30:T514,# Activity Analysis _**Part of the [VAST Challenge 2022](https://vast-challenge.github.io/2022/index.html) supporting notebook collection.** This page is considers how best to show and understant activity patterns of participants._ We can view each participant's activity log as extracted from their travel journal. Each journal entry contains the checkin and checkout times at a given venue. We know the venue type (e.g. apartment, pub, employer location etc.) from which we can surmise the activity. Times during the day without activity are therefore assumed to be travel between checked in activities. To characterise the variation in different participants' activities, we can sort them by various summary measures, such as total travel time, or the number of distinct activities: * Joviality score at start of period * Average number of checked in activities per day (`activities`) * Average minutes spent per day travelling (`travel`) * Average minutes spent per day in social activity (`social`) * Average minutes spent per day at eating venues (`eat`) * Average minutes spent per day at home (`home`) * Average minutes spent per day at work (`work`) * Average time of day participant leaves home each morning (`leave`) * Average time of day participant starts work each morning (`start`)31:Tfa1,{ const [w, h] = [1152, 1200]; const log = sortedLog[person].log; const margin = 150; const xScale = (minutes) => (w * minutes) / 1440; const yScale = (days) => (h * days) / 450; const timeMarker = (r, hours, doLabel = false) => { const mins = (hours - 3) * 60; r.push() .fill() .stroke(hours == 12 ? "black" : "grey") .strokeWidth(hours % 3 == 0 ? 0.5 : 0.2) .line([ [mins, 0], [mins, 453] ]); if (doLabel) { r.stroke() .fill("grey") .textAlign("middle") .textBaseline("top") .textSize(14) .text( hours == 0 || hours == 24 ? "midnight" : hours == 12 ? "midday" : hours % 24 < 12 ? (hours % 24) + "am" : hours - 12 + "pm", mins, 455 ); } r.pop(); }; const dateMarker = (r, date) => { const days = (date.getTime() - toDate("2022-03-01 00:00").getTime()) / (1440 * 60000); const fmt = date.getMonth() === 0 || (date.getMonth() == 2 && date.getFullYear() == 2022) ? { month: "short", year: "numeric" } : { month: "short" }; r.push() .fill() .stroke("grey") .strokeWidth(0.5) .line([ [0, days], [1450, days] ]) .stroke() .fill("grey") .textBaseline("middle") .textSize(14) .text(date.toLocaleString("en", fmt), 1450, days) .pop(); }; const bars = (a) => { let [x, y] = timePosition(a.timeMinutes); const [_, next] = timePosition(a.timeMinutes + 1440); const dayOffset = next - y; const coords = [ [ [x, y], [Math.min(1440, x + a.duration), y] ] ]; // Some activities cross day boundaries and can even span several days // so cut off anything beyond end of day and add it to the next day. let overshoot = x + a.duration - 1440; while (overshoot > 0) { y += dayOffset; coords.push([ [ [0, y], [Math.min(1440, overshoot), y] ] ]); overshoot -= 1440; } return coords; }; const r = new Renderer(w, h) .setScales(xScale, yScale) .scale(0.9, 0.95) .translate(40, 30) .strokeWidth((0.95 * h) / 450); // Draw weekend markers let date = toDate("2022-03-01 00:00"); r.stroke("#ddd"); for (let i = 0; i < 450; i++) { if (date.getDay() == 6) { r.lines([ [ [-10, i], [1440, i] ], [ [-10, i + 1], [1440, i + 1] ] ]); } date.setDate(date.getDate() + 1); } // Draw activity bars log.forEach((a) => r.stroke(activityColours(a.type, 0.8)).lines(bars(a))); // Draw time markers for (let h = 3; h <= 27; h++) { timeMarker(r, h, h % 3 == 0); } let d = toDate("2022-03-01 00:00"); for (let m = 0; m < 15; m++) { dateMarker(r, d); d.setMonth(d.getMonth() + 1); } // Label participant r.stroke() .fill("black") .textSize(14) .textBaseline("bottom") .text( "Person " + sortedLog[person].id + " has " + (sortedLog[person].numActivities / sortedLog[person].numDays).toFixed( 1 ) + " activities pd. Travel: " + Math.round(sortedLog[person].travelTime / sortedLog[person].numDays) + " mpd. Socialising: " + Math.round(sortedLog[person].socialTime / sortedLog[person].numDays) + " mpd. Eating: " + Math.round(sortedLog[person].eatTime / sortedLog[person].numDays) + " mpd. Working: " + Math.round(sortedLog[person].workTime / sortedLog[person].numDays) + " mpd. Home: " + Math.round(sortedLog[person].homeTime / sortedLog[person].numDays) + " mpd. Joviality: " + sortedLog[person].joviality.toFixed(2), 0, -5 ); return r.render(); }32:T9e5,{ const mapSize = 900; const r = new Renderer(mapSize, mapSize) .setProjection(d3.geoIdentity().reflectY(true)) .fill() .stroke("#aaa") .strokeWidth(0.1) .geoShape( buildingPolygons, "properties.buildingType", buildingColours(_, 0.4) ); // Show home and job locations