25:["$","title",null,{"children":"Detroit Data Sketches | SASAKI | Observable"}] 49:T49d,chart = { let plot = Plot.plot({ width: 1200, y: { grid: true, label: "↑ Speed (mph)" }, marks: [ Plot.line(trafficCam_data.filter(d=>d.truck_speed > 0), {x: "date", y: "truck_speed", strokeWidth: 0.75, stroke: "tomato"}), Plot.line(trafficCam_data.filter(d=>d.car_speed > 0), {x: "date", y: "car_speed", strokeWidth: 0.75, stroke: "forestGreen"}), Plot.line(trafficCam_data.filter(d=>d.bus_speed > 0), {x: "date", y: "bus_speed", strokeWidth: 0.75, stroke: "purple"}), Plot.line(trafficCam_data.filter(d=>d.moto_speed > 0), {x: "date", y: "moto_speed", strokeWidth: 0.75, stroke: "cornflowerBlue"}), Plot.ruleY([35]) //speed limit on E Jefferson Ave ] }) //Create highlight hover interaction using d3 d3.select(plot) .selectAll("path") .on("pointerenter", function () { d3.select(plot).selectAll("path").attr("opacity", 0.1); //Unable to grab same class using d3.selectAll("circle").filter((d)=>{return d.className == this.className}) d3.select(this).attr("opacity", 1); }); d3.select(plot).on("pointerleave", function () { d3.select(plot).selectAll("path").attr("opacity", 1); }); return plot; }4a:T45f,ntdm_data_15min = { let tempArr = [ntdm_data[0],]; // create new array with first observation that has no previous counts to aggregate with // loop through array, get every third object (aligned with 15-min intervals), store it in new array with summed values from previous two objects ntdm_data.forEach(function(d,i){ if (i%3 == 0 & i > 0){ tempArr.push({ date: d.date, weekday: d.weekday, cars_ltr: (ntdm_data[i].cars_ltr + ntdm_data[i-1].cars_ltr + ntdm_data[i-2].cars_ltr), cars_rtl: (ntdm_data[i].cars_rtl + ntdm_data[i-1].cars_rtl + ntdm_data[i-2].cars_rtl), trucks_ltr: (ntdm_data[i].trucks_ltr + ntdm_data[i-1].trucks_ltr + ntdm_data[i-2].trucks_ltr), trucks_rtl: (ntdm_data[i].trucks_rtl + ntdm_data[i-1].trucks_rtl + ntdm_data[i-2].trucks_rtl), all_ltr: (ntdm_data[i].all_ltr + ntdm_data[i-1].all_ltr + ntdm_data[i-2].all_ltr), all_rtl: (ntdm_data[i].all_rtl + ntdm_data[i-1].all_rtl + ntdm_data[i-2].all_rtl), all: (ntdm_data[i].all + ntdm_data[i-1].all + ntdm_data[i-2].all) }) } }) return tempArr; }4b:T708,trafficCam_data = d3.csvParse(await FileAttachment("data_export_PT15M_2023-09-17_2023-09-23.csv").text()).map(d=>{ let p = {}; p.date = trafficDateParse(d.UTC), //forumlates local dateTime from unix time code p.weekday = reformatDayOfWeek(d.Weekday), p.zone_id = d["Zone ID"], p.zone_name = d["Zone Name"], p.site_name = d["Site Name"], p.tot_count = +d["Total Counts"], p.truck_count = +d["Trucks (Counts)"], p.car_count = +d["Cars (Counts)"], p.bus_count = +d["Buses (Counts)"], p.moto_count = +d["Motorcycles (Counts)"], p.ped_count = +d["Persons (Counts)"], p.bike_count = +d["Bicycles (Counts)"], p.carsTrucks_count = (p.truck_count + p.car_count), //aggregated count of cars and trucks to align with NTDM data p.motorVeh_count = (p.truck_count + p.car_count + p.bus_count + p.moto_count), //aggregated count of all motor vehicles p.nonMotor_count = (p.bike_count + p.ped_count), //aggregated count of non-motorized road users p.avg_speed = +d["Average Speed (mph)"], p.truck_speed = +d["Trucks (Speed in mph)"], p.car_speed = +d["Cars (Speed in mph)"], p.bus_speed = +d["Buses (Speed in mph)"], p.moto_speed = +d["Motorcycles (Speed in mph)"], p.ped_speed = +d["Persons (Speed in mph)"], p.bike_speed = +d["Bikes (Speed in mph)"], p.tot_stopped = +d["Total Stopped Objects"], p.truck_stopped = +d["Trucks (Stopped)"], p.car_stopped = +d["Cars (Stopped)"], p.buses_stopped = +d["Buses (Stopped)"], p.moto_stopped = +d["Motorcycles (Stopped)"], p.ped_stopped = +d["Persons (Stopped)"], p.bike_stopped = (parseInt(d["Bikes (Stopped)"]) + parseInt(d["Bicycles (Stopped)"])) //not sure how/why those are different but I summed them return p; })4c:[{"id":"d2b4be4f7b600838d410770be84a78f71bd4a43b9bb57079b41b59e6d756be6a5c8cf2eba70b7a65afa6d1b9d4c05e4482e291400b4b75eacf298f679a7da721","href":"https://static.observableusercontent.com/files/d2b4be4f7b600838d410770be84a78f71bd4a43b9bb57079b41b59e6d756be6a5c8cf2eba70b7a65afa6d1b9d4c05e4482e291400b4b75eacf298f679a7da721","name":"data_export_PT15M_2023-09-17_2023-09-23.csv","type":"text/csv","lastModified":1696262399651.465,"size":120007},{"id":"d8baf82d4a13654f9c2fca0935240c4ced71fd54354154338f9297e6aae923214a22202ac5d14bcb63b1ebeefd6f20614cdaae5c2ced830481350fd9c47444a5","href":"https://static.observableusercontent.com/files/d8baf82d4a13654f9c2fca0935240c4ced71fd54354154338f9297e6aae923214a22202ac5d14bcb63b1ebeefd6f20614cdaae5c2ced830481350fd9c47444a5","name":"ntdm.csv","type":"text/csv","lastModified":1696269784087.247,"size":122212}] 