30:T454,comment = { if (covariate == "Arbeitslosenquote (Ältere)") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } else if (covariate == "Langzeitbeschäftigungslosigkeit") { return md`### Erläuterungen Wenig überraschend gibt es einen positiven Zusammenhang zwischen Arbeitslosigkeit und Langzeitbeschäftigungslosigkeit. Allerdings ist zu beobachten, dass in einigen Arbeitsmarktbezirken`; } else if (covariate == "Arbeitslosenquote (Junge)") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } else if (covariate == "life_expectancy") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } else if (covariate == "poor_health_proportion") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } else if (covariate == "poor_health_disparity") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } else if (covariate == "percent_bachelors") { return md`### Erläuterungen Hier könnte ein Kommentar vorbereitet werden.`; } }31:T1811,function make_map(opts = {}) { let { max_size = 900, link_chart = null } = opts; let map_width = width < max_size ? width : max_size; let height = 0.575 * map_width; let div = d3 .create("div") .style("width", `${map_width}px`) .style("height", `${height}px`) .style("overflow", "hidden") .style("position", "relative"); let svg = div .append("svg") .style("overflow", "hidden") .attr("viewBox", [0, 0, map_width, height]); let proj = d3 .geoIdentity() .reflectY(true) .fitSize([map_width, height], stateTiles); let path = d3.geoPath().projection(proj); let map = svg.append("g"); let density_map = new Map(); map .selectAll("path.tile") .data(stateTiles.features) .join("path") .attr("class", "tile") .attr("data-fips", (o) => o.properties.id) .attr("data-name", (o) => o.properties.name) .attr("data-rrs", function (d) { let fd = data.filter((o) => o.id == d.properties.id); if (fd.length > 0) { return fd[0].alq_g / 100; } else { return "NA"; } }) .attr("data-covariate", function (d) { //if (covariate != "---") { let fd = filtered_data.filter((o) => o.id == d.properties.id); if (fd.length > 0) { return fd[0][covariate] /100; } //else { //return "NA"; //} } }) .attr("d", path) .attr("fill", (o) => state_color(o.properties.id) //covariate == "---" //? rr_shade(o.properties.id) //: state_color(o.properties.id) ) .attr("stroke-width", 1) // Abstand zwischen Hexagon .attr("stroke", "white") .attr("stroke-linejoin", "round") .on("touchmove", (e) => e.preventDefault()) .on("pointerenter mouseenter", function (evt, o) { evt.preventDefault(); //if (covariate != "---") { let class_selector = ".color" + state_color(o.properties.id).slice(1); legend .select(class_selector) .attr("stroke", "black") .attr("stroke-width", 2) .raise(); if (link_chart) { let selected_circles = d3 .select(link_chart) .select(`#${o.properties.name}`) .attr("fill", "#F3D039") .attr("stroke", "black") .attr("r", 0.007 * width) .raise(); } } }) .on("mouseout", function (evt, o) { let class_selector = ".color" + state_color(o.properties.id).slice(1); legend .select(class_selector) .attr("stroke", "white") .attr("stroke-width", 0.5); if (link_chart) { let selected_circles = d3 .select(link_chart) .select(`#${o.properties.name}`) .attr("fill", "silver") .attr("r", 0.003 * width); } }); map .selectAll("text") .data(stateTiles.features) .join("text") .text((o) => o.properties.id) // name in hexagon .attr("font-size", `${0.020 * map_width}px`) .attr("x", function (o) { return path.centroid(o)[0]; }) .attr("text-anchor", "middle") .attr("y", function (o) { return path.centroid(o)[1]; }) .attr("dy", 0.008 * map_width) .attr("pointer-events", "none"); map .selectAll("path.tile") .nodes() .forEach(function (t) { let content = html`