r.fill(purposeColours("toHome")).stroke("black").strokeWidth(1); homeLocations[sortedLog[person].id].forEach((loc) => { if (loc) { r.ellipse(...loc, 80); } }); r.fill(purposeColours("commute")).stroke("black").strokeWidth(1); jobLocations[sortedLog[person].id].forEach((loc) => { if (loc) { const [x, y] = loc; r.polygon([ [x - 70, y - 70], [x + 70, y - 70], [x, y + 70] ]); } }); // Select trajectories from the given person const id = sortedLog[person].id; const commutePts = commuteTrajectories .filter((o) => o.id == id) .map((o) => o.pts); const socialPts = socialTrajectories .filter((o) => o.id == id) .map((o) => o.pts); const eatingPts = eatingTrajectories .concat(eatReturnTrajectories) .filter((o) => o.id == id) .map((o) => o.pts); const homePts = toHomeTrajectories .concat(eatReturnTrajectories) .filter((o) => o.id == id) .map((o) => o.pts); r.strokeWidth(2).fill(); const curvature = 0.2; homePts.map((traj) => r.stroke(purposeColours("toHome", 0.6)).bezierLine(traj, curvature) ); socialPts.map((traj) => r.stroke(purposeColours("social", 0.6)).bezierLine(traj, curvature) ); eatingPts.map((traj) => r.stroke(purposeColours("eating", 0.6)).bezierLine(traj, curvature) ); commutePts.map((traj) => r.stroke(purposeColours("commute", 0.6)).bezierLine(traj, curvature) ); // Supplementary info addAreaLabels(r); addRouteLabels(r); addScaleBar(r); addLegendDiscrete(r); addLegendDiscrete(r, { clrFn: purposeColours, cats: ["commute", "social", "eating", "toHome"], symbol: "line", y: 800 }); addLegendDiscrete(r, { clrFn: (_) => purposeColours("toHome"), cats: ["Home"], symbol: "circle", y: 1900 }); addLegendDiscrete(r, { clrFn: (_) => purposeColours("commute"), cats: ["Workplace"], symbol: "triangle", y: 1700 }); r.stroke().fill("grey").textAlign("left").textSize(14); let yPos = 7900; bio.forEach((bRow) => r.text(bRow, -3000, (yPos -= 200))); r.translate(20, mapSize - 530); addActivityHisto(sortedLog[person], r, 280, 300); return r.render(); }33:T72b,We can observe a number of general patterns: * Most people have a weekday and a weekend pattern of behaviour, each of which are remarkably consistent over the 15 months. * It is common for there to be more social activity in the first few weeks (March 2022) than the remaining period. This can be viewed by ordering activity view by `Socialising`. Participants 418, 782 and 866 are examples of more sociable people with this behaviour and participants 518 and 871 examples of less sociable people also more social in the first few weeks. Participants 188, 451, 470, 491 and 748 are extreme examples of people who only socialised in the first few weeks. * Most people show consistent eating-out patterns throughout the period. Typically, consistency is strongest for when eating takes place during working hours. The majority of people show either three (e.g. 439, 441, 698, 752) or one (eg. participants 182, 364, 717) regular eating-out times. Those with three regular eating-out times tend to eat out twice in any one day, but alternate pre-work and evening eating out depending on weekend/weekdays. * Time spent at work is quite consistent among participants, largely varying between 342 and 352 minutes per day. The start time for most working is largely consistent for any given participant and as a group, varies between a 7am and 9am start. * Commute time varies much more widely, ranging from over 2 hours each way (e.g. participants 651, 683, 728, 820) to just a few minutes (e.g. participants 210, 221, 737, 1007). * We see a wide diversity in the range and frequency of different activities taken by participants. For example participants 441 and 654 show lots of separate eating, socialising and working activities throughout the period whereas 238 and 433 show little other than a regular working and eating pattern.34:T4bd,bio = { const id = sortedLog[person].id; const atts = participantTbl .params({ id: id }) .filter((d) => d.participantId == id) .objects()[0]; const hSize = atts.householdSize == 1 ? "by themselves" : "in a household of " + atts.householdSize + " people"; const children = atts.haveKids == "TRUE" ? " with children." : " and have no children."; const ed = atts.educationLevel == "HighSchoolOrCollege" ? "left education after high school or college." : atts.educationLevel == "Bachelors" ? "has a degree." : atts.educationLevel == "Graduate" ? "has a higher degree." : "left school with few qualifications."; const jov = atts.joviality > 0.83 ? "very happy." : atts.joviality > 0.67 ? "happy" : atts.joviality > 0.5 ? "somewhat happy" : atts.joviality > 0.33 ? "somewhat unhappy" : atts.joviality > 0.16 ? "unhappy" : "very unhappy."; return [ "Person " + id + " is " + atts.age + " years old and " + ed, "They live " + hSize + children, "They started the