31:T54d,writeText({ "Bangladesh": "Bangladesh is dense. It's a small country, but more people live there than in Russia, and in a space one hundred times smaller. Its capital city, Dhaka, is one of the most population-dense cities in the world.

This concentration, combined with the prevalence of natural hazards in South Asia, make it especially vulnerable to climate change.", "Nepal": "Nepal is small, but it has a tremendous amount of geographic diversity — from the Himalayan Mountains in the north to the Gangetic Plains in the south.

This range of climates and terrains, combined with 17% of its people living in [multidimensional poverty](https://www.undp.org/nepal/publications/nepal-multidimensional-poverty-index-2021), make it uniquely challenging to adapt to a changing climate.", "Sri Lanka": "The small island nation of Sri Lanka is one of the hottest countries in the world.

Their complex, [monsoon-dominated climate](https://climateknowledgeportal.worldbank.org/country/sri-lanka/climate-data-historical) brings dramatic seasonal changes in precipitation, and 22% of their 22 million people remain [undernourished](https://climateknowledgeportal.worldbank.org/sites/default/files/2021-05/15507-WB_Sri%20Lanka%20Country%20Profile-WEB.pdf).

Altogether, Sri Lanka is highly vulnerable to climate change." })32:T5bd,densityPlot1 = { let height = 200 let arrowSize = 5.8 let arrowDy = -35 return addTooltips(Plot.plot({ width: maxWidth, height, marginTop: 40, x: { axis: "top", type: "log", tickFormat: d => { if (d == 1e4) {return "10k"} if (d == 1e5) {return "100k"} if (d == 1e6) {return "1M"} if (d == 1e7) {return "10M"} if (d == 1e8) {return "100M"} if (d == 1e9) {return "1B"} if (d == 1e10) {return "10B"} }, nice: true, label: "2022 Population →" }, y: { axis: null, domain: [arrowSize, 0] // size the arrow }, marks: [ Plot.dot(countryDensity.filter(d => d.pop2022 > 100e3), Plot.dodgeY({ x: d => d.pop2022, r: "area", anchor: "top", fill: d => d.country === pickCountryCaseStudy ? "#D7301F" : "#dfdfdf", title: d => `${d.country}\n${d3.format(".2s")(d.pop2022)} people\n${d3.format(".2s")(d.area)} km²`, })), Plot.text(countryDensity.filter(d => d.country === pickCountryCaseStudy), Plot.dodgeY({ x: d => d.pop2022, text: d => d.country, dy: -height, fill: "#D7301F", fontWeight: "bold" })), Plot.arrow(countryDensity.filter(d => d.country === pickCountryCaseStudy), { x1: "pop2022", x2: "pop2022", y1: 0, y2: 1, stroke: "#D7301F", dy: arrowDy }) ] })) }33:T43f,{ let suppress = dataDisastersSelected.length === 0 ? true : false return Plot.plot({ width: maxWidth, height: 90, marginRight: 0, marginLeft: 0, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, color: { legend: !suppress, domain: disasterColorDomain2, range: disasterColorRange2 }, marks: [ // missing data text Plot.frame({stroke: suppress ? "black" : null}), Plot.text(dataDisastersSelected.length === 0 ? [`Data Missing for ${pickCountryCaseStudy}`] : [], { x: 0.5, y: 0.5, text: d => d, fontSize: 24 }), Plot.barX(dataDisastersSelected, { x: "perc", y: "Name", fill: "category", order: "value", reverse: true }), Plot.textX(dataDisastersSelected, Plot.stackX({ x: "perc", y: "Name", text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", fontSize: 12, fontWeight: "bold", fill: "white" })) ] }) }34:T4f7,{ // trying to show missing data better let data = disasterSouthAsia.filter(d => d.country === pickCountryCaseStudyIso3) return addTooltips(Plot.plot({ width: maxWidth, height: 300, marginLeft: 100, insetLeft: 10, x: { grid: true, nice: true, tickFormat: "d", label: null }, y: { label: null, // type: "log" }, color: { domain: disasterColorDomain2, range: disasterColorRange2 }, marks: [ // missing data text Plot.text(data.length === 0 ? [`Data Missing for ${pickCountryCaseStudy}`] : [], { x: 0.5, y: 0.5, text: d => d, fontSize: 24 }), // main circle Plot.dot(data, { x: "Category", y: "type", fill: "type", opacity: 0.7, title: d => `${d.Category}: ${d.type}\n${d3.format(".2s")(d.count)} people`, r: "count", sort: {y: "r", reverse: true} }), // outer border Plot.dot(data, { x: "Category", y: "type", opacity: 1, title: d => `${d.Category}: ${d.type}\n${d3.format(".2s")(d.count)} people`, stroke: "type", r: "count", sort: {y: "r", reverse: true} }) ] })) }35:Tb00,{ let pickedQuestion = surveyQuestions[6] let data = dataClimateSurvey.filter(d => { return d["Question Text"] === pickedQuestion & [pickCountryCaseStudy].includes(d.country) }) // all values if (showSurveyDetail1) { let responseDomain = data.map(d => d.option) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginRight: 0, marginLeft: 0, height: 90, color: { legend: true, domain: data.map(d => d.option), range: fullColorRange.length > responseDomain.length ? [fullColorRange.slice(0, responseDomain.length - 1), fullColorRange.slice(-1)].flat() : fullColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ // missing data text Plot.text(data.length === 0 ? [`Data Missing for ${pickCountryCaseStudy}`] : [], { x: 0.5, y: 0.5, text: d => d, fontSize: 24 }), Plot.barX(data, { x: "perc", y: "country", fill: "option", // reverse: true, title: d => `${d3.format(".0f")(d["perc"])}% : ${d["option"]}` }), Plot.textX(data, Plot.stackX({ x: "perc", y: "country", text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", fontSize: 12, fontWeight: "bold", fill: "#fff" })) ] })) } // simplified version let consolidatedResponses = ["Very serious threat", "Somewhat serious threat"] // hard code smaller text for this response let colorDomain = pickCountryCaseStudy === "Sri Lanka" ? ["Very or somewhat serious threat", "Other"] : ["Very or somewhat serious threat", "Other Responses"] let simple = data let simplified = T.tidy( simple, T.mutate({option: d => consolidatedResponses.includes(d.option) ? colorDomain[0] : colorDomain[1]}), T.groupBy(["option", "Question Text"], [ T.summarize({perc: T.sum("perc")}) ]) ) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginLeft: 0, marginRight: 0, height: 80, color: { domain: colorDomain, range: consolidatedColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(simplified, { x: "perc", fill: "option", order: colorDomain }), Plot.textX(simplified, Plot.stackX({ x: "perc", text: d => `${d.option} (${d3.format(".0f")(d.perc)}%)`, fontWeight: "bold", fontSize: 14, fill: d => d.option === colorDomain[1] ? colorDomain[0] : colorDomain[1] })) ] })) }36:T98f,{ let pickedQuestion = surveyQuestions[4] let data = dataClimateSurvey.filter(d => { return d["Question Text"] === pickedQuestion & [pickCountryCaseStudy].includes(d.country) }) if (showSurveyDetail1) { let responseDomain = data.map(d => d.option) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginLeft: 0, marginRight: 0, height: 90, color: { legend: true, domain: data.map(d => d.option), range: fullColorRange.length > responseDomain.length ? [fullColorRange.slice(0, responseDomain.length - 1), fullColorRange.slice(-1)].flat() : fullColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(data, { x: "perc", y: "country", fill: "option", // reverse: true, title: d => `${d3.format(".0f")(d["perc"])}% : ${d["option"]}` }), Plot.textX(data, Plot.stackX({ x: "perc", y: "country", text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", fontSize: 12, fontWeight: "bold", fill: "#fff" })) ] })) } // simplified version let consolidatedResponses = ["A great deal", "A moderate amount"] let colorDomain = ["A great or moderate amount of harm", "Other responses"] let simple = data let simplified = T.tidy( simple, T.mutate({option: d => consolidatedResponses.includes(d.option) ? colorDomain[0] : colorDomain[1]}), T.groupBy(["option", "Question Text"], [ T.summarize({perc: T.sum("perc")}) ]) ) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginLeft: 0, marginRight: 0, height: 80, color: { domain: colorDomain, range: consolidatedColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(simplified, { x: "perc", fill: "option", order: colorDomain }), Plot.textX(simplified, Plot.stackX({ x: "perc", text: d => `${d.option} (${d3.format(".0f")(d.perc)}%)`, fontWeight: "bold", fontSize: 14, fill: d => d.option === colorDomain[1] ? colorDomain[0] : colorDomain[1] })) ] })) }37:T970,{ let pickedQuestion = surveyQuestions[11] let data = dataClimateSurvey.filter(d => { return d["Question Text"] === pickedQuestion & [pickCountryCaseStudy].includes(d.country) }) if (showSurveyDetail1) { let responseDomain = data.map(d => d.option) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginLeft: 0, marginRight: 0, height: 90, color: { legend: true, domain: responseDomain, range: fullColorRange.length > responseDomain.length ? [fullColorRange.slice(0, responseDomain.length - 1), fullColorRange.slice(-1)].flat() : fullColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(data, { x: "perc", y: "country", fill: "option", // reverse: true, title: d => `${d3.format(".0f")(d["perc"])}% : ${d["option"]}` }), Plot.textX(data, Plot.stackX({ x: "perc", y: "country", text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", fontSize: 12, fontWeight: "bold", fill: "#fff" })) ] })) } // simplified version let consolidatedResponses = ["Very high", "High"] let colorDomain = ["Very high or high priority", "Other responses"] let simple = data let simplified = T.tidy( simple, T.mutate({option: d => consolidatedResponses.includes(d.option) ? colorDomain[0] : colorDomain[1]}), T.groupBy(["option", "Question Text"], [ T.summarize({perc: T.sum("perc")}) ]) ) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginLeft: 0, marginRight: 0, height: 80, color: { domain: colorDomain, range: consolidatedColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(simplified, { x: "perc", fill: "option", order: colorDomain }), Plot.textX(simplified, Plot.stackX({ x: "perc", text: d => `${d.option} (${d3.format(".0f")(d.perc)}%)`, fontWeight: "bold", fontSize: 14, fill: d => d.option === colorDomain[1] ? colorDomain[0] : colorDomain[1] })) ] })) }38:Td7c,{ let pickedQuestion = surveyQuestions[14] let data = T.tidy( dataClimateSurvey.filter(d => { return d["Question Text"] === pickedQuestion & [pickCountryCaseStudy].includes(d.country) }), T.mutate({sort: (d) => joinGroupDomain.indexOf(d.option)}), T.arrange("sort") ) if (showSurveyDetail1) { let responseDomain = dataClimateSurvey.filter(d => { return d["Question Text"] === pickedQuestion & [pickCountryCaseStudy].includes(d.country) }).map(d => d.option) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginRight: 0, marginLeft: 0, height: 90, color: { legend: true, domain: [ "I am participating in an effort like this now", "I definitely would do it", "I probably would do it", "Not sure", "I probably would not do it", "I definitely would not do it", "Refused" ], range: fullColorRange.length > responseDomain.length ? [fullColorRange.slice(0, responseDomain.length - 1), fullColorRange.slice(-1)].flat() : fullColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(data, { x: "perc", y: "country", fill: "option", order: [ "I am participating in an effort like this now", "I definitely would do it", "I probably would do it", "Not sure", "I probably would not do it", "I definitely would not do it", "Refused" ], // reverse: true, title: d => `${d3.format(".0f")(d["perc"])}% : ${d["option"]}` }), Plot.textX(data, Plot.stackX({ x: "perc", y: "country", text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", fontSize: 12, fontWeight: "bold", fill: "#fff" })) ] })) } // simplified version let consolidatedResponses = ["I am participating in an effort like this now", "I definitely would do it", "I probably would do it"] let colorDomain = ["Participating now, would definitely join a group, or would probably join a group", "Other responses"] let simple = dataClimateSurvey.filter(d => d.country === pickCountryCaseStudy & d["Question Text"] === pickedQuestion) let simplified = T.tidy( simple, T.mutate({option: d => consolidatedResponses.includes(d.option) ? colorDomain[0] : colorDomain[1]}), T.groupBy(["option", "Question Text"], [ T.summarize({perc: T.sum("perc")}) ]) ) return addTooltips(Plot.plot({ caption: pickedQuestion, width: maxWidth, marginRight: 0, marginLeft: 0, height: 80, color: { domain: colorDomain, range: consolidatedColorRange }, x: { label: "", tickFormat: null, ticks: 0 }, y: { axis: null }, marks: [ Plot.barX(simplified, { x: "perc", fill: "option", order: colorDomain }), Plot.textX(simplified, Plot.stackX({ x: "perc", text: d => `${d.option} (${d3.format(".0f")(d.perc)}%)`, fontWeight: "bold", fontSize: 14, fill: d => d.option === colorDomain[1] ? colorDomain[0] : colorDomain[1] })) ] })) }39:T81f,{ let countryDomain = [pickCountryCaseStudy, pickCompareCountry1] let responseDomain = dataClimateSurvey.filter(d => { return d["Question Text"] === pickQuestion1 & [pickCountryCaseStudy].includes(d.country) }).map(d => d.option) return addTooltips(Plot.plot({ caption: pickQuestion1, marginLeft: 100, insetLeft: 10, width: maxWidth, height: 150, color: { legend: true, domain: pickQuestion1 === surveyQuestions[14] ? [ "I am participating in an effort like this now", "I definitely would do it", "I probably would do it", "Not sure", "I probably would not do it", "I definitely would not do it", "Refused" ] : responseDomain, range: fullColorRange.length > responseDomain.length ? [fullColorRange.slice(0, responseDomain.length - 1), fullColorRange.slice(-1)].flat() : fullColorRange }, x: { label: "Percent of Respondents (%) →" }, y: { label: null, domain: countryDomain }, marks: [ Plot.barX(dataClimateSurvey.filter(d => { return d["Question Text"] === pickQuestion1 & countryDomain.includes(d.country) }), { x: "perc", y: "country", fill: "option", order: pickQuestion1 === surveyQuestions[14] ? [ "I am participating in an effort like this now", "I definitely would do it", "I probably would do it", "Not sure", "I probably would not do it", "I definitely would not do it", "Refused" ] : null, title: d => `${d3.format(".0f")(d["perc"])}% : ${d["option"]}` }), // Plot.textX(dataClimateSurvey.filter(d => { // return d["Question Text"] === pickQuestion1 & ["Bangladesh"].includes(d.country) // }), Plot.stackX({ // x: "perc", // y: "country", // text: d => d.perc > 3 ? `${d3.format(".0f")(d.perc)}%` : "", // fontSize: 12, // fontWeight: "bold", // fill: "#fff" // })) ] })) }3a:T927,beadPlotRecent1 = addTooltips(Plot.plot({ nice: true, width: maxWidth, height: 500, x: { // domain: [0, 0.8], label: "Increased Readiness →" }, y: { // domain: [0, 0.8], reverse: true, label: "↑ Less Vulnerability" }, color: { label: null, domain: countryColorDomainFull, range: countryColorRangeFull }, marks: [ // Plot.frame(), Plot.text(pickCountries1.length == 0 ? ["Pick some countries below ↓"] : [], { x: 0.5, y: 0.5, text: d => d, fontSize: 24 }), // dotted line, start/end years Plot.arrow(bookendYearsRecent.filter(d => pickCountries1.map(d => d.ISO3).includes(d.ISO3)), { x1: "readiness_start", y1: "vulnerability_start", x2: "readiness_end", y2: "vulnerability_end", stroke: "Name", bend: true, title: d => `${d.Name}` }), // full timeseries trail Plot.line(dataMatrix.filter(d => pickCountries1.map(d => d.ISO3).includes(d.ISO3) & d.year > 2013), { x: "readiness", y: "vulnerability", // color by DMC stroke: "Name", opacity: 0.2, r: 5, curve: "catmull-rom", title: d => `${d.Name}` }), // year dots Plot.dot(dataMatrix.filter(d => d.year === sliderDateRangeBeadMaze & pickCountries1.map(d => d.ISO3).includes(d.ISO3) & d.year > 2013), { x: "readiness", y: "vulnerability", // color by DMC fill: "Name", opacity: 0.7, r: 5, title: d => `${d.Name}\n${d.year}\n${d3.format(".2f")(d.readiness)}/${d3.format(".2f")(d.vulnerability)}` }), // timeseries trail dots Plot.dot(dataMatrix.filter(d => pickCountries1.map(d => d.ISO3).includes(d.ISO3) & d.year > 2013), { x: "readiness", y: "vulnerability", // color by DMC fill: "Name", opacity: 0.2, r: 2, curve: "catmull-rom", title: d => `${d.Name}\n${d.year}` }), // country labels Plot.text(showCountryLabels ? dataMatrix.filter(d => d.year === 2020 & pickCountries1.map(d => d.ISO3).includes(d.ISO3)) : [], { x: "readiness", y: "vulnerability", // color by DMC fill: "Name", text: "Name", fontSize: 12, opacity: 0.9, r: 5, textAnchor: "start", dx: 10 }), ] }))3b:T997,{ let data = indicatorsFlat.filter(d => dmcIso3.includes(d.ISO3) & d.year == 2020 & d.Name === pickCountryCaseStudy & d.indicatorType === "vulnerability") let compareData = indicatorsFlat.filter(d => dmcIso3.includes(d.ISO3) & d.year == 2020 & d.Name === optionsDotPlot.pickCountryBySector & d.indicatorType === "vulnerability") return addTooltips(Plot.plot({ width: maxWidth, insetLeft: 10, marginLeft: 120, x: { domain: [0, 1], label: "Vulnerability →" }, y: { label: null, grid: true }, color: { domain: countryColorDomain, range: countryColorRange }, marks: [ // vulnerability score Plot.ruleX(vulnerability.filter(d => d.Name === pickCountryCaseStudy & d.year === 2020), { x: "vulnerability", stroke: "Name", strokeDasharray: [5, 5] }), // vulnerability score, compare Plot.ruleX(vulnerability.filter(d => d.Name === optionsDotPlot.pickCountryBySector & d.year === 2020), { x: "vulnerability", stroke: "grey", strokeDasharray: [5, 5] }), Plot.text(vulnerability.filter(d => d.Name === pickCountryCaseStudy & d.year === 2020), { x: "vulnerability", frameAnchor: "top", lineHeight: 1.2, dy: -32, fill: "Name", text: d => `Aggregate\nVulnerability Score (${d3.format(".2f")(d.vulnerability)})`, fontSize: 12, }), // comparison data Plot.dotX(compareData, Plot.groupY({x: "mean"}, { x: 'score', y: 'indicatorSector', fill: '#bbb', r: 5, // title: "Name" })), // average indicator value, chosen country Plot.dotX(data, Plot.groupY({x: "mean"}, { x: 'score', y: 'indicatorSector', fill: 'Name', r: 5, sort: {y: "x", reduce: "mean", reverse: true}, // title: "Name" })), // indicators Plot.dot(optionsDotPlot.showAllIndicators ? data : [], { x: 'score', y: 'indicatorSector', stroke: 'Name', r: 5, opacity: 0.8, title: d => `${d.indicatorName}`, sort: {y: "x", reduce: "mean", reverse: true} }), ] })) }3c:T557,
${writeText({ "Bangladesh": `These data reveal Bangladesh's vulnerability in areas like rural population and agricultural capacity, which have been tested by ${addNewTabLink("extreme flooding", "https://www.bbc.com/future/article/20201201-bangladesh-the-devastating-floods-essential-for-life")} in recent years.