${t.getAttribute("data-name")}

Arbeitslosenquote: ${d3.format(",.1%")(t.getAttribute("data-rrs"))}

${`

${covariate.replace(/_/g, " ")}: ${d3.format(",.1%")( t.getAttribute("data-covariate") )}

` }

`; tippy(t, { content: content, theme: "light-border", maxWidth: 300 }); }); let s = map_width / 6; let legend = svg .append("g") .attr("transform", `translate(${50},${30})`); // position of mini legend let arrow_group = legend.append("g"); arrow_group // from http://thenewcode.com/1068/Making-Arrows-in-SVG .append("svg:defs") .append("marker") .attr("id", "arrowhead") .attr("markerWidth", 0.5 * 10) .attr("markerHeight", 0.5 * 7) .attr("refX", 0) .attr("refY", 0.5 * 3.5) .attr("orient", "auto") .append("polygon") // .attr("points", `0 0, 10 3.5, 0 7`); .attr("points", `0 0, ${0.5 * 10} ${0.5 * 3.5}, 0 ${0.5 * 7}`); let patches = legend.append("g"); let arrows = legend.append("g"); { let n = 5; let dd = s / n; let to_rr = d3 .scaleLinear() .domain([0, n - 1]) .range([rr_scores.min, rr_scores.max]); let to_covariate = d3 .scaleLinear() .domain([0, n - 1]) .range([covariate_values.min, covariate_values.max]); for (let i = 0; i < n; i++) { for (let j = 0; j < n; j++) { patches .append("rect") .attr( "class", "color" + color(to_rr(i), to_covariate(n - j - 1)).slice(1) ) .attr("x", i * dd) .attr("y", j * dd) .attr("width", s / n) .attr("height", s / n) .attr("fill", color(to_rr(i), to_covariate(n - j - 1))) .attr("stroke", "white") .attr("stroke-width", 0.9); // Abstand zwischen Kastln in kleiner Legende } } arrows .append("line") .attr("x1", 0) .attr("x2", s) .attr("y1", s) .attr("y2", s) .attr("stroke", "black") .attr("stroke-width", 2) .attr("marker-end", "url(#arrowhead)"); arrows .append("line") .attr("x1", 0) .attr("x2", 0) .attr("y1", s) .attr("y2", 0) .attr("stroke", "black") .attr("stroke-width", 2) .attr("marker-end", "url(#arrowhead)"); arrows .append("text") .attr("x", 0.7 * s) .attr("y", 1.2 * s) .attr("font-size", 0.010 * width) .text("Arbeitslosenquote"); arrows .append("text") .attr("x", -0.016 * width) .attr("y", -0.012 * width) .attr("font-size", 0.010 * width) .text(covariate); } return div.node(); }32:T78c,function make_chart(opts = {}) { let {w = 800 } = opts; let h = 0.8 * w; let plot; // if (covariate != "---") { let xs = filtered_data.map((o) => o.alq_g); let xmin = d3.min(xs); let xmax = d3.max(xs); let xrange = xmax - xmin; xmin = xmin - xrange / 10; xmax = xmax + xrange / 10; let ys = filtered_data.map((o) => o[covariate]); let ymin = d3.min(ys); let ymax = d3.max(ys); let yrange = ymax - ymin; ymin = ymin - yrange / 10; ymax = ymax + yrange / 10; let f = (x) => regression.coef[1] * x + regression.coef[0]; plot = Plot.plot({ width: w, height: h, x: { domain: [xmin, xmax] , label: "Arbeitslosenquote"}, y: { domain: [ymin, ymax] }, marks: [ Plot.line([ [xmin + xrange / 40, f(xmin + xrange / 40)], [xmax, f(xmax)] ]), Plot.dot(filtered_data, { x: "alq_g", y: covariate, fill: "silver", stroke: "black", title: (o) => o.name, // Pop Up in der Regression r: 3 * Math.sqrt(width / 1152) }), Plot.ruleX([xmin]), Plot.ruleY([ymin]) ] }); d3.select(plot) .selectAll("circle") .on("pointerenter", function () { d3.select(this) .attr("fill", "#F3D039") .attr("stroke", "black") .attr("r", 7 * Math.sqrt(width / 1152)) .raise(); }) .on("pointerleave", function () { d3.select(this) .attr("fill", "silver") .attr("r", 3 * Math.sqrt(width / 1152)); }) .nodes() .forEach(function (c) { let d3Circle = d3.select(c); let value = d3Circle.select("title").text(); d3Circle.attr("id", value); tippy(c, { content: d3Circle.attr("id"), theme: "light-border" }); // d3Circle.select("title").remove(); }); } // else return plot; }33:T9a27,stateTiles = { let stateTiles = 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.2409],[666500.5188,474637.8948]],[[449807.5402,522756.26],[449807.5402,503508.914]],[[416470.1589,503508.914],[416470.1589,522756.26],[433138.8496,532379.9331],[449807.5402,522756.26]],[[449807.5402,522756.26],[466476.2309,532379.9331],[483144.9215,522756.26]],[[483144.9215,522756.26],[483144.9215,503508.914]],[[483144.9215,522756.26],[499813.6122,532379.9331]],[[499813.6122,532379.9331],[516482.3029,522756.26]],[[516482.3029,522756.26],[516482.3029,503508.914]],[[516482.3029,522756.26],[533150.9935,532379.9331]],[[533150.9935,532379.9331],[549819.6842,522756.26]],[[549819.6842,522756.26],[549819.6842,503508.914]],[[549819.6842,522756.26],[566488.3748,532379.9331]],[[566488.3748,532379.9331],[583157.0655,522756.26]],[[583157.0655,522756.26],[583157.0655,503508.914]],[[583157.0655,522756.26],[599825.7561,532379.9331]],[[599825.7561,532379.9331],[616494.4468,522756.26]],[[616494.4468,522756.26],[616494.4468,503508.914]],[[616494.4468,522756.26],[633163.1375,532379.9331]],[[633163.1375,532379.9331],[649831.8281,522756.26]],[[649831.8281,522756.26],[649831.8281,503508.914]],[[533150.9935,551627.2791],[533150.9935,532379.9331]],[[499813.6122,532379.9331],[499813.6122,551627.2791],[516482.3029,561250.9522],[533150.9935,551627.2791]],[[533150.9935,551627.2791],[549819.6842,561250.9522],[566488.3748,551627.2791]],[[566488.3748,551627.2791],[566488.3748,532379.9331]],[[566488.3748,551627.2791],[583157.0655,561250.9522],[599825.7561,551627.2791],[599825.7561,532379.9331]],[[633163.1375,532379.9331],[633163.1375,551627.2791],[649831.8281,561250.9522],[666500.5188,551627.2791],[666500.5188,532379.9331],[649831.8281,522756.26]]],"bbox":[116433.7271,291788.1071,683169.2094,561250.9522]}; let tiles = topojson.feature(stateTiles, stateTiles.objects.foo); //tiles.features = tiles.features.filter((o) => o.properties.id != "DC"); return tiles; }34:[{"id":"c4102dcbbd15e3e4e1ba2188808bb62f7876a2a0f2611a3749a03e99a1ee00d9b3605872b6fa4be5ab0c938fc07d52a23b8b725b6af75e60977ece115532066a","href":"https://static.observableusercontent.com/files/c4102dcbbd15e3e4e1ba2188808bb62f7876a2a0f2611a3749a03e99a1ee00d9b3605872b6fa4be5ab0c938fc07d52a23b8b725b6af75e60977ece115532066a","name":"AMB_data@17.csv","type":"text/csv","lastModified":1652359149914.265,"size":15076}] 2e:{"title":"Arbeitsmarktbezirke Hex-Map","cells":[{"id":1058,"value":"# Arbeitsmarktbezirke Hex-Map\n\nZusammenhang zwischen regionaler Arbeitslosenquote und ausgewählten, regionalen Arbeitsmarktindikatoren im Jahr 2016","pinned":false,"hidden":false,"mode":"md"},{"id":26,"value":"viewof covariate = Inputs.select(\n [].concat(Object.keys(data[0]).slice(3)),\n { label: \"Indikator:\", format: (s) => s.replace(/_/g, \" \") }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":86,"value":"viz = {\n let w = width < 1200 ? width : 1200;\n let small_screen = 800;\n let div = d3.create(\"div\");\n let map_container = div.append(\"div\");\n let chart = make_chart({ w: w < small_screen ? w : 0.3 * w }); // Merke ifelse, condition ? YES : NO \n console.log(w);\n map_container\n .style(\"display\", w < small_screen ? \"block\" : \"inline-block\")\n .style(w < small_screen ? \"margin-bottom\" : \"margin-right\", `${0.05 * w}px`)\n .append(() =>\n make_map({\n max_size: w < small_screen ? w : 0.6 * w - 20,\n link_chart: chart\n })\n );\n div\n .append(\"div\")\n .style(\"display\", w < small_screen ? \"block\" : \"inline-block\")\n .style(\"margin-bottom\", `${0.06 * w}px`)\n .append(() => chart);\n\n return div.