2b:null 2c:["$","title",null,{"children":"ER Roadside Survey Data analysis | ktringuyen | Observable"}] 31:T8bb,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null ); const tripCountData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), avg_daily_distance: d => aq.op.mean(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with at least 1 trip .objects(); const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; const maxTrips = Math.max(...tripCountData.map(d => d.trip_count)); return Plot.plot({ title: "Origin-Destination Trip Count Matrix", subtitle: `Total number of trips between locations`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "Greens", domain: [1, maxTrips], legend: true, label: "Number of Trips" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(tripCountData, { x: "destination_country_new", y: "origin_country_new", fill: "trip_count", stroke: "white", strokeWidth: 2, inset: 0.5, title: d => `${d.origin_country_new} → ${d.destination_country_new}\nTrip Count: ${d.trip_count}\nTotal Cost: ${d.total_cost?.toLocaleString() || 'N/A'} AED\nAvg Distance: ${d.avg_daily_distance?.toFixed(0) || 'N/A'} km` }), // Text labels showing trip count Plot.text(tripCountData, { x: "destination_country_new", y: "origin_country_new", text: d => d.trip_count, fill: d => d.trip_count > maxTrips * 0.5 ? "white" : "black", fontSize: 12, }) ] }); }32:T9cd,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null ); const tripCountData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), avg_daily_distance: d => aq.op.mean(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with at least 1 trip .objects(); // Calculate total trips across all routes const totalTrips = tripCountData.reduce((sum, d) => sum + d.trip_count, 0); // Add percentage calculation const tripPercentageData = tripCountData.map(d => ({ ...d, trip_percentage: (d.trip_count / totalTrips) * 100 })); // Domain ordered from north to south const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; // Calculate max percentage for color scale const maxPercentage = Math.max(...tripPercentageData.map(d => d.trip_percentage)); return Plot.plot({ title: "Origin-Destination Trip Distribution Matrix (%)", subtitle: `Percentage of total trips between locations`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, color: { type: "sequential", scheme: "Greens", domain: [0, maxPercentage], legend: true, label: "%" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(tripPercentageData, { x: "destination_country_new", y: "origin_country_new", fill: "trip_percentage", stroke: "white", strokeWidth: 2, inset: 0.5, title: d => `${d.origin_country_new} → ${d.destination_country_new}` }), // Text labels showing percentage Plot.text(tripPercentageData, { x: "destination_country_new", y: "origin_country_new", text: d => `${d.trip_percentage.toFixed(1)}%`, fill: d => d.trip_percentage > maxPercentage * 0.5 ? "white" : "black", fontSize: 10, fontWeight: "bold" }), ] }); }33:T902,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.survey_location === selected_site ); const tripCountData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), avg_daily_distance: d => aq.op.mean(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with at least 1 trip .objects(); const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; const maxTrips = Math.max(...tripCountData.map(d => d.trip_count)); return Plot.plot({ title: `Origin-Destination Trip Count Matrix filter by ${selected_site}`, subtitle: `Total number of trips between locations`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "Greens", domain: [1, maxTrips], legend: true, label: "Number of Trips" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(tripCountData, { x: "destination_country_new", y: "origin_country_new", fill: "trip_count", stroke: "white", strokeWidth: 2, inset: 0.5, title: d => `${d.origin_country_new} → ${d.destination_country_new}\nTrip Count: ${d.trip_count}\nTotal Cost: ${d.total_cost?.toLocaleString() || 'N/A'} AED\nAvg Distance: ${d.avg_daily_distance?.toFixed(0) || 'N/A'} km` }), // Text labels showing trip count Plot.text(tripCountData, { x: "destination_country_new", y: "origin_country_new", text: d => d.trip_count, fill: d => d.trip_count > maxTrips * 0.5 ? "white" : "black", fontSize: 12, }) ] }); }34:Ta0b,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.survey_location === selected_site ); const tripCountData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), avg_daily_distance: d => aq.op.mean(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with at least 1 trip .objects(); // Calculate total trips across all routes const totalTrips = tripCountData.reduce((sum, d) => sum + d.trip_count, 0); // Add percentage calculation const tripPercentageData = tripCountData.map(d => ({ ...d, trip_percentage: (d.trip_count / totalTrips) * 100 })); // Domain ordered from north to south const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; // Calculate max percentage for color scale const maxPercentage = Math.max(...tripPercentageData.map(d => d.trip_percentage)); return Plot.plot({ title: `Origin-Destination Trip Distribution Matrix (%) - ${selected_site}`, subtitle: `Percentage of total trips between locations`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, color: { type: "sequential", scheme: "Greens", domain: [0, maxPercentage], legend: true, label: "%" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(tripPercentageData, { x: "destination_country_new", y: "origin_country_new", fill: "trip_percentage", stroke: "white", strokeWidth: 2, inset: 0.5, title: d => `${d.origin_country_new} → ${d.destination_country_new}` }), // Text labels showing percentage Plot.text(tripPercentageData, { x: "destination_country_new", y: "origin_country_new", text: d => `${d.trip_percentage.toFixed(1)}%`, fill: d => d.trip_percentage > maxPercentage * 0.5 ? "white" : "black", fontSize: 10, fontWeight: "bold" }), ] }); }35:T444,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.destination_country !== null && d.distance_category_industry !== null); const data = aq.from(tmp) .derive({ source: d => d.origin_country + ' (O)', target: d => d.destination_country + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])]; return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }36:T44f,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.origin_country_new !== null && d.distance_category_industry !== null); const data = aq.from(tmp) .derive({ source: d => d.origin_country_new + ' (O)', target: d => d.destination_country_new + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])]; return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }37:T4ab,{ const tmp = rsiCleaned.filter((d) => d.origin_country_new !== null && d.destination_country_new !== null && d.distance_category_industry !