By tracking these detailed stories, countries can find the most effective places to organize their adaptation and resilience efforts.`, "Nepal": `These data reveal Nepal's vulnerability in areas like climate change-induced disease preparedness and agricultural capacity, which have been tested by ${addNewTabLink("extreme flooding", "https://www.unicef.org/stories/flooding-affects-millions-bangladesh-india-and-nepal")} in recent years.

By tracking these detailed stories, countries can find the most effective places to organize their adaptation and resilience efforts.`, "Sri Lanka": `These data reveal Sri Lanka's vulnerability in areas like grain yields, road infrastructure, and water resource management, which have been tested by ${addNewTabLink("extreme flooding", "https://reliefweb.int/report/sri-lanka/sri-lanka-floods-final-report-dref-n-mdrlk012")} in recent years.

By tracking these detailed stories, countries can find the most effective places to organize their adaptation and resilience efforts.` })}3d:T812,writeText({ "Bangladesh": `Fortunately, countries worldwide are taking steps to become climate change-resilient. Bangladesh, for example, is experimenting with new ${addNewTabLink("crop types", "https://reliefweb.int/report/bangladesh/salt-solution-growing-salt-tolerant-vegetables-bangladesh")}, ${addNewTabLink("farming methods", "https://gca.org/these-farmers-in-bangladesh-are-floating-their-crops-to-adapt-to-climate-change/")}, and ${addNewTabLink("forecasting methods for storms", "https://www.newagebd.net/article/166819/bangladesh-gets-storm-lightning-forecast-tool")}, which will increase their resilience and readiness to adapt to the changing climate.`, "Nepal": `Fortunately, countries worldwide are taking steps to become climate change-resilient. Nepal, for example, is making strides with ${addNewTabLink("community-driven biodiversity conservation approaches", "https://www.worldwildlife.org/magazine/issues/spring-2017/articles/adapting-to-climate-change-in-nepal")} and ${addNewTabLink("climate-smart agriculture technologies", "https://cdkn.org/resource/policy-brief-climate-smart-agriculture-nepal?loclang=en_gb")}, which will increase their resilience and readiness to adapt to the changing climate.`, "Sri Lanka": `Fortunately, countries worldwide are taking steps to become climate change-resilient. Sri Lanka has embraced deep partnerships with ADB and USAID to enhance its capacity to endure environmental shocks and stresses. Through these partnerships, Sri Lanka is undergoing initiatives to ${addNewTabLink("modernize early warning systems", "https://www.worldbank.org/en/news/press-release/2021/09/20/sri-lanka-world-bank-sign-agreement-to-strengthen-climate-climate-resilience")} and ${addNewTabLink("scale up innovative management strategies", "https://www.usaid.gov/sri-lanka/climate-change-adaptation")}. As a result, Sri Lanka continues to make ${addNewTabLink( "good progress" , "https://www.adb.org/publications/sustainable-development-after-covid-19-sdg-bonds)")} towards increasing its resilience to climate change.` })3e:T952,--- ## Methods & Sources
### Data Links & Citation We utilized data from several key data sources for our analyses and are listed below. Links to these sources can also be found in-context below each chart throughout this notebook. **For data on natural disasters:**
"Bangladesh - Vulnerability." *Climate Change Knowledge Portal*, last accessed on Nov. 2022, (Source). **For data on population/areas, by country:**
"Countries by Population Density | Countries by Density 2022." *World Population Review*, last accessed on 3 Nov. 2022, (Source). **For data on sentiment toward climate change, by country:**
"Climate Change Opinion Survey." *Meta | Data For Good*, last accessed on 3 Nov. 2022, (Source). **For aggregate metric on ability to adapt to changing climates:**
"Country Index." *Notre Dame Global Adaptation Initiative*, last accessed on 3 Nov. 2022, (Source).
### Data Notes One particular note about a dataset that we wanted to call attention to is our limiting of the ND-GAIN timeseries. In the ND-GAIN index, one indicator for the readiness component is the "Ease of doing business index", which is made using World Bank Doing Business indicators. This indicator aims to determine how a country is "capable of attracting adaptation investment". There is a noticeable jump in values for many countries with this indicator, which causes the readiness component to vary significantly in the mid 2010s. This causes fluctuations in ND-GAIN readiness values and index scores, so we limited to the most recent years. The indicator has been discontinued and its removal/consolidation could help smooth out earlier timeseries data. Going forward, it would be beneficial to recalculate the scores to fix this discrepancy. 3f:Tf4a,
### Techniques & Process In our process, we use a lot of different methods, techniques, and approaches. Here is a quick glimpse into a few key areas of our thought process for this challenge. **Finding a holistic angle**
The first step we took was identifying a lens that differed from the more common climate change narrative centered around rising air temperatures. We looked at the problem from a variety of angles, big and small, and wanted to find data that took a more holistic lens, factoring in the multitude of ways a global problem like climate change affects many different parts of a country at risk. Through extensive research, this led us to the Notre Dame Global Adaptation Initiative (ND-GAIN) metric. We were interested in the resilience of a country, knowing that the countries most affected have unique stories for dealing with and adapting to the changing climate conditions. **Creating a suite of case studies**
Starting out, we had selected Bangladesh as a focus case study. Following a suggestion from a mentor, we expanded the list of case studies to other member countries in South Asia, where data was available across all sources — three total. Including the other member countries with missing data would have resulted in large gaps in the narrative. We enabled the ability to select a country case study and have all the narrative text and charts update accordingly. We also heard that Nepal is a country whose progress on increasing climate change resilience often gets overlooked, compared to other South Asian countries. To help increase its visibility and prominence in our submission, we now default to Nepal. **Deepening the context**
We know that intersections of data tell a more full picture, so we layered in additional datasets to include natural disasters (via Climate Change Knowledge Portal) and how people who are dealing with climate change are actually feeling (via Meta’s “Climate Change Opinion Survey”). **Visualizing, iterating, and empathizing**
We wanted to do the experimenting in Observable, so we explored and visualized the data with Observable Plot. A main driver behind this decision was the ability to iterate on the charts in real-time and quickly as a team. We know the power of data visualization comes from clear and compelling data communication, so we chose a combination of more traditional chart types (line charts, bar charts, etc), and some more visually compelling and experimental charts such as beeswarms and a “bead maze” plot that provide more nuanced looks into the data. Following a suggestion from a mentor, we added a bar chart made of [isotypes](https://en.wikipedia.org/wiki/Isotype_(picture_language) to add a more human-relatable view of the data. Through relatable iconography, we know visualizations can resonate deeply with users, and help to emphasize the people behind the numbers. Overall, we strove to consolidate complex data presentations into simple charts up-front, with the ability to reveal the underlying complexity on-demand through additional controls. **Stream-lining our existing processes**
We took this challenge as an opportunity to merge some of our internal processes we have set up to work effectively with Observable. Setting up a remote data repository on Bitbucket, we implemented a process to dynamically feed data to the Observable notebook via an API. As data sources changed or were updated, we used the data repository to import datasets versus reloading static file attachments. This import strategy made it easier to get data into Observable quickly, avoiding a more manual process updating data sources with external data wrangling. We ultimately published this as a [Bitbucket API in Observable](https://observablehq.com/@periscopic/bitbucket-api-in-observable) helper function to make it easier to fetch data from Bitbucket repositories.

