h, i) => { const end = i === headings.length - 1 return Object.assign( html`${DOM.text(h.textContent)}${end ? '' : delim}`, {onclick: e => (e.preventDefault(), h.scrollIntoView())} ); })}`); } } const observer = new MutationObserver(observed); observer.observe(document.body, {childList: true, subtree: true}); observed(); return () => observer.disconnect(); }); }58:Ta438,nbText = { // text in the notebook // recommendation: only use objects for better tab-completion return { legends: { language: { en: "language", fr: "langue" }, toc: { en: "in this notebook", fr: "dans ce notebook" }, adminSelections: { country: { en: "country", fr: "pays" }, region: { en: "region", fr: "région" }, subregion: { en: "subregion", fr: "sous-région" }, allCountries: { en: "All Countries", fr: "Tous les pays" }, allRegions: { en: "All Regions", fr: "Toutes les régions" }, allBasins: { en: "All Basins", fr: "Tous les bassins" } }, anomalyNotes: { ipccAr6: { en: "Anomaly data shows difference from IPCC AR6 (1995-2014) baseline mean", fr: "Les données d'anomalie montrent une différence par rapport à la moyenne de référence du GIEC AR6 (1995-2014)" } }, anomalyText: { anomaly: { en: "anomaly", fr: "anomalie" } }, hazardVariables: { ndwl0: { name: { en: "Number of Waterlogging Days", fr: "Nombre de jours d'engorgement" }, labelAxis: { en: "Number of days", fr: "Nombre de jours" }, labelShort: { en: "Days", fr: "Jours" }, unit: { en: null, fr: null } }, ndws: { name: { en: "Number of Days of Water Stress", fr: "Nombre de jours de stress hydrique" }, labelAxis: { en: "Number of days", fr: "Nombre de jours" }, labelShort: { en: "Days", fr: "Jours" }, unit: { en: null, fr: null } }, ntx35: { name: { en: "Number of Heat Stress Days for Maize", fr: "Nombre de jours de stress thermique pour le maïs" }, labelAxis: { en: "Number of days", fr: "Nombre de jours" }, labelShort: { en: "Days", fr: "Jours" }, unit: { en: null, fr: null } }, ntx40: { name: { en: "Number of Heat Stress Days for Generic Crops", fr: "Nombre de jours de stress thermique pour les cultures génériques" }, labelAxis: { en: "Number of days", fr: "Nombre de jours" }, labelShort: { en: "Days", fr: "Jours" }, unit: { en: null, fr: null } }, ptot: { name: { en: "Total Precipitation", fr: "Précipitations totales" }, labelAxis: { en: "Precipitation (mm)", fr: "Précipitation (mm)" }, labelShort: { en: "Precipitation", fr: "Précipitation" }, unit: { en: "mm", fr: "mm" } }, tavg: { name: { en: "Mean Temperature", fr: "Température moyenne" }, labelAxis: { en: "Mean temperature (°C)", fr: "Température moyenne (°C)" }, labelShort: { en: "Mean Temperature", fr: "Température moyenne" }, unit: { en: "°C", fr: "°C" } }, thi: { name: { en: "Cattle Thermal Humidity Index", fr: "Indice d'humidité thermique des bovins" }, labelAxis: { en: "Cattle Thermal Humidity Index", fr: "Indice d'humidité thermique des bovins" }, labelShort: { en: "Cattle THI", fr: "Bovins THI" }, unit: { en: null, fr: null } }, tmax: { name: { en: "Max Temperature", fr: "Température maximale" }, labelAxis: { en: "Max temperature (°C)", fr: "Température maximale (°C)" }, labelShort: { en: "Max Temperature", fr: "Température maximale" }, unit: { en: "°C", fr: "°C" } } }, fSeasonString: { en: ({season}={}) => `Season = ${season}`, fr: ({season}={}) => `Saison = ${season}`, } }, sections: { quickInsightsGeneral: { fHeader: { en: ({ selection } = {}) => md`Quick Insights: ${selection}`, fr: ({ selection } = {}) => md`Aperçu Rapide: ${selection}` } }, intro: { title: { en: "Overview", fr: "Vue d’Ensemble" }, introText: { en: `As part of the Paris Alignment, public and private investors will increasingly match investments to national and international climate commitments. A critical part of developing future proposals and investments is to develop a climate rationale. The climate rationale describes in detail the current and projected changes in climate and the impacts that may arise from these changes. In this notebook, you will select a country and a sub-sector related to agriculture (crops, livestock, water resources), and get key graphs and brief interpretations that will help you quickly build a compelling climate rationale for your project. The notebook is divided in five sections, namely: 1. Recent changes in key climate indicators 2. Projected changes in key climate indicators 3. Exposure of crops, livestock, water resources, and people, to climate hazards 4. Projected impacts on crops, livestock, water resources 5. Potential adaptation solutions linked to specific hazards and sectors `, fr: `Dans le cadre de l’accord de Paris, les investisseurs publics et privés aligneront de plus en plus leurs investissements sur les engagements climatiques nationaux et internationaux. Une partie essentielle du développement des futures propositions et investissements est de formuler une justification climatique. La justification climatique décrit en détail les changements climatiques actuels et projetés ainsi que les impacts qui peuvent en découler. Dans ce notebook, vous sélectionnerez un pays et un sous-secteur lié à l'agriculture (cultures, élevage, ressources en eau) et obtiendrez des graphiques clés ainsi que de brèves interprétations qui vous aideront à construire rapidement une justification climatique convaincante pour votre projet. Le notebook est divisé en cinq sections, à savoir: 1. Évolution récente des indicateurs clés du climat 2. Évolution prévue des indicateurs clés du climat 3. Exposition des cultures, du bétail, des ressources en eau et des populations aux aléas climatiques 4. Impacts prévus sur les cultures, le bétail, les ressources en eau 5. Solutions d'adaptation potentielles liées à des aléas et à des secteurs spécifiques ` } }, keyFacts: { title: { en: "Key Demographic and Economic Facts", fr: "Principaux faits démographiques et économiques" }, introText: { en: "Select the geography below and get a set of quick demographic and economic facts to get a general context of the country targeted by your GCF project proposal.", fr: "Sélectionnez la zone géographique ci-dessous et obtenez un ensemble de données démographiques et économiques rapides pour obtenir un contexte général du pays ciblé par votre proposition de projet GCF." }, insights: { pop: { en: ({ country, popTotal, popRural, percRural } = {}) => md`**${country}** has a total population of **${popTotal}**, with **${popRural}** (**${percRural}**) living in rural areas.`, fr: ({ country, popTotal, popRural, percRural } = {}) => md`**${country}** compte une population totale de **${popTotal}** d'habitants, dont **${popRural}** (**${percRural}**) vivent dans les zones rurales.` }, pov: { en: ({ percPov, country, povertyString, percNonPov } = {}) => md`An estimated **${percPov}** of **${country}**’s population currently lives under **${povertyString} USD/day**, whereas **${percNonPov}** of the population is considered to be outside of poverty.`, fr: ({ percPov, country, povertyString, percNonPov } = {}) => md`On estime que **${percPov}** de la population **${country}** vit actuellement avec moins de **${povertyString} USD par jour**, tandis que **${percNonPov}** de la population est considérée comme étant en dehors de la pauvreté.` }, gdp: { main: { en: ({ rank, country } = {}) => md`Agriculture is the **${rank}** most important sector for the economy of **${country}**, critically and substantially contributing to employment in rural areas.`, fr: ({ rank, country } = {}) => md`L’agriculture est le **${rank}** secteur le plus important de l’économie **${country}**, contribuant de manière essentielle et substantielle à l’emploi dans les zones rurales.` }, missing: { en: ({ country } = {}) => md`Agriculture is **not listed** as a GDP sector for **${country}**.`, fr: ({ country } = {}) => md`L'agriculture **n'est pas répertoriée** comme secteur du PIB pour **${country}**.` } }, land: { en: ({ country, type, ha, perc } = {}) => md`The most prevalent land use in **${country}** is **${type}**, which covers **${ha} km²** or **${perc}** of the total area.`, fr: ({ country, type, ha, perc } = {}) => md`L'utilisation des terres la plus répandue **${country}** est **${type}**, qui couvrent **${ha} km²**, soit **${perc}** de la superficie totale.` }, landTable: { intro: { en: ({ country } = {}) => md`Below is a full breakdown of land use in **${country}**:`, fr: ({ country } = {}) => md`Vous trouverez ci-dessous une répartition complète de l’utilisation des terres **${country}**:` }, table: { group: { en: "Group", fr: "Groupe" }, sub: { en: "Group", fr: "Groupe" } }, table: { group: { en: "Group", fr: "Groupe" }, subgroup: { en: "Sub-Group", fr: "Sous-groupe" }, land: { en: "Land Use (km²)", fr: "Utilisation du sol (km²)" }, perc: { en: "% Total", fr: "% Total" } } } }, plots: { pop: { title: { en: "Population", fr: "Population" }, fCaption: { en: ({ year } = {}) => `Data is from ${year}`, fr: ({ year } = {}) => `Les données datent de ${year}` }, population: { rural: { en: "Rural", fr: "Rurale" }, urban: { en: "Urban", fr: "Urbaine" } } }, pov: { title: { en: "Poverty", fr: "Pauvreté" }, fCaption: { en: ({ year, threshold } = {}) => `Data uses purchasing power parity (PPP) values for ${year}. Poverty threshold is ${threshold} USD/day.`, fr: ({ year, threshold } = {}) => `Les données utilisent les valeurs de parité de pouvoir d'achat (PPA) pour ${year}. Le seuil de pauvreté est de ${threshold} USD/jour.` }, poverty: { poverty: { en: "Poverty", fr: "Pauvreté" }, notPoverty: { en: "Not in Poverty", fr: "Pas dans la pauvreté" } } }, gdp: { title: { en: "GDP", fr: "PIB" }, fCaption: { en: ({ year } = {}) => `Data is from ${year}, measured in 2015 US dollars`, fr: ({ year } = {}) => `Les données datent de ${year} et sont mesurées en dollars américains de 2015.