== null && d.daily_distance < selected_distance); const data = aq.from(tmp) .derive({ source: d => d.origin_country_new + ' (O)', target: d => d.destination_country_new + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])]; return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }38:T54e,{ const transformedData = results_df.flatMap(d => [ { Detector: d.Detector, Type: "Freight Count", Count: d.Freight_Count }, { Detector: d.Detector, Type: "Survey Count", Count: d.Survey_Count } ]); return Plot.plot({ title: "Freight Count by ATC Detector", width:width/1.5, height: height/1.5, marginLeft: 80, marginRight: 80, marginBottom: 50, marginTop:20, color: { ...globalStyles, range: ["#b7d7cc", "#8fb3a3"], // Two colors for the two count types domain: ["Freight Count", "Survey Count"] }, x: { label: "Site", tickSize: 14, domain: [...new Set(results_df.map(d => d.Detector))].sort(), }, y: { label: "Count", labelAnchor: "center", grid: true, tickSize: 14, ticks: 5, domain: [0, 55000] }, marks: [ Plot.barY(transformedData, { x: "Detector", y: "Count", fill: "Type", filter: d => d.Type === "Freight Count", opacity: 0.8, }), Plot.text(transformedData, { x: "Detector", y: "Count", filter: d => d.Type === "Freight Count", text: d => d.Count.toLocaleString(), dy: -5, textAnchor: "middle", fill: "black", fontSize: 10, }), Plot.ruleY([0]), ] }); }39:T56d,{ const transformedData = results_df.flatMap(d => [ { Detector: d.Detector, Type: "Freight Count", Count: d.Freight_Count }, { Detector: d.Detector, Type: "Survey Count", Count: d.Survey_Count } ]); return Plot.plot({ title: "Survey Count by ATC Detector", width:width/1.5, height: height/1.5, marginLeft: 80, marginRight: 80, marginBottom: 50, marginTop:20, color: { ...globalStyles, range: ["#b7d7cc", "#b7d7cc"], // Two colors for the two count types domain: ["Freight Count", "Survey Count"] }, x: { label: 'Site', labelAnchor: "center", tickSize: 14, domain: [...new Set(results_df.map(d => d.Detector))].sort(), }, y: { label: "Count", grid: true, labelAnchor: "center", tickSize: 14, ticks: 5, domain: [0, 500] }, marks: [ Plot.barY(transformedData, { x: "Detector", y: "Count", fill: "Type", filter: d => d.Type === "Survey Count", opacity: 0.8, }), Plot.text(transformedData, { x: "Detector", y: "Count", filter: d => d.Type === "Survey Count", text: d => d.Count.toLocaleString(), dy: -5, textAnchor: "middle", fill: "black", fontSize: 10, }), Plot.ruleY([0]), ] }); }3a:T4ce,{ const tmp = aq.from(rsiCleaned) .groupby('survey_location') .rollup({ count: aq.op.count() }).objects(); return Plot.plot({ title: "Freight Count By Survey Location", width:width/1.5, height: height/1.5, marginLeft: 50, marginRight: 80, marginBottom: 50, marginTop:20, color: { ...globalStyles, range: ["#b7d7cc", "#8fb3a3"], // Two colors for the two count types domain: ["Freight Count", "Survey Count"] }, x: { label: 'Counts', labelAnchor:'center', tickSize: 10, domain: [0, 450] }, y: { label: "Survey Location", grid: true, tickSize: 14, ticks: 5, domain: [...new Set(tmp.map(d => d.survey_location))].sort(), }, marks: [ Plot.barX(tmp, { x: "count", y: "survey_location", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(tmp, { x: "count", y: "survey_location", text: d => d.count.toLocaleString(), dx: 15, textAnchor: "middle", fill: "black", fontSize: 10, }), Plot.ruleY([0]), ] }); }3b:T434,{ return Plot.plot({ title: "Sample Rate by ATC detector", width:width/1.5, height: height/1.5, marginLeft: 50, marginRight:80, marginBottom: 30, color: { ...globalStyles, range: discreteScheme, domain: [...new Set(results_df.map(d => d.Detector))].sort() }, x: { label:"%", grid: true, domain: [0,5], tickSize:"14px", ticks: 5, labelAnchor:"center" }, y: { label: "Site", tickSize:14, domain:[...new Set(results_df.map(d => d.Detector))].sort(), }, marks: [ Plot.barX(results_df, { x: "Sample_Rate_Percent", y: "Detector", fill: "#b7d7cc", opacity:.8, }), Plot.text(results_df, { x: "Sample_Rate_Percent", y: "Detector", text: d => `${d.Sample_Rate_Percent.toFixed(2)} %`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleY([0]), ] }); }3c:T884,{ const tmp = aq.from(rsiCleaned) .derive({ timestamp_hour: aq.escape(d => new Date(d.timestamp).getHours()) }) .groupby('timestamp_hour') .rollup({count: aq.op.count()}) .objects(); const total = tmp.reduce((acc,v )=> acc += v.count, 0) let data = []; for (let i = 0; i < 24 ; i++) { let v = ((tmp.find(d => d.timestamp_hour == i) || 0).count || 0) * 100/total; data.push({ Hour: i, value:v }); } return Plot.plot({ title: "Freight and Survey Percentage by Hour", width:width/1.5, height: height/1.5, marginLeft: 20, marginRight: 80, marginBottom: 40, color: { ...globalStyles, range: ["#de425b", "#488f31"], domain: ["Survey", "ATC"], legend:true, }, x: { label: "Time of Day", labelAnchor:"center", grid: true, tickSize: 14, domain: [0, 23], ticks: 24, }, y: { label: "%", labelAnchor:"center", ticks:5, }, marks: [ // Survey percentage line Plot.line(data, { x: "Hour", y: "value", stroke: "#de425b", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(data, { x: "Hour", y: "value", fill: "#de425b", r: 3, }), // Add text labels for key data points (optional - you can remove if too cluttered) Plot.text(data.filter(d => d.Hour!==0), { x: "Hour", y: "value", text: d => `${d.value.toFixed(1)}%`, dy: -10, fontSize: 9, }), Plot.line(comparison_df, { x: "Hour", y: "Freight_Percentage", stroke: "#488f31", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(comparison_df, { x: "Hour", y: "Freight_Percentage", fill: "#488f31", r: 3, }), Plot.text(comparison_df.filter(d => d.Hour!==0), { x: "Hour", y: "Freight_Percentage", text: d => `${d.Freight_Percentage.toFixed(1)}%`, dy: 15, fontSize: 9, }), Plot.ruleX([0]), Plot.ruleY([0]), ] }); }3d:T966,{ const tmp = rsiCleaned.filter(d => d.origin_country === "United Arab Emirates" && d.destination_country!=="United Arab Emirates"); // Extract hour from timestamp and group by it const rsi = aq.from(tmp) .derive({ timestamp_hour: aq.escape(d => new Date(d.timestamp).getHours()) }) .groupby('timestamp_hour') .rollup({ 'count': aq.op.count() }).objects(); const total_rsi = rsi.reduce((acc, v) => acc += v.count, 0); let data = [] for (let i = 0; i < 24; i++) { let r_data = ((rsi.find(d => d.timestamp_hour === i) || {count: 0})['count'] || 0) * 100 / total_rsi; let t_data = tomtom.find(d => d.hour === i) || {external: 0}; data.push({ hour: i, rsi: r_data, tomtom: t_data['external'] || 0 }); } return Plot.plot({ title: "International trips distribution TomTom vs Survey (from Timestamp)", width: width/1.5, height: height/1.5, marginLeft: 20, marginRight: 80, marginBottom: 40, color: { ...globalStyles, range: ["#de425b", "#000000"], domain: ["Survey", "TomTom"], legend: true, }, x: { label: "Time of Day", labelAnchor: "center", grid: true, tickSize: 14, domain: [0, 23], ticks: 24, }, y: { label: "%", labelAnchor: "center", ticks: 5, }, marks: [ Plot.line(data, { x: "hour", y: "rsi", stroke: "#de425b", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(data, { x: "hour", y: "rsi", fill: "#de425b", r: 3, }), Plot.text(data.filter(d => d.hour !== 0), { x: "hour", y: "rsi", text: d => `${d.rsi.toFixed(1)}%`, filter: d=> d.rsi > 0, dy: 15, fontSize: 9, }), Plot.line(data, { x: "hour", y: "tomtom", stroke: "#000000", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(data, { x: "hour", y: "tomtom", fill: "#000000", r: 3, }), Plot.text(data.filter(d => d.tomtom !== 0), { x: "hour", y: "tomtom", text: d => `${d.tomtom.toFixed(1)}%`, dy: -10, fontSize: 9, }), Plot.ruleX([0]), Plot.ruleY([0]), ] }); }3e:T963,{ const tmp = rsiCleaned.filter(d => d.origin_country === "United Arab Emirates" && d.destination_country === "United Arab Emirates"); // Extract hour from timestamp and group by it const rsi = aq.from(tmp) .derive({ timestamp_hour: aq.escape(d => new Date(d.timestamp).getHours()) }) .groupby('timestamp_hour') .rollup({ 'count': aq.op.count() }).objects(); const total_rsi = rsi.reduce((acc, v) => acc += v.count, 0); let data = [] for (let i = 0; i < 24; i++) { let r_data = ((rsi.find(d => d.timestamp_hour === i) || {count: 0})['count'] || 0) * 100 / total_rsi; let t_data = tomtom.find(d => d.hour === i) || {internal: 0}; data.push({ hour: i, rsi: r_data, tomtom: t_data['internal'] || 0 }); } return Plot.plot({ title: "Internal trips distribution TomTom vs Survey (from Timestamp)", width: width/1.5, height: height/1.5, marginLeft: 20, marginRight: 80, marginBottom: 40, color: { ...globalStyles, range: ["#de425b", "#000000"], domain: ["Survey", "TomTom"], legend: true, }, x: { label: "Time of Day", labelAnchor: "center", grid: true, tickSize: 14, domain: [0, 23], ticks: 24, }, y: { label: "%", labelAnchor: "center", ticks: 5, }, marks: [ Plot.line(data, { x: "hour", y: "rsi", stroke: "#de425b", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(data, { x: "hour", y: "rsi", fill: "#de425b", r: 3, }), Plot.text(data.filter(d => d.hour !