31:Tc8e,{ const mapSize = 900; const r = new Renderer(mapSize, mapSize) .setProjection(d3.geoIdentity().reflectY(true)) .stroke("#aaa") .strokeWidth(0.1) .geoShape( buildingPolygons, "properties.buildingType", buildingColours(_, 0.3), borderObj(mapExtent) ); // Size symbols according to speed. const speedWidth = d3.scaleLinear().domain([0, 10]).range([1, 50]); const speedClr = d3.scaleSequential(d3.interpolateInferno).domain([0, 10]); if (clock.getTime() != tPositions.prevClock.getTime()) { const last5 = tFloor(clock); const next5 = tCeil(clock); if (clock.getTime() - tPositions.prevClock.getTime() !== 60000) { // Clock scrubber has been moved forward or backward, so recalculate positions from start. tPositions.ptr = 0; lastPositions.length = 0; while (tPositions.time[tPositions.ptr].getTime() < last5.getTime()) { lastPositions[tPositions.id[tPositions.ptr]] = [ tPositions.x[tPositions.ptr], tPositions.y[tPositions.ptr] ]; tPositions.ptr++; } } // Pointer is now referencing time that is either at or beyond rounded clock time // so update latest positions while rounded down time matches time of person data. tPositions.prevClock = clock; while ( tPositions.ptr < tPositions.id.length && tPositions.time[tPositions.ptr].getTime() === last5.getTime() ) { lastPositions[tPositions.id[tPositions.ptr]] = [ tPositions.x[tPositions.ptr], tPositions.y[tPositions.ptr], tPositions.x1[tPositions.ptr], tPositions.y1[tPositions.ptr], purposeColours(tPositions.purpose[tPositions.ptr]), tPositions.speed[tPositions.ptr] ]; tPositions.ptr++; } } // Draw each person at their last known location lastPositions.forEach((pts) => { if (pts[2]) { // Moving const x = interp(pts[0], pts[2], clock); const y = interp(pts[1], pts[3], clock); const [dx, dy] = vector1m(pts); const clr = symbolClr == "purpose" ? pts[4] : speedClr(pts[5]); r.strokeWidth(speedWidth(pts[5]) / 10) .stroke(clr) .line([ [x, y], [x + dx, y + dy] ]); r.stroke().fill(clr).ellipse(x, y, speedWidth(pts[5])); } else { // Static r.stroke().fill("black").ellipse(pts[0], pts[1], 10); } }); // Show clock r.textSize(24) .stroke() .fill("#333") .text( clock.toLocaleString("en", { weekday: "short", hour: "numeric", hour12: false, minute: "numeric", month: "short", year: "numeric", day: "numeric" }), -300, 7600 ); // Supplementary info addAreaLabels(r); addRouteLabels(r); addLegendDiscrete(r); if (symbolClr === "purpose") { addLegendDiscrete(r, { clrFn: purposeColours, cats: ["commute", "social", "eating", "toHome"], symbol: "circle", y: 800 }); } else { addLegendContinuous(r, { clrFn: speedClr, labels: [1, 5, 10], y: 800, title: "Speed (km/h)" }); } addScaleBar(r); return r.render(); }32:T470,{ const mapSize = 640; const drawZone = (r, [xMin, yMin, xMax, yMax]) => { r.stroke("black") .strokeWidth(0.5) .fill("rgba(250,0,0,0.5") .polygon([ [xMin, yMin], [xMax, yMin], [xMax, yMax], [xMin, yMax] ]); }; const r = new Renderer(mapSize, mapSize) .setProjection(d3.geoIdentity().reflectY(true)) .fill() .stroke("#aaa") .strokeWidth(0.1) .geoShape( buildingPolygons, "properties.buildingType", buildingColours(_, 0.4) ); drawZone(r, [-2700, 5800, -2200, 5900]); drawZone(r, [-900, 4900, -800, 5400]); drawZone(r, [-1500, 4200, -1000, 4300]); drawZone(r, [900, 4480, 1400, 4580]); drawZone(r, [900, 4000, 1400, 4100]); drawZone(r, [-2010, 2900, -1910, 3400]); drawZone(r, [-1900, 2600, -1400, 2700]); drawZone(r, [590, 2800, 690, 3300]); drawZone(r, [-700, 1590, -200, 