` }, missing: { en: "No data", fr: "Aucune donnée" }, gdp: { ag: { en: "Agriculture", fr: "Agriculture" }, ind: { en: "Industry", fr: "Industrie" }, man: { en: "Manufacturing", fr: "Production" }, serve: { en: "Services", fr: "Services" } } }, land: { title: { en: "Land Use", fr: "Utilisation des terres" }, fCaption: { en: ({ year } = {}) => `Data is from ${year}`, fr: ({ year } = {}) => `Les données datent de ${year}` }, type: { ag: { en: "Agriculture", fr: "Agriculture" }, forest: { en: "Forest Land", fr: "Terres forestières" }, other: { en: "Other Land", fr: "Autre Terre" } } }, tooltips: { country: { en: "Country", fr: "Pays" }, type: { en: "Type", fr: "Type" }, sector: { en: "Sector", fr: "Secteur" }, gdp: { en: "GDP", fr: "PIB" }, landUse: { en: "Land use", fr: "Utilisation des terres" }, population: { en: "Population", fr: "Population" } } } }, recentChangesClimateIndicators: { title: { en: "Recent Changes in Key Climate Indicators", fr: "Évolution récente des principaux indicateurs climatiques" }, introText: { en: "Historical changes in climate conditions are the first step to contextualizing a GCF proposal. This section will provide you an overview of the historical changes in key climate variables for the target country of your proposal.", fr: "Les changements historiques des conditions climatiques constituent la première étape de la contextualisation d'une proposition GCF. Cette section vous fournira un aperçu des changements historiques des variables climatiques clés pour le pays cible de votre proposition." }, inputs: { selectSeason: { label: { en: "Season", fr: "Saison" } }, selectClimateVar: { label: { en: "Climate Variable", fr: "Variable climatique" } }, toggleAnomaly: { label: { en: "Anomaly Data", fr: "Données d'anomalie" } }, toggleShowLine: { label: { en: "Show Line Chart", fr: "Afficher le graphique linéaire" } } }, insights: { warming: { fWarming: { en: ({ country, temp, tempDecade } = {}) => md`**${country}** warmed by **${temp}°C** between 1995 and 2014, which implies **${tempDecade}°C** per decade.`, fr: ({ country, temp, tempDecade } = {}) => md`**${country}** s'est réchauffé de **${temp}°C** entre 1995 et 2014, ce qui implique **${tempDecade}°C** par décennie.` } }, precChange: { fPrecChange: { en: ({ country, precString } = {}) => md`Precipitation changes during the same period have also occurred, with **${precString}** per decade on average across **${country}**.`, fr: ({ country, precString } = {}) => md`Les précipitations ont également changé au cours de la même période, avec **${precString}** par décennie en moyenne sur **${country}**.` }, rain: { more: { en: "more rain", fr: "de pluie en plus" }, less: { en: "less rain", fr: "de pluie en moins" } } }, precVariability: { fPrecVariability: { en: ({ droughtString, percDrought, wetString, percWet }) => // md`Precipitation variability and extreme events contribute to vulnerability. During the analysis period, there have been **${droughtString}** of extreme drought (**${percDrought} of total**) and **${wetString}** of extremely wet conditions (**${percWet} of total**). These extreme events have dire consequences on agricultural production and livelihoods.`, md`Precipitation variability and extreme events contribute to vulnerability. Extreme drought events happen for **${percDrought} of the analysis period**, and extremely wet conditions for **${percWet} of the period**. These extreme events have dire consequences on agricultural production and livelihoods.`, fr: ({ droughtString, percDrought, wetString, percWet }) => md`La variabilité des précipitations et les événements extrêmes contribuent à la vulnérabilité. Au cours de la période d’analyse, il y a eu **${droughtString}** de sécheresse extrême (**${percDrought} du total**) et **${wetString}** de conditions extrêmement humides (**${percWet} du total**). Ces événements extrêmes ont des conséquences désastreuses sur la production agricole et les moyens de subsistance.` }, period: { singular: { en: "period", fr: "période" }, plural: { en: "periods", fr: "périodes" } } }, heatStress: { fHeatStress: { en: ({ selection, areaString } = {}) => md`Heat stress for livestock and crops is a concern in **${selection}**, with ${areaString} exposed to severe or extreme heat stress.`, fr: ({ selection, areaString } = {}) => md`Le stress thermique pour le bétail et les cultures est une préoccupation **${selection}**, avec ${areaString} exposés à un stress thermique sévère ou extrême.` }, support: { and: { en: "and", fr: "et" }, unitSubstrings: { livestock: { en: "physical livestock area", fr: "superficie physique du bétail" }, crop: { en: "physical crop area", fr: "superficie physique des cultures" } }, fSubset: { en: ({ perc, label } = {}) => `**${perc}% of the ${label}**`, fr: ({ perc, label } = {}) => `**${perc}% de la ${label}**` } } } } }, futureClimateProjections: { title: { en: "Future Climate Projections", fr: "Projections climatiques futures" }, introText: { en: "Like for historical trends, future projections for the same key variables are needed to contextualize the proposed adaptation and mitigation solutions. This section explores different climate futures using data from the Global Climate Models (GCMs) used in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC).", fr: "Comme pour les tendances historiques, des projections futures pour les mêmes variables clés sont nécessaires pour contextualiser les solutions d’adaptation et d’atténuation proposées. Cette section explore différents futurs climatiques à l’aide de données provenant des modèles climatiques mondiaux (MCG) utilisés dans le sixième rapport d’évaluation (AR6) du Groupe d’experts intergouvernemental sur l’évolution du climat (GIEC)." }, chart: { tooltip: { year: { en: "Year", fr: "Année" }, scenario: { en: "Scenario", fr: "Scénario" } } }, inputs: { selectSeason: { label: { en: "Season", fr: "Saison" } }, selectClimateVar: { label: { en: "Climate Variable", fr: "Variable climatique" } }, toggleAnomaly: { label: { en: "Anomaly Data", fr: "Données d'anomalie" } }, toggleShowMinMax: { label: { en: "Show Min/Max", fr: "Afficher Min/Max" } }, toggleShowTrend: { label: { en: "Show Trend", fr: "Afficher la tendance" } }, checkboxScenario: { label: { en: "Scenarios", fr: "Scénarios" } } }, insights: { fWarming: { en: ({ selectedRegion, trajectoryHigh, trajectoryModerate, trajectoryLow }) => md`**${selectedRegion}** is projected to warm by **${trajectoryHigh}ºC** by 2050 in a high-emissions trajectory (SSP370). Under a moderately stringent mitigation scenario (SSP245), the warming by 2050 is **${trajectoryModerate}ºC**, whereas under a very stringent mitigation scenario (SSP126) the warming by 2050 is **${trajectoryLow}ºC**.`, fr: ({ selectedRegion, trajectoryHigh, trajectoryModerate, trajectoryLow }) => md`**${selectedRegion}** devrait connaître un réchauffement de **${trajectoryHigh}°C** d'ici 2050 dans le cadre d'une trajectoire à fortes émissions (SSP370). Dans le cadre d'un scénario d'atténuation modérément rigoureux (SSP245), le réchauffement d'ici 2050 est de **${trajectoryModerate}°C**, tandis que dans le cadre d'un scénario d'atténuation très rigoureux (SSP126), le réchauffement d'ici 2050 est de **${trajectoryLow}°C**.` }, fUncertainty: { en: ({ selectedRegion, min, max } = {}) => md`These temperature projections carry some uncertainty, with individual models for **${selectedRegion}** under the high-emissions trajectory (SSP370) showing a range of warming between **${min}ºC** (coolest) and **${max}ºC** (warmest).`, fr: ({ selectedRegion, min, max } = {}) => md`Ces projections de température comportent une certaine incertitude, les modèles individuels pour **${selectedRegion}** sous la trajectoire de fortes émissions (SSP370) montrant une fourchette de réchauffement comprise entre **${min}°C** (le plus froid) et **${max}°C** (le plus chaud).` }, precProjections: { fPrecProj: { en: ({ year, precString } = {}) => `Precipitation projections for ${year} under SSP370 show **${precString} mm/year** on average in the entire area.`, fr: ({ year, precString } = {}) => `Les projections de précipitations pour ${year} selon le SSP370 montrent **${precString} mm/an** en moyenne sur l’ensemble de la zone.` }, dirStrings: { inc: { en: "increases of", fr: "des augmentations de" }, dec: { en: "decreases of", fr: "une diminution de" } } }, fHeatStress: { en: ({ crop2, crop5, livestock2, livestock5 } = {}) => md`Heat stress is projected to increase, with the area under severe or greater levels of heat stress increasing by **${crop5}** for crops and **${livestock5}** for livestock by 2050 under a very high emissions (SSP585) trajectory. Efforts in climate mitigation efforts (SSP245) would mean increases in the amount of area experiencing heat stress would be limited to **${crop2}** for crops and **${livestock2}** for livestock.`, fr: ({ crop2, crop5, livestock2, livestock5 } = {}) => md`Le stress thermique devrait augmenter, avec une augmentation de la superficie soumise à des niveaux sévères ou plus élevés de **${crop5}** pour les cultures et de **${livestock5}** pour l'élevage d'ici 2050 dans le cadre d'une trajectoire d'émissions très élevées (SSP585). Les efforts d'atténuation du changement climatique (SSP245) signifieraient que l'augmentation de la superficie soumise à un stress thermique serait limitée à **${crop2}** pour les cultures et à **${livestock2}** pour l'élevage.` }, fRainfall: { en: ({ dec, inc } = {}) => `Some **${dec}** of the physical area experiences rainfall reductions, whereas **${inc}** experiences rainfall increases.`, fr: ({ dec, inc } = {}) => `Environ **${dec}** de la superficie physique connaît une diminution des précipitations, tandis que **${inc}** connaît une augmentation des précipitations.` } } }, cropLivestockHazardExposure: { title: { en: "Crop and Livestock Exposure to Climate Hazards", fr: "Exposition des cultures et du bétail aux risques climatiques" }, introText: { en: "Crops and livestock may be exposed to a variety of hazards and stresses, and these may co-occur for a given geography. This section allows exploring a variety of climate hazard variables that enable a deeper understanding of the potential implications of climate change for crops and livestock.", fr: "Les cultures et l’élevage peuvent être exposés à une variété de risques et de stress, qui peuvent coexister dans une zone géographique donnée. Cette section permet d’explorer diverses variables de risques climatiques qui permettent de mieux comprendre les implications potentielles du changement climatique pour les cultures et l’élevage." }, chart: { vop: { en: "VoP", fr: "VDP" }, caption: { en: "Monetary VoP values from 2020 are represented in 2015 international dollars. Source: MapSPAM 2020", fr: "Les valeurs monétaires de la VDP de 2020 sont exprimées en dollars internationaux de 2015. Source: MapSPAM" }, tooltip: { vop: { en: "VoP", fr: "VDP" }, crop: { en: "Crop", fr: "Culture" }, timeframe: { en: "Timeframe", fr: "Calendrier" } }, historic: { en: "Historic", fr: "Historique" } }, inputs: { selectHazard: { label: { en: "Hazard", fr: "Danger" } }, selectScenario: { label: { en: "Scenario", fr: "Scénario" } }, radioSortYCrops: { label: { en: "Sort Y", fr: "Trier Y" }, data: { alpha: { en: "Alphabetical", fr: "Alphabétique" }, total: { en: "Total Value of Production (VoP)", fr: "Valeur totale de la production (VDP)" } } }, toggleGenericCrop: { label: { en: "Include generic crop", fr: "Inclure la culture générique" }, data: { visible: { en: "Visible", fr: "Visible" } } } }, insights: { exposure: { fExposure: { en: ({ hazard, selectedRegion, cropString, livestockString, crop, livestock } = {}) => md`Exposure to **${hazard}** hazards in **${selectedRegion}** affects **${cropString}** and **${livestockString}**, with total value of production exposed being **${crop}** for crops and **${livestock}** for livestock.