2f:Tae7,p5(sketch => { let canvas_width = 1000; let canvas_height = 1200; let disease_panel_array = []; var stroke_icon, person_img, obs_icon, resp_icon, thra_icon, heart_icon, liver_icon, baby_icon; let total_ded_2019 = 11060; let red_factor = 10; let stroke_text = "" // Source of deaths : https://www.unep.org/interactive/air-pollution-note/ let stroke_ded = parseInt(total_ded_2019 * 0.07 / red_factor); let obs_text = "" let obs_ded = parseInt(total_ded_2019 * 0.1 / red_factor); let resp_ded = parseInt(total_ded_2019 * 0.08 / red_factor); let thra_ded = parseInt(total_ded_2019 * 0.06 / red_factor); let heart_ded = parseInt(total_ded_2019 * 0.06 / red_factor); let liver_ded = parseInt(total_ded_2019 * 0.04 / red_factor); let baby_ded = parseInt(total_ded_2019 * 0.03 / red_factor); sketch.preload = function() { stroke_icon = sketch.loadImage(stroke_img_URL); obs_icon = sketch.loadImage(obs_image_URL); resp_icon = sketch.loadImage(lungs_img_URL); thra_icon = sketch.loadImage(lungs_img_URL); heart_icon = sketch.loadImage(heart_img_URL); liver_icon = sketch.loadImage(liver_img_URL); baby_icon = sketch.loadImage(alien_img_URL); person_img = sketch.loadImage(person_img_URL); } sketch.setup = function() { sketch.createCanvas(canvas_width, canvas_height); sketch.imageMode(sketch.CENTER); sketch.textAlign(sketch.CENTER); disease_panel_array.push(new DiseasePanel(sketch, 10, 10, "Stroke (7%)", stroke_text, stroke_ded, stroke_icon , "crimson", person_img)); disease_panel_array.push(new DiseasePanel(sketch, 190, 10, "Chronic \nobstructive\n pulmonary\n disease (10%)", obs_text, obs_ded, obs_icon , color_yellow, person_img)); disease_panel_array.push(new DiseasePanel(sketch, 190+180, 10, "Lower\nrespiratory\ninfections (8%)", "", resp_ded, resp_icon , color_red, person_img)); disease_panel_array.push(new DiseasePanel(sketch, 190+180*2, 10, "Tracheal,\n bronchus,\n and lung\n cancer (6%)", "", thra_ded, thra_icon , "#ffae00", person_img)); disease_panel_array.push(new DiseasePanel(sketch, 10, 650, "Ischemic\n heart\n disease (6%)", "", heart_ded, heart_icon , "maroon", person_img)); disease_panel_array.push(new DiseasePanel(sketch, 190, 650, "Type II\n diabetes (4%)", "", liver_ded, liver_icon , color_yellow, person_img)); disease_panel_array.push(new DiseasePanel(sketch, 190+180, 650, "Neonatal\ndisorders (3%)", "", baby_ded, baby_icon , color_red, person_img)); //sketch.noLoop(); } sketch.draw = function() { sketch.background("white"); for(let i = 0; i < disease_panel_array.length; i++) { disease_panel_array[i].display(); } } })30:T685,## PM2.5 pollution Sources Some of the main sources of PM2.5 pollution globally include: 1. Combustion sources: Vehicles, power plants, industrial processes, and residential heating and cooking all release PM2.5 through the burning of fossil fuels. 2. Dust and soil: Construction sites, windblown dust from agricultural fields, and natural sources like dust storms can contribute to PM2.5 levels. 3. Forest fires: Wildfires and deforestation can release large amounts of PM2.5 into the air. 4. Residential activities: Use of household cleaning products and personal care products can release PM2.5 through volatilization. 5. Biological sources: Pollen, mold spores, bacteria, and other organic particles can contribute to PM2.5 levels in the air. 6. Sea spray: Salts and other particles from sea spray can contribute to PM2.5 levels near coastal areas. It's important to note that PM2.5 levels can vary greatly by region and can be influenced by factors such as weather conditions, economic activity, and government regulations. ### The Democratic Republic of the Congo main sources - Use of biomass for cooking and heating: Many households in the DRC rely on burning solid fuels such as wood, charcoal, and dung for cooking and heating. - Transportation: The use of vehicles and motorcycles, particularly diesel-powered ones. - Industrial activities: Industries such as mining, manufacturing, and power generation