32:T614,map = { const container = html`

`; yield container; const map = L.map(container).setView([0,0], 10); var markers = cities.map(p => { return L.geoJSON({ "type": "Point", "coordinates": [ p['lon'], p['lat'] ] }, { pointToLayer: function( feature, latlng ) { return L.circleMarker(latlng, { color: ppmcolorscale( p.ppm ), radius: p.city_internet_population / 500000, fillOpacity: 0.4 }).bindPopup( `City: ${p.city},${p.country}
Nr. probes: ${p.atlas_probe_count}
Internet users: ${d3.format(",.2r")(p.city_internet_population)}
Probes per 1M users: ${d3.format(".2f")( p.ppm )} ${ probe_application_url( p ) }` ) } }) }) var group = L.featureGroup(markers).addTo(map); setTimeout(function () { map.fitBounds(group.getBounds()); }, 1000); L.tileLayer('https://stamen-tiles-{s}.a.ssl.fastly.net/toner-lite/{z}/{x}/{y}{r}.{ext}', { attribution: 'Map tiles by Stamen Design, CC BY 3.0 — Map data © OpenStreetMap contributors', subdomains: 'abcd', minZoom: 0, maxZoom: 20, ext: 'png' }).addTo( map ); }33:[] 30:{"title":"RIPE Atlas probes deployment stats for large cities","cells":[{"id":0,"value":"md`# RIPE Atlas probes deployment stats for large cities\nThis is a visualisation/analysis that tries to answer to what extent RIPE Atlas probes are installed in places with large populations. What differences do we see between cities?\n\nThe table below shows the analysis. We took cities with over half a million inhabitants in the geonames database, and we tried to estimate the number of Internet users (based on Worldbank data). For all these cities we count how many probes are within ${data.radius}km of the city centre (as defined in the geonames database).\n\nIn the analysis below we use a target of 1 probes per 1M users (ppm) as default 'low threshold' and 5 probes as 'high threshold', for instance to color the cities on the worldmap. Cities with less then 1 ppm are colored red, cities with less then 5 ppm are colored orange. To set this threshold differently set it in the select box below.\n\nNote: This is based on an analysis on ${data.date}. It is future work to automate this further.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"viewof pr_thresh_low = Inputs.range([0, 100], {label: \"Low threshold (nr. probes per 1M users)\", value: 1})","pinned":false,"hidden":false,"mode":"ojs"},{"id":119,"value":"viewof pr_thresh_high = Inputs.range([0, 100], {label: \"High threshold (nr. probes per 1M users)\", value: 5})","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"viewof cities_table = Inputs.table(cities, {\n columns: ['city','country','city_population','city_internet_population',`atlas_probe_count`,'ppm'],\n header: {'city': \"City name\",\n 'country': \"Country\",\n 'city_population': \"Population\",\n 'city_internet_population': \"Est. Internet Users\",\n 'atlas_probe_count': `Probe count (within ${data.radius}km)`,\n 'ppm': \"Probes per million users\"\n \n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"The scatterplot below shows there is not a lot of correlation between the count of users and the count of probes","pinned":false,"hidden":false,"mode":"md"},{"id":19,"value":"Plot.plot({\n marks: [\n Plot.dot(cities, {x: \"city_internet_population\", y: \"atlas_probe_count\", opacity: 0.2})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":75,"value":"ppmcolorscale = d3.scaleThreshold()\n .domain([pr_thresh_low, pr_thresh_high, d3.max( cities.map( d => d.ppm ) ) ])\n .range(['red','orange','green'])","pinned":true,"hidden":false,"mode":"ojs"},{"id":142,"value":"function probe_application_url( p ) {\n var txt = \"\"\n if (p.ppm < pr_thresh_high) {\n txt += \"
\"\n txt += \"If you can put a probe in this city, please apply here\"\n }\n return txt\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":124,"value":"cities = data['cities'].map( el => {\n if ( el['city_internet_population'] > 0 ) { // ppm = probes per million users\n el['ppm'] = el['atlas_probe_count'] * 1_000_000 / el['city_internet_population'] \n } else {\n el['ppm'] = 0\n }\n return el\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":3,"value":"data = fetch(\"https://raw.githubusercontent.com/emileaben/ripe-atlas-vs-city-population/main/cities.atlas.r25.m500000.json\").then( d => d.json() ) /* .then( d => d.map( el => {\n if ( el['city_internet_population'] > 0 ) { // ppm = probes per million users\n el['ppm'] = el['atlas_probe_50km_count'] * 1_000_000 / el['city_internet_population'] \n } else {\n el['ppm'] = 0\n }\n return el\n }));\n*/","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"L = {\n const L = await require(\"leaflet@1/dist/leaflet.js\");\n if (!L._style) {\n const href = await require.resolve(\"leaflet@1/dist/leaflet.css\");\n document.head.appendChild(L._style = html``);\n }\n return L;\n}","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}