`, fr: ({ hazard, selectedRegion, cropString, livestockString, crop, livestock } = {}) => md`L’exposition à **${hazard}** dangers dans **${selectedRegion}** affecte **${cropString}** et **${livestockString}**, la valeur totale de la production exposée étant de **${crop}** pour les cultures et de **${livestock}** pour l’élevage.` }, lookupStrings: { crop: { singular: { en: "crop", fr: "culture" }, plural: { en: "crops", fr: "cultures" } }, livestock: { singular: { en: "livestock species", fr: "espèce d'élevage" }, plural: { en: "livestock species", fr: "espèces d'élevage" } } } }, mostExposed: { fMain: { en: ({ type, hazard, crop, value } = {}) => `The most exposed ${type} to **${hazard}** is **${crop}** with **${value}** in production value exposed.`, fr: ({ type, hazard, crop, value } = {}) => `${type} la plus exposée à **${hazard}** est **${crop}** avec **${value}** en valeur de production exposée.` }, typeStrings: { crop: { en: "crop", fr: "La culture" }, livestock: { en: "livestock", fr: "Le bétail" } } } } }, climateChangeImpacts: { title: { en: "Climate Change Impacts on Crops, Livestock, Water Resources, and Food Security", fr: "Impacts du changement climatique sur les cultures, l'élevage, les ressources en eau et la sécurité alimentaire" }, introText: { en: "The exposure of crops and livestock to climate hazards is likely to lead to reductions in productivity with potentially negative economic consequences. This section explores the specific impacts of climate change on crop and livestock production, as well as on two key sectors related to agriculture, namely, water resources and food security.", fr: "L’exposition des cultures et de l’élevage aux aléas climatiques est susceptible d’entraîner une baisse de la productivité, avec des conséquences économiques potentiellement négatives. Cette section examine les impacts spécifiques du changement climatique sur la production végétale et animale, ainsi que sur deux secteurs clés liés à l’agriculture, à savoir les ressources en eau et la sécurité alimentaire." }, chart: { // independent of sectors tooltipLabels: { min: { en: "Min", fr: "Min" }, max: { en: "Max", fr: "Max" }, avg: { en: "Avg.", fr: "Moy." }, mean: { en: "Mean", fr: "Moyenne" }, median: { en: "Median", fr: "Médiane" }, snapshot: { en: "Data Snapshot", fr: "Aperçu des données" } } }, inputs: { selectSector: { label: { en: "Sector", fr: "Secteur" }, data: { crop_yield: { label: { en: "Crop Yield", fr: "Rendement des cultures" }, xLabel: { en: "Change in Crop Yield (%)", fr: "Évolution du rendement des cultures (%)" }, yLabel: { en: "Frequency", fr: "Fréquence" }, indicator: { irr: { en: "Irrigated", fr: "Irrigué" }, rfd: { en: "Rainfed", fr: "Pluviale" } } }, crop_suit: { label: { en: "Crop Suitability", fr: "Adéquation des cultures" }, xLabel: { en: "Change in Suitability (%)", fr: "Changement d'adéquation (%)" }, yLabel: { en: "Frequency", fr: "Fréquence" } }, water: { label: { en: "Water Resources", fr: "Ressources en eau" }, xLabel: { en: "Change in Water Resource", fr: "Changement dans les ressources en eau" }, yLabel: { en: "Frequency", fr: "Fréquence" } } } }, selectScenario: { label: { en: "Scenario", fr: "Scénario" } }, selectIndicator: { labelWater: { en: "Indicator", fr: "Indicateur" }, labelCrop: { en: "Crop", fr: "Culture" }, } }, insights: { cyChanges: { text: { en: md`**Crop yield changes**: Below are the average crop yield change and most negatively impacted crop under a moderate (SSP370) and high emissions (SSP585) trajectory.`, fr: md`**Changements dans le rendement des cultures**: Vous trouverez ci-dessous les changements moyens du rendement des cultures et les cultures les plus affectées négativement dans le cadre d'une trajectoire d'émissions modérées (SSP370) et élevées (SSP585).` }, table: { cols: { ssp: { en: "Scenario", fr: "Scénario" }, yield_type: { en: "Yield Type", fr: "Type de rendement" }, yield_avg: { en: "Avg. Crop Diff (ton/ha)", fr: "Différence moyenne entre les cultures (tonnes/ha)" }, yield_min: { en: "Min change (ton/ha)", fr: "Changement minimal (tonnes/ha)" }, minCropString: { en: "Most Negative Crop (ton/ha)", fr: "Culture la plus négative (tonnes/ha)" }, avgPercChange: { en: "Avg. Change (%)", fr: "Variation moyenne (%)" } } } }, cySuitability: { text: { en: md`**Crop suitability changes**: Below are the average crop suitability changes and most negatively impacted crops under a moderately-low (SSP245) and high emissions (SSP585) trajectory.`, fr: md`**Changements dans l'adéquation des cultures**: Vous trouverez ci-dessous les changements moyens dans l'adéquation des cultures et les cultures les plus affectées négativement dans le cadre d'une trajectoire d'émissions modérément faibles (SSP245) et élevées (SSP585).` }, table: { cols: { scenario: { en: "Scenario", fr: "Scénario" }, avg_crop_diff: { en: "Avg. Crop Diff (km²)", fr: "Différence moyenne entre les cultures (km²)" }, minCropString: { en: "Most Negative Crop (km²)", fr: "Culture la plus négative (km²)" } } } }, cattleProd: { fCattleProd: { en: ({ milk1, milk5, meat1, meat5 }) => md`Milk and meat production for cattle are likely to be impaired by heat stress, with reductions of up to **${milk1} (milk)** and **${meat1} (meat)** for a low emissions trajectory (SSP126), and reductions of up to **${milk5} (milk)** and **${meat5} (meat)** for a high emissions trajectory (SSP585) (USD 2020).`, fr: ({ milk1, milk5, meat1, meat5 }) => md`La production de lait et de viande de bovins est susceptible d'être altérée par le stress thermique, avec des réductions allant jusqu'à **${milk1} (lait)** et **${meat1} (viande)** pour une trajectoire à faibles émissions (SSP126), et des réductions allant jusqu'à **${milk5} (lait)** et **${meat5} (viande)** pour une trajectoire à fortes émissions (SSP585) (USD 2020).` } }, water: { text: { en: md`**Impacts on water resources**: Below is a table of average percent changes to different impacts on water resources across different scenarios:`, fr: md`**Impacts sur les ressources en eau**: Vous trouverez ci-dessous un tableau des pourcentages moyens de changement des différents impacts sur les ressources en eau dans différents scénarios:` }, table: { cols: { scenario: { en: "Scenario", fr: "Scénario" }, variable: { en: "Water Resource", fr: "Ressource en eau" }, mean: { en: "Avg. Change (%)", fr: "Variation moyenne (%)" } } } } } }, summary: { title: { en: "Summary", fr: "Résumé" }, fBody: { en: ({ admin, tempIncLo, tempIncHi, vopExposed, cropYieldLo, cropYieldHi, cropList }) => md`Climate has been changing over the last century in **${admin}**. Future climate projections from the latest IPCC report suggest increases in temperature in the range **${tempIncLo}** and **${tempIncHi}**, depending on the emissions trajectories. Rainfall changes are also expected. These impacts will exacerbate current climate hazards, which by 2050 together affect **${vopExposed}** in value of production for crops and livestock. Crop yield reductions in the range **${cropYieldLo}** and **${cropYieldHi}** are projected for key crops such as **${cropList}** by 2050. Challenges in water resources and food security are also expected. In designing the project that addresses these challenges, there are a number of adaptation solutions that should be taken into consideration.`, fr: ({ admin, tempIncLo, tempIncHi, vopExposed, cropYieldLo, cropYieldHi, cropList }) => md`Le climat **${admin}** a changé au cours du siècle dernier. Les projections climatiques futures du dernier rapport du GIEC suggèrent des augmentations de température de l'ordre de **${tempIncLo}** à **${tempIncHi}**, en fonction des trajectoires d'émissions. Des changements dans les précipitations sont également attendus. Ces impacts exacerbent les risques climatiques actuels, qui d'ici 2050 affectent **${vopExposed}** en valeur de production pour les cultures et l'élevage. Des baisses de rendement des cultures de l'ordre de **${cropYieldLo}** et **${cropYieldHi}** sont prévues pour des cultures clés telles que **${cropList}** d'ici 2050. Des défis en matière de ressources en eau et de sécurité alimentaire sont également attendus. Lors de la conception du projet qui répond à ces défis, un certain nombre de solutions d'adaptation doivent être prises en considération.` }, support: { and: { en: "and", fr: "et" }, unit: { en: "ton/ha", fr: "tonnes/ha" } } } } }; }59:T628f,nbTextMethods = { // methods and sources in the notebook return { title: { en: "Methods & Sources", fr: "Méthodes & Sources" }, sources: { title: { en: "Sources", fr: "Sources" }, sections: { population: { title: { en: "Population", fr: "Population" }, text: { en: "Population data is from the WorldPop Unconstrained Global Mosaic for 2020. The population data is masked Africapolis data to separate the population groups into rural, urban, and total. The final raster dataset used is at a resolution of 0.083 degrees (5 arc minutes).", fr: "Les données démographiques proviennent de la mosaïque mondiale sans contrainte WorldPop pour 2020. Les données démographiques sont des données Africapolis masquées pour séparer les groupes de population en zones rurales, urbaines et totales. Le jeu de données raster final utilisé a une résolution de 0,083 degré (5 minutes d'arc)." } }, poverty: { title: { en: "Poverty", fr: "Pauvreté" }, text: { en: md`Poverty data is derived from the [World Bank global subnational poverty atlas](https://datacatalog.worldbank.org/search/dataset/0042041/global_subnational_poverty_atlas_gsap) dataset for a poverty line at $3.65 USD. The data used was at admin 1 level.`, fr: md`Les données sur la pauvreté sont tirées de [l'ensemble de données de l'atlas mondial de la pauvreté infranationale de la Banque mondiale](https://datacatalog.worldbank.org/search/dataset/0042041/global_subnational_poverty_atlas_gsap) pour un seuil de pauvreté de 3,65 USD. Les données utilisées étaient au niveau admin 1.