1690]); addAreaLabels(r); addRouteLabels(r); addLegendDiscrete(r); addLegendDiscrete(r, { clrFn: (_) => "rgba(250,0,0,0.5", cats: ["Sensor zone"], y: 700 }); addScaleBar(r, { x: -3700 }); return r.render(); }33:T4f9,tPositions = { const tbl = week === "March 7, 2022" ? week02Tbl : week === "December 5, 2022" ? week41Tbl : week === "April 24, 2023" ? week61Tbl : null; const tps = { ptr: 0, prevClock: toDate("2022-03-01 00:00"), time: tbl.array("time").map(toDate), id: tbl.array("participantId"), x: tbl.array("x"), y: tbl.array("y"), x1: Array(tbl.numRows()).fill(null), y1: Array(tbl.numRows()).fill(null), speed: tbl.array("speed"), purpose: tbl.array("purpose") }; // Populate x1,y1 arrays with the next 5-minute positions if they exist. let ptr0 = 0; // Current point in array let ptr1 = 1; // Point 5 minutes ahead of current one in array let t1 = incTime(tps.time[0]).getTime(); while (ptr0 < tps.id.length && ptr1 < tps.id.length) { while ( tps.time[ptr1] && (tps.time[ptr1].getTime() < t1 || (tps.time[ptr1].getTime() === t1 && tps.id[ptr1] < tps.id[ptr0])) ) { ptr1++; } if ( tps.time[ptr1] && tps.time[ptr1].getTime() === t1 && tps.id[ptr1] === tps.id[ptr0] ) { tps.x1[ptr0] = tps.x[ptr1]; tps.y1[ptr0] = tps.y[ptr1]; } ptr0++; t1 = incTime(tps.time[ptr0]).getTime(); } return tps; }34:T8e8,function drawDaySensorChart(sensorId, isEW, r) { const [w, h] = [600, 300]; const border = 25; const readings = fiveMinuteSensorTbl .objects() .filter((o) => o.sensor == sensorId); const maxVal = 2400; const xScale = d3 .scaleLinear() .domain([0, 1440]) .range([border, w - border]); const yScale1 = d3 .scaleLinear() .domain([0, maxVal]) .range([h / 2, border]); const yScale2 = d3 .scaleLinear() .domain([0, maxVal]) .range([h / 2, h - border]); const y0 = yScale1(0); r.push(); // Show sensor readings r.strokeWidth(2); readings.forEach((o) => { const x = xScale((o.minutes + 1260) % 1440); r.stroke("#b55") .line([ [x, y0], [x, yScale1(o.count1)] ]) .stroke("#55b") .line([ [x, y0], [x, yScale2(o.count2)] ]); }); // Show grid lines const lineCoords = []; for (let y = -2500; y <= 2500; y += 500) { lineCoords.push([ [border, y < 0 ? yScale2(Math.abs(y)) : yScale1(Math.abs(y))], [w - border, y < 0 ? yScale2(Math.abs(y)) : yScale1(Math.abs(y))] ]); } for (let x = 0; x <= 1440; x += 180) { lineCoords.push([ [xScale(x), yScale1(2500)], [xScale(x), yScale2(2500)] ]); } r.stroke("lightgrey").strokeWidth(0.8).lines(lineCoords); r.stroke().fill("grey").textAlign("right").textBaseline("middle").textSize(8); for (let y = 0; y <= 2500; y += 500) { r.text(y, xScale(-5), yScale1(y)).text(y, xScale(-5), yScale2(y)); } const tLabels = [ "3am", "6am", "9am", "midday", "3pm", "6pm", "9pm", "midnight", "3am" ]; let i = 0; r.textAlign("middle").textBaseline("top"); for (let x = 0; x <= 1440; x += 180) { r.text(tLabels[i++], xScale(x), yScale2(2600)); } // Title text r.stroke() .fill("grey") .textSize(14) .textAlign("left") .textBaseline("top") .text("Sensor " + sensorId.substring(1), border, 0); // Legend r.fill("#b55") .textBaseline("middle") .textAlign("right") .textSize(9) .text(isEW ? "Heading east" : "Heading north", w - border - 3, h * 0.38) .fill("#55b") .text(isEW ? "Heading west" : "Heading south", w - border - 3, h * 0.63); return r.pop(); }35:T821,function drawDailySensorChart(sensorId, isEW, r) { const [w, h] = [600, 300]; const border = 25; const readings = dailySensorTbl.objects().filter((o) => o.sensor == sensorId); const maxVal = 300; const xScale = d3 .scaleLinear() .domain([1, 