40:T816,indicatorNaming = ({ id_food_01: "Projected change of cereal yields", id_food_02: "Projected population change", id_food_03: "Food import dependency", id_food_04: "Rural population", id_food_05: "Agriculture capacity", id_food_06: "Child malnutrition", id_wate_01: "Projected change of annual runoff", id_wate_02: "Projected change of annual groundwater recharge", id_wate_03: "Fresh water withdrawl rate", id_wate_04: "Water dependency ratio", id_wate_05: "Dam capacity", id_wate_06: "Access to reliable drinking water", id_heal_01: "Projected change of deaths from climate change induced diseases", id_heal_02: "Projected change in vectorborne diseases", id_heal_03: "Dependency on external resource for health services", id_heal_04: "Slum Population", id_heal_05: "Medical staff", id_heal_06: "Access to improved sanitation facilities", id_ecos_01: "Projected change of biome distribution", id_ecos_02: "Projected change of marine biodiversity", id_ecos_03: "Natural capital dependency", id_ecos_04: "Ecological footprint", id_ecos_05: "Protected biome", id_ecos_06: "Engagement in international environmental conventions", id_habi_01: "Projected change of warm periods", id_habi_02: "Projected change of flood hazard", id_habi_03: "Urban concentration", id_habi_04: "Age dependency ratio", id_habi_05: "Quality of trade and transport infrastructure", id_habi_06: "Paved roads", id_infr_01: "Projected change of hydropower generation capacity", id_infr_02: "Projected change of sea level rise impacts", id_infr_03: "Dependency on imported energy", id_infr_04: "Population living under 5m above sea level", id_infr_05: "Electricity access", id_infr_06: "Disaster preparedness", id_econ_01: "Doing business", id_gove_01: "Political stability and nonviolence", id_gove_02: "Control of corruption", id_gove_03: "Regulatory quality", id_gove_04: "Rule of law", id_soci_01: "Social inequality ", id_soci_02: "Ict infrastructure", id_soci_03: "Education", id_soci_04: "Innovation", })41:[{"id":"b8f862cd7033ff70cc927abcd66134390ec25945f0414877733a09181d036819b930c3d6bfdbf02c878fa12e0c214eaf89b1a3330af501fd646dc8fdedb3bc67","href":"https://static.observableusercontent.com/files/b8f862cd7033ff70cc927abcd66134390ec25945f0414877733a09181d036819b930c3d6bfdbf02c878fa12e0c214eaf89b1a3330af501fd646dc8fdedb3bc67","name":"rawReadiness.csv","type":"text/csv","lastModified":1667493755945.721,"size":92294},{"id":"d05784fd556d0384222f352c684c5095e8bc25199a049868d386b5d6d1a12499ce88d1544ad82585e28a059119da0d01d1939ad5c6bbc5214456db07bb227b1d","href":"https://static.observableusercontent.com/files/d05784fd556d0384222f352c684c5095e8bc25199a049868d386b5d6d1a12499ce88d1544ad82585e28a059119da0d01d1939ad5c6bbc5214456db07bb227b1d","name":"disasters.csv","type":"text/csv","lastModified":1667493755945.721,"size":1283},{"id":"013602798fdbc76eacf6e60a297b6d57d09e2c148bcec97b652cdd5605a725f4cf9af3dc3a890d6ae628f4d577cf91ae7e36324c52ee0ea32bcb48cfe17221fa","href":"https://static.observableusercontent.com/files/013602798fdbc76eacf6e60a297b6d57d09e2c148bcec97b652cdd5605a725f4cf9af3dc3a890d6ae628f4d577cf91ae7e36324c52ee0ea32bcb48cfe17221fa","name":"disasterSouthAsia.csv","type":"text/csv","lastModified":1667493755945.721,"size":18872},{"id":"0edaf84c71beac9099015703ec9df068a54ca22c1a6a7c49713745f32d255f781c01cf9d858f79ddf632b72fee4f9e9dd7f4ddb595c77859d699e709f763ab8f","href":"https://static.observableusercontent.com/files/0edaf84c71beac9099015703ec9df068a54ca22c1a6a7c49713745f32d255f781c01cf9d858f79ddf632b72fee4f9e9dd7f4ddb595c77859d699e709f763ab8f","name":"regionData.csv","type":"text/csv","lastModified":1667493755945.721,"size":20182},{"id":"7d3eb57c88919bc4758887a6828491d3b5afcf18234bb4b01ca799ad293e5b36f6e2b21d1a847414b36d6db24284d271cf90ccc38e8af51630a9a534cb54d635","href":"https://static.observableusercontent.com/files/7d3eb57c88919bc4758887a6828491d3b5afcf18234bb4b01ca799ad293e5b36f6e2b21d1a847414b36d6db24284d271cf90ccc38e8af51630a9a534cb54d635","name":"dataNdGain.csv","type":"text/csv","lastModified":1667493755945.721,"size":178942},{"id":"6d2efa23dd5e483041c9cd78f0d0356dc49b13820aa0112be1e76fa76373620447622ba437160486b5b07a0fecb7671a0b1b7b70227a32c8b2bad7c34f26eea8","href":"https://static.observableusercontent.com/files/6d2efa23dd5e483041c9cd78f0d0356dc49b13820aa0112be1e76fa76373620447622ba437160486b5b07a0fecb7671a0b1b7b70227a32c8b2bad7c34f26eea8","name":"countryDensity.csv","type":"text/csv","lastModified":1667493755945.721,"size":6848},{"id":"0db77d520c634f901d0c7a9f943b29d468ea522d0d298d564aacb14d514df9cd29a29aa77faa41fa9eef9620d932a6eeef17214178c68e561511e27dcaed6905","href":"https://static.observableusercontent.com/files/0db77d520c634f901d0c7a9f943b29d468ea522d0d298d564aacb14d514df9cd29a29aa77faa41fa9eef9620d932a6eeef17214178c68e561511e27dcaed6905","name":"climate_change_opinion_survey_2022_aggregated.xlsx","type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","lastModified":1667493755945.721,"size":198960},{"id":"87c2c55ea4a6d7afdea30affd31cb1a9aa6ab52cd4f4b154a2081c3ec862195b9abf7ecf6170b84dc2a28a45972be69b00149073698803c97cd3e870567daa32","href":"https://static.observableusercontent.com/files/87c2c55ea4a6d7afdea30affd31cb1a9aa6ab52cd4f4b154a2081c3ec862195b9abf7ecf6170b84dc2a28a45972be69b00149073698803c97cd3e870567daa32","name":"indicatorNest.json","type":"application/json","lastModified":1667493755945.721,"size":21286767},{"id":"8a1d24d7dd907df72e5ed0930c4833c2e3c18fb825d65f0496cf5fab6f71056c14412b834bd22f8b81e2e13b61403eb294696c8832bd19b8a9a67585b9973d85","href":"https://static.observableusercontent.com/files/8a1d24d7dd907df72e5ed0930c4833c2e3c18fb825d65f0496cf5fab6f71056c14412b834bd22f8b81e2e13b61403eb294696c8832bd19b8a9a67585b9973d85","name":"rawVulnerability.csv","type":"text/csv","lastModified":1667493755945.721,"size":87858},{"id":"edf5dfed635d7743bd93309da8f85d08cdeaa2caee38c0f2f43bf406afb1c3816be7c5e736999da62032a502e122045b86f97a984c4199deeb658bc6a139d754","href":"https://static.observableusercontent.com/files/edf5dfed635d7743bd93309da8f85d08cdeaa2caee38c0f2f43bf406afb1c3816be7c5e736999da62032a502e122045b86f97a984c4199deeb658bc6a139d754","name":"Landslide.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1981},{"id":"bbb1cc37080c152d9b4eff5fd05636f92b476fc5ee41b4c622e04e980e7ea8781f4731f008745099733975bf8c1db8c6f28c7c71b9b0eed266494ae591aaad31","href":"https://static.observableusercontent.com/files/bbb1cc37080c152d9b4eff5fd05636f92b476fc5ee41b4c622e04e980e7ea8781f4731f008745099733975bf8c1db8c6f28c7c71b9b0eed266494ae591aaad31","name":"Epidemic.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1984},{"id":"711b1668480f69a25477e0c7e32290a457856022b85027c7e27e25ba3dc6e398069b330fd06d2f7e60c827dc9cc1ce8995b74416498e8af754ba9c517891355f","href":"https://static.observableusercontent.com/files/711b1668480f69a25477e0c7e32290a457856022b85027c7e27e25ba3dc6e398069b330fd06d2f7e60c827dc9cc1ce8995b74416498e8af754ba9c517891355f","name":"Flood.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1978},{"id":"926064bd0bd702076e5afca4e2f2833d82cbaf8f96e82bdb984472af50d091def062d439f57f711f2d1ce7d19b56d855642040fbc33731bc83d3a26b9d465d79","href":"https://static.observableusercontent.com/files/926064bd0bd702076e5afca4e2f2833d82cbaf8f96e82bdb984472af50d091def062d439f57f711f2d1ce7d19b56d855642040fbc33731bc83d3a26b9d465d79","name":"Miscellaneous accident.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1988},{"id":"657a84544aef0025e49b59dafe3bf23416ab7c9193d6b4367b6836a8053fb981c0515a818f96933baad5f08af0e41dedbd2a821eb5419e71469d64c13d581b8f","href":"https://static.observableusercontent.com/files/657a84544aef0025e49b59dafe3bf23416ab7c9193d6b4367b6836a8053fb981c0515a818f96933baad5f08af0e41dedbd2a821eb5419e71469d64c13d581b8f","name":"person-fill-svgrepo-com.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":671},{"id":"8e965654067c1cc21351b064cf5c87e9fe4d3fa2d2a663d8fe425960e2b7ee55871a6a86c32aae738c30f9d24495ed01119e6f07650ac74454476c9f55b2e635","href":"https://static.observableusercontent.com/files/8e965654067c1cc21351b064cf5c87e9fe4d3fa2d2a663d8fe425960e2b7ee55871a6a86c32aae738c30f9d24495ed01119e6f07650ac74454476c9f55b2e635","name":"Extreme temperature.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1985},{"id":"95d2bcc90bdc8dfe7f6c4a207d764645bb5292677c8bd2e94d0250996e9bcdef858d7816ff34f92940612cc5f660a8851d27b20438c70bf131b93756e869d1e8","href":"https://static.observableusercontent.com/files/95d2bcc90bdc8dfe7f6c4a207d764645bb5292677c8bd2e94d0250996e9bcdef858d7816ff34f92940612cc5f660a8851d27b20438c70bf131b93756e869d1e8","name":"Earthquake.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1986},{"id":"46314a256b55d737de37aaa08c6c0c73a8f1fe3b7742f55ac6463176bd99d7f6d43218d0de2a873205436ada487d8f9110f9a9d837c59fc5b8da6cf3b09e94e1","href":"https://static.observableusercontent.com/files/46314a256b55d737de37aaa08c6c0c73a8f1fe3b7742f55ac6463176bd99d7f6d43218d0de2a873205436ada487d8f9110f9a9d837c59fc5b8da6cf3b09e94e1","name":"Storm.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1985},{"id":"0499253e4948508da2f1d82b66a862f432d75fc4fa950f31e4a010e709f24fd18b3173797ffe2e471a96ff8f978bfcc78fc305c1a4ecda27b5ee1d7a96f8294e","href":"https://static.observableusercontent.com/files/0499253e4948508da2f1d82b66a862f432d75fc4fa950f31e4a010e709f24fd18b3173797ffe2e471a96ff8f978bfcc78fc305c1a4ecda27b5ee1d7a96f8294e","name":"Drought.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1976},{"id":"c7cf6d5667ea2d59c0fc0951d3e94529529de2f2b4bf7050fb6622500e5bc3e615579f8462ba1bbdc6df8047dfc6787bca51af2940188a4062e680c3ed66f177","href":"https://static.observableusercontent.com/files/c7cf6d5667ea2d59c0fc0951d3e94529529de2f2b4bf7050fb6622500e5bc3e615579f8462ba1bbdc6df8047dfc6787bca51af2940188a4062e680c3ed66f177","name":"Wildfire.svg","type":"image/svg+xml","lastModified":1669249143213.79,"size":1986}] 2f:{"title":"Runner-Up, Team Periscopic - Building Resilience to Climate Change in South Asia","cells":[{"id":0,"value":"# Runner-Up, Team Periscopic -
Building Resilience to Climate Change in South Asia","pinned":false,"hidden":false,"mode":"md"},{"id":4205,"value":"Climate change is not a myth. Global changes are upending entire regions, and South Asia is particularly vulnerable. In fact, more than 750 million people in the region have been impacted by a climate-related disaster in the past twenty years. \n\nLet's take a closer look at the changing climate in South Asia: which disasters are happening, how people are feeling, and where we can improve.\n
","pinned":false,"hidden":false,"mode":"md"},{"id":6490,"value":"
\n
\n## When Disaster Strikes, What Is It?","pinned":false,"hidden":false,"mode":"md"},{"id":1259,"value":"\nFor the developing member countries in South Asia, more than half of the natural disasters are floods or storms. They account for almost two-thirds of all natural disasters in the past forty years.\n
\n
","pinned":false,"hidden":false,"mode":"md"},{"id":5315,"value":"