` } }, gdp: { title: { en: "GDP", fr: "PIB" }, text: { en: md`GDP data by sector from the World Bank World Development Indicators [“Value added” dataset](https://wdi.worldbank.org/table/4.2). This data is at the admin 0 (country) level.`, fr: md`Données du PIB par secteur issues de [l'ensemble de données “Valeur ajoutée”](https://wdi.worldbank.org/table/4.2) des indicateurs de développement dans le monde de la Banque mondiale. Ces données sont au niveau admin 0 (pays).` } }, landUse: { title: { en: "Land Use", fr: "Utilisation des terres" }, text: { en: md`The land use data uses multiple sectors and subsectors from the FAO [country level land use area dataset](https://www.fao.org/faostat/en/#data/RL). This data is at the admin 0 (country) level.`, fr: md`Les données sur l'utilisation des terres utilisent plusieurs secteurs et sous-secteurs de [l'ensemble de données sur l'utilisation des terres au niveau des pays de la FAO](https://www.fao.org/faostat/en/#data/RL). Ces données sont au niveau admin 0 (pays).` } }, historicalFutureClimateHazards: { title: { en: "Historical and future climate hazards", fr: "Risques climatiques historiques et futurs" }, text: { en: md`[Climate hazards](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/hazard_catalog/catalog.json) were calculated using five datasets [CHIRPS](https://www.chc.ucsb.edu/data/chirps)/[CHIRTS](https://www.chc.ucsb.edu/data/chirtsdaily), [AgERA5](https://doi.org/10.24381/cds.6c68c9bb), [CMIP6 projections](https://github.com/SantanderMetGroup/ATLAS), and [crop calendars](https://zenodo.org/records/5062513). Using the R workflows mentioned below the hazard values were summarized for each 3 month window as well as the annual average and extracted by administrative boundaries to generate a tabular parquet dataset.`, fr: md`[Les risques climatiques](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/hazard_catalog/catalog.json) ont été calculés à l'aide de cinq ensembles de données : [CHIRPS](https://www.chc.ucsb.edu/data/chirps)/[CHIRTS](https://www.chc.ucsb.edu/data/chirtsdaily), [AgERA5](https://doi.org/10.24381/cds.6c68c9bb), [projections CMIP6](https://github.com/SantanderMetGroup/ATLAS) et [calendriers de cultures](https://zenodo.org/records/5062513). À l'aide des flux de travail R mentionnés ci-dessous, les valeurs de risque ont été résumées pour chaque fenêtre de 3 mois ainsi que la moyenne annuelle et extraites par limites administratives pour générer un ensemble de données de parquet tabulaire.` } }, exposureToRisk: { title: { en: "Exposure to risk (Value of Production)", fr: "Exposition au Risques (Valeur de Production)" }, text: { en: md`[Crop value of production](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/mapspam2017/mapspam2017_vop_parquet/mapspam2017_vop_parquet.json) data comes from MapSPAM 2017 V2r3 (Spatial Production Allocation Model) where production values are multiplied by country specific FAOstat international crop prices in (2005 International Dollars). An [international dollar](https://en.wikipedia.org/wiki/International_dollar) (2005) is a hypothetical unit of currency that has the same purchasing power parity as the U.S. dollar had in the United States in 2005, allowing for comparison of what consumers can buy in different countries with the same amount of money. This dataset is an updated version of MapSPAM specifically tailored for this project. It includes bug fixes and incorporates country-specific values of production instead of global averages. [Livestock value of production](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/GLW3_livestock/GLW3_livestock_vopPQ/GLW3_livestock_vopPQ.json) in 2005 International Dollars were provided by the authors of [Herrero et al. (2013)](https://www.pnas.org/doi/full/10.1073/pnas.1308149110/).`, fr: md`[Les données sur la valeur de la production des cultures](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/mapspam2017/mapspam2017_vop_parquet/mapspam2017_vop_parquet.json) proviennent de MapSPAM 2017 V2r3 (Modèle d'Allocation de Production Spatiale) où les valeurs de production sont multipliées par les prix internationaux des cultures de la FAOstat spécifiques à chaque pays en (dollars internationaux de 2005). Un [dollar international](https://en.wikipedia.org/wiki/International_dollar) (2005) est une unité monétaire hypothétique qui a la même parité de pouvoir d'achat que le dollar américain aux États-Unis en 2005, ce qui permet de comparer ce que les consommateurs peuvent acheter dans différents pays avec la même somme d'argent. Cet ensemble de données est une version mise à jour de MapSPAM spécialement conçue pour ce projet. Il comprend des corrections de bogues et incorpore des valeurs de production spécifiques à chaque pays au lieu de moyennes mondiales. [La valeur de la production animale](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/exposure_catalog/GLW3_livestock/GLW3_livestock_vopPQ/GLW3_livestock_vopPQ.json) en dollars internationaux de 2005 a été fournie par les auteurs de [Herrero et al. (2013)](https://www.pnas.org/doi/full/10.1073/pnas.1308149110/). ` } }, waterResources: { title: { en: "Water Resources", fr: "Ressources en eau" }, text: { en: md`Water resource data is calculated using data from [isimip3b](https://www.isimip.org/outputdata/?simulation_round=ISIMIP3b). The data used includes 3 hydrological models and 5 GCMs across the variables of discharge, evapotranspiration, and surface runoff. The raw data was resampled to a resolution of 0.083 (5 arc minutes) before use.`, fr: md`Les données sur les ressources en eau sont calculées à l'aide des données d'[isimip3b](https://www.isimip.org/outputdata/?simulation_round=ISIMIP3b). Les données utilisées comprennent 3 modèles hydrologiques et 5 modèles de circulation générale (GCM) pour les variables de débit, d'évapotranspiration et de ruissellement de surface. Les données brutes ont été rééchantillonnées à une résolution de 0,083 (5 minutes d'arc) avant utilisation.` } }, cropYieldProjections: { title: { en: "Crop Yield Projections", fr: "Projections de rendement des cultures" }, text: { en: md`Data for projected crop yield is from the Global Yield Gap Atlas (GYGA) and is [licensed accordingly](https://www.yieldgap.org/licensing). The data is free for non-commercial use and users are directed to the GYGA site to access and download this data. The data used in this notebook is at a resolution of Admin 2.`, fr: md`Les données sur le rendement projeté des cultures proviennent du Global Yield Gap Atlas (GYGA) et sont sous [licence en conséquence](https://www.yieldgap.org/licensing). Les données sont gratuites pour une utilisation non commerciale et les utilisateurs sont dirigés vers le site du GYGA pour accéder à ces données et les télécharger. Les données utilisées dans ce carnet sont à une résolution d'Admin 2.` } }, cropSuitabilityProjections: { title: { en: "Crop Suitability Projections", fr: "Projections sur l'adéquation des cultures" }, text: { en: md`Projected crop suitability is extracted from the CropSuite dataset (Zabel, F, Knüttel, M., & Poschlod, B., 2024). The dataset is the output of a fuzzy suitability model for 48 different crops under rainfed and irrigated conditions. The data was resampled to a resolution of 0.083 degrees.`, fr: md`L'adéquation des cultures projetées est extraite de l'ensemble de données CropSuite (Zabel, F, Knüttel, M. et Poschlod, B., 2024). L'ensemble de données est le résultat d'un modèle d'adéquation flou pour 48 cultures différentes dans des conditions pluviales et irriguées. Les données ont été rééchantillonnées à une résolution de 0,083 degré.` } }, meatAndMilkProjections: { title: { en: "Meat and Milk Projections", fr: "Projections sur la viande et le lait" }, text: { en: md`Meat and Milk projections are calculated using [Gridded Livestock of the World (GLW)](https://dataverse.harvard.edu/dataverse/glw), [Global Livestock Production systems](https://dataverse.harvard.edu/dataverse/GLPS), FAO economic deflator values, and economic loss data from Thornton et al. 2022`, fr: md`Les projections relatives à la viande et au lait sont calculées à l'aide du [Gridded Livestock of the World (GLW)](https://dataverse.harvard.edu/dataverse/glw), [des systèmes mondiaux de production animale](https://dataverse.harvard.edu/dataverse/GLPS), des valeurs du déflateur économique de la FAO et des données sur les pertes économiques de Thornton et al. 2022` } }, boundaries: { title: { en: "Boundaries", fr: "Délimitations" }, text: { en: md`[Administrative areas](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/boundary_catalog/geoBoundaries_SSA/collection.json) used in this notebook come from [geoBoundaries 6.0.0](https://github.com/wmgeolab/geoBoundaries). The gbHumanitarian boundaries are used and if not available then the gbOpen boundaries are substituted. The Basin Areas used in this notebook are from the [Global Runoff Data Centre (GRDC)](https://grdc.bafg.de/GRDC/EN/02_srvcs/22_gslrs/223_WMO/wmo_regions_node.html) WMO Basins and Sub-Basins V3 which includes and builds on data from HydroSHEDS dataset.`, fr: md`Les [zones administratives](https://radiantearth.github.io/stac-browser/#/external/digital-atlas.s3.amazonaws.com/stac/public_stac/boundary_catalog/geoBoundaries_SSA/collection.json) utilisées dans ce notebook proviennent de [geoBoundaries 6.0.0](https://github.com/wmgeolab/geoBoundaries). Les frontières gbHumanitarian sont utilisées et, si elles ne sont pas disponibles, les frontières gbOpen sont substituées. Les zones de bassin utilisées dans ce carnet proviennent du [Centre mondial de données sur le ruissellement (GRDC)](https://grdc.bafg.de/GRDC/EN/02_srvcs/22_gslrs/223_WMO/wmo_regions_node.html) de l'OMM Bassins et sous-bassins V3, qui inclut et s'appuie sur les données de l'ensemble de données HydroSHEDS.` } } } }, methods: { title: { en: "Methods", fr: "Méthodologie" }, sections: { quickDemoEconFacts: { title: { en: "Quick Demographic/Economic Facts", fr: "Quelques faits démographiques et économiques" }, text: { en: md`The population raster was extracted to the country level using sum as the aggregation statistic to calculate the total urban and rural populations in each country. The poverty data was rasterized and extracted using the same method. No aggregation was needed for the GDP or land use statistics. The land use data was split into main and sub classes and converted to hectares. The country level percentages for each of the main land use classes, population, number of people in poverty, and GDP per sector were then calculated.