25:["$","title",null,{"children":"Air pollution (PM2.5) in Democratic Republic of the Congo | Vogelmann | Observable"}] 49: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(); } } })4a:Ta79,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; 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; let cross_img, skull_img; //this.p5.image(this.image_icon, this.x + this.w/2, this.y + 60, 100, 100); sketch.preload = function() { cross_img = sketch.loadImage(cross_img_URL); skull_img = sketch.loadImage(skull_img_URL); } 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_bordeaux); //else if(current_month >= total_months[gender] - months_removed) sketch.fill(color_red); sketch.square(posx, posy, month_square_size); if(current_month >= total_months[gender] - months_removed){ sketch.tint(color_red); sketch.image(cross_img, posx, posy, month_square_size, month_square_size); } else if(months_intersection != 0 & current_month >= months_lived & current_month < total_months[gender] - months_removed) { sketch.tint("lightgrey"); sketch.image(skull_img, posx, posy, month_square_size, 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; } } } })4b: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`

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`; return htl.html`

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

`; }4c: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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29:{"id":"181fb946acb40cab","slug":null,"title":" Air pollution (PM2.5) in Democratic Republic of the Congo ","thumbnail":"49c13bcff3a79c33f9d971298795d731aabfe5a528af5ceb2a62ce6f8f4bc466","access_level":"public","create_time":"$D2023-02-04T21:21:07.307Z","update_time":"$D2023-09-25T14:19:25.394Z","view_time":null,"first_public_time":"$D2023-02-04T21:21:07.307Z","has_unpublished_edits":null,"fork":{"id":"12f1109dedf393e8","slug":null,"title":" Air pollution (PM2.5) in Democratic Republic of the Congo ","owner":{"id":"75f7d2ff35e926a2","login":"wilberquito","name":"Wil"}},"stars":1,"starred":false,"owner":{"type":"team","id":"cf0cffacb5e3b6d7","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":"starter_2024","bio":"🐦⚙️ Level 28 Aarakocra Tinkerer ||\n🔢♾️ Mathematician & Data Scientist ||\n🚀🤖 PhD (AI + Astrodynamics)\n☕💀 Fueled by caffeine and spite.","followed_by_you":false,"follows_you":false},"version":836,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"ddb96b3865d46af6","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":null,"bio":"🐦⚙️ Level 28 Aarakocra Tinkerer ||\n🔢♾️ Mathematician & Data Scientist ||\n🚀🤖 PhD (AI + Astrodynamics)\n☕💀 Fueled by caffeine and spite.","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"ddb96b3865d46af6","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"f9a8ef1628983b82","slug":"msc-tasks-exercises","title":"MSc DS tasks & exercises","owner":{"id":"cf0cffacb5e3b6d7","login":"vogelmann","name":"Vogelmann"}},"type":"notebook","body":{"titling":"auto","title":" Air pollution (PM2.5) in Democratic Republic of the Congo ","cells":[{"id":0,"value":"# Air pollution (PM2.5) in Democratic Republic of the Congo ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":270,"value":"obs_cover_mesaDeTrabajo1 = FileAttachment(\"obs_cover_Mesa de trabajo 1.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":288,"value":"toc({\n title: 'Index',\n headers: 'h2'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"## Democratic Republic of the Congo is the most Air Polluted country in Africa","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":300,"value":"choro = bertin.draw({\n params: { \n extent: Africa,\n projection: d3.geoEckert3()},\n layers: [\n {\n type: \"layer\",\n geojson: Africa,\n fill: {\n type: \"choro\",\n values: \"pm\",\n nbreaks: 9,\n method: 'jenks',\n //pal: pal,\n colors: 'YlOrRd',\n leg_round: -2,\n leg_title: `PM2.5 in 2018 (μg/m³)`,\n leg_x: 180,\n leg_y: 550\n },\n\n tooltip: [\"$country\", \"pm generated in 2018:\",\"$pm\", \"(μg/m³)\"]\n },\n { type: \"graticule\" },\n\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":306,"value":"### Size of a PM2.5","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":775,"value":"hair_pm = FileAttachment(\"hair.PNG\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"### Particle and disease prototype\nHow the air pollution is impacting the people from Democratic Republic of the Congo,well the congo have 3.2μg/m³ of PM2.5, when the fine paricules is a important factor in deaths from chronic obstructive pulmonary disease, lower respiratory infections, stroke, tracheal,bronchus and lung cancer. it alsa a contributing factor on type 2 diabetes and neonatal disorders.\n\n**13 deaths per 100,000 people** attributable to fine particle pollution in 2019\n\nPercent of deaths from each disease attributable to fine particle outdoor air pollution in 2019.