Natural Disasters in South Asia

","pinned":false,"hidden":false,"mode":"html"},{"id":6229,"value":"Plot.plot({\n width: maxWidth,\n height: 80,\n marginLeft: 0,\n marginRight: 0,\n\n color: {\n legend: true,\n domain: disasterColorDomain,\n range: disasterColorRange\n },\n x: {\n label: \"\",\n tickFormat: null,\n ticks: 0\n },\n marks: [\n Plot.barX(dataDisasters, {\n x: \"perc\",\n fill: \"category\"\n }),\n Plot.textX(dataDisasters, Plot.stackX({\n x: \"perc\",\n text: d => d.perc > 3 ? `${d3.format(\".0f\")(d.perc)}%` : \"\",\n fontSize: 12,\n fontWeight: \"bold\",\n fill: \"white\"\n }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6679,"value":"
\n
\n## Country Case Studies","pinned":false,"hidden":false,"mode":"md"},{"id":6678,"value":"This story explores the prevalence of natural disasters in three South Asian countries, and how their local people feel about climate change. ","pinned":false,"hidden":false,"mode":"md"},{"id":6677,"value":"viewof pickedCountry1 = view`

${[\"selection\", Inputs.radio(Object.keys(pickedCountryLabels), {value: Object.keys(pickedCountryLabels)[0]})]}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6676,"value":"
\n
\n
\n## Focusing on ${pickCountryCaseStudy}","pinned":false,"hidden":false,"mode":"md"},{"id":6675,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":5276,"value":"
\n

Countries Sized by Area and Arranged by Population

\n","pinned":false,"hidden":false,"mode":"html"},{"id":6674,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":5565,"value":"Source: World Population Review","pinned":false,"hidden":false,"mode":"md"},{"id":6673,"value":"

\n## ${writeText({\n \"Bangladesh\": `In ${pickCountryCaseStudy}, Floods and Storms are Huge`,\n \"Nepal\": `In ${pickCountryCaseStudy}, Floods and Landslides are Huge`,\n \"Sri Lanka\": `In ${pickCountryCaseStudy}, Floods Dominate`\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":5483,"value":"writeText({\n \"Bangladesh\": `Storms and floods make up two-thirds of Bangladesh's ${totalIncidentsSelected} natural disaster incidents in the past four decades.`,\n \"Nepal\": `Floods and landslides make up over half of Nepal's ${totalIncidentsSelected} natural disaster incidents in the past four decades.`,\n \"Sri Lanka\": `Floods alone make up over two-thirds of Sri Lanka's ${totalIncidentsSelected} natural disaster incidents in the past four decades.`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5271,"value":"
\n

Natural Disasters in ${pickCountryCaseStudy}

","pinned":false,"hidden":false,"mode":"html"},{"id":6670,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":5573,"value":"Source: Climate Change Knowledge Portal","pinned":false,"hidden":false,"mode":"md"},{"id":6668,"value":"
\n${writeText({\n \"Bangladesh\": \"The impact of these incidents is enormous on the Bangladeshi people. While droughts have affected many in the past, storms and floods consistently result in thousands being affected.\",\n \"Nepal\": \"The impact of these incidents is enormous on the Nepali people. While earthquakes have affected millions in the past, floods consistently result in tens or hundreds of thousands being affected.\",\n \"Sri Lanka\": \"The impact of floods on the Sri Lankan people is enormous. While occasional droughts affect millions of people, floods are far more consistent and affect hundreds of thousands virtually every year.\"\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":5788,"value":"
\n

Number of People Affected by Selected Natural Disasters Over Time in ${pickCountryCaseStudy}

","pinned":false,"hidden":false,"mode":"html"},{"id":6666,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":5581,"value":"Source: Climate Change Knowledge Portal","pinned":false,"hidden":false,"mode":"md"},{"id":6665,"value":"
\nBehind these numbers is an entire community permanently changed. The people affected by natural disasters have their lives uprooted, and over time the impact is enormous.","pinned":false,"hidden":false,"mode":"md"},{"id":6664,"value":"
\n

Total Number of People Affected Over Time by Selected Natural Disasters in ${pickCountryCaseStudy}

","pinned":false,"hidden":false,"mode":"html"},{"id":6663,"value":"viewof endDateSlider1 = {\n pickCountryCaseStudy // reset case study\n return Scrubber(_.range(1982, 2020), {delay: 500, loopDelay: 3000})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6662,"value":"$${await FileAttachment(\"person-fill-svgrepo-com.svg\").image({width: 15})} = ${d3.format(\".2s\")(iconSize)} people","pinned":false,"hidden":false,"mode":"md"},{"id":6661,"value":"Plot.plot({\n caption: `1981-${endDateSlider1}`,\n width,\n marginLeft: 80,\n height: 440,\n x: {\n domain: [0, 60],\n axis: null\n },\n y: {\n reverse: true,\n domain: [-1, 5],\n ticks: 0,\n label: null\n },\n facet: {\n axis: \"left\",\n data: iconDataLongFacet1,\n y: \"type\",\n marginLeft: 80,\n label: null\n },\n fy: {\n domain: countryDisasterTotalEnd.filter(d => d.total > iconSize).map(d => d.type).sort()\n },\n marks: [\n // Plot.frame(),\n Plot.image(\n iconDataLongFacet1,\n Plot.stackX({\n y: \"layer\",\n src: d => icon[d.type] ? icon[d.type] : icon.other,\n width: 12,\n opacity: d => d.newYear ? 1 : 0.5\n })\n ),\n Plot.text(\n iconDataLongFacet1, \n Plot.selectLast({\n x: d => Math.floor(d.ind / layerSize),\n text: d => `${d3.format(\".2s\")((d.ind + 1) * iconSize)}`,\n textAnchor: \"start\",\n dx: 25\n })\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6481,"value":"
\n
\n
\n## The People of ${pickCountryCaseStudy} are Concerned","pinned":false,"hidden":false,"mode":"md"},{"id":5253,"value":"\nThere has to be change, and people must be at the root of climate action.\n\nData from a survey done in partnership between Meta and the Yale Program on Climate Change Communication provide insight into how individual people view climate change.\n","pinned":false,"hidden":false,"mode":"md"},{"id":5515,"value":"
\n

\n${writeText({\n \"Bangladesh\": \"Responses from Bangladeshi People on Climate Change\",\n \"Nepal\": \"Responses from Nepali People on Climate Change\",\n \"Sri Lanka\": \"Responses from Sri Lankan People on Climate Change\"\n})}\n

\n\n","pinned":false,"hidden":false,"mode":"html"},{"id":5353,"value":"viewof showSurveyDetail1 = {\n pickCountryCaseStudy // reset case study\n return Inputs.toggle({label: \"Show More Detail?\", value: false})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1528,"value":"
\n${writeText({\n \"Bangladesh\": \"Bangladeshis view climate change as a **serious threat**.\",\n \"Nepal\": \"Nepalis view climate change as a **serious threat**.\",\n \"Sri Lanka\": \"Sri Lankans view climate change as a **serious threat**.\"\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":6655,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1590,"value":"
\n${writeText({\n \"Bangladesh\": \"Bangladeshis agree that climate change will put them directly **in harm's way**.\",\n \"Nepal\": \"Nepalis agree that climate change will put them directly **in harm's way**.\",\n \"Sri Lanka\": \"Sri Lankans agree that climate change will put them directly **in harm's way**.\",\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":5386,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":3333,"value":"
\n${writeText({\n \"Bangladesh\": \"Bangladeshis agree the government should make climate change a **top priority**.\",\n \"Nepal\": \"Nepalis agree the government should make climate change a **top priority**.\",\n \"Sri Lanka\": \"Sri Lankans agree the government should make climate change a **top priority**.\"\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":6301,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1618,"value":"
\n${writeText({\n \"Bangladesh\": \"Bangladesh is united in organizing to enact change and **take action**.\",\n \"Nepal\": \"Nepal is united in organizing to enact change and **take action**.\",\n \"Sri Lanka\": \"Sri Lankans is united in organizing to enact change and **take action**.\",\n})}","pinned":false,"hidden":false,"mode":"md"},{"id":6318,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":5785,"value":"Source: Climate Change Opinion Survey\n
","pinned":false,"hidden":false,"mode":"html"},{"id":6478,"value":"
\n
\n
\n## Other Countries Seem to Be Less Concerned","pinned":false,"hidden":false,"mode":"md"},{"id":5636,"value":"The United States is one of the largest emitters of greenhouse gases, but their impact will have a greater effect on countries like ${pickCountryCaseStudy}. ","pinned":false,"hidden":false,"mode":"md"},{"id":5672,"value":"
\n

Comparing Responses from Bangladesh and Other Countries

","pinned":false,"hidden":false,"mode":"html"},{"id":5425,"value":"viewof pickQuestionType1 = Inputs.select(Object.keys(surveyQuestionPicks), {label: \"Survey Question\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5427,"value":"viewof pickCompareCountry1 = Inputs.select(\n dataClimateSurvey.map(d => d.country), {\n unique: true, \n label: \"Compare With\",\n value: \"United States\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6341,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":6055,"value":"Source: Climate Change Opinion Survey","pinned":false,"hidden":false,"mode":"html"},{"id":1628,"value":"
\nThe people of ${pickCountryCaseStudy} are united in their attitude toward climate change. The threat is imminent, and this consensus of opinions will help them to adopt solutions.","pinned":false,"hidden":false,"mode":"md"},{"id":1220,"value":"
\n
\n
\n## Countries are Adapting to Climate Change","pinned":false,"hidden":false,"mode":"md"},{"id":1117,"value":"Researchers at the University of Notre Dame have created a metric to measure how prepared a country is for the effects of climate change, called the global adaptation initiative index (ND-GAIN).\n\nHigher scores are better, and many ND-GAIN indices have increased over time. In the chart below, the developing member countries in Asia are highlighted.","pinned":false,"hidden":false,"mode":"md"},{"id":5302,"value":"
\n