`, fr: md`Le raster de population a été extrait au niveau national en utilisant la somme comme statistique d'agrégation pour calculer les populations urbaines et rurales totales dans chaque pays. Les données sur la pauvreté ont été rasterisées et extraites en utilisant la même méthode. Aucune agrégation n'a été nécessaire pour les statistiques du PIB ou de l'utilisation des terres. Les données sur l'utilisation des terres ont été divisées en classes principales et sous-classes, puis converties en hectares. Les pourcentages au niveau national pour chacune des principales classes d'utilisation des terres, de la population, du nombre de personnes vivant dans la pauvreté, et du PIB par secteur ont ensuite été calculés.` } }, annualSeasonalHazards: { title: { en: "Annual and Seasonal Hazards", fr: "Risques annuels et saisonniers" }, text: { en: md`A detailed and replicable R workflow for generating raw climate hazards data is available at the AdaptationAtlas/Hazards. A reproducible workflow to process and calculate the seasonal hazards can be found in a different repository. For each hazard and model we calculate summary statistics (mean, max, etc.) over each annual or 3 month time frame. Following this, we calculate the ensemble statistics across all of the available models.The climate anomaly is calculated using the deviation of the ensemble from the historical baseline.`, fr: md`Un flux de travail R détaillé et reproductible pour générer des données brutes sur les risques climatiques est disponible dans l'AdaptationAtlas/Hazards. Un flux de travail reproductible pour traiter et calculer les risques saisonniers est disponible dans un autre référentiel. Pour chaque risque et modèle, nous calculons des statistiques récapitulatives (moyenne, max, etc.) sur chaque période annuelle ou trimestrielle. Ensuite, nous calculons les statistiques d'ensemble sur tous les modèles disponibles. L'anomalie climatique est calculée en utilisant l'écart de l'ensemble par rapport à la ligne de base historique.` } }, cropLivestockExposure: { title: { en: "Crop and Livestock VOP Exposure", fr: "Exposition des cultures et du bétail aux VDP" }, text: { en: md`The process for calculating exposure to risk comprises several critical steps: 1. **Annual or Seasonal Summarization**: Raw hazard data is aligned with the [GGCMI Phase 3 crop calendar for maize](https://zenodo.org/records/5062513), then climate hazards are summarized at either annual or seasonal timescales. Future updates of the Atlas will incorporate crop-specific calendars. 2. **Occurrence**: For each season or year in a time series, we classify the risk of hazard occurrence (present/absent) across three severity levels (moderate, severe, extreme). Note, hazard severity is fixed to “severe” in this notebook. The specific hazards used in this analysis are: - **Dry** = number of soil water stress days [NDWS](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Severe risk = NDWS > 20 days (same across all crops and livestock). - **Heat (crops)** = days where maximum temperature > 35ºC [NTx35](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Severe risk = NTx35 > 20 days (same across all crops). - **Heat** (livestock) = thermal humidity index [THI](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Severe risk is livestock class specific, see Table 2 of [Thorton et al. 2021](https://doi.org/10.1111/gcb.15825). - **Wet** = number of waterlogging days [NDWL0](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Severe risk = NDWL0 > 5 days (same across all crops and livestock). - More details on hazard definitions and thresholds for severity classes can found in the atlas/hazards wiki. Future 2024 updates of the Atlas will add crop specific maximum temperature thresholds and a notebook will be created to allow users to explore hazard risks in more detail. 3. **Risk**: We average the classified hazards over the years or seasons encompassed by the time-series to obtain an estimation of occurrence risk. 4. **Compound Risks**: To assess the risk of compound hazards, we multiply the risks associated with dry, heat, and wet conditions. This multiplication highlights areas where multiple hazards may coincide, posing greater risk to agricultural outputs. 5. **Integration with Economic Data**: Finally, we multiply the compound crop hazard risk data with crop value of production data. For example, if a crop is exposed to a hazard in 50% of growing seasons and the value of production is $2M/year then, on average, $1M of production is exposed to the hazard. The exposed value of production is then extracted by administrative areas to indicate potential economic impacts on crop production. `, fr: md`Le processus de calcul des risques comprend plusieurs étapes critiques: 1. **Synthèse annuelle ou saisonnière**: Les données brutes sur les risques sont alignées sur le [calendrier des cultures de la phase 3 de GGCMI pour le maïs](https://zenodo.org/records/5062513), puis les risques climatiques sont résumés à l'échelle annuelle ou saisonnière. Les futures mises à jour de l'atlas intégreront des calendriers spécifiques aux cultures. 2. **Occurrence**: Pour chaque saison ou année d'une série temporelle, nous classons le risque d'occurrence de l'aléa (présent/absent) selon trois niveaux de gravité (modéré, grave, extrême). Dans ce notebook, la gravité des aléas est fixée à "grave". Les risques spécifiques utilisés dans cette analyse sont les suivants: - **Sec** = nombre de jours de stress hydrique du sol [NDWS](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Risque grave = NDWS > 20 jours (identique pour toutes les cultures et le bétail). - **Chaleur (cultures)** = jours où la température maximale est > 35ºC [NTx35](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Risque grave = NTx35 > 20 jours (identique pour toutes les cultures). - **Chaleur (bétail)** = indice d'humidité thermique [THI](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Le risque grave est spécifique à la classe de bétail, voir le tableau 2 de [Thorton et al. 2021](https://doi.org/10.1111/gcb.15825). - **Humidité** = nombre de jours d'engorgement [NDWL0](https://github.com/AdaptationAtlas/hazards/wiki/Hazards-definitions). Risque grave = NDWL0 > 5 jours (identique pour toutes les cultures et le bétail). - Pour plus de détails sur les définitions des risques et les seuils des classes de gravité, voir le wiki atlas/risques. Les futures mises à jour de l'atlas en 2024 ajouteront des seuils de température maximale spécifiques aux cultures et un notebook sera créé pour permettre aux utilisateurs d'explorer les risques de manière plus détaillée. 3. **Risque**: Nous faisons la moyenne des aléas classés sur les années ou les saisons concernées par la série chronologique afin d'obtenir une estimation du risque d'occurrence. 4. **Risques Composés**: Pour évaluer les risques composés, nous multiplions les risques associés aux conditions de sécheresse, de chaleur et d'humidité. Cette multiplication met en évidence les zones où des risques multiples peuvent coïncider, ce qui représente un risque plus important pour la production agricole. 5. **Intégration avec les Données Économiques**: Enfin, nous multiplions les données sur les risques composés pour les cultures par les données sur la valeur de la production des cultures. Par exemple, si une culture est exposée à un danger pendant 50 % des saisons de croissance et que la valeur de la production est de 2 millions de dollars par an, alors, en moyenne, 1 million de dollars de la production est exposée au danger. La valeur de la production exposée est ensuite extraite par zone administrative pour indiquer les impacts économiques potentiels sur la production agricole.` } }, climateChange: { title: { en: "Climate Change Impacts on Crops, Livestock, Water Resources, and Food Security", fr: "Impacts du changement climatique sur les cultures, l'élevage, les ressources en eau et la sécurité alimentaire" }, text: { en: md`The ISIMIP, GYGA, and CropSuite datasets were extracted by the necessary boundary datasets (Admin Boundaries for GYGA and CropSuite and Basins for ISIMIP). Summary statistics and histogram bins were then calculated using the values falling within each boundary. For ISIMIP and CropSuite, these values are based on the gridded raster values within each admin unit. These values are based on the contained admin 2 regions for GYGA as that is the lowest resolution data provided. Projected losses in Meat and Milk production were calculated using the workflow found [here](https://github.com/AdaptationAtlas/hazards_prototype/blob/19974511da331840f2e52938daee365cabcbafa1/R/livestock_heat_impacts.R). First, the economic loss values are adjusted for inflation from 2005 to 2020 using economic deflator ratios from FAO. Following this, livestock distribution for each production system and country is extracted using admin boundaries and GLPS maps. These data are merged with the dry matter intake and economic losses to reflect losses proportionate to the cattle distribution across regions and production systems.`, fr: md`Les ensembles de données ISIMIP, GYGA et CropSuite ont été extraits par les ensembles de données de limites nécessaires (limites administratives pour GYGA et CropSuite et bassins pour ISIMIP). Les statistiques récapitulatives et les compartiments d'histogramme ont ensuite été calculés à l'aide des valeurs comprises dans chaque limite. Pour ISIMIP et CropSuite, ces valeurs sont basées sur les valeurs raster quadrillées dans chaque unité administrative. Ces valeurs sont basées sur les 2 régions administratives contenues pour GYGA car il s'agit des données de résolution les plus basses fournies. Les pertes projetées dans la production de viande et de lait ont été calculées à l'aide du flux de travail trouvé ici. Tout d'abord, les valeurs des pertes économiques sont ajustées en fonction de l'inflation de 2005 à 2020 à l'aide des ratios de déflateur économique de la FAO. Ensuite, la répartition du bétail pour chaque système de production et pays est extraite à l'aide des limites administratives et des cartes GLPS. Ces données sont fusionnées avec l'apport en matière sèche et les pertes économiques pour refléter les pertes proportionnelles à la répartition du bétail dans les régions et les systèmes de production.