== 0), { x: "hour", y: "rsi", text: d => `${d.rsi.toFixed(1)}%`, filter: d => d.rsi >0, dy: 15, fontSize: 9, }), Plot.line(data, { x: "hour", y: "tomtom", stroke: "#000000", strokeWidth: 2, curve: "monotone-x", }), Plot.dot(data, { x: "hour", y: "tomtom", fill: "#000000", r: 3, }), Plot.text(data.filter(d => d.tomtom !== 0), { x: "hour", y: "tomtom", text: d => `${d.tomtom.toFixed(1)}%`, dy: -10, fontSize: 9, }), Plot.ruleX([0]), Plot.ruleY([0]), ] }); }3f:T755,{ const tmp = aq.from(rsiCleaned) .derive({ timestamp_hour: aq.escape(d => new Date(d.timestamp).getHours()) }) .groupby('timestamp_hour', 'survey_location') .rollup({count: aq.op.count()}) .objects(); // Get unique hours and locations for proper ordering const hours = Array.from({length: 24}, (_, i) => i); // Force all hours 0-23 const locations = [...new Set(tmp.map(d => d.survey_location))].sort(); const completeGrid = []; for (const hour of hours) { for (const location of locations) { const existing = tmp.find(d => d.timestamp_hour === hour && d.survey_location === location ); completeGrid.push({ timestamp_hour: hour, survey_location: location, count: existing ? existing.count : 0 }); } } return Plot.plot({ title: "Survey Count by Hour and Location (from Timestamp)", width: width/2, height: height, marginLeft: 40, marginRight: 100, marginBottom: 60, marginTop: 50, color: { type: "linear", scheme: "viridis", reverse: true, legend: true, label: "Number of Surveys" }, y: { label: "Time of Day", domain: hours, tickSize: 14, }, x: { label: "Survey Location", domain: locations, tickSize: 14, }, marks: [ // Heatmap rectangles Plot.rect(completeGrid, { y: "timestamp_hour", x: "survey_location", fill: "count", stroke: "white", strokeWidth: 0.5, inset: 0.5, }), // Annotations (numbers on each cell) Plot.text(tmp, { y: "timestamp_hour", x: "survey_location", text: d => d.count, fill: "white", fontSize: 11, fontWeight: "500", textAnchor: "middle", }), ] }); }40:T5b5,{ const tmp = aq.from(freight_only) .groupby('start_time_hour', 'atc_identifier') .rollup({count: d => aq.op.sum(d.count)}) .objects(); // return tmp; // Get unique hours and locations for proper ordering const hours = [...new Set(tmp.map(d => d.start_time_hour))].sort((a, b) => a - b); const locations = [...new Set(tmp.map(d => d.atc_identifier))].sort(); return Plot.plot({ title: "ATC Count by Hour and Location", width: width/2, height: height, marginLeft: 40, marginRight: 100, marginBottom: 60, marginTop: 50, color: { type: "linear", scheme: "viridis", reverse: true, legend: true, label: "Number of Counts" }, y: { label: "Time of Day", domain: hours, tickSize: 14, }, x: { label: "ATC Site", domain: locations, tickSize: 14, }, marks: [ // Heatmap rectangles Plot.rect(tmp, { x: "atc_identifier", y: "start_time_hour", fill: "count", stroke: "white", // Grid lines like seaborn strokeWidth: 0.5, inset: 0.5, // Small gap between cells }), Plot.text(tmp, { y: "start_time_hour", x: "atc_identifier", text: d => d.count, fill: "white", // White text for visibility on dark background fontSize: 11, fontWeight: "500", textAnchor: "middle", }), ] }); }41:Ta99,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.distance_category_industry !== null); const rawData = aq.from(tmp) .groupby("origin_country", "distance_category_industry") .count({ as: "value" }) .objects(); let data, yLabel, yDomain; if (showPercentage) { const originTotals = aq.from(rawData) .groupby("origin_country") .rollup({ total_trips: d => aq.op.sum(d.value) }) .objects(); const totalLookup = {}; originTotals.forEach(d => { totalLookup[d.origin_country] = d.total_trips; }); data = rawData.map(d => ({ ...d, displayValue: (d.value / totalLookup[d.origin_country]) * 100, total_for_origin: totalLookup[d.origin_country] })); yLabel = "%"; yDomain = [0, 100]; } else { data = rawData.map(d => ({ ...d, displayValue: d.value })); yLabel = "Trips"; yDomain = [0, Math.max(...data.map(d => d.displayValue)) * 1.1]; } const fyDomain = [...new Set(data.map(d => d.origin_country))]; const distanceOrder = [ '0-10km', '10-50km', '50-100km', '100-150km', '150-250km', '250-500km', '500-1000km', '1000-2000km', '2000-2500km' ]; return Plot.plot({ title: showPercentage ? "Distribution of Distance Categories by Origin Country (%)" : "Survey Count by Distance Category and Origin Country", subtitle: showPercentage ? "Percentages calculated within each origin country" : undefined, width: 1200, height: 700, marginBottom: 50, marginRight: 150, marginTop: showPercentage ? 50 : 30, style: {...globalStyles, fontSize: "14px"}, color: { range: discreteScheme, legend: true, domain: fyDomain }, x: { label: null, domain: distanceOrder, grid: true }, y: { label: null, domain: showPercentage? [0,110]: yDomain, ticks: 0, }, fy: { axis: "right", label: null, domain: fyDomain }, marks: [ Plot.barY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'origin_country', fill: 'origin_country' }), Plot.textY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'origin_country', filter: d => showPercentage ? d.displayValue > 5 : d.displayValue > 20, dy: -8, text: d => showPercentage ? `${d.displayValue.toFixed(1)}%` : d3.format(".1s")(d.displayValue), fill: showPercentage ? 'black' : undefined, fontSize: 10 }), Plot.ruleY([0]) ] }); }42:T94a,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("origin_country") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('avg_distance')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.avg_distance, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Origin Country", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.cargo_type_agg))].sort() }, x: { label: "Average Distance (km)", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.origin_country), }, marks: [ Plot.barX(avgDistanceData, { x: "avg_distance", y: "origin_country", fill: "#b7d7cc", opacity:.8, title: d => `${d.origin_country}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "avg_distance", y: "origin_country", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].origin_country, text: `Avg: ${overallAvg.toFixed(0)} km` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }43:T935,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("origin_country") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('trip_count')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.trip_count, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Origin Country", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.cargo_type_agg))].sort() }, x: { label: "Trips", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.origin_country), }, marks: [ Plot.barX(avgDistanceData, { x: "trip_count", y: "origin_country", fill: "#b7d7cc", opacity:.8, title: d => `${d.origin_country}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "trip_count", y: "origin_country", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].origin_country, text: `Avg: ${overallAvg.toFixed(0)} trips` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }44:Tac1,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.distance_category_industry !