450]) .range([border, w - border]); const yScale1 = d3 .scaleLinear() .domain([0, maxVal]) .range([h / 2, border]); const yScale2 = d3 .scaleLinear() .domain([0, maxVal]) .range([h / 2, h - border]); const y0 = yScale1(0); r.push(); // Show sensor readings r.strokeWidth(2); readings.forEach((o) => { const x = xScale(o.day); r.stroke("#b55") .line([ [x, y0], [x, yScale1(o.count1)] ]) .stroke("#55b") .line([ [x, y0], [x, yScale2(o.count2)] ]); }); // Show grid lines const lineCoords = []; for (let y = -300; y <= 300; y += 100) { lineCoords.push([ [border, y < 0 ? yScale2(Math.abs(y)) : yScale1(Math.abs(y))], [w - border, y < 0 ? yScale2(Math.abs(y)) : yScale1(Math.abs(y))] ]); } for (let x = 0; x <= 450; x += 30) { lineCoords.push([ [xScale(x), yScale1(300)], [xScale(x), yScale2(300)] ]); } r.stroke("lightgrey").strokeWidth(0.8).lines(lineCoords); r.stroke().fill("grey").textAlign("right").textBaseline("middle").textSize(8); for (let y = 0; y <= 300; y += 100) { r.text(y, xScale(-5), yScale1(y)).text(y, xScale(-5), yScale2(y)); } r.textAlign("middle").textBaseline("top"); for (let x = 0; x <= 450; x += 30) { r.text(x, xScale(x), yScale2(310)); } // Title text r.stroke() .fill("grey") .textSize(14) .textAlign("left") .textBaseline("top") .text("Sensor " + sensorId.substring(1), border, 0); // Legend r.fill("#b55") .textBaseline("middle") .textAlign("right") .textSize(9) .text(isEW ? "Heading east" : "Heading north", w - border - 3, h * 0.12) .fill("#55b") .text(isEW ? "Heading west" : "Heading south", w - border - 3, h * 0.88); return r.pop(); }36:[{"id":"9266c6348868bd6c2f3cb0e88b13d41f418256006f91f42855b0c2c040bbe74f338408e054beb2c86a8c04ca1feb0e3af9e0edbd1631528e7f1d23ff50fcfe49","href":"https://static.observableusercontent.com/files/9266c6348868bd6c2f3cb0e88b13d41f418256006f91f42855b0c2c040bbe74f338408e054beb2c86a8c04ca1feb0e3af9e0edbd1631528e7f1d23ff50fcfe49","name":"week41.csv","type":"text/csv","lastModified":1656662341895.944,"size":6884027},{"id":"8521c6e430e49f95c9e164f1b2e2fcf733c8a5b276850853400fa4db8ddd2ab593c9cd2d5c90e87affaa2c2e62593137c557948f05b51a63397e9d260ea61e6c","href":"https://static.observableusercontent.com/files/8521c6e430e49f95c9e164f1b2e2fcf733c8a5b276850853400fa4db8ddd2ab593c9cd2d5c90e87affaa2c2e62593137c557948f05b51a63397e9d260ea61e6c","name":"sensors@2.csv","type":"text/csv","lastModified":1657633110232.404,"size":8741764},{"id":"5e977f8797be0609f517c7c49598743971f9102d61d2c297beb5a6b2570d1f07bb2735c87d60f32f874217662e357db711f0e701e6934895fa78a4491e96db99","href":"https://static.observableusercontent.com/files/5e977f8797be0609f517c7c49598743971f9102d61d2c297beb5a6b2570d1f07bb2735c87d60f32f874217662e357db711f0e701e6934895fa78a4491e96db99","name":"week02.csv","type":"text/csv","lastModified":1656569927715.551,"size":9682831},{"id":"3ae73e7dc878e8ce38d5cb0ce488e298871794464406e1a64993014b95cdabd6c076902e5c26d5dc34018c9d77d58fd8be1fd7c8fc9ee134bf3fac527c737a31","href":"https://static.observableusercontent.com/files/3ae73e7dc878e8ce38d5cb0ce488e298871794464406e1a64993014b95cdabd6c076902e5c26d5dc34018c9d77d58fd8be1fd7c8fc9ee134bf3fac527c737a31","name":"week61.csv","type":"text/csv","lastModified":1656569938559.895,"size":6916184}] 2f:{"title":"Location Dynamics","cells":[{"id":0,"value":"# Location Dynamics\n\n_**Part of the [VAST Challenge 2022](https://vast-challenge.github.io/2022/index.html) supporting notebook collection.