` } } } } }; }5a:[{"id":"5db4be91a234b7dafdcc70cc7c99a81220c13af27958fc132a372e44f91bda43c0c1c89702f46784af6eb15e280105c507240ed173a026649f3d6c5b061b97ef","href":"https://static.observableusercontent.com/files/5db4be91a234b7dafdcc70cc7c99a81220c13af27958fc132a372e44f91bda43c0c1c89702f46784af6eb15e280105c507240ed173a026649f3d6c5b061b97ef","name":"asData_raw.json","type":"application/json","lastModified":1752059041101.122,"size":50991},{"id":"4c6c3520c6c54752be0b2ddc86befe82153c54739bb40dd43243733c28954ad63ae6971b6e5b8998559a038fc9d0460559869a82f0c1881bc4bb57e40b1833a4","href":"https://static.observableusercontent.com/files/4c6c3520c6c54752be0b2ddc86befe82153c54739bb40dd43243733c28954ad63ae6971b6e5b8998559a038fc9d0460559869a82f0c1881bc4bb57e40b1833a4","name":"formulate-a-climate-rationale.webp","type":"image/webp","lastModified":1750929142769.586,"size":796340},{"id":"3a46eab102a6201e11b449c24fc7b5d95cc375146288c96e3d8b7d6aaeb908585507f698f6a0be75046ebefd3b0d40069a029769624d047fe37294a400f5184f","href":"https://static.observableusercontent.com/files/3a46eab102a6201e11b449c24fc7b5d95cc375146288c96e3d8b7d6aaeb908585507f698f6a0be75046ebefd3b0d40069a029769624d047fe37294a400f5184f","name":"abAtlas-td-translation-data-v3@3.json","type":"application/json","lastModified":1750929142769.586,"size":44267},{"id":"a07e0f6770fee6bc2c7d844b81db0a1d18a6979d1c4282a6420a7e05b0e0d92cecc9c3fe5d90a23d9f17e1b8a95682b678d1cb5facc6e88d4e934b607b7b1326","href":"https://static.observableusercontent.com/files/a07e0f6770fee6bc2c7d844b81db0a1d18a6979d1c4282a6420a7e05b0e0d92cecc9c3fe5d90a23d9f17e1b8a95682b678d1cb5facc6e88d4e934b607b7b1326","name":"formulate-a-climate-rationale-fr.webp","type":"image/webp","lastModified":1750929142769.586,"size":794702},{"id":"4ccae1b2a0237a073dea359962ed5d3c0824453f241487cdb4550c9d930e2fd7c07dd8618ae70a1b28dc204ead093c946db482617a2f63b5201fc55408225a1e","href":"https://static.observableusercontent.com/files/4ccae1b2a0237a073dea359962ed5d3c0824453f241487cdb4550c9d930e2fd7c07dd8618ae70a1b28dc204ead093c946db482617a2f63b5201fc55408225a1e","name":"ptot_na.json","type":"application/json","lastModified":1750933249169.853,"size":410495},{"id":"151f8d70bf1e8c88dbe2bb49fd8f83b9cbe8fadc3120fb88955e972b05366bf2b6e8a840885bf24ed8544d116c4ba9d68eec4d4f718c1d984712b1de5a8621e6","href":"https://static.observableusercontent.com/files/151f8d70bf1e8c88dbe2bb49fd8f83b9cbe8fadc3120fb88955e972b05366bf2b6e8a840885bf24ed8544d116c4ba9d68eec4d4f718c1d984712b1de5a8621e6","name":"fst_plotDataAll.json","type":"application/json","lastModified":1751985874502.65,"size":52895},{"id":"f152b5eead0f9a5fe8606e209adaee3c84c057793be467475d53f273d2a20907d8d08656d4d5b9aa33756196e7265ea03c754619937fac3d51be9b9e8e01ccba","href":"https://static.observableusercontent.com/files/f152b5eead0f9a5fe8606e209adaee3c84c057793be467475d53f273d2a20907d8d08656d4d5b9aa33756196e7265ea03c754619937fac3d51be9b9e8e01ccba","name":"disputed_areas.csv","type":"text/csv","lastModified":1751989834487.736,"size":411}] 2d:{"title":"Formulate a Climate Rationale for Projects","cells":[{"id":6769,"value":"# Formulate a Climate Rationale for Projects","pinned":false,"hidden":false,"mode":"md"},{"id":0,"value":"heroImage = {\n const imgSource = {\n en: FileAttachment(\"formulate-a-climate-rationale.webp\").url(),\n fr: FileAttachment(\"formulate-a-climate-rationale-fr.webp\").url(),\n };\n const imgUrl = _lang(imgSource);\n return htl.html`

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md`${_lang(nbText.sections.keyFacts.introText)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2291,"value":"// format admin selections\n// bind to appendix inputs, add template to format\nfstCountrySelect = Inputs.form(\n [\n Inputs.bind(\n Inputs.select(asData_admin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof as_i_admin0\n ),\n // Inputs.bind(\n // Inputs.select(asData_admin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n // viewof as_i_admin1\n // ),\n ],\n {\n template: inputTemplate()\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":328,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":335,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":338,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"fstInsightHeader = {\n const selection = fst_selections.admin0;\n const text = _lang(nbText.sections.quickInsightsGeneral.fHeader)({\n selection: selection.admin0_name\n });\n return md`### ${text}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2880,"value":"fstInsight1 = {\n const country = fst_selections.admin0.admin0_name;\n const iso3 = fst_selections.admin0.iso3;\n const countryTranslated = Lang.toSentenceCase(_lang(country_translations[iso3].article1));\n const dataPopulation = fst_data_pop_wide[0];\n const totalPop = dataPopulation.population_rural_pop + dataPopulation.population_urban_pop;\n const ruralPop = dataPopulation.population_rural_pop;\n const ruralPerc = dataPopulation.percentage_rural_pop / 100;\n\n const formatPop = formatNumCompactSigFigs();\n const formatPerc = d3.format(\".1%\")\n\n const i = {\n country: countryTranslated,\n popTotal: formatPop(totalPop),\n popRural: formatPop(ruralPop),\n percRural: formatPerc(ruralPerc)\n }\n\n const insight = _lang(nbText.sections.keyFacts.insights.pop)({\n country: i.country,\n popTotal: i.popTotal,\n popRural: i.popRural,\n percRural: i.percRural\n })\n \n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2879,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":2885,"value":"fstInsight3 = {\n const country = fst_selections.admin0.admin0_name;\n const iso3 = fst_selections.admin0.iso3;\n const countryTranslated1 = _lang(country_translations[iso3].article1);\n const countryTranslated3 = _lang(country_translations[iso3].article3);\n \n const data = fst_data_agricultureGdp;\n const rank = data.rank;\n\n const i = {\n rank: numberToOrdinal(rank, {lang: language.key, gender: \"m\"}),\n country1: countryTranslated1,\n country3: countryTranslated3,\n }\n\n if (!data.gdp || data.percentage === null) {\n return _lang(nbText.sections.keyFacts.insights.gdp.missing)({\n country: i.country1\n })\n } else {\n return _lang(nbText.sections.keyFacts.insights.gdp.main)({\n country: i.country3,\n rank: i.rank\n });\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3131,"value":"fstInsight4 = {\n const country = fst_selections.admin0.admin0_name;\n const iso3 = fst_selections.admin0.iso3;\n const countryTranslated = _lang(country_translations[iso3].article2);\n const data = fst_plotData.land;\n const maxLand = d3.max(data, d => d.ha);\n const mostLandUse = data.filter((d) => d.ha == maxLand)[0];\n\n // return mostLandUse\n\n const formatHa = formatNumCompactSigFigs();\n const formatPerc = d3.format(\".1%\")\n\n // const groupTranslated = mostLandUse.group;\n const groupTranslated = _lang(td.land_group.values?.[mostLandUse.group]);\n \n const i = {\n country: countryTranslated,\n type: groupTranslated,\n ha: formatHa(mostLandUse.ha),\n perc: formatPerc(mostLandUse.percentage / 100)\n }\n\n const insight = _lang(nbText.sections.keyFacts.insights.land)({\n country: i.country,\n type: i.type,\n ha: i.ha,\n perc: i.perc\n })\n \n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":357,"value":"fstInsight5Intro = {\n const country = fst_selections.admin0.admin0_name;\n const iso3 = fst_selections.admin0.iso3;\n const countryTranslated = _lang(country_translations[iso3].article2);\n\n const insight = _lang(nbText.sections.keyFacts.insights.landTable.intro)({\n country: countryTranslated\n });\n\n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":148,"value":"wsBanner = md`# ${_lang(nbText.sections.recentChangesClimateIndicators.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":157,"value":"wsIntro = md`${_lang(nbText.sections.recentChangesClimateIndicators.introText)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2412,"value":"wsGeoSelects = // format admin selections\n// bind to appendix inputs, add template to format\nInputs.form(\n [\n Inputs.bind(\n Inputs.select(asData_admin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof as_i_admin0\n ),\n Inputs.bind(\n Inputs.select(asData_admin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n viewof as_i_admin1\n ),\n ],\n {\n template: inputTemplate()\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":574,"value":"wsSeasonVarSelects = Inputs.form([viewof ws_iSelectSeason, viewof ws_iSelectVariable], {\n template: inputTemplate()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":573,"value":"wsCheckboxes = Inputs.form([viewof ws_iToggleAnomaly, viewof ws_iToggleLine], {\n template: inputTemplate()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":397,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3343,"value":"buildInlineDownloadButton(ws_plotData, _lang(download_translation))","pinned":false,"hidden":false,"mode":"ojs"},{"id":469,"value":"wsInsightBanner = {\n const selection = getAdmin0WithAdmin1Parens();\n const text = _lang(nbText.sections.quickInsightsGeneral.fHeader)({\n selection: selection\n });\n const seasonStr = _lang(nbText.legends.fSeasonString)({season: ws_selections.season.season_string});\n return md`### ${text}\n_${seasonStr}_ ${styling}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":468,"value":"wsInsight1 = {\n const selection = getAdmin0WithAdmin1Parens({articleField: \"article1\"});\n \n const _admin0 = ws_selections.admin0.admin0_name;\n const _admin1 = as_i_admin1.admin1_name;\n const _season = ws_iSelectSeason.season;\n\n const insightData = ws_insightData_tempTrends.find(\n d => d.admin0_name == _admin0 && d.admin1_name == _admin1 && d.season == _season\n )\n\n const iWarming = insightData?.value_diff_tot\n const iDecade = insightData?.value_diff_year * 10\n\n const format = d3.format('.2f')\n\n const i = {\n country: Lang.toSentenceCase(selection),\n temp: format(iWarming),\n tempDecade: format(iDecade)\n }\n\n const insight = _lang(nbText.sections.recentChangesClimateIndicators.insights.warming.fWarming)({\n country: i.country,\n temp: i.temp,\n tempDecade: i.tempDecade\n })\n\n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":467,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":466,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2918,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"plcBanner = md`# ${_lang(nbText.sections.futureClimateProjections.