2b:null 2c:["$","title",null,{"children":"Access to Family planning | Observable | Observable"}] 31:T51f,rename = new Map([ ["Antigua and Barbuda", "Antigua and Barb."], ["Bolivia (Plurinational State of)", "Bolivia"], ["Bosnia and Herzegovina", "Bosnia and Herz."], ["Central African Republic", "Central African Rep."], ["Cook Islands", "Cook Is."], ["Democratic People's Republic of Korea", "North Korea"], ["Democratic Republic of the Congo", "Dem. Rep. Congo"], ["Dominican Republic", "Dominican Rep."], ["Equatorial Guinea", "Eq. Guinea"], ["Eswatini", "eSwatini"], // formerly known as Swaziland ["Iran (Islamic Republic of)", "Iran"], ["Lao People's Democratic Republic", "Laos"], ["Marshall Islands", "Marshall Is."], ["Republic of Korea", "South Korea"], ["Russian Federation", "Russia"], ["Saint Kitts and Nevis", "St. Kitts and Nevis"], ["Saint Vincent and the Grenadines", "St. Vin. and Gren."], ["Sao Tome and Principe", "São Tomé and Principe"], ["Solomon Islands", "Solomon Is."], ["South Sudan", "S. Sudan"], ["Syrian Arab Republic", "Syria"], ["Türkiye", "Turkey"], ["United Kingdom of Great Britain and Northern Ireland", "United Kingdom"], ["United Republic of Tanzania", "Tanzania"], ["Venezuela (Bolivarian Republic of)", "Venezuela"], ["Viet Nam", "Vietnam"], ["occupied Palestinian territory, including east Jerusalem", "Palestine"], ])32:[{"id":"d0273f3c7a504fecb982e17a51c5313916464467e2db6de2761b9b8b5b873ca86b5b1306d959a4a65bd832dfb00b02b5e363e953f579aa27b802ace37cddefda","href":"https://static.observableusercontent.com/files/d0273f3c7a504fecb982e17a51c5313916464467e2db6de2761b9b8b5b873ca86b5b1306d959a4a65bd832dfb00b02b5e363e953f579aa27b802ace37cddefda","name":"family-planning.csv","type":"text/csv","lastModified":1678269972391.5,"size":4629}] 2f:{"id":"652e557706830bb5","slug":"access-to-family-planning","title":"Access to Family planning","thumbnail":"3c7af4524c0317b4f89a0beb19d8fc1d8855e51ed562d301183881b4ce5cd166","access_level":"public","create_time":"$D2023-03-08T10:05:38.321Z","update_time":"$D2023-03-08T15:25:22.144Z","view_time":null,"first_public_time":"$D2023-03-08T17:18:27.935Z","fork":true,"stars":13,"starred":false,"owner":{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024"},"version":519,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"45a379fcfcb14253","login":"fil","name":"Fil","avatar_url":"https://avatars-next.observableusercontent.com/d5b1777907117a2bdf2a07eb98600d7a9beb21639fde42b9753e95c88c7731a6","tier":null}],"members":[{"type":"individual","id":"45a379fcfcb14253","login":"fil","name":"Fil","avatar_url":"https://avatars-next.observableusercontent.com/d5b1777907117a2bdf2a07eb98600d7a9beb21639fde42b9753e95c88c7731a6","tier":null,"role":"editor"},{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":16,"collection":{"id":"97996058c85841a2","slug":"plot-examples","title":"Plot Examples","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"type":"notebook","body":{"title":"Access to Family planning","cells":[{"id":0,"value":"# Access to Family planning\n## Choropleth map with Observable Plot\n\n*Women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods (%), 2020.*
\nData: [United Nations Population Division, World Health Organisation](https://apps.who.int/gho/data/view.main.FAMILYPLANNINGv?lang=en).\n\nFor a D3 version, see [World Choropleth](https://observablehq.com/@d3/world-choropleth).","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":9,"value":"choropleth = Plot.plot({\n width: 928,\n projection: { type: \"equal-earth\", rotate: [-10, 0] },\n color: {\n type: \"quantize\",\n n: 10,\n unknown: \"#ddd\",\n label: \"Share of Women with access to modern methods of family planning (%)\",\n legend: true,\n width: 360\n },\n marks: [\n Plot.sphere({ fill: \"#f0faff\", stroke: \"currentColor\" }),\n Plot.geo(land, { fill: \"currentColor\", dx: 1, dy: 1 }), // shade\n Plot.graticule(),\n Plot.geo(countries, {\n fill: (d) => byName.get(d.properties.name)?.value,\n stroke: \"currentColor\",\n strokeWidth: 0.25,\n title: (d) => `${d.properties.name}: ${byName.get(d.properties.name)?.value.toFixed(1) ?? \"No data\"}`\n }),\n\n // Gray swatch legend for no data\n () =>\n svg`No data.`\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":227,"value":"A [choropleth map](https://en.wikipedia.org/wiki/Choropleth_map) shows each geographic feature (here, the countries) with a distinct color based on the data associated with that feature. A few precautions are necessary when joining the shapes with the data, to ensure that every data point has an associated geographic feature. Features that have no associated data will be shown as such (with a gray color), and the legend will mention that.