== null); const rawData = aq.from(tmp) .groupby("origin_country_new", "distance_category_industry") .count({ as: "value" }) .objects(); let data, yLabel, yDomain; if (showPercentage) { const originTotals = aq.from(rawData) .groupby("origin_country_new") .rollup({ total_trips: d => aq.op.sum(d.value) }) .objects(); const totalLookup = {}; originTotals.forEach(d => { totalLookup[d.origin_country_new] = d.total_trips; }); data = rawData.map(d => ({ ...d, displayValue: (d.value / totalLookup[d.origin_country_new]) * 100, total_for_origin: totalLookup[d.origin_country_new] })); yLabel = "%"; yDomain = [0, 115]; } else { data = rawData.map(d => ({ ...d, displayValue: d.value })); yLabel = "Trips"; yDomain = [0, Math.max(...data.map(d => d.displayValue)) * 1.1]; } const fyDomain = [...new Set(data.map(d => d.origin_country_new))]; const distanceOrder = [ '0-10km', '10-50km', '50-100km', '100-150km', '150-250km', '250-500km', '500-1000km', '1000-2000km', '2000-2500km' ]; return Plot.plot({ title: showPercentage ? "Distribution of Distance Categories by Origin Country (%)" : "Survey Count by Distance Category and Origin Country", subtitle: showPercentage ? "Percentages calculated within each origin country" : undefined, width: 1200, height: 700, marginBottom: 50, marginRight: 150, marginTop: showPercentage ? 50 : 30, style: {...globalStyles, fontSize: "14px"}, color: { range: discreteScheme, legend: true, domain: fyDomain }, x: { label: null, domain: distanceOrder, grid: true }, y: { label: null, domain: showPercentage? [0,110]: yDomain, ticks: 0, }, fy: { axis: "right", label: null, domain: fyDomain }, marks: [ Plot.barY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'origin_country_new', fill: 'origin_country_new' }), Plot.textY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'origin_country_new', filter: d => showPercentage ? d.displayValue > 5 : d.displayValue > 20, dy: -4, text: d => showPercentage ? `${d.displayValue.toFixed(1)}%` : d3.format(".1s")(d.displayValue), fill: showPercentage ? 'black' : undefined, fontSize: 10 }), Plot.ruleY([0]) ] }); }45:T976,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("origin_country_new") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('avg_distance')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.avg_distance, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Origin Country Disaggregate by UAE", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.cargo_type_agg))].sort() }, x: { label: "Average Distance (km)", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.origin_country_new), }, marks: [ Plot.barX(avgDistanceData, { x: "avg_distance", y: "origin_country_new", fill: "#b7d7cc", opacity:.8, title: d => `${d.origin_country_new}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "avg_distance", y: "origin_country_new", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].origin_country, text: `Avg: ${overallAvg.toFixed(0)} km` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }46:T94d,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("origin_country_new") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('trip_count')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.trip_count, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Origin Country", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.cargo_type_agg))].sort() }, x: { label: "Trips", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.origin_country_new), }, marks: [ Plot.barX(avgDistanceData, { x: "trip_count", y: "origin_country_new", fill: "#b7d7cc", opacity:.8, title: d => `${d.origin_country_new}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "trip_count", y: "origin_country_new", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].origin_country, text: `Avg: ${overallAvg.toFixed(0)} trips` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }47:Tad2,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with distance data .objects(); // Domain ordered from north to south const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; const allDistances = avgDistanceData.map(d => d.avg_distance); const minDistance = Math.min(...allDistances); const maxDistance = Math.max(...allDistances); const avgOverallDistance = allDistances.reduce((sum, dist) => sum + dist, 0) / allDistances.length; return Plot.plot({ title: "Origin-Destination Average Distance Matrix", subtitle: `Average trip distance in kilometers`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, color: { type: "sequential", scheme: "Reds", domain: [minDistance, maxDistance], legend: true, label: "Average Distance (km)" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(avgDistanceData, { x: "destination_country_new", y: "origin_country_new", fill: "avg_distance", stroke: "white", strokeWidth: 2, inset: 0.5 }), // Text labels showing average distance Plot.text(avgDistanceData, { x: "destination_country_new", y: "origin_country_new", text: d => `${d.avg_distance.toFixed(0)}`, fill: d => d.avg_distance > avgOverallDistance ? "white" : "black", fontSize: 11, }), // // Trip count labels (smaller text) // Plot.text(avgDistanceData, { // x: "destination_country_new", // y: "origin_country_new", // text: d => `(${d.trip_count})`, // fill: d => d.avg_distance > avgOverallDistance ? "white" : "black", // fontSize: 8, // dy: 10 // Offset below the distance value // }) ] }); }48:T113d,{ const commodityCategories = { 'Beverages &\nWater': ['Beverages (other than water)', 'Drinking water (bottled)', 'Drainage water', 'Water for irrigation'], 'Chemicals &\nMaterials': ['Ceramics', 'Chemicals for oil industry', 'Glass (no packaging)', 'Other chemicals', 'PE granulate', 'Plastics, insulation (no packaging)'], 'Construction\nMaterials': ['Aggregate', 'Bituminous construction material', 'Bricks, pipes made of cement', 'Building site equipment', 'Cement', 'Construction materials made of wood', 'Pipes made of steel', 'Plastering materials'], 'Consumer \nGoods & Others': ['Furnishing, white goods', 'Non-food products (textiles etc.)', 'Packaging', 'Recyclable waste', 'Waste from household', 'Wood'], 'Energy &\nFuel': ['Petrol'], 'Food &\nAgriculture': ['Fish', 'Fodder', 'Fodder, crops', 'Fruits and dates', 'Milk, eggs', 'Other food', 'Plants', 'Red meat', 'Vegetables', 'White meat'], 'Metals &\nManufacturing': ['Iron, steel', 'Machinery', 'Steel products', 'Tin, aluminium'], 'Vehicles &\nEquipment': ['Exported/damaged cars', 'Imported cars', 'Other vehicles'], }; // Transform data for arrow diagram const arrowData = []; let yPosition = 0; Object.entries(commodityCategories).forEach(([category, commodities]) => { // Add category node (left side) arrowData.push({ type: 'category', category: category, text: category, x: 0, y: yPosition + (commodities.length - 1) / 2, // Center category among its commodities count: commodities.length }); // Add commodity nodes (right side) and arrows commodities.forEach((commodity, index) => { const commodityY = yPosition + index; // Add commodity node arrowData.push({ type: 'commodity', category: category, text: commodity, x: 2, y: commodityY }); // Add arrow arrowData.push({ type: 'arrow', category: category, x1: 0.5, y1: yPosition + (commodities.length - 1) / 2, x2: 1.8, y2: commodityY }); }); yPosition += commodities.length + 1; // Add spacing between categories }); return Plot.plot({ title: "Aggregated Commodity Categories → Individual Commodities Categories", width: 1200, height: Math.max(600, yPosition * 20), marginLeft: 200, marginRight: 300, color: { range: discreteScheme, domain: Object.keys(commodityCategories) }, x: { domain: [-0.5, 2.5], axis: null }, y: { domain: [-1, yPosition], axis: null }, marks: [ // Draw arrows Plot.arrow(arrowData.filter(d => d.type === 'arrow'), { x1: "x1", y1: "y1", x2: "x2", y2: "y2", stroke: "category", strokeWidth: 2, strokeOpacity: 