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":182,"value":"plcIntro = md`${_lang(nbText.sections.futureClimateProjections.introText)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2440,"value":"plcGeoSelects = // format admin selections\n// bind to appendix inputs, add template to format\nInputs.form(\n [\n Inputs.bind(\n Inputs.select(asData_admin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof as_i_admin0\n ),\n Inputs.bind(\n Inputs.select(asData_admin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n viewof as_i_admin1\n ),\n ],\n {\n template: inputTemplate()\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":588,"value":"plcSeasonVarSelects = Inputs.form([viewof plc_iSelectSeason, viewof plc_iSelectVariable], {\n template: inputTemplate()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"plcCheckboxes = Inputs.form([viewof plc_iToggleScenario, viewof plc_iToggleAnomaly, viewof plc_iToggleMinMax], { //viewof plc_iToggleTrend\n template: inputTemplate()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5866,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":3347,"value":"buildInlineDownloadButton(tf_data, _lang(download_translation))","pinned":false,"hidden":false,"mode":"ojs"},{"id":499,"value":"plcInsightBanner = {\n const selection = getAdmin0WithAdmin1Parens();\n const text = _lang(nbText.sections.quickInsightsGeneral.fHeader)({\n selection: selection\n });\n const season = plc_iSelectSeason\n const seasonStr = _lang(nbText.legends.fSeasonString)({season: plc_iSelectSeason.season_string})\n return md`### ${text}\n_${seasonStr}_`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2926,"value":"plcInsight1 = {\n const selectedRegion = getAdmin0WithAdmin1Parens({articleField: \"article1\"});\n const data = plc_insights.filter(d => d.hazard == 'TAVG');\n const findScenario = (scenario, df = data, scenarioField = \"scenario\") => {\n return df.find((x) => x[scenarioField] == scenario);\n };\n\n const trajectoryHigh = findScenario(\"ssp370\")?.mean_anomaly.toFixed(2);\n const trajectoryModerate = findScenario(\"ssp245\")?.mean_anomaly.toFixed(2);\n const trajectoryLow = findScenario(\"ssp126\")?.mean_anomaly.toFixed(2);\n\n const i = {\n selectedRegion: Lang.toSentenceCase(selectedRegion),\n trajectoryHigh: trajectoryHigh,\n trajectoryModerate: trajectoryModerate,\n trajectoryLow: trajectoryLow,\n }\n\n const insight = _lang(nbText.sections.futureClimateProjections.insights.fWarming)({\n selectedRegion: i.selectedRegion,\n trajectoryHigh: i.trajectoryHigh,\n trajectoryModerate: i.trajectoryModerate,\n trajectoryLow: i.trajectoryLow,\n })\n\n return insight\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":497,"value":"plcInsight2 = {\n const selectedRegion = getAdmin0WithAdmin1Parens({articleField: \"article1\"});\n const data = plc_insights.filter((d) => d.hazard == \"TAVG\");\n const findScenario = (scenario, df = data, scenarioField = \"scenario\") => {\n return df.find((x) => x[scenarioField] == scenario);\n };\n\n const trajectory = findScenario(\"ssp370\");\n\n const i = {\n selectedRegion: selectedRegion,\n min: trajectory?.min_anomaly,\n max: trajectory?.max_anomaly\n };\n\n const insight = _lang(nbText.sections.futureClimateProjections.insights.fUncertainty)({\n selectedRegion: i.selectedRegion,\n max: i.max,\n min: i.min\n })\n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3171,"value":"plcInsight3 = {\n // combined precipitation insights\n const projection = plc_insight_makePrecipicationProjections();\n const reduction = plc_insight_makePrecipicationRainfall();\n\n return md`${projection} ${reduction}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2227,"value":"plcInsight4 = {\n const data = plc_insight_heatStress;\n const crop2 = data.ntx.find(d => d.scenario == 'ssp245')\n const crop5 = data.ntx.find(d => d.scenario == 'ssp585')\n const livestock2 = data.thi.find(d => d.scenario == 'ssp245')\n const livestock5 = data.thi.find(d => d.scenario == 'ssp585')\n\n const formatPerc = (d) => d3.format(\".1f\")(d) + '%'\n \n const i = {\n crop2: formatPerc(crop2?.perc_area_total),\n crop5: formatPerc(crop5?.perc_area_total),\n livestock2: formatPerc(livestock2?.perc_area_total),\n livestock5: formatPerc(livestock5?.perc_area_total),\n }\n\n const insight = _lang(nbText.sections.futureClimateProjections.insights.fHeatStress)({\n crop2: i.crop2,\n crop5: i.crop5,\n livestock2: i.livestock2,\n livestock5: i.livestock5,\n })\n\n return insight\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":192,"value":"ehmBanner = md`# ${_lang(nbText.sections.cropLivestockHazardExposure.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"ehmIntro = md`${_lang(nbText.sections.cropLivestockHazardExposure.introText)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2448,"value":"ehmGeoSelects = // format admin selections\n// bind to appendix inputs, add template to format\nInputs.form(\n [\n Inputs.bind(\n Inputs.select(asData_admin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof as_i_admin0\n ),\n Inputs.bind(\n Inputs.select(asData_admin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n viewof as_i_admin1\n ),\n ],\n {\n template: inputTemplate()\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":601,"value":"ehmHazScenarioSelects = Inputs.form([viewof ehm_iHazard, viewof ehm_iScenario], {\n template: inputTemplate()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":440,"value":"viewof ehm_iSortY = {\n const lookup = (d) => _lang(nbText.sections.cropLivestockHazardExposure.inputs.radioSortYCrops.data?.[d]);\n const data = [\n { key: \"alpha\", label: lookup(\"alpha\"), sort: { y: \"y\", reverse: false } },\n { key: \"vop\", label: lookup(\"total\"), sort: { y: \"fill\", reverse: true } }\n ];\n return Inputs.radio(data, {\n label: _lang(nbText.sections.cropLivestockHazardExposure.inputs.radioSortYCrops.label),\n format: (x) => x.label,\n value: data.find((x) => x.key === \"vop\")\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":456,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3351,"value":"buildInlineDownloadButton(ehm_prepData, _lang(download_translation))","pinned":false,"hidden":false,"mode":"ojs"},{"id":455,"value":"ehmInsightBanner = {\n const selection = getAdmin0WithAdmin1Parens();\n const text = _lang(nbText.sections.quickInsightsGeneral.fHeader)({\n selection: selection\n });\n return md`### ${text}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":454,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":453,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"ccBanner = md`# ${_lang(nbText.sections.climateChangeImpacts.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"ccIntro = md`${_lang(nbText.sections.climateChangeImpacts.introText)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2247,"value":"// pick a sector\nviewof cc_iSector = Inputs.select(cc_sectors, {\n label: _lang(nbText.sections.climateChangeImpacts.inputs.selectSector.label),\n format: (x) => x.label,\n value: cc_sectors.find(x => x.key == 'crop_suit')\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2459,"value":"ccGeoSelects = // format admin selections\n// bind to appendix inputs, add template to format\n// SPECIAL: the admin1 is determined by the sector selection\n{\n const admin0 = Inputs.bind(\n Inputs.select(asData_admin0, {\n label: adminRegions.labels.admin0,\n format: (x) => x.label\n }),\n viewof as_i_admin0\n );\n const admin1Standard = Inputs.bind(\n Inputs.select(asData_admin1, {\n label: adminRegions.labels.admin1,\n format: (x) => x.label\n }),\n viewof as_i_admin1\n );\n const admin1Basins = Inputs.bind(\n Inputs.select(cc_qAdmin1Basins, {\n label: \"Basin\",\n format: (x) => x.label\n }),\n viewof cc_i_admin1Basins\n );\n\n let admin1 = cc_iSector.key == \"water\" \n ? admin1Basins\n : admin1Standard\n \n return Inputs.form([admin0, admin1], {\n template: inputTemplate()\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3317,"value":"viewof scenario_form = {\n let temp_names = { rfd: \"Rainfed\", irr: \"Irrigated\" };\n let elements = [\n Inputs.bind(\n Inputs.select(cc_qScenarioTimeframe, {\n label: _lang(\n nbText.sections.climateChangeImpacts.inputs.selectScenario.label\n ),\n format: (x) => x.label\n }),\n viewof cc_iScenario\n )\n ];\n if (cc_iSector.key === \"crop_yield\") {\n elements.push(\n Inputs.select([\"irr\", \"rfd\"], {\n label: _lang(\n nbText.sections.climateChangeImpacts.insights.cyChanges.table.cols\n .yield_type\n ),\n format: (v) =>\n _lang(\n nbText.sections.climateChangeImpacts.inputs.selectSector.data\n .crop_yield.indicator[v]\n )\n })\n );\n }\n return Inputs.form(elements, { template: inputTemplate() });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3316,"value":"Climate_summaryTable = {\n let scaleValues = cc_iSector.key === \"water\" ? false : true;\n return generate_table(cc_plotData, cc_iSector.key, scaleValues);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3355,"value":"buildInlineDownloadButton(cc_plotData, _lang(download_translation))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2287,"value":"ccInsightBanner = {\n const showParens = cc_iSector.key == \"water\" ? false : true // only admin0 for water (by basins)\n const selection = getAdmin0WithAdmin1Parens({showParens});\n const text = _lang(nbText.sections.quickInsightsGeneral.fHeader)({\n selection: selection\n });\n return md`### ${text}`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2986,"value":"ccInsightBody = {\n // show insights based on selected sector\n const sector = cc_iSector.key\n // water resources (based on basins)\n if (sector == \"water\") return cc_showCCInsights_water()\n // non-water (based on regions)\n else if (sector == \"crop_suit\") return cc_showCCInsights_cropSuit()\n else if (sector == \"crop_yield\") return cc_showCCInsights_cropYield()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":217,"value":"summaryBanner = md`# ${_lang(nbText.sections.summary.