study feeling " + jov, "They are part of interest group " + atts.interestGroup + "." ]; }35:T8c7,activityLog = { const activities = activityTbl .join(locationTbl, ["location", "locationId"], [aq.not("location"), "type"]) .objects(); const travelTime = (log) => log[log.length - 1].timeMinutes - log[0].timeMinutes - (log.reduce((total, activity) => total + activity.duration, 0) - log[log.length - 1].duration); const activityTime = (type, log) => log.reduce( (total, activity) => total + (activity.type == type ? activity.duration : 0), 0 ); const commuteStart = (log) => { const ts = []; let prevHome = 0; log.forEach((activity) => { if (activity.type == "emp") { const day = Math.floor(activity.timeMinutes / 1440); if (ts[day] == undefined) { ts[day] = prevHome; } } else if (activity.type == "apt") { prevHome = (activity.timeMinutes + activity.duration) % 1440; } }); const vals = ts.filter(Boolean); return vals.length == 0 ? 99999 : vals.reduce((total, x) => total + x, 0) / vals.length; }; const workStart = (log) => { const ts = []; log.forEach((activity) => { if (activity.type == "emp") { const day = Math.floor(activity.timeMinutes / 1440); if (ts[day] == undefined) { ts[day] = activity.timeMinutes % 1440; } } }); const vals = ts.filter(Boolean); return vals.reduce((total, x) => total + x, 0) / vals.length; }; const logs = []; for (let id = 0; id <= 1010; id++) { logs.push({ id: id, joviality: joviality[id], log: [] }); } activities.forEach((activity) => logs[activity.participantId].log.push(activity) ); // Add log attributes for each person so we can sort by them. logs.forEach((log) => { log.numDays = Math.floor(log.log[log.log.length - 1].timeMinutes / 1440) + 1; log.numActivities = log.log.length; log.travelTime = travelTime(log.log); log.socialTime = activityTime("pub", log.log); log.eatTime = activityTime("eat", log.log); log.workTime = activityTime("emp", log.log); log.homeTime = activityTime("apt", log.log); log.commuteStart = commuteStart(log.log); log.workStart = workStart(log.log); }); return logs; }36:T465,minVals = { const minVals = { joviality: maxVals.joviality, numActivities: maxVals.numActivities, travelTime: maxVals.travelTime, socialTime: maxVals.socialTime, eatTime: maxVals.eatTime, workTime: maxVals.workTime, homeTime: maxVals.homeTime, commuteStart: maxVals.commuteStart, workStart: maxVals.workStart }; activityLog .filter((o) => o.numDays == 450) .forEach((log) => { minVals.joviality = Math.min(minVals.joviality, log.joviality); minVals.numActivities = Math.min( minVals.numActivities, log.numActivities ); minVals.travelTime = Math.min(minVals.travelTime, log.travelTime); minVals.socialTime = Math.min(minVals.socialTime, log.socialTime); minVals.eatTime = Math.min(minVals.eatTime, log.eatTime); minVals.workTime = Math.min(minVals.workTime, log.workTime); minVals.homeTime = Math.min(minVals.homeTime, log.homeTime); minVals.commuteStart = Math.min(minVals.commuteStart, log.commuteStart); minVals.workStart = Math.min(minVals.workStart, log.workStart); }); return minVals; }37:T81f,function addActivityHisto(log, r, w, h) { r.push(); switch (orderType) { case "Number of activities": r.fill("#bbc"); return drawHisto(log.numActivities / log.numDays, activityFreqs, r, { xTitle: "Average activities per day", w: w, h: h }); case "Travelling": r.fill("#aaa"); return drawHisto(log.travelTime / log.numDays, travelFreqs, r, { xTitle: "Average travel time per day (minutes)", w: w, h: h }); case "Socialising": r.fill(activityColours("pub", 0.7)); return drawHisto(log.socialTime / log.numDays, socialFreqs, r, { xTitle: "Average social time per day (minutes)", w: w, h: h }); case "Eating": r.fill(activityColours("eat", 0.7)); return drawHisto(log.eatTime / log.numDays, eatFreqs, r, { xTitle: "Average eating time per day (minutes)", w: w, h: h }); case "Working": r.fill(activityColours("emp", 0.7)); return drawHisto(log.workTime / log.numDays, workFreqs, r, { xTitle: "Average working time per day (minutes)", w: w, h: h }); case "Home time": r.fill(activityColours("apt", 0.7)); return drawHisto(log.homeTime / log.numDays, homeFreqs, r, { xTitle: "Average time at home per day (minutes)", w: w, h: h }); case "Commute start": r.fill(activityColours("emp", 0.6)); return drawHisto(log.commuteStart, commuteStartFreqs, r, { xTitle: "Average commute start time (minutes since midnight)", w: w, h: h }); case "Work start": r.fill(activityColours("emp", 0.8)); return drawHisto(log.workStart, workStartFreqs, r, { xTitle: "Average work start time (minutes since midnight)", w: w, h: h }); case "Joviality": r.fill("#bcb"); return drawHisto(log.joviality, jovialityFreqs, r, { xTitle: "Initial joviality", w: w, h: h }); } return r.pop(); }38:T5b9,function