Preparedness of Countries Over Time

\n","pinned":false,"hidden":false,"mode":"html"},{"id":4969,"value":"viewof sliderDateRange = {\n pickCountryCaseStudy // reset case study\n return Scrubber(_.range(2014, 2021), {delay: 500, loopDelay: 3000})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4961,"value":"addTooltips(Plot.plot({\n width: maxWidth,\n nice: true,\n marginLeft: 50,\n insetLeft: 10,\n x: {\n label: \"ND-GAIN Index →\",\n grid: true,\n domain: [25, 80]\n },\n y: {\n label: null\n },\n color: {\n legend: true,\n domain: countryColorDomain,\n range: countryColorRange\n },\n marks: [\n Plot.textX([sliderDateRange], {\n x: 78,\n text: d => d3.format(\"d\")(d),\n fill: \"white\",\n fontSize: 30,\n lineAnchor: \"middle\",\n textAnchor: \"start\"\n }),\n Plot.dot(dataNdGainWithRegion.filter(d => d.year === sliderDateRange & !dmcIso3.includes(d.ISO3)), {\n x: \"ndGain\", \n y: \"region\",\n fill: \"grey\",\n opacity: 0.3,\n r: 5,\n title: d => `${d.Name}`\n }),\n Plot.dot(dataNdGainWithRegion.filter(d => d.year === sliderDateRange & dmcIso3.includes(d.ISO3)), {\n x: \"ndGain\", \n y: \"region\",\n fill: \"Name\",\n opacity: 0.6,\n r: 5,\n title: d => `${d.Name}`\n })\n ]\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":6052,"value":"Source: Notre Dame Global Adaptation Initiative","pinned":false,"hidden":false,"mode":"html"},{"id":1178,"value":"
\nWhile the indices for the selected South Asian countries are in the bottom half of the scale, they have all made improvements in recent years.","pinned":false,"hidden":false,"mode":"md"},{"id":5860,"value":"
\n

Preparedness of South Asian Countries Over Time

\n","pinned":false,"hidden":false,"mode":"html"},{"id":4272,"value":"Plot.plot({\n width: maxWidth,\n marginRight: 120,\n insetLeft: 10,\n insetBottom: 10,\n x: {\n label: null,\n tickFormat: \"d\",\n ticks: 5 // limit interim tick values\n },\n y: {\n grid: true,\n label: \"↑ ND-GAIN Index\"\n },\n color: {\n domain: countryColorDomain,\n range: countryColorRange\n },\n marks: [\n Plot.line(dataNdGainDmc.filter(d => d.year > 2013), {\n x: \"year\",\n y: \"ndGain\",\n stroke: \"Name\"\n }),\n Plot.text(dataPercentChange.filter(d => d.year === 2020), {\n x: \"year\",\n y: \"ndGain\",\n text: d => `${d.Name} (${d.percChange > 0 ? '+' : ''}${d3.format(\".1f\")(d.percChange)}%)`,\n textAnchor: \"start\",\n fontSize: 12,\n fill: \"Name\",\n dx: 5\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6057,"value":"Source: Notre Dame Global Adaptation Initiative","pinned":false,"hidden":false,"mode":"html"},{"id":5615,"value":"
\n
\n
\n## What is Resilience?","pinned":false,"hidden":false,"mode":"md"},{"id":1250,"value":"The ND-GAIN index is composed of rankings that are collected into two categories:\n- **Vulnerability**: how vulnerable a country is to climate change\n- **Readiness**: the ability to improve resilience to climate change\n\nIn the plot below, countries most adapted to changing climates will be positioned toward the top-right, and South Asian countries have all improved in recent years.\n
","pinned":false,"hidden":false,"mode":"md"},{"id":5606,"value":"
\n

Country Vulnerability and Readiness for Climate Change Over Time

","pinned":false,"hidden":false,"mode":"html"},{"id":5019,"value":"viewof sliderDateRangeBeadMaze = {\n reset\n pickCountryCaseStudy // reset case study\n return Scrubber(_.range(2014, 2021), {delay: 500, loopDelay: 3000})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4739,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":4745,"value":"viewof pickCountries1 = {\n reset\n return Inputs.table(bookendYears, {\n required: false,\n columns: [\"Name\"],\n value: bookendYears.filter(d => dmcIso3.includes(d.ISO3)),\n height: 150,\n header: {\"Name\": \"Select countries to add to the chart above\"}\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4753,"value":"viewof showCountryLabels = {\n reset\n return Inputs.toggle({label: \"Country Labels?\", value: true})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1270,"value":"viewof reset = Inputs.button(\"Reset Chart\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":6059,"value":"Source: Notre Dame Global Adaptation Initiative","pinned":false,"hidden":false,"mode":"html"},{"id":3690,"value":"
\nOverall, the path to climate resiliency involves many incremental improvements to address a large problem. South Asia ADB member countries are making these improvements to reduce their vulnerability.","pinned":false,"hidden":false,"mode":"md"},{"id":6639,"value":"Vulnerability is not homogenous, however. We can further unpack the individual metrics and explore what is causing a country to be vulnerable to climate change. Here is a closer look at the vulnerability score for ${pickCountryCaseStudy}:","pinned":false,"hidden":false,"mode":"md"},{"id":6638,"value":"viewof optionsDotPlot = {\n let countyPicks = _.uniq(vulnerability.filter(d => dmcIso3.includes(d.ISO3) & d.Name !== pickCountryCaseStudy).map(d => d.Name))\n let allValues = [\"None\", countyPicks].flat()\n \n let inputCompare = Inputs.select(allValues, {\n label: `Compare with`,\n value: \"none\",\n width: 150,\n })\n \n return Inputs.form({\n showAllIndicators: Inputs.toggle({label: `Show Detailed Indicators for ${pickCountryCaseStudy}`, value: false}),\n pickCountryBySector: inputCompare\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6637,"value":"
\n