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3164,"value":"summarySelects = // format admin selections\n// bind to appendix inputs, add template to format\nInputs.form(\n [\n Inputs.bind(\n Inputs.select(asData_admin0, { label: adminRegions.labels.admin0, format: x => x.label }),\n viewof as_i_admin0\n ),\n Inputs.bind(\n Inputs.select(asData_admin1, { label: adminRegions.labels.admin1, format: x => x.label }),\n viewof as_i_admin1\n ),\n ],\n {\n template: inputTemplate()\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":220,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":228,"value":"msBanner = md`# ${_lang(nbTextMethods.title)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3114,"value":"ms_s_banner = getMethodTitle(nbTextMethods.sources)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3113,"value":"ms_s_pop = getMethodSection(nbTextMethods.sources.sections.population)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3112,"value":"ms_s_pov = getMethodSection(nbTextMethods.sources.sections.poverty)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3111,"value":"ms_s_gdp = getMethodSection(nbTextMethods.sources.sections.gdp)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3110,"value":"ms_s_land = getMethodSection(nbTextMethods.sources.sections.landUse)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3109,"value":"ms_s_historicalHazards = getMethodSection(nbTextMethods.sources.sections.historicalFutureClimateHazards)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3108,"value":"ms_s_exposure = getMethodSection(nbTextMethods.sources.sections.exposureToRisk)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3107,"value":"ms_s_water = getMethodSection(nbTextMethods.sources.sections.waterResources)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3106,"value":"ms_s_cropYieldRed = getMethodSection(nbTextMethods.sources.sections.cropYieldProjections)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3105,"value":"ms_s_cropSuit = getMethodSection(nbTextMethods.sources.sections.cropSuitabilityProjections)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3104,"value":"ms_s_meatMilkProj = getMethodSection(nbTextMethods.sources.sections.meatAndMilkProjections)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3103,"value":"ms_s_boundaries = getMethodSection(nbTextMethods.sources.sections.boundaries)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3102,"value":"ms_m_banner = getMethodTitle(nbTextMethods.methods)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3101,"value":"ms_m_facts = getMethodSection(nbTextMethods.methods.sections.quickDemoEconFacts)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3100,"value":"ms_m_hazards = getMethodSection(nbTextMethods.methods.sections.annualSeasonalHazards)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3099,"value":"ms_m_cropLivestockVop = getMethodSection(nbTextMethods.methods.sections.cropLivestockExposure)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3098,"value":"ms_m_climate = getMethodSection(nbTextMethods.methods.sections.climateChange)","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"---\n# Appendix\nThis large section includes all the cells that power the notebook.","pinned":false,"hidden":false,"mode":"md"},{"id":240,"value":"---\n## Data Setup","pinned":false,"hidden":false,"mode":"md"},{"id":246,"value":"db = await initDb(dbSetup)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6837,"value":"db2 = {\n document.body.style.cursor = \"wait\"\n let cli = await DuckDBClient.of()\n await cli.query(`CREATE VIEW test_hist AS SELECT * FROM read_parquet(\"${resolveWindowsCacheIssue(\"s3://digital-atlas/testData_atlas/tmp_climate-rational/haz_3months_adm_mean_historic_anomaly-historic_ensemble_seasons.parquet\")}\")`)\n document.body.style.cursor = \"\"\n return cli\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6966,"value":"import {resolveWindowsCacheIssue} from \"72281be2f13a0a72\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3396,"value":"import {initDb} from \"@adaptationatlas/ab-climate-rationale-data-sources\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3399,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":247,"value":"// lookup array of objects, find result\nfunction findObjectByField(array, {field=\"key\", value}={}) {\n return array.find(obj => obj[field] === value);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":252,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":2300,"value":"### Global admin selections","pinned":false,"hidden":false,"mode":"md"},{"id":3060,"value":"// specific locations to exclude\nasData_excludeLocations = [\n { admin0_name: \"Equatorial Guinea\", admin1_name: \"Annobón\" }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2298,"value":"asData_raw = FileAttachment(\"asData_raw.json\").json()\n// asData_raw = {\n// const table = dataTable.chirtsSimple.name;\n// const query = await db.query(`\n// with base as (\n// select admin0_name\n// , iso3\n// , admin1_name\n// , count(*)\n// from tbl_chirts_simple\n// group by 1, 2, 3\n// )\n \n// select admin0_name\n// , iso3\n// , admin1_name\n// from base\n// order by 1, 2, 3\n// `)\n// // filter out any specific locations\n// const specificLocations = asData_excludeLocations;\n// const tidy = query.filter(d => !specificLocations.some(x => x.admin0_name == d.admin0_name && x.admin1_name == d.admin1_name))\n\n// return tidy\n// }","pinned":false,"hidden":false,"mode":"ojs"},{"id":2297,"value":"asData_admin0 = {\n const raw = asData_raw.filter(d => d.admin1_name == null)\n // adding labels\n const formatted = raw.map(d => ({\n admin0_name: d.admin0_name,\n iso3: d.iso3,\n label: _lang(country_translations[d.iso3]?.general)\n }))\n return formatted.filter((d) => Object.values(countryToIso3).includes(d.iso3))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3667,"value":"countryToIso3 = new Object({\n \"Angola\": \"AGO\",\n \"Benin\": \"BEN\",\n \"Botswana\": \"BWA\",\n \"Burkina Faso\": \"BFA\",\n \"Burundi\": \"BDI\",\n \"Cameroon\": \"CMR\",\n \"Central African Republic\": \"CAF\",\n \"Chad\": \"TCD\",\n \"Congo - Brazzaville\": \"COG\",\n \"Congo - Kinshasa\": \"COD\",\n \"Côte d’Ivoire\": \"CIV\",\n \"Côte d'Ivoire\": \"CIV\",\n \"Djibouti\": \"DJI\",\n \"Equatorial Guinea\": \"GNQ\",\n \"Eritrea\": \"ERI\",\n \"Eswatini\": \"SWZ\",\n \"Ethiopia\": \"ETH\",\n \"Gabon\": \"GAB\",\n \"Gambia\": \"GMB\",\n \"Ghana\": \"GHA\",\n \"Guinea\": \"GIN\",\n \"Guinea-Bissau\": \"GNB\",\n \"Kenya\": \"KEN\",\n \"Lesotho\": \"LSO\",\n \"Liberia\": \"LBR\",\n \"Madagascar\": \"MDG\",\n \"Malawi\": \"MWI\",\n \"Mali\": \"MLI\",\n \"Mauritania\": \"MRT\",\n \"Mozambique\": \"MOZ\",\n \"Namibia\": \"NAM\",\n \"Niger\": \"NER\",\n \"Nigeria\": \"NGA\",\n \"Rwanda\": \"RWA\",\n \"Senegal\": \"SEN\",\n \"Sierra Leone\": \"SLE\",\n \"Somalia\": \"SOM\",\n \"South Africa\": \"ZAF\",\n \"South Sudan\": \"SSD\",\n \"Sudan\": \"SDN\",\n \"Tanzania\": \"TZA\",\n \"Togo\": \"TGO\",\n \"Uganda\": \"UGA\",\n \"Zambia\": \"ZMB\",\n \"Zimbabwe\": \"ZWE\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2296,"value":"asData_admin1 = {\n const raw = asData_raw.filter(d => d.admin0_name == as_i_admin0.admin0_name)\n // adding labels\n const formatted = raw.map(d => ({\n admin0_name: d.admin0_name,\n admin1_name: d.admin1_name,\n label: d.admin1_name == null ? refAdmin.all.admin1 : d.admin1_name,\n }))\n return formatted\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2295,"value":"// info for the admin selectors\nadminRegions = {\n return {\n labels: {\n admin0: Lang.toSentenceCase(\n _lang(nbText.legends.adminSelections.country)\n ),\n admin1: Lang.toSentenceCase(\n _lang(nbText.legends.adminSelections.region)\n )\n },\n allLabels: {\n admin1: _lang(nbText.legends.adminSelections.allRegions),\n }\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2293,"value":"viewof as_i_admin0 = {\n return Inputs.select(asData_admin0, { label: _lang(nbText.legends.adminSelections.country), format: x => x.label });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2292,"value":"viewof as_i_admin1 = {\n return Inputs.select(asData_admin1, { label: _lang(nbText.legends.adminSelections.region), format: x => x.label });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"### General","pinned":false,"hidden":false,"mode":"md"},{"id":262,"value":"// alphabetic sort, nulls first\nsortByField = (a,b, {field}={}) => {\n // If a.name is null and b.name is not null, a should come first\n if (a[field] === null && b[field] !== null) return -1;\n // If b.name is null and a.name is not null, b should come first\n if (b[field] === null && a[field] !== null) return 1;\n // If both are null, they are equal\n if (a[field] === null && b[field] === null) return 0;\n // If neither is null, compare alphabetically\n if (a[field] < b[field]) return -1;\n if (a[field] > b[field]) return 1;\n return 0;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":265,"value":"// create where clause that handles null cases\nsqlWhereNull = ({value, field}={}) => {\n return value === null \n ? `and ${field} is null`\n : `and ${field} = '${value}'`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"refAdmin = {\n const lookup = (field) => _lang(nbText.legends.adminSelections?.[field])\n return {\n all: {\n admin0: lookup(\"allCountries\"),\n admin1: lookup(\"allRegions\"),\n basin: lookup(\"allBasins\")\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":451,"value":"// get country or region name, passing object\ngetAdminSelection = (selections) => {\n if (selections.admin1.admin1_name) {\n return selections.admin1.admin1_name\n } else {\n return selections.admin0.admin0_name\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2833,"value":"getAdmin0WithAdmin1Parens = ({articleField=\"general\", showParens=true}={}) => {\n // getting admin0, with admin1 in parens\n // can specify admin0article lookup\n const a0 = as_i_admin0\n const a1 = as_i_admin1\n\n const a0WithArticle = _lang(country_translations?.[a0.iso3]?.[articleField])\n \n if (a1.admin1_name == null) return a0WithArticle\n if (!showParens) return a0WithArticle\n return `${a0WithArticle} (${a1.label})`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":572,"value":"inputTemplate = ({ gap = \"2em\" } = {}) => {\n // template for input display\n return (inputs) => html`

${inputs}

`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2482,"value":"function hideTableCheckboxes(tbl) {\n // hide the checkboxes from an Observable Table Input\n // Ex. tbl = Inputs.table(penguins)\n\n tbl.append(\n htl.html`\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3332,"value":"import {buildInlineDownloadButton} from \"1bda5f9b60c64223\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3405,"value":"ptot_na = FileAttachment(\"ptot_na.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6897,"value":"daaata = db.query(`\n select iso3\n , year\n , \"group\"\n , sub_group\n , ha\n from ${dataTable.factsSummaryTable_land.name}\n where 1=1\n -- and sub_group is not null\n and year = (select max(year) from ${dataTable.factsSummaryTable_land.name})\n`)","pinned":true,"hidden":false,"mode":"ojs"},{"id":6903,"value":"new Set(daaata.map((d) => d.year))","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W5a","stdlib":"1","titling":"auto"}