29:{"id":"4f057226a95a0869","slug":null,"title":"Detroit Data Sketches","thumbnail":"bcfcdcae966aaf2780c95b1a5a97f885ebadd6038e2deba9a7bac7af86c94b2a","access_level":"public","create_time":"$D2023-10-02T15:58:56.770Z","update_time":"$D2023-10-06T14:13:49.789Z","view_time":null,"first_public_time":"$D2023-10-02T15:58:56.770Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"0a7b68d04bd1e1f3","login":"sasaki","name":"SASAKI","avatar_url":"https://avatars-next.observableusercontent.com/962868b7383a27060cfd7320e6c9cb7927ff86b632c8ba37627be8b55b48f8b7","tier":"starter_2024","bio":"","followed_by_you":false,"follows_you":false},"version":967,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"ab76ebed74c31607","login":"mk-sasaki","name":"mk-sasaki","avatar_url":"https://avatars-next.observableusercontent.com/d6d06a6bfb608af0e2a13fbfdffef3dc4f5d03dab1a6902c90ab399a2dc48004","tier":null,"bio":"","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"ab76ebed74c31607","login":"mk-sasaki","name":"mk-sasaki","avatar_url":"https://avatars-next.observableusercontent.com/d6d06a6bfb608af0e2a13fbfdffef3dc4f5d03dab1a6902c90ab399a2dc48004","tier":null,"role":"editor"},{"type":"team","id":"0a7b68d04bd1e1f3","login":"sasaki","name":"SASAKI","avatar_url":"https://avatars-next.observableusercontent.com/962868b7383a27060cfd7320e6c9cb7927ff86b632c8ba37627be8b55b48f8b7","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Detroit Data Sketches","cells":[{"id":0,"value":"# Detroit Data Sketches","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":226,"value":"#### data description\n\n**`ntdm_data`** comes from the Noise and Traffic Data Module, which uses acoustic sensors for traffic quantification and qualification. It provides counts of vehicles by class (car, truck, all) and direction (left-to-right (ltr) or right-to-left (rtl)). Sensor was located on the corner of Atwater & Chene St. Data is recorded at 5-minute intervals from *${d3.min(ntdm_data, d => d.date)}* to *${d3.max(ntdm_data, d => d.date)}*. **`ntdm_data_15min`** has rolled up counts at 15-minute intervals to match traffic camera data.\n\n**`trafficCam_data`** comes from traffic cameras which provide counts of road users by class (car, bus, motorcycle, pedestrian, bicycle, all), as well as average speeds (mph) and a count of the number of objects stopped (30s+) within the field of view. Cameras are located at the corner of Jefferson Ave & Chene St. Data is recorded at 15-minute intervals from *${d3.min(trafficCam_data, d => d.date)}* to *${d3.max(trafficCam_data, d => d.date)}*. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":452,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"## Plots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":177,"value":"**Traffic Camera Road User Counts (15-min interval):**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"Plot.line(trafficCam_data,{x: \"date\", y: \"tot_count\", tip: true}).plot({y: {grid: true}})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":694,"value":"**Traffic Camera Road User Counts by Mode (15-min interval):**\n
Dashed black line is all road users, orange is car, purple is bus, blue is motorcycle, green is trucks, gold is pedestrian, pink is bicycles","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":684,"value":"Plot.plot({\n grid: true,\n marks:[\n Plot.line(trafficCam_data, {x: \"date\", y: \"tot_count\", stroke: \"black\", strokeDasharray: 3}), // total counts\n Plot.line(trafficCam_data, {x: \"date\", y: \"truck_count\", stroke: \"forestGreen\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"car_count\", stroke: \"tomato\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"bus_count\", stroke: \"purple\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"moto_count\", stroke: \"conflowerBlue\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"ped_count\", stroke: \"goldenRod\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"bike_count\", stroke: \"hotPink\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"**Traffic Camera Daily Road User Histogram:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":403,"value":"Plot.plot({\n marginLeft: 50,\n marks: [\n Plot.rectY(trafficCam_data, {x: \"date\", y: \"tot_count\", interval: \"day\"}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":281,"value":"**NTDM Vehicle Counts:**\n