drawHisto(logVal, freqs, r, options) { const defaults = { w: 360, h: 400, xTitle: "Activity Characteristic", yTitle: "Number of people", fMax: Math.max(...freqs.values()) }; options = Object.assign({}, defaults, options); const bins = [...freqs.keys()]; const binWidth = bins[1] - bins[0]; const [bMin, bMax] = [Math.min(...bins), Math.max(...bins)]; const border = 40; const xSc = d3 .scaleLinear() .domain([bMin, bMax]) .range([border, options.w - border]); const ySc = d3 .scaleLinear() .domain([0, options.fMax]) .range([options.h - border, border]); r.push().push().stroke("white").strokeWidth(0.5).setScales(xSc, ySc); [...freqs.entries()].map(([c, f], i) => r.polygon([ [bins[i], 0], [bins[i] + binWidth, 0], [bins[i] + binWidth, f], [bins[i], f] ]) ); r.stroke("black") .strokeWidth(2) .line([ [logVal, 0], [logVal, options.fMax] ]); return r .textSize(options.w / 50) .stroke("#999") .strokeWidth(0.2) .fill("grey") .axisBottom(xSc, ySc) .axisLeft(xSc, ySc) .textBaseline("top") .textAlign("middle") .textSize(options.w / 35) .stroke() .text(options.xTitle, (bMin + bMax) / 2, options.fMax / -14) .pop() .stroke() .fill("grey") .textAlign("middle") .textBaseline("middle") .rotate(-90) .text(options.yTitle, options.h / -2, 10) .pop(); }39:Tc8a,function drawPCP(log) { const [w, h] = [700, 300]; const border = 10; const axisGap = (w - 2 * border) / 8; const r = new Renderer(w, h).translate(30, 30).scale(0.9); const scJoviality = d3 .scaleLinear() .domain([0, 1]) .range([h - border, border]); const scNumActivities = d3 .scaleLinear() .domain([minVals.numActivities, maxVals.numActivities]) .range([h - border, border]); const scTravelTime = d3 .scaleLinear() .domain([minVals.travelTime, maxVals.travelTime]) .range([h - border, border]); const scSocialTime = d3 .scaleLinear() .domain([minVals.socialTime, maxVals.socialTime]) .range([h - border, border]); const scEatTime = d3 .scaleLinear() .domain([minVals.eatTime, maxVals.eatTime]) .range([h - border, border]); const scWorkTime = d3 .scaleLinear() .domain([minVals.workTime, maxVals.workTime]) .range([h - border, border]); const scHomeTime = d3 .scaleLinear() .domain([minVals.homeTime, maxVals.homeTime]) .range([h - border, border]); const scCommuteStart = d3 .scaleLinear() .domain([minVals.commuteStart, maxVals.commuteStart]) .range([h - border, border]); const scWorkStart = d3 .scaleLinear() .domain([minVals.workStart, maxVals.workStart]) .range([h - border, border]); r.stroke("rgba(100,150,200,0.2").strokeWidth(0.5); activityLog .filter((o) => o.numDays == 450) .forEach((log) => { r.line([ [border + 0 * axisGap, scJoviality(log.joviality)], [border + 1 * axisGap, scNumActivities(log.numActivities)], [border + 2 * axisGap, scTravelTime(log.travelTime)], [border + 3 * axisGap, scSocialTime(log.socialTime)], [border + 4 * axisGap, scEatTime(log.eatTime)], [border + 5 * axisGap, scHomeTime(log.homeTime)], [border + 6 * axisGap, scWorkTime(log.workTime)], [border + 7 * axisGap, scCommuteStart(log.commuteStart)], [border + 8 * axisGap, scWorkStart(log.workStart)] ]); }); // Show highlighted log if (log.numDays == 450) { r.strokeWidth(2).stroke("red"); r.line([ [border + 0 * axisGap, scJoviality(log.joviality)], [border + 1 * axisGap, scNumActivities(log.numActivities)], [border + 2 * axisGap, scTravelTime(log.travelTime)], [border + 3 * axisGap, scSocialTime(log.socialTime)], [border + 4 * axisGap, scEatTime(log.eatTime)], [border + 5 * axisGap, scHomeTime(log.homeTime)], [border + 6 * axisGap, scWorkTime(log.workTime)], [border + 7 * axisGap, scCommuteStart(log.commuteStart)], [border + 8 * axisGap, scWorkStart(log.workStart)] ]); } // Draw axes const labels = [ "joviality", "activites", "travel", "social", "eat", "home", "work", "leave", "start" ]; r.fill("grey") .strokeWidth(2) .textSize(12) .textAlign("middle") .textBaseline("bottom"); let xPos = border; for (let i = 0; i < 9; i++) { r.stroke("black") .line([ [xPos, border], [xPos, h - border] ]) .stroke() .text(labels[i], xPos, border); xPos += axisGap; } return r.render(); }3a:[{"id":"0ebc6b010fd032a2677fb78465dbb53a9265fa3d630133feb2b73b59d31fa4952c0099d7ac888216fe820c2a866930d1199a1f89e702deaa2e8cec1fe35da27f","href":"https://static.observableusercontent.com/files/0ebc6b010fd032a2677fb78465dbb53a9265fa3d630133feb2b73b59d31fa4952c0099d7ac888216fe820c2a866930d1199a1f89e702deaa2e8cec1fe35da27f","name":"activity.csv","type":"text/csv","lastModified":1656742858507.661,"size":40882745}] 2e:{"title":"Activity Analysis","cells":[{"id":0,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":1377,"value":"## Choosing Participants\n\nMC2.3 requires us to select two participants and describe their daily routines. To select two people with contrasting routines, but