release PM2.5 through the burning of fossil fuels and other processes. - Agricultural activities: Dust from agricultural fields, as well as the use of fertilizers and pesticides. - Waste burning: The open burning of waste, particularly in urban areas.31:T7b4,p5(sketch => { let canvas_width = 800; let canvas_height = 800; let gender = gender_input; let months_lived = monthDiff(birth_date, new Date()); let months_removed = months_removed_input.months_lost_2019; let months_intersection = 0; let total_months = {"Male" : Math.max(months_lived, parseInt(59.42 * 12)), "Female" : Math.max(months_lived, parseInt(62.54*12))}; let month_square_size = 20; let month_square_separation = 3; let month_posx = canvas_width / 2 - 12 * (month_square_size + month_square_separation); let month_posy = 50; sketch.setup = function() { sketch.createCanvas(canvas_width, canvas_height); sketch.noLoop(); if(months_lived + months_removed >= total_months[gender]) { months_intersection = (months_lived + months_removed)- total_months[gender]; months_lived = months_lived - months_intersection; months_removed = months_removed - months_intersection; } } sketch.draw = function() { sketch.background("white"); sketch.draw_months(); } sketch.draw_months = function() { sketch.fill(255); let posx = month_posx; let posy = month_posy; let current_month = 0; for(let i = 1; i <= total_months[gender]; i++) { sketch.fill("white"); if(current_month < months_lived) sketch.fill(color_yellow); else if(months_intersection != 0 & current_month >= months_lived & current_month < total_months[gender] - months_removed) sketch.fill(color_blue); else if(current_month >= total_months[gender] - months_removed) sketch.fill(color_red); sketch.square(posx, posy, month_square_size); current_month = current_month + 1; posx = posx + month_square_size + month_square_separation; if(i % 24 == 0) { posy = posy + month_square_size + month_square_separation; posx = month_posx; } } } })32:T830,// Copyright 2021, Observable Inc. // Released under the ISC license. // https://observablehq.com/@d3/color-legend function Swatches(color, { columns = null, format, unknown: formatUnknown, swatchSize = 15, swatchWidth = swatchSize, swatchHeight = swatchSize, marginLeft = 0 } = {}) { const id = `-swatches-${Math.random().toString(16).slice(2)}`; const unknown = formatUnknown == null ? undefined : color.unknown(); const unknowns = unknown == null || unknown === d3.scaleImplicit ? [] : [unknown]; const domain = color.domain().concat(unknowns); if (format === undefined) format = x => x === unknown ? formatUnknown : x; function entity(character) { return `&#${character.charCodeAt(0).toString()};`; } if (columns !== null) return htl.html`

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

${label}

`; })}

`; return htl.html`

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

`; }33:T4be,class DiseasePanel { constructor(p5, x, y, title, description, num_persons, icon, color, person_img) { this.p5 = p5; this.x = x; this.y = y; this.w = 150; this.h = 250; this.color = color; this.title = title; this.description = description; this.num_persons = num_persons; this.image_icon = icon; this.person_img = person_img; } display() { this.p5.strokeWeight(0); this.p5.fill(140); this.p5.rect(this.x+3, this.y+3, this.w, this.h, 10); this.p5.strokeWeight(1); this.p5.fill("white"); this.p5.rect(this.x, this.y, this.w, this.h, 10); this.p5.tint(this.color); this.p5.image(this.image_icon, this.x + this.w/2, this.y + 60, 100, 100); this.p5.fill(150); this.p5.textSize(20); this.p5.textAlign(this.p5.CENTER); this.p5.text(this.title, this.x + this.w/2, this.y + this.h / 2 + 10); let posx = this.x + 20; let posy = this.y + this.h + 30; for(let i = 1; i <= this.num_persons; i++) { this.p5.image(this.person_img, posx, posy); posx = posx + 12; if(i % 10 == 0) { posy = posy + 30; posx = this.x + 20; } } } 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2d:{"title":" Air pollution (PM2.5) in Democratic Republic of the Congo ","cells":[{"id":0,"value":"# Air pollution (PM2.5) in Democratic Republic of the Congo ","pinned":false,"hidden":false,"mode":"md"},{"id":270,"value":"obs_cover_mesaDeTrabajo1 = FileAttachment(\"congo_.