Comparing Forms of Vulnerability in ${pickCountryCaseStudy}

","pinned":false,"hidden":false,"mode":"html"},{"id":6636,"value":"{\n if (optionsDotPlot.pickCountryBySector !== \"None\") \n return Swatches(\n d3.scaleOrdinal(\n [pickCountryCaseStudy, optionsDotPlot.pickCountryBySector],\n [countryColorRange[countryColorDomain.indexOf(pickCountryCaseStudy)], \"grey\"])\n )\n else\n return Swatches(\n d3.scaleOrdinal(\n [pickCountryCaseStudy],\n [countryColorRange[countryColorDomain.indexOf(pickCountryCaseStudy)]])\n )\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6635,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":6061,"value":"Source: Notre Dame Global Adaptation Initiative","pinned":false,"hidden":false,"mode":"html"},{"id":4686,"value":"$3c","pinned":false,"hidden":false,"mode":"md"},{"id":4682,"value":"Further information about the individual indicators can be seen in the ND-GAIN technical document.\n
\n
","pinned":false,"hidden":false,"mode":"md"},{"id":6632,"value":"### How ${pickCountryCaseStudy} is adapting","pinned":false,"hidden":false,"mode":"md"},{"id":6631,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":6475,"value":"
\n
\n
\n## For Vulnerable Nations, Resilience is Key","pinned":false,"hidden":false,"mode":"md"},{"id":1755,"value":"Climate change will affect everyone, but some nations are more directly at risk. To provide the best solutions, we must understand the situation on both a global and local level. \n\nFortunately, countries in South Asia and elsewhere are on their way to becoming more climate resilient. Many have taken concrete steps to define goals and plans for adaptation through ${addNewTabLink(\"National Adaptation Plans\", \"https://napcentral.org/submitted-naps\")} or ${addNewTabLink(\"Nationally Determined Contributions\", \"https://unfccc.int/NDCREG\")}. These can pave the way for progress.\n\nWe can create a more climate change-resilient world. It will take commitment, creativity, quality information, and unity — but every step is one that leads us closer to a future where we can all thrive. \n\nLet's keep working.\n
\n
\n
","pinned":false,"hidden":false,"mode":"md"},{"id":6628,"value":"### Interested in Other Countries?","pinned":false,"hidden":false,"mode":"md"},{"id":6627,"value":"This was a case study for ${pickCountryCaseStudy}, but there are two more countries to explore. ","pinned":false,"hidden":false,"mode":"md"},{"id":6626,"value":"[Select a new one ⤴️](https://observablehq.com/@periscopic/adb-visualizing-climate-change-resilience-challenge#cell-6679)","pinned":false,"hidden":false,"mode":"md"},{"id":6625,"value":"---\n## Thank You to Our Mentors and Judges\n
\nWe want to extend a big thank you to our mentors and judges. We had wonderful conversations and correspondence with our mentors, who provided insightful and poignant feedback that we took to heart and incorporated into this final version of our submission.\n
\n
\nTeam Periscopic looks forward to staying connected.\n
\n
\n
\n","pinned":false,"hidden":false,"mode":"md"},{"id":5359,"value":"$3e","pinned":false,"hidden":false,"mode":"md"},{"id":4808,"value":"$3f","pinned":false,"hidden":false,"mode":"md"},{"id":2620,"value":"---\n## Data Work","pinned":false,"hidden":false,"mode":"md"},{"id":5246,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":6621,"value":"### Isotype Chart","pinned":false,"hidden":false,"mode":"md"},{"id":6620,"value":"icon = ({\n other: await FileAttachment(\"person-fill-svgrepo-com.svg\").url(),\n [disasterColorDomain2[0]]: await FileAttachment(\"Flood.svg\").url(),\n [disasterColorDomain2[1]]: await FileAttachment(\"Landslide.svg\").url(),\n [disasterColorDomain2[2]]: await FileAttachment(\"Epidemic.svg\").url(),\n [disasterColorDomain2[3]]: await FileAttachment(\"Extreme temperature.svg\").url(),\n [disasterColorDomain2[4]]: await FileAttachment(\"Storm.svg\").url(),\n [disasterColorDomain2[5]]: await FileAttachment(\"Miscellaneous accident.svg\").url(),\n [disasterColorDomain2[6]]: await FileAttachment(\"Earthquake.svg\").url(),\n [disasterColorDomain2[7]]: await FileAttachment(\"Wildfire.svg\").url(),\n [disasterColorDomain2[8]]: await FileAttachment(\"Drought.svg\").url(),\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6619,"value":"pickCountryCaseStudyIso3_1 = dmcIso3[dmcIso3Names.indexOf(pickCountryCaseStudy)]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6618,"value":"peopleAffectedData = disasterSouthAsia.filter(d => d.country === pickCountryCaseStudyIso3_1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6617,"value":"// filter within a date range, minus most recent\ncountryDisasterTotal1 = {\n let filteredData = peopleAffectedData.filter(d => d.Category >= 1981 & d.Category <= endDateSlider1)\n let data = T.tidy(filteredData,\n T.mutate({newYear: \n (d => d.Category >= 1981 & d.Category <= (endDateSlider1 - 1)) || endDateSlider1 == 1981\n }),\n T.groupBy([\"type\", \"newYear\"], [\n T.summarize({total: T.sum(\"count\")})\n ]),\n // remove disasters that haven't happened\n T.filter(d => d.total > 0)\n )\n return data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6616,"value":"// filter within a date range\ncountryDisasterTotalEnd = {\n let filteredData = peopleAffectedData.filter(d => d.Category <= 2020)\n let data = T.tidy(filteredData,\n T.groupBy(\"type\", [\n T.summarize({total: T.sum(\"count\")})\n ]),\n // remove disasters that haven't happened\n T.filter(d => d.total > 0)\n )\n return data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6615,"value":"iconSize = {\n if (pickCountryCaseStudy == \"Bangladesh\") return 1e6\n if (pickCountryCaseStudy == \"Nepal\") return 100000\n if (pickCountryCaseStudy == \"Sri Lanka\") return 100000\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6614,"value":"iconData1 = {\n return T.tidy(countryDisasterTotal1,\n T.mutate({icons: d => Math.floor(d.total / iconSize)})\n )\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6613,"value":"// repeat the values based on icon count\niconDataLong1 = iconData1.map(d => Array(d.icons).fill(d)).flat()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6612,"value":"layerSize = 5","pinned":false,"hidden":false,"mode":"ojs"},{"id":6611,"value":"iconDataLongFacet1 = T.tidy(\n iconDataLong1,\n T.groupBy([\"type\"], [\n T.mutate({ind: (d, i) => i}),\n T.mutate({layer: d => Math.floor(d.ind % layerSize) })\n ])\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6610,"value":"### Narrative","pinned":false,"hidden":false,"mode":"md"},{"id":6609,"value":"function addNewTabLink(text, link) {\n return `${text}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6608,"value":"### Case Study","pinned":false,"hidden":false,"mode":"md"},{"id":6607,"value":"selectedCountries = [\"Bangladesh\", \"Nepal\", \"Sri Lanka\"] // picks for case study","pinned":false,"hidden":false,"mode":"ojs"},{"id":6606,"value":"function writeText(textObj) {\n let text = \"🚨 Missing Text\"\n if ([\"Bangladesh\", \"Sri Lanka\", \"Nepal\"].includes(pickCountryCaseStudy) & textObj[pickCountryCaseStudy] !== undefined) {\n text = textObj[pickCountryCaseStudy]\n }\n return md`${text}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6605,"value":"totalIncidentsSelected = dataDisastersSelected.reduce((acc, d) => acc + d.total, 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6604,"value":"dmcIso3Names = [\"Bangladesh\", \"Bhutan\", \"India\", \"Maldives\", \"Nepal\", \"Sri Lanka\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6603,"value":"dmcIso3NamesPossessive = [\"Bangladeshi\", \"Bhutanese\", \"Indian\", \"Maldivian\", \"Nepalese\", \"Sri Lankan\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6602,"value":"pickCountryCaseStudyIso3 = dmcIso3[dmcIso3Names.indexOf(pickCountryCaseStudy)]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6601,"value":"pickCountryCaseStudyIso3Possessive = dmcIso3NamesPossessive[dmcIso3Names.indexOf(pickCountryCaseStudy)]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6600,"value":"dataDisastersSelected = T.tidy(\n disastersNamed,\n T.filter(d => d.iso3 === pickCountryCaseStudyIso3),\n T.groupBy([\"category\"], [\n T.summarize({total: T.sum(\"incidents\")})\n ]),\n T.mutateWithSummary({allIncidents: T.sum(\"total\")}),\n T.mutate({perc: d => d.total / d.allIncidents * 100}),\n T.arrange(T.desc(\"perc\"))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6599,"value":"disasterColors = ({\n \"Flood\": \"#023858\",\n \"Epidemic\": \"#045A8D\",\n \"Storm\": \"#1C9099\",\n \"Miscellaneous accident\": \"#7BCCC4\",\n \"Earthquake\": \"#D8B365\",\n \"Extreme temperature\": \"#D95F0E\",\n \"Wildfire\": \"#D7301F\",\n \"Drought\": \"#993404\",\n \"Landslide\": \"#662506\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6598,"value":"disasterColorDomain2 = dataDisastersSelected.map(d => d.category)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6597,"value":"disasterColorRange2 = disasterColorDomain2.map(d => disasterColors[d])","pinned":false,"hidden":false,"mode":"ojs"},{"id":4327,"value":"### Plotting","pinned":false,"hidden":false,"mode":"md"},{"id":5334,"value":"naturalDisasterColorPalette = {\n // write out a color palette as array\n let p1 = ['#023858', '#045A8D', '#4EB3D3', '#1C9099', '#7BCCC4', '#D8B365', '#D95F0E', '#993404', '#662506']\n let p2 = [\n '#1C9099', // Storm\n '#023858', // Flood\n '#045A8D', // Epidemic\n '#7BCCC4', // misc\n '#D8B365', // quake\n '#D95F0E', // temp\n '#D7301F', // wildfire\n '#993404', // drought\n '#662506', // landslide\n ]\n return p2\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5347,"value":"consolidatedColorRange = ['#023858', '#C6DBEF']","pinned":false,"hidden":false,"mode":"ojs"},{"id":5358,"value":"fullColorRange = ['#023858','#045A8D','#0570B0','#4292C6','#74A9CF','#C6DBEF','#DFDFDF']","pinned":false,"hidden":false,"mode":"ojs"},{"id":5962,"value":"joinGroupDomain = [\n \"I am participating in an effort like this now\",\n \"I definitely would do it\",\n \"I probably would do it\",\n \"Not sure\",\n \"I probably would not do it\",\n \"I definitely would not do it\",\n \"Refused\"\n ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":5165,"value":"maxWidth = 1100","pinned":false,"hidden":false,"mode":"ojs"},{"id":6272,"value":"#### Disaster Coloring","pinned":false,"hidden":false,"mode":"md"},{"id":6225,"value":"disasterColorDomain = [\n \"Flood\",\n \"Storm\",\n \"Epidemic\",\n \"Miscellaneous accident\",\n \"Extreme temperature\",\n \"Landslide\",\n \"Earthquake\",\n \"Drought\",\n \"Wildfire\",\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6226,"value":"disasterColorRange = [\n \"#023858\",\n \"#1C9099\",\n \"#045A8D\",\n \"#7BCCC4\",\n \"#D95F0E\",\n \"#662506\",\n \"#D8B365\",\n \"#993404\",\n \"#D7301F\",\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6247,"value":"disasterColorBangDomain = [\n \"Storm\",\n \"Flood\", \n \"Epidemic\",\n \"Miscellaneous accident\",\n \"Extreme temperature\",\n \"Earthquake\",\n \"Landslide\",\n \"Drought\",\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6249,"value":"disasterColorBangRange = [\n \"#1C9099\",\n \"#023858\",\n \"#045A8D\",\n \"#7BCCC4\",\n \"#D95F0E\",\n \"#D8B365\",\n \"#662506\",\n \"#993404\",\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6034,"value":"#### Country Coloring","pinned":false,"hidden":false,"mode":"md"},{"id":5988,"value":"countryColorDomain = countryNames.filter(d => dmcIso3.includes(d.ISO3)).map(d => d.Name)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5996,"value":"countryColorRange = ['#D7301F', '#993404', '#D8B365', '#7BCCC4', '#023858', '#045A8D']","pinned":false,"hidden":false,"mode":"ojs"},{"id":6017,"value":"otherCountries = countryNames.filter(d => !dmcIso3.includes(d.ISO3)).map(d => d.Name)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6012,"value":"countryColorDomainFull = [countryColorDomain, otherCountries].flat()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6020,"value":"countryColorRangeFull = [countryColorRange, Array(otherCountries.length).fill(\"grey\")].flat()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6078,"value":"#### Indicator Coloring","pinned":false,"hidden":false,"mode":"md"},{"id":6063,"value":"sectorColorDomain = [\"Health\", \"Water\", \"Ecosystem services\", \"Food\", \"Human habitat\", \"Infrastructure\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6065,"value":"sectorColorRange = ['#023858', '#045A8D', '#1C9099', '#D8B365', '#D95F0E', '#662506']","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"### Countries to highlight","pinned":false,"hidden":false,"mode":"md"},{"id":34,"value":"[**Developing Member Countries** from ADB South Asia Department](https://challenges.adb.org/en/challenges/dataviz4climate/pages/about-adb?lang=en)","pinned":false,"hidden":false,"mode":"md"},{"id":26,"value":"dmcIso3 = [\"BGD\", \"BTN\", \"IND\", \"MDV\", \"NPL\", \"LKA\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3086,"value":"### Population Density by country","pinned":false,"hidden":false,"mode":"md"},{"id":4308,"value":"countryDensityRaw = FileAttachment(\"countryDensity.