who are not extreme outliers in terms of behaviour, we can show all people summarised by the 9 measures above as a parallel coordinates plot. Interactive selection (with slider) allows us to quickly browse individuals and see how their routines (red) compare with the collection as a whole (blue). Browsing order can be determined by selecting the measure by which to sort participants.","pinned":false,"hidden":false,"mode":"md"},{"id":1341,"value":"drawPCP(sortedLog[person])","pinned":false,"hidden":false,"mode":"ojs"},{"id":148,"value":"viewof person = Inputs.range([0, 1010], {\n label:\n orderType == \"Person ID\"\n ? \"Person Id\"\n : orderType == \"Joviality\"\n ? \"Joviality sequence\"\n : orderType == \"Commute start\" || orderType == \"Work start\"\n ? \"Time sequence\"\n : \"Activity sequence\",\n step: 1,\n value: 0\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":405,"value":"viewof orderType = Inputs.radio(\n [\n \"Person ID\",\n \"Joviality\",\n \"Number of activities\",\n \"Travelling\",\n \"Socialising\",\n \"Eating\",\n \"Home time\",\n \"Working\",\n \"Commute start\",\n \"Work start\"\n ],\n {\n label: \"Order by\",\n value: \"Person ID\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1452,"value":"**Person 266** spends much less time than average travelling between locations. This is in part because their home and workplace locations are very close (within easy walking distance) but also because their favoured venues for both socialising and eating tend to be quite close by (in the southern part of areas B and C). It is also worthy of note that their daily travel does not involve any of the named routes (1 to 8) that act as bottlenecks for much travel around the city. They start the survey period with a higher than average joviality score and spend more time socialising than most other people.","pinned":false,"hidden":false,"mode":"md"},{"id":1457,"value":"**Person 845** in contrast spends much more of their time travelling (nearly 3 hours per day on average). A significant contribution to this is because their home is in area F (SW) but their workplace in area D (NE) and the regular commuting journey involves traversing routes 8, 7 and 5 all of which act as bottlenecks connecting different parts of the city. Compared to most people in the survey, they spend less time socialising (mean of 20 minutes per day over the 15th months) and started the survey period feeling unhappy.","pinned":false,"hidden":false,"mode":"md"},{"id":1372,"value":"## Activity Calendars \n\nWe show each person's activity history as a calendar view with hours through the day horizontally and days of the 15 month sample period vertically. Weekends marked with light grey horizontal bars.\n\nAs above, we can control the browsing order by choosing the activity measure to sort participants. This also allows us to group the participants with incomplete records (131 of them).","pinned":false,"hidden":false,"mode":"md"},{"id":1406,"value":"{\n const r = new Renderer(150, 150)\n .fit([\n [0, 0],\n [1500, 1500]\n ])\n .scale(1.3);\n\n addLegendDiscrete(r, {\n clrFn: (_) => purposeColours(\"toHome\"),\n cats: [\"At home\"],\n x: 10,\n y: 100\n });\n addLegendDiscrete(r, {\n clrFn: (_) => \"white\",\n cats: [\"Travel\"],\n x: 10,\n y: 300\n });\n addLegendDiscrete(r, {\n clrFn: (_) => purposeColours(\"commute\"),\n cats: [\"At work\"],\n x: 10,\n y: 500\n });\n addLegendDiscrete(r, {\n clrFn: (_) => purposeColours(\"eating\"),\n cats: [\"At eating venue\"],\n x: 10,\n y: 700\n });\n addLegendDiscrete(r, {\n clrFn: (_) => purposeColours(\"social\"),\n cats: [\"Socialising\"],\n x: 10,\n y: 900\n });\n\n addLegendDiscrete(r, {\n clrFn: (_) => \"lightgrey\",\n cats: [\"Weekend\"],\n symbol: \"line\",\n x: 10,\n y: 1100\n });\n\n return r.render();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":90,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1353,"value":"Inputs.bind(\n Inputs.range([0, 1010], {\n label:\n orderType == \"Person ID\"\n ? \"Person Id\"\n : orderType == \"Joviality\"\n ? \"Joviality sequence\"\n : orderType == \"Commute start\" || orderType == \"Work start\"\n ? \"Time sequence\"\n : \"Activity sequence\",\n step: 1,\n value: 0\n }),\n viewof person\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1359,"value":"Inputs.bind(\n Inputs.radio(\n [\n \"Person ID\",\n \"Joviality\",\n \"Number of activities\",\n \"Travelling\",\n \"Socialising\",\n \"Eating\",\n \"Home time\",\n \"Working\",\n \"Commute start\",\n \"Work start\"\n ],\n {\n label: \"Order by\",\n value: \"Person ID\"\n }\n ),\n viewof orderType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1461,"value":"From the calendar view **Person 266** spends much more