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":996,"value":"*This project began as a deliverable for Girona University's Hackathon 2023 contest for Data Science Master Students.*\n\n[Click here to see the hackathon solution](https://observablehq.com/d/12f1109dedf393e8)","pinned":false,"hidden":false,"mode":"md"},{"id":836,"value":"Made by Judit Quintana, Wilber E. Bermeo, Isaac de Palau, Feng Lin and Samuel Loaiza","pinned":false,"hidden":false,"mode":"md"},{"id":288,"value":"toc({\n title: 'Index',\n headers: 'h2'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":296,"value":"## Democratic Republic of the Congo is the most Air Polluted country in Africa\nAccording to the Air Quality Life Index (AQLI), the African country with the higher levels of PM2.5 is the Democratic Republic of the Congo.","pinned":false,"hidden":false,"mode":"md"},{"id":300,"value":"choro = bertin.draw({\n params: { \n extent: Africa,\n projection: d3.geoEckert3()},\n layers: [\n {\n type: \"layer\",\n geojson: Africa,\n fill: {\n type: \"choro\",\n values: \"pm\",\n nbreaks: 9,\n method: 'jenks',\n //pal: pal,\n colors: 'YlOrRd',\n leg_round: -2,\n leg_title: `PM2.5 in 2018 (μg/m³)`,\n leg_x: 180,\n leg_y: 550\n },\n\n tooltip: [\"$country\", \"pm generated in 2018:\",\"$pm\", \"(μg/m³)\"]\n },\n { type: \"graticule\" },\n\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"## What is Particulate Matter and how it affects you?\nAirborne particulate matter (PM) is not a single pollutant, but rather is a mixture of many chemical species. It is a complex mixture of solids and aerosols composed of small droplets of liquid, dry solid fragments, and solid cores with liquid coatings. Particles vary widely in size, shape and chemical composition, and may contain inorganic ions, metallic compounds, elemental carbon, organic compounds, and compounds from the earth’s crust. Particles are defined by their diameter for air quality regulatory purposes. Those with a diameter of 10 microns or less (PM10) are inhalable into the lungs and can induce adverse health effects. Fine particulate matter is defined as particles that are 2.5 microns or less in diameter (PM2.5). Therefore, PM2.5 comprises a portion of PM10.","pinned":false,"hidden":false,"mode":"md"},{"id":306,"value":"### Size of a PM2.5","pinned":false,"hidden":false,"mode":"md"},{"id":884,"value":"Plot.plot({\n height: 580,\n width: 900,\n grid: false,\n x: {\n label: null,\n axis: null,\n domain: [0,6]\n },\n y: {\n label: null,\n axis: null,\n domain: [0.8,1.2]\n },\n r:{\n range: [0,200]\n },\n color: {\n range: [color_red,color_blue],\n },\n marks: [\n Plot.dot(length_comparision, {\n y: \"y\",\n x: \"x\",\n r: \"r\", \n fill: d => d.x == 2,\n strokeWidth: 2,\n mixBlendMode: \"multiply\",\n title: \"z\"\n }),\n Plot.text(\n length_comparision,\n {\n y: \"y\",\n x: \"x\",\n text: \"z\",\n fill: \"black\",\n fontSize: 25,\n dy: -250,\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"### Particle and disease\nHow is the air pollution impacting the people of the Democratic Republic of the Congo?\n\nThe Congo has 3.2μg/m³ of PM2.5. Small air particles are an important factor of deaths from chronic obstructive pulmonary disease, lower respiratory infections, stroke, tracheal, bronchus and lung cancer. It is also a contributing factor in type 2 diabetes and neonatal disorders.\n\n**13 deaths per 100,000 people** attributable to fine particle pollution in 2019.\n\nIn 2019, fine particle outdoor air pollution was responsible for a certain percentage of deaths from each disease. \n\nℹ️ *Each human figure represents 100 deaths.