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1238,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":84,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"regression = {\n //if (covariate != \"---\") \n {\n return jstat.models.ols(\n filtered_data.map((o) => o[covariate]),\n filtered_data.map((o) => [1, o[\"alq_g\"]])\n );\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":175,"value":"//colors = [\"#D3D3D3\",\"#BACDBA\",\"#A2C7A1\",\"#8AC188\",\"#72BB6F\",\"#DBD2AC\",\"#C3CC98\",\"#ABC584\",\"#93BF71\",\"#7BB95D\",\"#E3D186\",\"#CBCB77\",\"#B3C468\",\"#9BBE5A\",\"#84B74B\",\"#EBD05F\",\"#D3CA56\",\"#BCC34C\",\"#A4BC43\",\"#8DB539\",\"#F3D039\",\"#DBC934\",\"#C4C230\",\"#ADBB2C\",\"#96B428\"] \n\ncolors = [\"#D3D3D3\",\"#B3C5D3\",\"#94B7D4\",\"#74A9D4\",\"#559BD5\",\"#BACDBA\",\"#A1C0BC\",\"#87B4BE\",\"#6EA8C1\",\"#559BC3\",\"#A2C7A1\",\"#8FBCA5\",\"#7BB1A9\",\"#68A7AD\",\"#559CB2\",\"#8AC188\",\"#7CB88E\",\"#6FAF94\",\"#62A69A\",\"#559DA0\",\"#72BB6F\",\"#6AB377\",\"#63AC7F\",\"#5CA587\",\"#559E8F\"] ","pinned":false,"hidden":false,"mode":"ojs"},{"id":237,"value":"function state_color(id) {\n // if (covariate != \"---\") \n {\n let info = filtered_data.filter((o) => o.id == id);\n if (info.length > 0) {\n return color(info[0].alq_g, info[0][covariate]);\n } else {\n return \"black\";\n }\n } \n //else \n //{\n //return rr_shade(data.filter((o) => o.id == id));\n //}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":235,"value":"color = (rr, cov) => colors[j(cov) + i(rr) * 5]","pinned":false,"hidden":false,"mode":"ojs"},{"id":180,"value":"i = d3\n .scaleQuantile()\n .domain([rr_scores.min, rr_scores.max])\n .range(d3.range(Math.sqrt(colors.length)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":188,"value":"rr_scores = {\n let rr_scores = data.map((o) => o.alq_g);\n rr_scores.min = d3.min(rr_scores);\n rr_scores.max = d3.max(rr_scores);\n return rr_scores;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"j = d3\n .scaleQuantile()\n .domain([covariate_values.min, covariate_values.max])\n .range(d3.range(Math.sqrt(colors.length)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":231,"value":"covariate_values = {\n let covariate_values = filtered_data.map((o) => o[covariate]);\n covariate_values.min = d3.min(covariate_values);\n covariate_values.max = d3.max(covariate_values);\n return covariate_values;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":45,"value":"filtered_data = data.filter((o) => o[covariate] > 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3,"value":"data = FileAttachment(\"AMB_data@17.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"jstat = require(\"jstat\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1078,"value":"style = html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":595,"value":"tippy_style = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"tippy = require(\"tippy.js@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":70,"value":"topojson = require(\"topojson-client@3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":445,"value":"import { Legend } from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}