\n\nℹ️ *Each human figure represents 100 deaths.*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":204,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":388,"value":"## PM2.5 pollution Sources\n1. Combustion sources: Vehicles, power plants, industrial processes, and residential heating and cooking all release PM2.5 through the burning of fossil fuels.\n2. Dust and soil: Construction sites, windblown dust from agricultural fields, and natural sources like dust storms can contribute to PM2.5 levels.\n3. Forest fires: Wildfires and deforestation can release large amounts of PM2.5 into the air.\n4. Residential activities: Use of household cleaning products and personal care products can release PM2.5 through volatilization.\n5. Biological sources: Pollen, mold spores, bacteria, and other organic particles can contribute to PM2.5 levels in the air.\n6. Sea spray: Salts and other particles from sea spray can contribute to PM2.5 levels near coastal areas.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":765,"value":"## Democratic Republic of the Congo pollution hotspots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":767,"value":"{\n const map = vl.markGeoshape({fill: \"ivory\", stroke: \"black\"})\n .data(vl.topojson(countries_url).feature(\"countries1\"))\n \n const city_points = vl.markPoint({filled: true, opacity: 0.5})\n .data(pm_cod_2018)\n .transform(vl.filter(\"datum.pm_per_pop >= 0\"))\n .encode(vl.latitude().fieldQ(\"lat\"),\n vl.longitude().fieldQ(\"lng\"),\n vl.color().value(\"red\"),\n vl.size().fieldQ(\"pm_per_pop\").scale({range:[100, 2000]}),\n vl.tooltip(\"city\"))\n \n return vl.layer(map, city_points)\n .project(vl.projection(\"mercator\").scale(2000).center(22, -3))\n .width(700)\n .height(700)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":396,"value":"## Your life in years as a Democratic Republic of the Congo citizen","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"viewof gender_input = Inputs.radio([\"Male\", \"Female\"], {label: \"Gender\", value : \"Male\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"viewof birth_date = Inputs.date({label: \"What's your date of birth?\", value: \"1990-01-01\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"viewof months_removed_input = Inputs.range([0, 50*12], {label: \"Removed life months (provisional)\", step: 1, value : 48})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"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_bordeaux]))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"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 it also 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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":589,"value":"## Conclusion\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":468,"value":"## Methodologies used for the diferent scenarios","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":627,"value":"### Team Owl methodology","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":630,"value":"- Analisys of the geospatial data \n- Study of rasters funcionallity\n- Maps have been generated for the whole world from 1999 to 2019. For each pixel of the raster, we looked to see if they belonged to the sea and if not, we looked to see which country they belonged to.\n- Downsampling x20\n- Calculation of maximum, minimum and average PM2.5 aggregation.\n- With this, polygons of countries, provinces and autonomous communities have been generated.\n\n### Team Pandas methodology\nUsing a Random Regression model \nWith the following data: \n - Population\n - Region\n - Life Expentancy\n - PM2.5 avg\n\nAfter this Gives the Life Expentacny of every country given the population and the contamination.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":815,"value":"### Team Pandas","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":188,"value":"## Resources","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":423,"value":"## Development","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":619,"value":"### Dataset CONGO","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":621,"value":"congo_2019 = FileAttachment(\"congo_2019.csv\").csv(); // RAW DATA - DO NOT USE!","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":634,"value":"congo_data = congo_2019.filter(d => d.id_country == 'COD');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":682,"value":"Object.entries(emisiones_2019).sort(function(a,b){return b[1]-a[1]});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"### Aux functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":359,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":757,"value":"function city_latitude(row){\n if (congo_coor.filter(x => x['city'] == row.name_2).length > 0) {\n return congo_coor.filter(x => x['city'] == row.name_2)[0]['latitude']}\n return ''}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":759,"value":"function city_longitude(row){\n if (congo_coor.filter(x => x['city'] == row.name_2).length > 0) {\n return congo_coor.filter(x => x['city'] == row.name_2)[0]['longitude']}\n return ''}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":761,"value":"function pm_per_pop(row){\n if (row['pm_2018'] >0 && row['population']> 0 ) {\n return row['pm_2018']*1000000 / row['population']}\n return ''}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":763,"value":"pm_cod_2018 = pm_cod.map(x =>(\n {city : x.name_2,\n pm : x.pm_2018,\n pm_per_pop: pm_per_pop(x),\n year : 2018,\n lat : city_latitude(x),\n lng : city_longitude(x),\n iso_code: x.iso_alpha3,\n pop: x.population\n\t}\n)).filter(d => d.lat != '' && d.lng != '')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"### Aux classes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":210,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"### Globals","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":195,"value":"Population of the Dem. Rep. of Congo in 2020:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"congo_pop_2020 = 98850000","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"### PM size ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":637,"value":"bubbleComponentRadius = 50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":575,"value":"render = d3Render.default","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":579,"value":"d3Render = require('d3-render@v0.2.4')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":599,"value":"circleComponent = ({\n key,\n r,\n cx,\n cy,\n fill,\n randomDelay = Math.random() * 300\n}) => {\n return {\n append: 'circle',\n key,\n r: { enter: r, exit: 0 },\n cx,\n cy,\n fill,\n duration: 1000,\n // Add some randomness movement of circles\n delay: randomDelay\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":558,"value":"bubbleComponent = ({\n name,\n id,\n value,\n r,\n x,\n y,\n fill,\n colour,\n duration = 1000\n}) => {\n return {\n append: 'g',\n key: id,\n transform: {\n enter: `translate(${x + 1},${y + 1})`,\n exit: `translate(${width / 2},${450 / 2})`\n },\n duration,\n delay: Math.random() * 300,\n children: [\n circleComponent({ key: id, r, fill, duration }),\n ...labelComponent({\n key: id,\n countryName: name,\n isoCode: id,\n value,\n r,\n colour,\n duration\n })\n ]\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":606,"value":"labelComponent = ({ isoCode, countryName, value, r, colour }) => {\n // Don't show any text for radius under 12px\n if (r < 12) {\n return [];\n }\n\n const circleWidth = r * 2;\n const nameWidth = countryName.length * 10;\n const shouldShowIso = nameWidth > circleWidth;\n const newCountryName = shouldShowIso ? isoCode : countryName;\n const shouldShowValue = r > 18;\n\n let nameFontSize;\n\n if (shouldShowValue) {\n nameFontSize = shouldShowIso ? '10px' : '12px';\n } else {\n nameFontSize = '8px';\n }\n\n return [\n textComponent({\n key: isoCode,\n text: newCountryName,\n fontSize: nameFontSize,\n y: shouldShowValue ? '-0.2em' : '0.3em',\n fillOpacity: 1,\n colour\n }),\n ...(shouldShowValue\n ? [\n textComponent({\n key: isoCode,\n text: format(value),\n fontSize: '10px',\n y: shouldShowIso ? '0.9em' : '1.0em',\n fillOpacity: 0.7,\n colour\n })\n ]\n : [])\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":608,"value":"format = value => {\n const newValue = d3.format(\"0.2s\")(value);\n\n if (newValue.indexOf('m') > -1) {\n return parseInt(newValue.replace('m', '')) / 1000;\n }\n\n return newValue;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":610,"value":"textComponent = ({\n key,\n text,\n x = 0,\n y = 0,\n fontWeight = 'bold',\n fontSize = '12px',\n textAnchor = 'middle',\n fillOpacity = 1,\n colour,\n r,\n duration = 1000\n}) => {\n return {\n append: 'text',\n key,\n text,\n x,\n y,\n textAnchor,\n fontFamily: 'sans-serif',\n fontWeight,\n fontSize,\n fillOpacity: { enter: fillOpacity, exit: 0 },\n fill: colour,\n duration,\n style: {\n pointerEvents: 'none'\n }\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":227,"value":"### life espectancy files","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":234,"value":"Life_expectancy = FileAttachment(\"Life_expectancy-10.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"### Data Fen","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":454,"value":"pm = FileAttachment(\"aqli_global_data.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":452,"value":"countries_with_regional_code = FileAttachment(\"countries_with_regional_code.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":450,"value":"function country_continent(row){\n if (countries_with_regional_code.filter(x => x['alpha-3'] == row.iso_alpha3).length > 0) {\n return countries_with_regional_code.filter(x => x['alpha-3'] == row.iso_alpha3)[0]['region']}\n return ''}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":444,"value":"world = d3.json(\n \"https://raw.githubusercontent.com/neocarto/bertin/main/data/world.geojson\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":438,"value":"Africa = (\n { type: \"FeatureCollection\",\n features: bertin.merge(world, \"ISO3\", pm_2018.filter(d => d.continent == 'Africa'), \"iso_code\").features.filter(d => d.properties.continent == 'Africa')\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"regions = d3.csv(\n \"https://raw.githubusercontent.com/neocarto/bertin/main/data/data.csv\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":164,"value":"### Style","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":182,"value":"Primary colors:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":166,"value":"color_blue = \"#007fff\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"color_red = \"#ce1021\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":172,"value":"color_yellow = \"#f7d618\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"
\nAuxiliar colors:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":178,"value":"color_bordeaux = \"#5c3a4b\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"### Images","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":470,"value":"obs_image_URL = await FileAttachment(\"internal-organ.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"stroke_img_URL = await FileAttachment(\"brain.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"lungs_img_URL = await FileAttachment(\"lungs.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":529,"value":"heart_img_URL = await FileAttachment(\"heart-organ.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"liver_img_URL = await FileAttachment(\"liver.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":554,"value":"alien_img_URL = await FileAttachment(\"embryo.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":565,"value":"cross_img_URL = await FileAttachment(\"cross-mark.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":368,"value":"person_img_URL = await FileAttachment(\"person.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":804,"value":"skull_img_URL = await FileAttachment(\"heart-plus.png\").url();","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"
\n---\n### Addendum...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":446,"value":"bertin = require(\"bertin@1.5.9\");","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":442,"value":"d3 = require(\"d3@7\", \"d3-geo-projection@4\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":291,"value":"import { toc } from \"@nebrius/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"import {p5} from \"@tmcw/p5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"import {slider, color} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":749,"value":"d4 = require(\"d3-dsv@1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":751,"value":"countries_url = \"https://raw.githubusercontent.com/deldersveld/topojson/master/world-countries-sans-antarctica.json\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":753,"value":"pm_cod = FileAttachment(\"aqli_regional_data_COD.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":755,"value":"congo_coor = FileAttachment(\"data.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"viewof colorHighlight = color({\n value: \"#ff0080\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 24:null