*","pinned":false,"hidden":false,"mode":"md"},{"id":204,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":388,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":765,"value":"## Democratic Republic of the Congo pollution hotspots","pinned":false,"hidden":false,"mode":"md"},{"id":1066,"value":"We have determined the hotspots where the greatest impact should be taken into account. For determiyin it we have taken into account where were the highest emissions and where most people live. \n\n**pm / population_density**","pinned":false,"hidden":false,"mode":"md"},{"id":1007,"value":"map = Choropleth(pm_cod, {\n id: d => d.name_2,\n value: d=> (d.pm_2018/d.population)*1000,\n domain: [0,0.5],\n range: d3.schemeReds[3],\n features: cod,\n title: (f, d) => `${d.name_2}, ${d.name_1}`,\n width: width,\n height: width-300,\n projection: d3.geoEqualEarth().scale(2000).rotate([-25,0])\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":690,"value":"## TOP 10 polluting cities in DRC\n\n1. **[Gemena](https://en.wikipedia.org/wiki/Gemena)** (42.4 μg/m³)\n2. **[Mobayi-Mbongo](https://en.wikipedia.org/wiki/Mobayi-Mbongo)** (41.1 μg/m³)\n3. **[Bosobolo](https://en.wikipedia.org/wiki/Bosobolo)** (40.7 μg/m³)\n4. **[Budjala](https://en.wikipedia.org/wiki/Budjala)** (40.9 μg/m³)\n5. **[Gbadolite](https://en.wikipedia.org/wiki/Gbadolite)** (40.3 μg/m³)\n6. **[Kungu](https://en.wikipedia.org/wiki/Kungu)** (40.1 μg/m³)\n7. **[Libenge](https://en.wikipedia.org/wiki/Libenge)** (39.2 μg/m³)\n8. **[Oshwe](https://en.wikipedia.org/wiki/Oshwe)** (38.9 μg/m³)\n9. **[Kutu](https://en.wikipedia.org/wiki/Kutu)** (38.8 μg/m³)\n10. **[Bomongo](https://en.wikipedia.org/wiki/Bomongo)** (38.6 μg/m³)","pinned":false,"hidden":false,"mode":"md"},{"id":396,"value":"## You life in years as a Democratic Republic of the Congo citizen","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"viewof gender_input = Inputs.radio([\"Male\", \"Female\"], {label: \"Gender\", value : \"Male\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"viewof birth_date = Inputs.date({label: \"What's your date of birth?\", value: \"1999-01-01\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":957,"value":"viewof le_cod_per_region_Searcher = Inputs.search(\n le_cod_per_region, \n {\n label: \"Search your region\",\n multiple: true,\n autocomplete: true,\n columns: ['region_name', 'months_lost_2019']\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":149,"value":"viewof months_removed_input = Inputs.select(le_cod_per_region_Searcher, { label: \"Region\", format: x => x.region_name})","pinned":false,"hidden":false,"mode":"ojs"},{"id":352,"value":"Swatches(d3.scaleOrdinal([\"Your current lived months\", \"Months losed because of PM\", \"Months lived beyond your life expectancy\"], [color_yellow,color_red,color_blue]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":464,"value":"## Why the we have choosed the Democratic Republic of the Congo?\n\nWe have decided to make the exploration of The Democratic Republic of the Congo because is the most air polluted country in the continent of Africa.\nAlso has not mesures to solve it.\n\nActions taken towards cleaner air:\n- Clean production incentives - **No**\n- Vehicle emission standards - **No standards**\n- Solid waste burning - **No controlls**\n- Incentives for residential cooking and heating - **No**\n- Sustainable agricultural practices - **No**\n- Air quality management strategies - **No**\n- Air quality monitoring - **No monitoring**\n- Air quality standards - **No**\n","pinned":false,"hidden":false,"mode":"md"},{"id":589,"value":"## Conclusions\n","pinned":false,"hidden":false,"mode":"md"},{"id":593,"value":"The Democratic Republic of the Congo does not have environmental standards on air quality and can therefore apply for funding for air quality standardization and management projects. So the future implementation of air quality standards can directly affect the life expectancy of the citizens of the congo. \nReformulation of air quality management efforts to take into account the multiple development benefits associated with reducing emissions for cleaner air and lengthening the lifespan of citizens of the Democratic Republic of Congo.","pinned":false,"hidden":false,"mode":"md"},{"id":188,"value":"## Resources","pinned":false,"hidden":false,"mode":"md"},{"id":597,"value":"**UN Environment Programme - (September 07, 2021).