** This page considers dynamic patterns of participant location over time._","pinned":false,"hidden":false,"mode":"md"},{"id":325,"value":"A problem with showing all movement of every person is that data volumes are large. Therefore we sample selected weeks' worth of movement to gain a general pattern of weekday and weekend movements. Three separate weeks are sampled, one near the start of the 15 month period (March 2022), one in the middle (December 2022), and one near the end (April 2023).","pinned":false,"hidden":false,"mode":"md"},{"id":201,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"viewof clock = {\n let start = \"n/a\";\n let end = \"n/a\";\n if (week === \"March 7, 2022\") {\n start = toDate(\"2022-03-07 00:00\");\n end = toDate(\"2022-03-13 23:55\");\n } else if (week === \"December 5, 2022\") {\n start = toDate(\"2022-12-05 00:00\");\n end = toDate(\"2022-12-11 23:55\");\n } else if (week === \"April 24, 2023\") {\n start = toDate(\"2023-04-24 00:00\");\n end = toDate(\"2023-04-30 23:55\");\n }\n return Scrubber(dateSequence(start, end), {\n autoplay: false,\n delay: 0,\n format: (date) =>\n date.toLocaleString(\"en\", {\n weekday: \"short\",\n month: \"short\",\n day: \"numeric\",\n year: \"numeric\",\n hour: \"numeric\",\n hour12: false,\n minute: \"numeric\"\n })\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":293,"value":"viewof symbolClr = Inputs.radio([\"purpose\", \"speed\"], {\n label: \"Colour by\",\n value: \"purpose\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":328,"value":"viewof week = Inputs.radio(\n [\"March 7, 2022\", \"December 5, 2022\", \"April 24, 2023\"],\n {\n label: \"Week to sample\",\n value: \"March 7, 2022\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":403,"value":"Some patterns to note:\n\n* Travel speeds are pretty constant at around 5km/h. This appears to the case even when movement volumes are low (1:30am). This suggests constraint of speed is not because of other movement, but more likely by mode (e.g. walking) or infrastructure (narrow roads) that don't have temporal variability.\n* Travel along the main named routes is two-way, as would appear to be the case for most, if not all, of the road network.\n* Commuting travel Monday-Friday are pretty regular with peak rush hour travel 6:30-8:30 and 16:30-18:30.\n* Weekday mid-morning travel (9:00-12:00) is somewhat less than we might expect.\n* Weekend movement patterns more continuous through the day, dominated as we might expect by social and eating related travel.\n* There is a data outage on Saturday March 12th between c. 15:45 and 23:15.\n* TODO: Discuss other activity types after looking at activity data.\n","pinned":false,"hidden":false,"mode":"md"},{"id":413,"value":"## Traffic Sensors\n\nFor continuous temporal variation in traffic volumes we create virtual 'sensors' at key points in the road network. Each sensor counts when a person enters the sensor region and from which of two directions along its long axis. The sensors cover the full road width and a distance of 500m which is the furthest a 6km/h person could travel in a 5 minute interval ensuring their movement is captured. The sensor locations are as follows:","pinned":false,"hidden":false,"mode":"md"},{"id":417,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":653,"value":"Laid out in their approximate geographic position we first look at the movement volume at 5-minute resolution averaged over all days for the 15 month period:","pinned":false,"hidden":false,"mode":"md"},{"id":456,"value":"{\n const