0.7, bend: 10 }), Plot.dot(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", r: d => Math.min(8 + d.count * 0.5, 15), fill: "category", stroke: "white", strokeWidth: 2 }), // Commodity nodes (smaller, right side) Plot.dot(arrowData.filter(d => d.type === 'commodity'), { x: "x", y: "y", r: 4, fill: "category", // fillOpacity: 0.6, stroke: "category", strokeWidth: 1 }), // Category labels Plot.text(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", text: d => d.text, // Fixed: use function to access text fill: "white", fontSize: 10, fontWeight: "bold", textAnchor: "middle", dx: -80 // Position to the left of the dot }), // Commodity labels Plot.text(arrowData.filter(d => d.type === 'commodity'), { x: "x", y: "y", text: d => d.text, // Fixed: use function to access text fill: "black", fontSize: 9, textAnchor: "start", dx: 8 // Position to the right of the dot }), // Add count labels for categories Plot.text(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", text: d => d.category, fill: "category", fontSize: 14, fontWeight: "bold", textAnchor: "start", dx: 10, dy: -5 }) ] }); } 49:Taac,{ const tmp = rsiCleaned.filter(d => d.cargo_type_agg !== null && d.distance_category_industry !== null); // Get raw counts const rawData = aq.from(tmp) .groupby("cargo_type_agg", "distance_category_industry") .count({ as: "value" }) .objects(); let data, yLabel, yDomain; if (showPercentage2) { const commodityTotals = aq.from(rawData) .groupby("cargo_type_agg") .rollup({ total_trips: d => aq.op.sum(d.value) }) .objects(); const totalLookup = {}; commodityTotals.forEach(d => { totalLookup[d.cargo_type_agg] = d.total_trips; }); data = rawData.map(d => ({ ...d, displayValue: (d.value / totalLookup[d.cargo_type_agg]) * 100, total_for_commodity: totalLookup[d.cargo_type_agg] })); yLabel = "%"; yDomain = [0, 115]; } else { data = rawData.map(d => ({ ...d, displayValue: d.value })); yLabel = "Number of Surveys"; yDomain = [0, Math.max(...data.map(d => d.displayValue)) * 1.1]; } const fyDomain = [...new Set(data.map(d => d.cargo_type_agg))].sort(); const distanceOrder = [ '0-10km', '10-50km', '50-100km', '100-150km', '150-250km', '250-500km', '500-1000km', '1000-2000km', '2000-2500km' ]; return Plot.plot({ // title: showPercentage2 // ? "Distribution of Distance Categories by Commodity Type (%)" // : "Survey Count by Distance Category and Commodity Types", // subtitle: showPercentage2 // ? "Percentages calculated within each commodity type" // : undefined, width: 1200, height: 800, marginBottom: 50, marginRight: 200, marginTop: showPercentage2 ? 50 : 30, style: {...globalStyles, fontSize: "14px"}, color: { range: discreteScheme, legend: true, domain: fyDomain }, x: { label: null, domain: distanceOrder, grid: true }, y: { label: null, domain: yDomain, ticks: 0 }, fy: { axis: "right", label: null, domain: fyDomain }, marks: [ Plot.barY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'cargo_type_agg', fill: 'cargo_type_agg' }), Plot.textY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'cargo_type_agg', filter: d => showPercentage2 ? d.displayValue > 5 : d.displayValue > 20, dy: -8, text: d => showPercentage2 ? `${d.displayValue.toFixed(1)}%` : d3.format(".1s")(d.displayValue), fill:'black', fontSize: 10 }), Plot.ruleY([0]) ] }); }4a:T48e,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.destination_country !== null && d.distance_category_industry !== null && d.cargo_type_agg === selected_commodity); const data = aq.from(tmp) .derive({ source: d => d.origin_country + ' (O)', target: d => d.destination_country + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])]; return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }4b:T4a5,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.distance_category_industry !== null && d.cargo_type_agg === selected_commodity); const data = aq.from(tmp) .derive({ source: d => d.origin_country_new + ' (O)', target: d => d.destination_country_new + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])].sort(); return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }4c:T94a,{ const tmp = rsiCleaned.filter(d => d.cargo_type_agg !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("cargo_type_agg") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('avg_distance')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.avg_distance, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Commodity Type", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.cargo_type_agg))].sort() }, x: { label: "Average Distance (km)", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.cargo_type_agg), }, marks: [ Plot.barX(avgDistanceData, { x: "avg_distance", y: "cargo_type_agg", fill: "#b7d7cc", opacity:.8, title: d => `${d.cargo_type_agg}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "avg_distance", y: "cargo_type_agg", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].cargo_type_agg, text: `Avg: ${overallAvg.toFixed(0)} km` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }4d:T8b4,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.destination_country !== null && d.trip_cost > 0 && d.daily_distance > 0 ); const costEfficiencyData = aq.from(tmp) .derive({ cost_per_km: d => d.trip_cost / d.daily_distance, route_pair: d => `${d.origin_country} → ${d.destination_country}` }) .groupby('route_pair', 'origin_country', 'destination_country') .rollup({ avg_cost_per_km: d => aq.op.mean(d.cost_per_km), trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 3) .orderby(aq.desc('avg_cost_per_km')) .objects(); return Plot.plot({ title: "Cost Efficiency by Origin-Destination Route", subtitle: `Routes ranked by average cost per km (highest to lowest) • ${costEfficiencyData.length} routes`, width: width/1.5, height: height/1.5, marginLeft: 250, marginRight: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "Reds", legend: true, label: "Cost per KM (AED)" }, x: { label: "Average Cost per Kilometer (AED)", grid: true }, y: { label: "Origin → Destination Route", domain: costEfficiencyData.map(d => d.route_pair) // Pre-sorted order }, marks: [ Plot.barX(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", fill: "avg_cost_per_km", title: d => `${d.route_pair}\nCost per KM: ${d.avg_cost_per_km.toFixed(2)} AED\nTotal trips: ${d.trip_count}\nTotal distance: ${d.total_distance.toLocaleString()} km\nTotal cost: ${d.total_cost.toLocaleString()} AED` }), Plot.text(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", text: d => `${d.avg_cost_per_km.toFixed(1)} (${d.trip_count} trips)`, dx: 5, textAnchor: "start", fill: "black", fontSize: 9 }), Plot.ruleX([aq.op.mean(costEfficiencyData.map(d => d.avg_cost_per_km))], { stroke: "red", strokeDasharray: "3,3", strokeWidth: 2 }) ] }); }4e:T8fe,{ const tmp = rsiCleaned.filter(d => d.origin_country !== null && d.destination_country !