** *Actions on Air Quality: A Global Summary of Policies and Programmes to Reduce Air Pollution* https://www.unep.org/resources/report/actions-air-quality-global-summary-policies-and-programmes-reduce-air-pollution","pinned":false,"hidden":false,"mode":"md"},{"id":405,"value":"**California Air Resources Board - (January 04, 2023).** *Inhalable Particulate Matter and Health (PM2.5 and PM10)* https://ww2.arb.ca.gov/resources/inhalable-particulate-matter-and-health","pinned":false,"hidden":false,"mode":"md"},{"id":418,"value":"**The AQLI - (January 04, 2023).** *How much longer would you live if you breathed clean air?* https://aqli.epic.uchicago.edu/the-index/","pinned":false,"hidden":false,"mode":"md"},{"id":412,"value":"**UNEP's - (January 04, 2023).** *Pollution Action Note – Data you need to know* https://www.unep.org/interactive/air-pollution-note/","pinned":false,"hidden":false,"mode":"md"},{"id":518,"value":"**Global Burden of Disease Study 2019** *Institute for Health Metrics and Evaluation (IHME)* \nhttps://doi.org/10.1038/s41467-021-23853-y","pinned":false,"hidden":false,"mode":"md"},{"id":881,"value":"---","pinned":false,"hidden":false,"mode":"md"},{"id":423,"value":"## Development","pinned":false,"hidden":false,"mode":"md"},{"id":619,"value":"### Dataset CONGO","pinned":false,"hidden":false,"mode":"md"},{"id":621,"value":"congo_2019 = FileAttachment(\"congo_2019.csv\").csv(); // RAW DATA - DO NOT USE!","pinned":false,"hidden":false,"mode":"ojs"},{"id":634,"value":"congo_data = congo_2019.filter(d => d.id_country == 'COD');","pinned":false,"hidden":false,"mode":"ojs"},{"id":644,"value":"emisiones_2019 = {\n let array_emisiones = {};\n let num_paises = Object.keys(congo_data).length;\n for(let i = 0; i < num_paises; i++)\n {\n let country_name = congo_data[i][\"region_name\"];\n array_emisiones[country_name] = congo_data[i][\"pollution\"];\n\n }\n\n return array_emisiones;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":952,"value":"le_cod_per_region = Object.values(pm_cod.reduce((a,obj) => {\n if (a[obj.name_1]){\n a[obj.name_1][\"llpp\"].push(parseFloat(obj.llpp_who_2018))\n } else {\n a[obj.name_1] = { id: obj.name_1, llpp: [parseFloat(obj.llpp_who_2018)]}\n }\n return a\n },{})).map(obj => {\n return (\n {\n region_name: obj.id,\n months_lost_2019: parseInt((obj.llpp.reduce((partialSum, a) => partialSum + a, 0)/obj.llpp.length) * 12)\n }\n )\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":753,"value":"pm_cod = FileAttachment(\"aqli_regional_data_COD.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1016,"value":"cod = FileAttachment(\"cod.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":132,"value":"### Aux functions","pinned":false,"hidden":false,"mode":"md"},{"id":135,"value":"function monthDiff(d1, d2) {\n var months;\n months = (d2.getFullYear() - d1.getFullYear()) * 12;\n months -= d1.getMonth();\n months += d2.getMonth();\n return months <= 0 ? 0 : months;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":359,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":208,"value":"### Aux classes","pinned":false,"hidden":false,"mode":"md"},{"id":210,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"### Globals","pinned":false,"hidden":false,"mode":"md"},{"id":195,"value":"Population of the Dem. Rep. of Congo in 2020:","pinned":false,"hidden":false,"mode":"md"},{"id":197,"value":"congo_pop_2020 = 98850000","pinned":false,"hidden":false,"mode":"ojs"},{"id":556,"value":"### PM size ","pinned":false,"hidden":false,"mode":"md"},{"id":904,"value":"length_comparision = [\n {\n x: 2,\n y: 1,\n z:\"Hair Furiculum\",\n r: 70,\n }, {\n x: 5, \n y: 1, \n z:\"PM 2.5\",\n r:1,\n }\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"### Africa map","pinned":false,"hidden":false,"mode":"md"},{"id":457,"value":"pm_2018 = pm.map(x =>(\n {country : x.country,\n continent : country_continent(x),\n pm : x.pm_2018 * 1000,\n year : 2018,\n population : x.population,\n iso_code : x.iso_alpha3,\n\t}\n)).filter(d => d.continent != '')","pinned":false,"hidden":false,"mode":"ojs"},{"id":454,"value":"pm = FileAttachment(\"aqli_global_data.