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":215,"value":"First, we read the data—Health-adjusted life expectancy, 2016— from a CSV FileAttachment, which has both the values and the country names.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":73,"value":"fp = FileAttachment(\"family-planning.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":399,"value":"In order to associate the values with the geographic features of the map, we index the dataset by country name, which will allow fast look-ups.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":11,"value":"byName0 = d3.index(fp, (d) => d.country)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":236,"value":"The following—minimalist—choropleth map uses the **unknown** scale option for countries with no available value.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":115,"value":"minimalist = Plot.geo(countries, {\n fill: (d) => byName0.get(d.properties.name)?.value\n}).plot({ projection: \"equirectangular\", color: { scheme: \"blues\", unknown: \"#ccc\" } })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":204,"value":"As we can see, there are quite a few gaps. A closer look at our dataset reveals that a few countries are named differently in the geographic shapes and in the health dataset. Here are the countries we’re missing because their names are different in the geo shape:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":499,"value":"html`${[...byName0.keys()].filter((name) => !countries.features.find((d) => d.properties.name === name)).sort().join(\" / \")}`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":512,"value":"We will rename them:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":240,"value":"$31","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":249,"value":"We create an updated byName index, including renamed countries, and double-check the missing elements:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":245,"value":"byName = d3.index(fp, (d) => rename.get(d.country) ?? d.country)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"renamed = Plot.geo(countries, {\n fill: (d) => byName.get(d.properties.name)?.value,\n stroke: \"red\",\n strokeWidth: (d) => byName.get(d.properties.name)?.value ? 0 : 1.5,\n sort: (d) => byName.get(d.properties.name)?.value,\n title: d => byName.get(d.properties.name)?.value ? \"\" : d.properties.name,\n}).plot({ projection: \"equirectangular\", color: { unknown: \"lime\", scheme: \"greys\" } })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":253,"value":"Conversely, we also make a table of the geographic features that are missing an associated health data point. The most populated is Taiwan, which is never present in the World Health Organization’s datasets. Kosovo, N. Cyprus, W. Sahara, Somaliland… are also often missing. For once, Palestine is present (although its value if from “2019-2020”, where the other countries are from “2020”. And, more surprisingly, Iceland is missing. The datasets are incomplete, and that is expected—we’ll show these features in gray with the **unknown** option. The tables below help us make sure that there is nothing _we_ missed when we joined the datasets. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":316,"value":"_Data points without a corresponding geo shape (should be empty):_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":182,"value":"Inputs.table(\n [...byName].filter(\n ([name]) => !countries.features.find((d) => d.properties.name === name)\n )\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":311,"value":"_Countries without associated data (to double-check which “big” countries are missing):_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":178,"value":"Inputs.table(\n countries.features\n .filter((d) => byName.get(d.properties.name)?.value == null)\n .map(({ id, properties }) => ({ id, ...properties }))\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":380,"value":"For the base map, we use [our preferred variant](https://observablehq.com/@visionscarto/geo#basemaps) of TopoJSON’s world-atlas files, in resolution 50m.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":5,"value":"import { land50m as land, countries50m as countries } from \"@visionscarto/geo\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W32","stdlib":"1","titling":"auto"}} 30:"$2f:body"