r = new Renderer(900, 1200).scale(0.7);\n\n drawDaySensorChart(\"s1\", true, r);\n r.translate(0, 350);\n drawDaySensorChart(\"s2\", false, r);\n r.translate(650, 0);\n drawDaySensorChart(\"s4\", true, r);\n r.translate(-650, 350);\n drawDaySensorChart(\"s3\", true, r);\n r.translate(650, 0);\n drawDaySensorChart(\"s5\", true, r);\n r.translate(-650, 350);\n drawDaySensorChart(\"s6\", false, r);\n r.translate(650, 0);\n drawDaySensorChart(\"s7\", false, r);\n r.translate(-650, 350);\n drawDaySensorChart(\"s6b\", true, r);\n r.translate(650, 0);\n drawDaySensorChart(\"s8\", true, r);\n return r.render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":720,"value":"We can examine longer term trends over the 15 month period by measuring average daily traffic volume for each day of the study:","pinned":false,"hidden":false,"mode":"md"},{"id":722,"value":"{\n const r = new Renderer(900, 1200).scale(0.7);\n\n drawDailySensorChart(\"s1\", true, r);\n r.translate(0, 350);\n drawDailySensorChart(\"s2\", false, r);\n r.translate(650, 0);\n drawDailySensorChart(\"s4\", true, r);\n r.translate(-650, 350);\n drawDailySensorChart(\"s3\", true, r);\n r.translate(650, 0);\n drawDailySensorChart(\"s5\", true, r);\n r.translate(-650, 350);\n drawDailySensorChart(\"s6\", false, r);\n r.translate(650, 0);\n drawDailySensorChart(\"s7\", false, r);\n r.translate(-650, 350);\n drawDailySensorChart(\"s6b\", true, r);\n r.translate(650, 0);\n drawDailySensorChart(\"s8\", true, r);\n return r.render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"---\n## Appendices","pinned":false,"hidden":false,"mode":"md"},{"id":28,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":131,"value":"_Travel purpose key: c = commute, s=social, e=eating, r=return from eating, h=to home:_","pinned":false,"hidden":false,"mode":"md"},{"id":394,"value":"#### Week 2 (starting March 7, 2022)","pinned":false,"hidden":false,"mode":"md"},{"id":333,"value":"viewof week02Tbl = aq\n .fromCSV(await FileAttachment(\"week02.csv\").text())\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":396,"value":"#### Week 41 (starting December 5, 2022)","pinned":false,"hidden":false,"mode":"md"},{"id":382,"value":"viewof week41Tbl = aq\n .fromCSV(await FileAttachment(\"week41.csv\").text())\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":400,"value":"#### Week 61 (starting April 24 2023)","pinned":false,"hidden":false,"mode":"md"},{"id":338,"value":"viewof week61Tbl = aq\n .fromCSV(await FileAttachment(\"week61.csv\").text())\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":221,"value":"lastPositions = []","pinned":false,"hidden":false,"mode":"ojs"},{"id":462,"value":"### Timestamped road sensor data","pinned":false,"hidden":false,"mode":"md"},{"id":425,"value":"viewof sensorTbl = aq\n .fromCSV(await FileAttachment(\"sensors@2.csv\").text())\n .derive({ minutes: (d) => op.hours(d.time) * 60 + op.minutes(d.time) })\n .derive({\n day: (d) => (op.year(d.time) - 2022) * 365 + op.dayofyear(d.time) - 59\n })\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":459,"value":"_Aggregated by minutes of the day at 5 minute intervals:_","pinned":false,"hidden":false,"mode":"md"},{"id":428,"value":"viewof fiveMinuteSensorTbl = sensorTbl\n .groupby([\"sensor\", \"minutes\"])\n .rollup({\n count1: (d) => op.sum(d.count1),\n count2: (d) => op.sum(d.count2)\n })\n .orderby([\"sensor\", \"minutes\"])\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":655,"value":"_Aggregated