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":452,"value":"countries_with_regional_code = FileAttachment(\"countries_with_regional_code.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":450,"value":"function country_continent(row){\n if (countries_with_regional_code.filter(x => x['alpha-3'] == row.iso_alpha3).length > 0) {\n return countries_with_regional_code.filter(x => x['alpha-3'] == row.iso_alpha3)[0]['region']}\n return ''}","pinned":false,"hidden":false,"mode":"ojs"},{"id":444,"value":"world = d3.json(\n \"https://raw.githubusercontent.com/neocarto/bertin/main/data/world.geojson\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":438,"value":"Africa = (\n { type: \"FeatureCollection\",\n features: bertin.merge(world, \"ISO3\", pm_2018.filter(d => d.continent == 'Africa'), \"iso_code\").features.filter(d => d.properties.continent == 'Africa')\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":751,"value":"countries_url = \"https://raw.githubusercontent.com/deldersveld/topojson/master/world-countries-sans-antarctica.json\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":164,"value":"### Style","pinned":false,"hidden":false,"mode":"md"},{"id":182,"value":"Primary colors: Congo Flag","pinned":false,"hidden":false,"mode":"md"},{"id":873,"value":"color_palette = FileAttachment(\"color_palette.PNG\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":166,"value":"color_blue = \"#007fff\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":169,"value":"color_red = \"#ce1021\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":172,"value":"color_yellow = \"#f7d618\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":243,"value":"### Images","pinned":false,"hidden":false,"mode":"md"},{"id":470,"value":"obs_image_URL = await FileAttachment(\"internal-organ.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":245,"value":"stroke_img_URL = await FileAttachment(\"brain.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":506,"value":"lungs_img_URL = await FileAttachment(\"lungs.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":529,"value":"heart_img_URL = await FileAttachment(\"heart-organ.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":544,"value":"liver_img_URL = await FileAttachment(\"liver.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":554,"value":"alien_img_URL = await FileAttachment(\"embryo.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":565,"value":"cross_img_URL = await FileAttachment(\"cross-mark.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":368,"value":"person_img_URL = await FileAttachment(\"person.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":804,"value":"skull_img_URL = await FileAttachment(\"heart-plus.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"### Imports","pinned":false,"hidden":false,"mode":"md"},{"id":446,"value":"bertin = require(\"bertin@1.5.9\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":442,"value":"d3 = require(\"d3@7\", \"d3-geo-projection@4\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":749,"value":"d4 = require(\"d3-dsv@1\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":291,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":22,"value":"import {p5} from \"@tmcw/p5\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"import {slider, color} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":746,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1009,"value":"import {Choropleth, Legend} from \"@d3/choropleth\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}