Gray line is 5-min intervals, black line is 15-min intervals","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":212,"value":"Plot.plot({\n grid: true,\n marks:[\n Plot.line(ntdm_data, {x: \"date\", y: \"all\", stroke: \"lightGray\"}), //5-min intervals\n Plot.line(ntdm_data_15min, {x: \"date\", y: \"all\", tip: true}) //15-min intervals\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":520,"value":"**NTDM Vehicle Counts by Day of the Week:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":508,"value":"Plot.plot({\n y: {\n grid: true,\n label: \"Count of Cars & Trucks\"\n },\n x: {\n domain: [\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"], // sets order of x-axis\n label: \"\"\n },\n marks: [\n Plot.ruleY([0]),\n Plot.barY(ntdm_dayOfWeek, {x: \"weekday\", y: \"all\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":284,"value":"**Comparison Vehicle Counts:**\n
*Note: this is kind of an apples-to-oranges comparison because the traffic cameras and NTDM are not necessarily accurate to the same degree.*\n
Blue line is traffic cam data, red line is NTDM data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":286,"value":"Plot.plot({\n grid: true,\n x: {label: \"Date & Time\"},\n y: {label: \"Count of Cars & Trucks\"},\n marks:[\n Plot.line(trafficCam_data, {x: \"date\", y: \"carsTrucks_count\" , stroke: \"skyBlue\"}), \n Plot.line(ntdm_data_15min, {x: \"date\", y: \"all\", stroke: \"tomato\"}) \n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"**Average Speed by Mode from Traffic Cameras:**\n
${``} Car, ${``} Bus, ${``} Motorcycle, ${``} Truck","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":345,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":936,"value":"**Counts by Mode from Traffic Cameras:**\n
${``} Car ${``} Bus ${``} Motorcycle ${``} Truck ${``} Pedestrian","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":921,"value":"Plot.plot({\n width: 1200,\n y: {\n grid: true,\n label: \"↑ Counts\"\n },\n marks: [\n Plot.line(trafficCam_data, {x: \"date\", y: \"truck_count\", strokeWidth: 0.75, stroke: \"forestGreen\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"car_count\", strokeWidth: 0.75, stroke: \"tomato\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"bus_count\", strokeWidth: 0.75, stroke: \"purple\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \"moto_count\", strokeWidth: 0.75, stroke: \"cornflowerBlue\"}),\n Plot.line(trafficCam_data, {x: \"date\", y: \" ped_count\", strokeWidth: 0.75, stroke: \"grey\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"**Traffic Camera Hourly Average Speeds:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":454,"value":"Plot.plot({\n height: 250,\n width: 800,\n marginTop: 20,\n height: 150,\n x: {round: true},\n color: {scheme: \"orRd\", legend: true},\n marks: [\n Plot.barX(trafficCam_data, {x: \"date\", fill: \"avg_speed\", interval: \"hour\", inset: 0, tip: true})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":550,"value":"**Traffic Camera Temporal Heatmap of All Road Users:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":527,"value":"Plot.plot({\n padding: 0,\n height: 250,\n width: 800,\n color: {scheme: \"PuRd\"},\n y: {tickFormat: Plot.formatWeekday(\"en\", \"short\")},\n x: {tickFormat: (d)=> reformatTime(d)},\n marks: [\n Plot.cell(trafficCam_data, Plot.group({fill: \"max\"}, {\n x: (d) => d.date.getUTCHours(),\n y: (d) => d.date.getUTCDay(),\n fill: \"tot_count\",\n inset: 0.5,\n tip: true\n }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"**Traffic Camera Temporal Heatmap of Pedestrians:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":601,"value":"Plot.plot({\n padding: 0,\n height: 250,\n width: 800,\n color: {scheme: \"BuPu\"},\n y: {tickFormat: Plot.formatWeekday(\"en\", \"short\")},\n x: {tickFormat: (d)=> reformatTime(d)},\n marks: [\n Plot.cell(trafficCam_data, Plot.group({fill: \"max\"}, {\n x: (d) => d.date.getUTCHours(),\n y: (d) => d.date.getUTCDay(),\n fill: \"ped_count\",\n inset: 0.5,\n tip: true\n }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":612,"value":"**Traffic