time socialising in the first 4 weeks. This appears to be part of pattern common across many participants. Perhaps this is due to initial excitement about participating in the survey that began to lose its novelty by April 2022. We see very regular work patterns (solid red bars with same start and end times) and contrasting weekend behaviour dominated by socialising on one day (usually Sunday) and staying at home the other (usually Saturday). Weekends tend to start at an eating venue at around 8:30-9:00am and when socialising followed by full day of 6 to 8 social events punctuated by brief visits home. The day's weekend socialising tends to finish around 11:00pm.","pinned":false,"hidden":false,"mode":"md"},{"id":1465,"value":"**Person845** starts their weekday commute from home in Area F (SW) at 7am taking an 1hr20m to travel to work at the opposite end of town (Area D in the NE). They have a brief lunch break at work between 1:15-1:50pm involving brief travel to an eating venue in the same area (D). They clock off work at around 4:50pm followed by another long commute arriving home at just after 6pm. Most evenings are spent at home but Monday nights and sometimes Tuesdays are spent socialising. This again involves significant travelling (around 1-1.5 hours each way) for around 2 hours of socialising. Weekend afternoons are commonly spent travelling to and from an eating venue (again with travel time exceeding time spent at destination). This weekend eating activity decreases slightly towards the latter period of the study.","pinned":false,"hidden":false,"mode":"md"},{"id":1450,"value":"## Mapping Activity\n\nWe can view the selected person's travel around the city.","pinned":false,"hidden":false,"mode":"md"},{"id":609,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1366,"value":"Inputs.bind(\n Inputs.range([0, 1010], {\n label:\n orderType == \"Person ID\"\n ? \"Person Id\"\n : orderType == \"Joviality\"\n ? \"Joviality sequence\"\n : orderType == \"Commute start\" || orderType == \"Work start\"\n ? \"Time sequence\"\n : \"Activity sequence\",\n step: 1,\n value: 0\n }),\n viewof person\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1369,"value":"Inputs.bind(\n Inputs.radio(\n [\n \"Person ID\",\n \"Joviality\",\n \"Number of activities\",\n \"Travelling\",\n \"Socialising\",\n \"Eating\",\n \"Home time\",\n \"Working\",\n \"Commute start\",\n \"Work start\"\n ],\n {\n label: \"Order by\",\n value: \"Person ID\"\n }\n ),\n viewof orderType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":550,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"---\n\n## Appendices","pinned":false,"hidden":false,"mode":"md"},{"id":8,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":21,"value":"activityTbl = aq.fromCSV(await FileAttachment(\"activity.csv\").text())","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"joviality = participantTbl.orderby(\"participantId\").array(\"joviality\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":574,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":762,"value":"_Each person may have 0 or more registered apartments over the period. This is an array of locations where the index is the person ID and the value at that index is a array of x,y locations or `undefined`s (when there is no apartment ID for that person):_","pinned":false,"hidden":false,"mode":"md"},{"id":714,"value":"homeLocations = {\n const locations = locationTbl\n .objects()\n .map((o) => [o.locationX, o.locationY]);\n\n return homeTbl\n .array(\"apartments\")\n .map((apts) => apts.map((apt) => locations[apt]));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":812,"value":"_Similarly, each person may have 0 or more jobs over the period. This is an array of locations where the index is the person ID and the value at that index is a array of x,y locations or `undefined`s (when there is no job for that person):_","pinned":false,"hidden":false,"mode":"md"},{"id":814,"value":"jobLocations = {\n const locations = jobTbl.objects().map((o) => [o.locationX, o.locationY]);\n\n return employmentTbl\n .array(\"jobs\")\n .map((jobs) => jobs.map((job) => locations[job]));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"_For each participant provide their entire timestamped activity log and summary measures. Gaps in activity are travel. Note a type of `sch` corresponds to locationId 0, which although one of the schools, in this context appears to represent unknown residential location. These are associated with the incomplete records._","pinned":false,"hidden":false,"mode":"md"},{"id":380,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1254,"value":"_Maximum and minimum values for each summary measure in all activity