by day over the 15 month period:_","pinned":false,"hidden":false,"mode":"md"},{"id":662,"value":"viewof dailySensorTbl = sensorTbl\n .groupby([\"sensor\", \"day\"])\n .rollup({\n count1: (d) => op.sum(d.count1),\n count2: (d) => op.sum(d.count2)\n })\n .orderby([\"sensor\", \"day\"])\n .view({ height: 150, limit: 2000 })","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"### Time Functions","pinned":false,"hidden":false,"mode":"md"},{"id":42,"value":"_Convert timestamp string into a Date object (converts to UTC ISO format for browser compatibility):_","pinned":false,"hidden":false,"mode":"md"},{"id":33,"value":"function toDate(timeStr) {\n return new Date(timeStr.replace(\" \", \"T\") + \":00Z\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"_Given a Date object provide a new one incremented by the given number of minutes (default 5 minutes):_","pinned":false,"hidden":false,"mode":"md"},{"id":92,"value":"function incTime(date, inc = 5) {\n return new Date(date.getTime() + inc * 60000);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"_Given a Date object provide the previous and next 5-minute rounded time:_","pinned":false,"hidden":false,"mode":"md"},{"id":50,"value":"function tFloor(date) {\n const newDate = new Date(date.getTime());\n newDate.setUTCMinutes(5 * Math.floor(newDate.getMinutes() / 5));\n return newDate;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"function tCeil(date) {\n const newDate = incTime(date);\n newDate.setUTCMinutes(5 * Math.floor(newDate.getMinutes() / 5));\n return newDate;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"_Generate a list of date objects from the given start to end inclusive separated by the given interval in minutes (default 1 minute):_","pinned":false,"hidden":false,"mode":"md"},{"id":106,"value":"function dateSequence(start, end, inc = 1) {\n const seq = [];\n let date = new Date(start.getTime());\n while (date.getTime() <= end.getTime()) {\n seq.push(new Date(date.getTime()));\n date = new Date(date.getTime() + inc * 60000);\n }\n return seq;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":627,"value":"### Drawing Functions","pinned":false,"hidden":false,"mode":"md"},{"id":452,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":675,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":211,"value":"### Other Functions","pinned":false,"hidden":false,"mode":"md"},{"id":213,"value":"_Given two values 5 minutes apart linearly interpolate new value according to how many minutes away from nearest 5 minute point and calculate the 1 minute vector between the two points._","pinned":false,"hidden":false,"mode":"md"},{"id":215,"value":"function interp(x0, x1, time) {\n const t = time.getMinutes() % 5;\n return (x0 * (5 - t) + x1 * t) / 5;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":218,"value":"function vector1m([x0, y0, x1, y1]) {\n return [x0 - (x0 * 4 + x1) / 5, y0 - (y0 * 4 + y1) / 5];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"### External libraries","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"import { Renderer } from \"@jwolondon/renderer\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"import {\n buildingPolygons,\n buildingColours,\n purposeColours,\n addScaleBar,\n addLegendDiscrete,\n addLegendContinuous,\n addAreaLabels,\n addRouteLabels,\n addDevelopmentLabels,\n borderObj,\n viewExtent,\n mapExtent\n} from \"@jwolondon/vc2022buildingimport\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}