== null && d.trip_cost > 0 && d.daily_distance > 0 && d.cargo_type_agg === selected_commodity2 ); const costEfficiencyData = aq.from(tmp) .derive({ cost_per_km: d => d.trip_cost / d.daily_distance, route_pair: d => `${d.origin_country} → ${d.destination_country}` }) .groupby('route_pair', 'origin_country', 'destination_country') .rollup({ avg_cost_per_km: d => aq.op.mean(d.cost_per_km), trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 3) .orderby(aq.desc('avg_cost_per_km')) .objects(); return Plot.plot({ title: `Cost Efficiency by Origin-Destination Route for ${selected_commodity}`, subtitle: `Routes ranked by average cost per km (highest to lowest) • ${costEfficiencyData.length} routes`, width: width/1.5, height: height/1.5, marginLeft: 250, marginRight: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "Reds", legend: true, label: "Cost per KM (AED)" }, x: { label: "Average Cost per Kilometer (AED)", grid: true }, y: { label: "Origin → Destination Route", domain: costEfficiencyData.map(d => d.route_pair) // Pre-sorted order }, marks: [ Plot.barX(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", fill: "avg_cost_per_km", title: d => `${d.route_pair}\nCost per KM: ${d.avg_cost_per_km.toFixed(2)} AED\nTotal trips: ${d.trip_count}\nTotal distance: ${d.total_distance.toLocaleString()} km\nTotal cost: ${d.total_cost.toLocaleString()} AED` }), Plot.text(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", text: d => `${d.avg_cost_per_km.toFixed(1)} (${d.trip_count} trips)`, dx: 5, textAnchor: "start", fill: "black", fontSize: 9 }), Plot.ruleX([aq.op.mean(costEfficiencyData.map(d => d.avg_cost_per_km))], { stroke: "red", strokeDasharray: "3,3", strokeWidth: 2 }) ] }); }4f:T8cd,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.trip_cost > 0 && d.daily_distance > 0 ); const costEfficiencyData = aq.from(tmp) .derive({ cost_per_km: d => d.trip_cost / d.daily_distance, route_pair: d => `${d.origin_country_new} → ${d.destination_country_new}` }) .groupby('route_pair', 'origin_country_new', 'destination_country_new') .rollup({ avg_cost_per_km: d => aq.op.mean(d.cost_per_km), trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 10) .orderby(aq.desc('avg_cost_per_km')) .objects(); return Plot.plot({ title: "Cost Efficiency by Origin-Destination Route", subtitle: `Routes ranked by average cost per km (highest to lowest) • ${costEfficiencyData.length} routes`, width: width/1.5, height: height/1.5, marginLeft: 250, marginRight: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "Reds", legend: true, label: "Cost per KM (AED)" }, x: { label: "Average Cost per Kilometer (AED)", grid: true }, y: { label: "Origin → Destination Route", domain: costEfficiencyData.map(d => d.route_pair) // Pre-sorted order }, marks: [ Plot.barX(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", fill: "avg_cost_per_km", title: d => `${d.route_pair}\nCost per KM: ${d.avg_cost_per_km.toFixed(2)} AED\nTotal trips: ${d.trip_count}\nTotal distance: ${d.total_distance.toLocaleString()} km\nTotal cost: ${d.total_cost.toLocaleString()} AED` }), Plot.text(costEfficiencyData, { x: "avg_cost_per_km", y: "route_pair", text: d => `${d.avg_cost_per_km.toFixed(1)} (${d.trip_count} trips)`, dx: 5, textAnchor: "start", fill: "black", fontSize: 9 }), Plot.ruleX([aq.op.mean(costEfficiencyData.map(d => d.avg_cost_per_km))], { stroke: "red", strokeDasharray: "3,3", strokeWidth: 2 }) ] }); }50:Tb4b,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.trip_cost > 0 && d.daily_distance > 0 ); const costEfficiencyData = aq.from(tmp) .derive({ cost_per_km: d => d.trip_cost / d.daily_distance }) .groupby('origin_country_new', 'destination_country_new') .rollup({ avg_cost_per_km: d => aq.op.mean(d.cost_per_km), trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 5) // Routes with 5+ trips .objects(); // Domain ordered from north to south (same as other charts) const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; // Calculate overall statistics for color scale const allCostPerKm = costEfficiencyData.map(d => d.avg_cost_per_km); const minCost = Math.min(...allCostPerKm); const maxCost = Math.max(...allCostPerKm); const avgCost = allCostPerKm.reduce((sum, cost) => sum + cost, 0) / allCostPerKm.length; return Plot.plot({ title: "Origin-Destination Cost Efficiency Matrix", subtitle: `Average cost per km (AED)`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, style: { ...globalStyles }, color: { type: "sequential", scheme: "YlOrRd", domain: [minCost, maxCost], legend: true, label: "Cost per KM (AED)" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(costEfficiencyData, { x: "destination_country_new", y: "origin_country_new", fill: "avg_cost_per_km", stroke: "white", strokeWidth: 2, inset: 0.5 }), // Text labels showing cost per km Plot.text(costEfficiencyData, { x: "destination_country_new", y: "origin_country_new", text: d => d.avg_cost_per_km.toFixed(1), fill: d => d.avg_cost_per_km > avgCost ? "white" : "black", // White text on dark cells fontSize: 10, fontWeight: "bold" }), // Trip count labels (smaller text) Plot.text(costEfficiencyData, { x: "destination_country_new", y: "origin_country_new", text: d => `(${d.trip_count})`, fill: d => d.avg_cost_per_km > avgCost ? "white" : "black", fontSize: 8, dy: 10 // Offset below the cost value }) ] }); }51:Ta45,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.trip_cost > 0 && d.daily_distance > 0 ); const flowData = aq.from(tmp) .derive({ cost_per_km: d => d.trip_cost / d.daily_distance }) .groupby('origin_country_new', 'destination_country_new') .rollup({ avg_cost_per_km: d => aq.op.mean(d.cost_per_km), trip_count: aq.op.count(), total_cost: d => aq.op.sum(d.trip_cost) }) .filter(d => d.trip_count >= 5) .objects(); // Create nodes (unique locations) const allLocations = [...new Set([ ...flowData.map(d => d.origin_country_new), ...flowData.map(d => d.destination_country_new) ])]; // Position nodes in a circle const nodePositions = {}; allLocations.forEach((location, i) => { const angle = (2 * Math.PI * i) / allLocations.length; const radius = 200; nodePositions[location] = { x: Math.cos(angle) * radius, y: Math.sin(angle) * radius, location: location }; }); // Add positions to flow data const flowWithPositions = flowData.map(d => ({ ...d, x1: nodePositions[d.origin_country_new].x, y1: nodePositions[d.origin_country_new].y, x2: nodePositions[d.destination_country_new].x, y2: nodePositions[d.destination_country_new].y })); const nodes = Object.values(nodePositions); return Plot.plot({ title: "Origin-Destination Flow Diagram", subtitle: "Arc thickness = trip volume, color = cost efficiency", width: 500, height: 500, color: { type: "sequential", scheme: "YlOrRd", legend: true, label: "Cost per KM (AED)" }, marks: [ // Flow arcs Plot.arrow(flowWithPositions, { x1: "x1", y1: "y1", x2: "x2", y2: "y2", stroke: "avg_cost_per_km", strokeWidth: d => Math.sqrt(d.trip_count) * 2, strokeOpacity: 0.7, bend: 20, title: d => `${d.origin_country_new} → ${d.destination_country_new}\nCost/KM: ${d.avg_cost_per_km.toFixed(2)} AED\nTrips: ${d.trip_count}` }), // Node circles Plot.dot(nodes, { x: "x", y: "y", r: 8, fill: "steelblue", stroke: "white", strokeWidth: 2 }), // Node labels Plot.text(nodes, { x: "x", y: "y", text: "location", dx: d => d.x > 0 ? 15 : -15, dy: d => d.y > 0 ? 5 : -5, textAnchor: d => d.x > 0 ? "start" : "end", fontSize: 10, fontWeight: "bold" }) ] }); }52:Teab,{ const commodityCategories = { 'Goods Truck': ['Light Goods Truck (≤ 5.5m)', 'Medium Goods Enclosed Truck (5.5–14m)', 'Medium Goods Flatbed Truck (5.5–14m)', 'Heavy Goods Enclosed Truck (11.5–19m)','Heavy Goods Flatbed Truck (11.5–19m)','Heavy Goods Tanker Truck (11.5–19m)'], 'Bulk Truck':['Medium Bulk Truck','Heavy Bulk Truck'], 'Tractor':['Tractor Only'], 'Container Truck':['Container Truck (1×TEU)','Container Truck (1×FEU or 2×TEU)','Multi-Container Truck (3–4×TEU)'], }; // Transform data for arrow diagram const arrowData = []; let yPosition = 0; Object.entries(commodityCategories).forEach(([category, commodities]) => { // Add category node (left side) arrowData.push({ type: 'category', category: category, text: category, x: 0, y: yPosition + (commodities.length - 1) / 2, // Center category among its commodities count: commodities.length }); // Add commodity nodes (right side) and arrows commodities.forEach((commodity, index) => { const commodityY = yPosition + index; // Add commodity node arrowData.push({ type: 'commodity', category: category, text: commodity, x: 2, y: commodityY }); // Add arrow arrowData.push({ type: 'arrow', category: category, x1: 0.5, y1: yPosition + (commodities.length - 1) / 2, x2: 1.8, y2: commodityY }); }); yPosition += commodities.length + 1; // Add spacing between categories }); return Plot.plot({ title: "Aggregated Vehicle Class", width: 1200, height: Math.max(600, yPosition * 20), marginLeft: 200, marginRight: 300, color: { range: discreteScheme, domain: Object.keys(commodityCategories) }, x: { domain: [-0.5, 2.5], axis: null }, y: { domain: [-1, yPosition], axis: null }, marks: [ // Draw arrows Plot.arrow(arrowData.filter(d => d.type === 'arrow'), { x1: "x1", y1: "y1", x2: "x2", y2: "y2", stroke: "category", strokeWidth: 2, strokeOpacity: 0.7, bend: 10 }), Plot.dot(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", r: d => Math.min(8 + d.count * 0.5, 15), fill: "category", stroke: "white", strokeWidth: 2 }), // Commodity nodes (smaller, right side) Plot.dot(arrowData.filter(d => d.type === 'commodity'), { x: "x", y: "y", r: 4, fill: "category", // fillOpacity: 0.6, stroke: "category", strokeWidth: 1 }), // Category labels Plot.text(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", text: d => d.text, // Fixed: use function to access text fill: "white", fontSize: 10, fontWeight: "bold", textAnchor: "middle", dx: -80 // Position to the left of the dot }), // Commodity labels Plot.text(arrowData.filter(d => d.type === 'commodity'), { x: "x", y: "y", text: d => d.text, // Fixed: use function to access text fill: "black", fontSize: 9, textAnchor: "start", dx: 8 // Position to the right of the dot }), // Add count labels for categories Plot.text(arrowData.filter(d => d.type === 'category'), { x: "x", y: "y", text: d => d.category, fill: "category", fontSize: 14, fontWeight: "bold", textAnchor: "start", dx: 10, dy: -5 }) ] }); } 53:Tab4,{ const tmp = rsiCleaned.filter(d => d.vehicle_type_name !== null && d.distance_category_industry !== null); const rawData = aq.from(tmp) .groupby("vehicle_type_name", "distance_category_industry") .count({ as: "value" }) .objects(); let data, yLabel, yDomain; if (showPercentage3) { const originTotals = aq.from(rawData) .groupby("vehicle_type_name") .rollup({ total_trips: d => aq.op.sum(d.value) }) .objects(); const totalLookup = {}; originTotals.forEach(d => { totalLookup[d.vehicle_type_name] = d.total_trips; }); data = rawData.map(d => ({ ...d, displayValue: (d.value / totalLookup[d.vehicle_type_name]) * 100, total_for_origin: totalLookup[d.vehicle_type_name] })); yLabel = "%"; yDomain = [0, 100]; } else { data = rawData.map(d => ({ ...d, displayValue: d.value })); yLabel = "Trips"; yDomain = [0, Math.max(...data.map(d => d.displayValue)) * 1.1]; } const fyDomain = ["Goods", "Bulk", "Tractor", "Container"]; const distanceOrder = [ '0-10km', '10-50km', '50-100km', '100-150km', '150-250km', '250-500km', '500-1000km', '1000-2000km', '2000-2500km' ]; return Plot.plot({ title: showPercentage3 ? "Distribution of Distance Categories by Vehicle Type (%)" : "Survey Count by Distance Category and Vehicle Type", subtitle: showPercentage3 ? "Percentages calculated within each origin country" : undefined, width: 1200, height: 700, marginBottom: 50, marginRight: 150, marginTop: showPercentage3 ? 50 : 30, style: {...globalStyles, fontSize: "14px"}, color: { range: discreteScheme, legend: true, domain: fyDomain }, x: { label: null, domain: distanceOrder, grid: true }, y: { label: null, domain: showPercentage3? [0,110]: yDomain, ticks: 0, }, fy: { axis: "right", label: null, domain: fyDomain }, marks: [ Plot.barY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'vehicle_type_name', fill: 'vehicle_type_name' }), Plot.textY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'vehicle_type_name', filter: d => showPercentage3 ? d.displayValue > 5 : d.displayValue > 20, dy: -8, text: d => showPercentage3 ? `${d.displayValue.toFixed(1)}%` : d3.format(".1s")(d.displayValue), fill: showPercentage3 ? 'black' : undefined, fontSize: 10 }), Plot.ruleY([0]) ] }); }54:T960,{ const tmp = rsiCleaned.filter(d => d.vehicle_type_name !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("vehicle_type_name") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('avg_distance')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.avg_distance, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Vehicle Type", width:width/1.5, height: height/1.5, marginLeft: 150, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.vehicle_type_name))].sort() }, x: { label: "Average Distance (km)", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.vehicle_type_name), }, marks: [ Plot.barX(avgDistanceData, { x: "avg_distance", y: "vehicle_type_name", fill: "#b7d7cc", opacity:.8, title: d => `${d.vehicle_type_name}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "avg_distance", y: "vehicle_type_name", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].vehicle_type_name, text: `Avg: ${overallAvg.toFixed(0)} km` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }55:T981,{ const tmp = rsiCleaned.filter(d => d.vehicle_type_full_name !== null && d.daily_distance > 0 ); const avgDistanceData = aq.from(tmp) .groupby("vehicle_type_full_name") .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), total_distance: d => aq.op.sum(d.daily_distance), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance) }) .orderby(aq.desc('avg_distance')) .objects(); const overallAvg = avgDistanceData.reduce((sum, d) => sum + d.avg_distance, 0) / avgDistanceData.length; return Plot.plot({ title: "Average Travel Distance by Vehicle Type", width:width/1.5, height: height/1.5, marginLeft: 200, marginRight:80, marginBottom: 30, color: {...globalStyles, range: discreteScheme, domain: [...new Set(avgDistanceData.map(d => d.vehicle_type_full_name))].sort() }, x: { label: "Average Distance (km)", grid: true, ticks:5, tickSize:"14px", }, y: { label: null, tickSize:14, domain: avgDistanceData.map(d => d.vehicle_type_full_name), }, marks: [ Plot.barX(avgDistanceData, { x: "avg_distance", y: "vehicle_type_full_name", fill: "#b7d7cc", opacity:.8, title: d => `${d.vehicle_type_full_name}\nAvg Distance: ${d.avg_distance.toFixed(0)} km\nTrips: ${d.trip_count}\nRange: ${d.min_distance}-${d.max_distance} km\nTotal Distance: ${d.total_distance.toLocaleString()} km` }), Plot.text(avgDistanceData, { x: "avg_distance", y: "vehicle_type_full_name", text: d => `${d.avg_distance.toFixed(0)} km\n(${d.trip_count} trips)`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11, // fontWeight: "bold" }), Plot.ruleX([overallAvg], { stroke: "red", strokeDasharray: "3,3", opacity: .7, strokeWidth: 1 }), Plot.ruleY([0]), Plot.text([{ x: overallAvg, y: avgDistanceData[avgDistanceData.length - 1].vehicle_type_name, text: `Avg: ${overallAvg.toFixed(0)} km` }], { x: "x", y: "y", text: "text", dx: 40, dy: -10, fill: "red", fontSize: 10, fontWeight: "bold" }) ] }); }56:T4d8,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.distance_category_industry !