logs:_","pinned":false,"hidden":false,"mode":"md"},{"id":1256,"value":"maxVals = {\n const maxVals = {\n joviality: 0,\n numActivities: 0,\n travelTime: 0,\n socialTime: 0,\n eatTime: 0,\n workTime: 0,\n homeTime: 0,\n commuteStart: 0,\n workStart: 0\n };\n\n activityLog\n .filter((o) => o.numDays == 450)\n .forEach((log) => {\n maxVals.joviality = Math.max(maxVals.joviality, log.joviality);\n maxVals.numActivities = Math.max(\n maxVals.numActivities,\n log.numActivities\n );\n maxVals.travelTime = Math.max(maxVals.travelTime, log.travelTime);\n maxVals.socialTime = Math.max(maxVals.socialTime, log.socialTime);\n maxVals.eatTime = Math.max(maxVals.eatTime, log.eatTime);\n maxVals.workTime = Math.max(maxVals.workTime, log.workTime);\n maxVals.homeTime = Math.max(maxVals.homeTime, log.homeTime);\n maxVals.commuteStart = Math.max(maxVals.commuteStart, log.commuteStart);\n maxVals.workStart = Math.max(maxVals.workStart, log.workStart);\n });\n\n return maxVals;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1263,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":453,"value":"_Sorted version of the activity log sorted by either person ID, number of activities or travel time depending on GUI selection._","pinned":false,"hidden":false,"mode":"md"},{"id":442,"value":"sortedLog = orderType == \"Number of activities\"\n ? activityLog.sort((a, b) => b.numActivities - a.numActivities)\n : orderType == \"Travelling\"\n ? activityLog.sort((a, b) => b.travelTime - a.travelTime)\n : orderType == \"Socialising\"\n ? activityLog.sort((a, b) => b.socialTime - a.socialTime)\n : orderType == \"Eating\"\n ? activityLog.sort((a, b) => b.eatTime - a.eatTime)\n : orderType == \"Working\"\n ? activityLog.sort((a, b) => b.workTime - a.workTime)\n : orderType == \"Home time\"\n ? activityLog.sort((a, b) => b.homeTime - a.homeTime)\n : orderType == \"Commute start\"\n ? activityLog.sort((a, b) => a.commuteStart - b.commuteStart)\n : orderType == \"Work start\"\n ? activityLog.sort((a, b) => a.workStart - b.workStart)\n : orderType == \"Joviality\"\n ? activityLog.sort((a, b) => b.joviality - a.joviality)\n : activityLog.sort((a, b) => a.id - b.id)","pinned":false,"hidden":false,"mode":"ojs"},{"id":850,"value":"_Frequency distributions of the various person activity measures:_","pinned":false,"hidden":false,"mode":"md"},{"id":843,"value":"jovialityFreqs = {\n const jovialities = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.joviality);\n const bins = [\n 0,\n 0.05,\n 0.1,\n 0.15,\n 0.2,\n 0.25,\n 0.3,\n 0.35,\n 0.4,\n 0.45,\n 0.5,\n 0.55,\n 0.6,\n 0.65,\n 0.7,\n 0.75,\n 0.8,\n 0.85,\n 0.9,\n 0.95,\n 1\n ];\n return freqTable(jovialities, bins, (x) => Math.floor(x * 20) / 20);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":861,"value":"activityFreqs = {\n const numActivities = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.numActivities / 450);\n const bins = [];\n const binSize = 0.5;\n for (let i = 0; i <= 10; i += binSize) {\n bins.push(i);\n }\n return freqTable(\n numActivities,\n bins,\n (x) => Math.floor(x / binSize) * binSize\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":887,"value":"travelFreqs = {\n const travelTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.travelTime / 450);\n const bins = [];\n const binSize = 20;\n for (let i = 0; i <= 400; i += binSize) {\n bins.push(i);\n }\n return freqTable(travelTimes, bins, (x) => Math.floor(x / binSize) * binSize);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":898,"value":"socialFreqs = {\n const socialTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.socialTime / 450);\n const bins = [];\n const binSize = 10;\n for (let i = 0; i <= 180; i += binSize) {\n bins.push(i);\n }\n return freqTable(socialTimes, bins, (x) => Math.floor(x / binSize) * binSize);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":908,"value":"eatFreqs = {\n const eatTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.eatTime / 450);\n const bins = [];\n const binSize = 2;\n for (let i = 0; i <= 42; i += binSize) {\n bins.push(i);\n }\n return freqTable(eatTimes, bins, (x) => Math.floor(x / binSize) * binSize);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":914,"value":"workFreqs = {\n const workTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.workTime / 450);\n const bins = [];\n const binSize = 1;\n for (let i = 340; i <= 360; i += binSize) {\n bins.push(i);\n }\n return freqTable(workTimes, bins, (x) => Math.floor(x / binSize) * binSize);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":924,"value":"homeFreqs = {\n const homeTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.homeTime / 450);\n // return Math.max(...homeTimes);\n const bins = [];\n const