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4396,"value":"countryDensity = T.tidy(\n countryDensityRaw,\n T.mutate({pop2022: d => Math.round(d.pop2022 * 1000)})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4030,"value":"### ND-GAIN","pinned":false,"hidden":false,"mode":"md"},{"id":4322,"value":"dataNdGain = FileAttachment(\"dataNdGain.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1165,"value":"dataNdGainDmc = dataNdGain.filter(d => dmcIso3.includes(d.ISO3))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2842,"value":"#### Getting Region Data\n[Source](https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/all/all.csv)","pinned":false,"hidden":false,"mode":"md"},{"id":4338,"value":"regionData = FileAttachment(\"regionData.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2848,"value":"dataNdGainWithRegion = T.tidy(\n dataNdGain,\n T.leftJoin(regionData, {by: {[\"alpha-3\"]: \"ISO3\"}})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4274,"value":"dataPercentChange = T.tidy(\n dataNdGainDmc,\n T.filter(d => [2014, 2020].includes(d.year)),\n T.groupBy([\"ISO3\"], [\n T.mutateWithSummary({prev: T.lag(\"ndGain\")})\n ]),\n T.filter(d => d.prev),\n T.mutate({percChange: d => (d.ndGain - d.prev) / d.prev * 100})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2639,"value":"### Disaster Data by Country (South Asia)","pinned":false,"hidden":false,"mode":"md"},{"id":4347,"value":"disasterSouthAsia = FileAttachment(\"disasterSouthAsia.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":543,"value":"### Natural Disasters facing countries\n- Data is from the [Climate Knowledge Portal on country vulnerability](https://climateknowledgeportal.worldbank.org/country/bangladesh/vulnerability) (example of Bangladesh), can see [source pdf](https://climateknowledgeportal.worldbank.org/media/document/metatag.pdf) for more info","pinned":false,"hidden":false,"mode":"md"},{"id":4356,"value":"disasters = FileAttachment(\"disasters.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1297,"value":"countryNames = T.tidy(\n bookendYears,\n T.select([\"ISO3\", \"Name\"])\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1300,"value":"disastersNamed = T.tidy(\n disasters,\n T.leftJoin(countryNames, {by: {ISO3: 'iso3'}})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3956,"value":"dataDisasters = T.tidy(\n disastersNamed,\n T.groupBy([\"category\"], [\n T.summarize({total: T.sum(\"incidents\")})\n ]),\n T.mutateWithSummary({allIncidents: T.sum(\"total\")}),\n T.mutate({perc: d => d.total / d.allIncidents * 100}),\n T.arrange(T.desc(\"perc\"))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4159,"value":"dataDisastersBangladesh = T.tidy(\n disastersNamed,\n T.filter(d => d.iso3 === \"BGD\"),\n T.groupBy([\"category\"], [\n T.summarize({total: T.sum(\"incidents\")})\n ]),\n T.mutateWithSummary({allIncidents: T.sum(\"total\")}),\n T.mutate({perc: d => d.total / d.allIncidents * 100}),\n T.arrange(T.desc(\"perc\"))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5078,"value":"totalIncidents = dataDisastersBangladesh.reduce((acc, d) => acc + d.total, 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2635,"value":"### Climate Survey","pinned":false,"hidden":false,"mode":"md"},{"id":1505,"value":"#### Getting Data","pinned":false,"hidden":false,"mode":"md"},{"id":1403,"value":"// read data from excel attachment\nrawClimateSurvey = FileAttachment(\"climate_change_opinion_survey_2022_aggregated.xlsx\").xlsx()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1451,"value":"surveyCodebook = rawClimateSurvey.sheet(\"Codebook\", {headers: true}).slice(0,16)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1434,"value":"dataClimateSurvey = T.tidy(\n rawClimateSurvey.sheetNames.slice(2).map(sheet => {\n let data = rawClimateSurvey.sheet(sheet, {headers: true})\n let parsed = T.tidy(\n data,\n T.filter(d => d[data.columns[1]] !== \"(Unweighted Base)\"),\n T.pivotLonger({\n cols: data.columns.slice(2),\n namesTo: \"country\",\n valuesTo: \"perc\"\n }),\n T.rename({[sheet]: \"option\"}),\n T.mutate({question: sheet}),\n T.select([\"question\", T.everything()])\n )\n return parsed\n}).flat(),\n T.leftJoin(surveyCodebook, {by: {[\"Variable\"]: \"question\"}})\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1548,"value":"surveyQuestions = _.uniq(dataClimateSurvey.map(d => d[\"Question Text\"]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4713,"value":"surveyQuestionPicks = ({\n \"Threat of climate change\": surveyQuestions[6],\n \"Personal harm\": surveyQuestions[4],\n \"Government priority\": surveyQuestions[11],\n \"Join an organized group\": surveyQuestions[14]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5460,"value":"pickQuestion1 = surveyQuestionPicks[pickQuestionType1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"### Bead Maze Plots, ND-GAIN Index Components","pinned":false,"hidden":false,"mode":"md"},{"id":4362,"value":"rawReadiness = FileAttachment(\"rawReadiness.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"readiness = T.tidy(\n rawReadiness,\n T.pivotLonger({\n cols: ['-ISO3', '-Name'],\n namesTo: \"year\",\n valuesTo: \"readiness\"\n }),\n T.mutate({year: d => +d.year})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4529,"value":"rawVulnerability = FileAttachment(\"rawVulnerability.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":92,"value":"vulnerability = T.tidy(\n rawVulnerability,\n T.pivotLonger({\n cols: ['-ISO3', '-Name'],\n namesTo: \"year\",\n valuesTo: \"vulnerability\"\n }),\n T.mutate({year: d => +d.year})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"dataMatrix = T.tidy(\n readiness,\n T.leftJoin(vulnerability),\n T.filter(d => d.readiness !== null & d.vulnerability !== null)\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":169,"value":"// getting the start and end values for each country\nbookendYears = T.tidy(\n dataMatrix,\n T.groupBy([\"ISO3\"], [\n T.mutateWithSummary({\n yearMax: T.max(\"year\"),\n yearMin: T.min(\"year\")\n }),\n T.filter(d => d.year === d.yearMin | d.year === d.yearMax)\n ]),\n // pivot into start/end values\n T.mutate({yearType: d => d.year === d.yearMin ? \"start\" : \"end\"}),\n T.select([\"-yearMax\", \"-yearMin\", \"-year\"]),\n T.groupBy([\"ISO3\"], T.pivotWider({\n namesFrom: [\"yearType\"],\n valuesFrom: [\"readiness\", \"vulnerability\"]\n }))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4213,"value":"// values for recent year progression\nbookendYearsRecent = T.tidy(\n dataMatrix,\n T.filter(d => d.year > 2013),\n T.groupBy([\"ISO3\"], [\n T.mutateWithSummary({\n yearMax: T.max(\"year\"),\n yearMin: T.min(\"year\")\n }),\n T.filter(d => d.year === d.yearMin | d.year === d.yearMax)\n ]),\n // pivot into start/end values\n T.mutate({yearType: d => d.year === d.yearMin ? \"start\" : \"end\"}),\n T.select([\"-yearMax\", \"-yearMin\", \"-year\"]),\n T.groupBy([\"ISO3\"], T.pivotWider({\n namesFrom: [\"yearType\"],\n valuesFrom: [\"readiness\", \"vulnerability\"]\n }))\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":628,"value":"### Case Study by Indicators","pinned":false,"hidden":false,"mode":"md"},{"id":4535,"value":"indicatorNest = FileAttachment(\"indicatorNest.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":4564,"value":"// from table https://gain.nd.edu/assets/254377/nd_gain_technical_document_2015.pdf\nindicatorSectors = ({\n econ: \"Economic readiness\",\n ecos: \"Ecosystem services\",\n food: \"Food\",\n gove: \"Governance readiness\",\n habi: \"Human habitat\",\n heal: \"Health\",\n infr: \"Infrastructure\",\n soci: \"Social readiness\",\n wate: \"Water\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4612,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":948,"value":"indicatorsFlat = T.tidy(\n indicatorNest.flat(),\n // components\n T.mutate({indicatorType: d => {\n if ([\n \"id_econ_01\",\n \"id_gove_01\",\n \"id_gove_02\",\n \"id_gove_03\",\n \"id_gove_04\",\n \"id_soci_01\",\n \"id_soci_02\",\n \"id_soci_03\",\n \"id_soci_04\"\n ].includes(d.indicator)) {\n return \"readiness\"\n } else {\n return \"vulnerability\"\n }\n }}),\n // sector\n T.mutate({\n indicatorCode: d => d.indicator.split(\"_\")[1],\n indicatorSector: d => indicatorSectors[d.indicatorCode]\n }), \n // name \n T.mutate({indicatorName: d => indicatorNaming[d.indicator]})\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6596,"value":"### Country Selector","pinned":false,"hidden":false,"mode":"md"},{"id":6595,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":6594,"value":"// apply radio button choice to pick case study\npickCountryCaseStudy = pickedCountryLabels[pickedCountry1.selection]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6593,"value":"pickedCountryLabels = ({\n [\"🇳🇵 Nepal\"]: \"Nepal\",\n [\"🇱🇰 Sri Lanka\"]: \"Sri Lanka\",\n [\"🇧🇩 Bangladesh\"]: \"Bangladesh\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":6592,"value":"buttonAccent = countryColorRange[countryColorDomain.indexOf(pickedCountryLabels[pickedCountry1.selection])]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6591,"value":"// get class for radio button\nns = \"theme-country\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":48,"value":"### Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":49,"value":"import {T} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":126,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":410,"value":"import {toc} from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4963,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":6590,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":6589,"value":"import { view } from \"@tomlarkworthy/view\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W41","stdlib":"1","titling":"auto"}