Camera Temporal Heatmap of Motorcycles:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":608,"value":"Plot.plot({\n padding: 0,\n height: 250,\n width: 800,\n color: {scheme: \"BuGn\"},\n y: {tickFormat: Plot.formatWeekday(\"en\", \"short\")},\n x: {tickFormat: (d)=> reformatTime(d)},\n marks: [\n Plot.cell(trafficCam_data, Plot.group({fill: \"max\"}, {\n x: (d) => d.date.getUTCHours(),\n y: (d) => d.date.getUTCDay(),\n fill: \"moto_count\",\n inset: 0.5,\n tip: true\n }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":625,"value":"**Traffic Camera Temporal Heatmap of Trucks:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":615,"value":"Plot.plot({\n padding: 0,\n height: 250,\n width: 800,\n color: {scheme: \"RdGy\"},\n y: {tickFormat: Plot.formatWeekday(\"en\", \"short\")},\n x: {tickFormat: (d)=> reformatTime(d)},\n marks: [\n Plot.cell(trafficCam_data, Plot.group({fill: \"max\"}, {\n x: (d) => d.date.getUTCHours(),\n y: (d) => d.date.getUTCDay(),\n fill: \"truck_count\",\n inset: 0.5,\n tip: true\n }))\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":648,"value":"**NTDM Directional Split:**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":636,"value":"Plot.plot({\n marginLeft: 55,\n y: {tickFormat: Math.abs,labelAnchor: \"center\", label: \" ← left to right (all) | right to left (all) →\"},\n marks: [\n Plot.rectY(ntdm_data_15min, {x: \"date\", y: \"all_rtl\", interval: \"hour\", fill: \"goldenRod\"}),\n Plot.rectY(ntdm_data_15min, {x: \"date\", y: (d)=>d.all_ltr*-1, interval: \"hour\", fill: \"orangeRed\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":312,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"#### helper functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":83,"value":"trafficDateParse = function(date){\n let timeParse = d3.timeParse(\"%s\");\n let parsed_date = timeParse(parseInt(date)/1000); //convert utc code to UTC date time\n return parsed_date;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":198,"value":"ntdmDateParse = d3.timeParse(\"%Y-%m-%dT%H:%M:%S%Z\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"reformatDayOfWeek = function(DoW){\n if (DoW[0] == \"1\"){\n return \"Sun\";\n } else if (DoW[0] == \"2\"){\n return \"Mon\";\n } else if (DoW[0] == \"3\"){\n return \"Tue\";\n } else if (DoW[0] == \"4\"){\n return \"Wed\";\n } else if (DoW[0] == \"5\"){\n return \"Thu\";\n } else if (DoW[0] == \"6\"){\n return \"Fri\";\n } else if (DoW[0] == \"7\"){\n return \"Sat\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"reformatTime = function(time){\n if (time > 0 & time < 13){\n return time.toString().concat(\"am\");\n } else if (time == 0){\n return \"12am\";\n } else {\n return (time-12).toString().concat(\"pm\");\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":955,"value":"sum = function sum(input){\n \n if (toString.call(input) !== \"[object Array]\")\n return false;\n \n var total = 0;\n for(var i=0;i d3.sum(v, d => d.all), d => d.weekday), ([weekday, all]) => ({ weekday, all })) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":193,"value":"ntdm_data = d3.csvParse(await FileAttachment(\"ntdm.csv\").text()).map(d=>{\n let p = {};\n p.date = ntdmDateParse(d.ISO_UTC), //forumlates local dateTime from unix time code\n p.weekday = d.weekday,\n p.cars_ltr = +d.cars_ltr,\n p.cars_rtl = +d.cars_rtl,\n p.trucks_ltr = +d.trucks_ltr,\n p.trucks_rtl = +d.trucks_rtl,\n p.all_ltr = +d.all_ltr,\n p.all_rtl = +d.all_rtl,\n p.all = +d.all\n \n return p; \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"raw_trafficCam_data = d3.csvParse(await FileAttachment(\"data_export_PT15M_2023-09-17_2023-09-23.csv\").text())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"raw_ntdm_data = d3.csvParse(await FileAttachment(\"ntdm.csv\").text())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":727,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":723,"value":"### Reflections & Comments","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":725,"value":"- Pre-aggregation rather than object-level data is very limiting\n- lack of spatial dimension (other than sensor location) misses out on key geospatial context\n- lack of confidence metric for classifications makes it unclear how accurate these categorizations are (need KODE to parse this in the final dev)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 24:null