binSize = 20;\n for (let i = 600; i <= 1100; i += binSize) {\n bins.push(i);\n }\n return freqTable(homeTimes, bins, (x) => Math.floor(x / binSize) * binSize);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":931,"value":"commuteStartFreqs = {\n const commuteStartTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.commuteStart);\n const pad = (num) => {\n const s = \"0\" + num;\n return s.substr(s.length - 2);\n };\n const minsToTime = (mins) =>\n pad(Math.floor(mins / 60)) + \":\" + pad(mins % 60);\n const bins = [];\n const binSize = 10;\n\n for (let i = 300; i <= 535; i += binSize) {\n // bins.push(minsToTime(i));\n bins.push(i);\n }\n return freqTable(\n commuteStartTimes,\n bins,\n (x) =>\n //minsToTime(Math.round(x / binSize) * binSize));\n Math.floor(x / binSize) * binSize\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":948,"value":"workStartFreqs = {\n const workStartTimes = activityLog\n .filter((o) => o.numDays > 400)\n .map((o) => o.workStart);\n const bins = [];\n const binSize = 10;\n for (let i = 400; i <= 540; i += binSize) {\n bins.push(i);\n }\n return freqTable(\n workStartTimes,\n bins,\n (x) => Math.floor(x / binSize) * binSize\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":54,"value":"### Data transformation functions","pinned":false,"hidden":false,"mode":"md"},{"id":61,"value":"_Convert a date object into number of minutes since 2022-03-01 00:00 (start of study period):_","pinned":false,"hidden":false,"mode":"md"},{"id":63,"value":"function minutesSinceStart(date) {\n return (date.getTime() - toDate(\"2022-03-01 00:00\").getTime()) / 60000;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"_Convert a time string in format `yyyy-mm-dd HH:MM` into a Date object:_","pinned":false,"hidden":false,"mode":"md"},{"id":69,"value":"function toDate(timeStr) {\n return new Date(timeStr.replace(\" \", \"T\") + \":00Z\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"_Given a time expressed as minutes since 2022-03-01 00:00, provide its minutes of the day and day for a calendar view:_","pinned":false,"hidden":false,"mode":"md"},{"id":59,"value":"function timePosition(t) {\n // 1440 is number of minutes in a day.\n // 450 days in study (1st Mar 2022 to 24 May 2023 inclusive)\n return [(t - 180) % 1440, Math.floor((t - 180) / 1440)];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"_Generate a sequence of timestamps between the given start and end at the given interval. Defaults to 5 minute intervals between 2022-03-01 00:00 and 2023-05-24 23:55._","pinned":false,"hidden":false,"mode":"md"},{"id":92,"value":"function dateSequence(\n start = toDate(\"2022-03-01 00:00\"),\n end = toDate(\"2023-05-24 23:55\"),\n inc = 5\n) {\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":838,"value":"_Generate a frequency table from the given array of values and bins. Can optionally provide a binning function for categorising continuous data:_","pinned":false,"hidden":false,"mode":"md"},{"id":840,"value":"function freqTable(vals, bins = [], bin = (x) => x) {\n const addToFreqTable = (fs, v, n = 1) => {\n const f = fs.get(v);\n return fs.set(v, f ? f + n : n);\n };\n\n // Map is sorted by keys in case any new bins are created that fall outside those provided.\n // Without sorting, bins would be returned in insert order rather than numeric order.\n return new Map(\n [\n ...vals\n .reduce(\n (fs, x) => addToFreqTable(fs, bin(x)),\n bins.reduce((bm, x) => bm.set(x, 0), new Map())\n )\n .entries()\n ].sort(([b1, f1], [b2, f2]) => b1 - b2)\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":964,"value":"### Rendering","pinned":false,"hidden":false,"mode":"md"},{"id":970,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1250,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"### External Libraries","pinned":false,"hidden":false,"mode":"md"},{"id":12,"value":"import { Renderer } from \"@jwolondon/renderer\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"import {\n buildingPolygons,\n buildingColours,\n purposeColours,\n activityColours,\n addScaleBar,\n addLegendDiscrete,\n addLegendContinuous,\n addAreaLabels,\n addRouteLabels,\n addDevelopmentLabels,\n borderObj,\n viewExtent,\n mapExtent\n} from \"@jwolondon/vc2022buildingimport\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"import {\n locationTbl,\n participantTbl,\n homeTbl,\n jobTbl,\n employmentTbl\n} from \"@jwolondon/vc2022datatables\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":612,"value":"import {\n commuteTrajectories,\n socialTrajectories,\n eatingTrajectories,\n eatReturnTrajectories,\n toHomeTrajectories\n} from \"@jwolondon/vc2022movement\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3a","stdlib":"1","titling":"auto"}