== null && d.vehicle_type_name === selected_vehicle_type); // return tmp; const data = aq.from(tmp) .derive({ source: d => d.origin_country_new + ' (O)', target: d => d.destination_country_new + ' (D)' }) .groupby('source', 'target') .count({ as: "value" }) .select('source', 'target', 'value') .objects(); const countriesDomain = [...new Set([ ...data.map(d => d.source), ...data.map(d => d.target) ])].sort(); // return countriesDomain; return SankeyChart( { links: data }, { nodeGroup: (d) => d.id.slice(0, -4), nodeLabel: (d) => d.id.slice(0, -4), nodeSort: (a, b) => { return ( countriesDomain.indexOf(a.id.slice(0, -4)) - countriesDomain.indexOf(b.id.slice(0, -4)) ); }, nodeWidth: 20, nodeStrokeWidth: 0, linkColor: "source", linkStrokeOpacity: 0.4, colors: discreteScheme, width, height } ) }57:Tb5d,{ const freight_sample = aq .from(freight_only) .groupby("vehicle_type_full_name") .rollup({ 'freight_count': d => aq.op.sum(d.count) }).objects(); const rsi_survey_count = aq .from(rsiCleaned) .groupby("vehicle_type_full_name") .rollup({ 'rsi_count': d => aq.op.count() }).objects(); const vehicleTypes = [...new Set([ ...freight_sample.map(d => d.vehicle_type_full_name), ...rsi_survey_count.map(d => d.vehicle_type_full_name) ])].filter(d => d != null).sort(); const freightMap = Object.fromEntries( freight_sample.map(d => [d.vehicle_type_full_name, d.freight_count]) ); const rsiMap = Object.fromEntries( rsi_survey_count.map(d => [d.vehicle_type_full_name, d.rsi_count]) ); const combinedData = vehicleTypes.map(vehicle => { const freightCount = freightMap[vehicle] || 0; const rsiCount = rsiMap[vehicle] || 0; const sampleRate = freightCount > 0 ? (rsiCount / freightCount) * 100 : 0; return { vehicle_type: vehicle, freight_count: freightCount, rsi_count: rsiCount, sample_rate_percent: sampleRate }; }).filter(d => d.freight_count > 0); const dataMap = Object.fromEntries( combinedData.map(d => [d.vehicle_type, d]) ); const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] yDomain.sort((a,b) => { const dataA = dataMap[a]; const dataB = dataMap[b]; if (!dataA && !dataB) return 0; if ((!dataA) || (!dataB)) return 1; return dataA.sample_rate_percent - dataB.sample_rate_percent; }) return Plot.plot({ title: "Sample Rate by Vehicle Type (RSI vs Freight)", width:width/1.5, height: height/1.5, marginLeft: 170, marginRight:80, marginBottom: 30, color: { ...globalStyles, range: discreteScheme }, x: { label: "Sample Rate (%)", grid: true, domain: [0, Math.max(...combinedData.map(d => d.sample_rate_percent)) * 1.1], tickSize: "14px", labelAnchor: "center" }, y: { label: null, tickSize: 14, domain: yDomain }, marks: [ Plot.barX(combinedData, { x: "sample_rate_percent", y: "vehicle_type", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(combinedData, { x: "sample_rate_percent", y: "vehicle_type", text: d => `${d.sample_rate_percent.toFixed(2)}% (${d.rsi_count}/${d.freight_count})`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11 }), Plot.ruleX([0]) ] }); }58:Ta73,{ const freight_sample = aq .from(freight_only) .groupby("vehicle_type_full_name") .rollup({ 'freight_count': d => aq.op.sum(d.count) }).objects(); const rsi_survey_count = aq .from(rsiCleaned) .groupby("vehicle_type_full_name") .rollup({ 'rsi_count': d => aq.op.count() }).objects(); const vehicleTypes = [...new Set([ ...freight_sample.map(d => d.vehicle_type_full_name), ...rsi_survey_count.map(d => d.vehicle_type_full_name) ])].filter(d => d != null).sort(); const freightMap = Object.fromEntries( freight_sample.map(d => [d.vehicle_type_full_name, d.freight_count]) ); const rsiMap = Object.fromEntries( rsi_survey_count.map(d => [d.vehicle_type_full_name, d.rsi_count]) ); const combinedData = vehicleTypes.map(vehicle => { const freightCount = freightMap[vehicle] || 0; const rsiCount = rsiMap[vehicle] || 0; const sampleRate = freightCount > 0 ? (rsiCount / freightCount) * 100 : 0; return { vehicle_type: vehicle, freight_count: freightCount, rsi_count: rsiCount, sample_rate_percent: sampleRate }; }).filter(d => d.freight_count > 0); const dataMap = Object.fromEntries( combinedData.map(d => [d.vehicle_type, d]) ); const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] return Plot.plot({ title: "Sample Rate by Vehicle Type (RSI vs Freight)", width:width/1.5, height: height/1.5, marginLeft: 170, marginRight:80, marginBottom: 30, color: { ...globalStyles, range: discreteScheme }, x: { label: "Sample Rate (%)", grid: true, domain: [0, Math.max(...combinedData.map(d => d.sample_rate_percent)) * 1.1], tickSize: "14px", labelAnchor: "center" }, y: { label: null, tickSize: 14, domain: yDomain }, marks: [ Plot.barX(combinedData, { x: "sample_rate_percent", y: "vehicle_type", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(combinedData, { x: "sample_rate_percent", y: "vehicle_type", text: d => `${d.sample_rate_percent.toFixed(2)}% (${d.rsi_count}/${d.freight_count})`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11 }), Plot.ruleX([0]) ] }); }59:Tc37,{ // Define vehicle type to load type mapping const vehicleToLoadType = { "Light Goods Truck": "Medium", "Medium Goods Enclosed Truck": "Medium", "Medium Goods Flatbed Truck": "Medium", "Medium Bulk Truck": "Medium", "Heavy Goods Enclosed Truck": "Heavy", "Heavy Goods Flatbed Truck": "Heavy", "Heavy Goods Tanker Truck": "Heavy", "Heavy Bulk Truck": "Heavy", "Tractor": "Heavy", "Container Truck": "Container", "Multi-Container": "Container" }; // First get the original data by vehicle type const freight_sample = aq .from(freight_only) .groupby("vehicle_type_full_name") .rollup({ 'freight_count': d => aq.op.sum(d.count) }).objects(); const rsi_survey_count = aq .from(rsiCleaned) .groupby("vehicle_type_full_name") .rollup({ 'rsi_count': d => aq.op.count() }).objects(); // Map vehicle types to load types and aggregate const freightByLoadType = {}; const rsiByLoadType = {}; freight_sample.forEach(d => { if (d.vehicle_type_full_name && vehicleToLoadType[d.vehicle_type_full_name]) { const loadType = vehicleToLoadType[d.vehicle_type_full_name]; freightByLoadType[loadType] = (freightByLoadType[loadType] || 0) + d.freight_count; } }); rsi_survey_count.forEach(d => { if (d.vehicle_type_full_name && vehicleToLoadType[d.vehicle_type_full_name]) { const loadType = vehicleToLoadType[d.vehicle_type_full_name]; rsiByLoadType[loadType] = (rsiByLoadType[loadType] || 0) + d.rsi_count; } }); // Create combined data for load types const loadTypes = ["Medium", "Heavy", "Container"]; const combinedData = loadTypes.map(loadType => { const freightCount = freightByLoadType[loadType] || 0; const rsiCount = rsiByLoadType[loadType] || 0; const sampleRate = freightCount > 0 ? (rsiCount / freightCount) * 100 : 0; return { load_type: loadType, freight_count: freightCount, rsi_count: rsiCount, sample_rate_percent: sampleRate }; }).filter(d => d.freight_count > 0); return Plot.plot({ title: "Sample Rate by Load Type (RSI vs Freight)", width: width/1.5, height: height/1.5, marginLeft: 100, marginRight: 80, marginBottom: 50, color: { ...globalStyles, range: discreteScheme }, x: { label: "Sample Rate (%)", grid: true, domain: [0, Math.max(...combinedData.map(d => d.sample_rate_percent)) * 1.1], tickSize: "14px", labelAnchor: "center" }, y: { label: null, tickSize: 14, domain: loadTypes }, marks: [ Plot.barX(combinedData, { x: "sample_rate_percent", y: "load_type", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(combinedData, { x: "sample_rate_percent", y: "load_type", text: d => `${d.sample_rate_percent.toFixed(2)}% (${d.rsi_count}/${d.freight_count})`, dx: 10, textAnchor: "start", fill: "black", fontSize: 12 }), Plot.ruleX([0]) ] }); }5a:T680,{ const freight_sample = aq .from(freight_only) .groupby("vehicle_type_full_name") .rollup({ 'freight_count': d => aq.op.sum(d.count) }).objects(); const rsi_survey_count = aq .from(rsiCleaned) .groupby("vehicle_type_full_name") .rollup({ 'rsi_count': d => aq.op.count() }).objects(); const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] return Plot.plot({ title: "Sample Rate by Vehicle Type (RSI vs Freight)", width:width/1.5, height: height/1.5, marginLeft: 170, marginRight:80, marginBottom: 30, color: { ...globalStyles, range: discreteScheme }, x: { label: "Count", grid: true, domain: [0, Math.max(...rsi_survey_count.map(d => d.rsi_count)) * 1.1], tickSize: "14px", labelAnchor: "center" }, y: { label: null, tickSize: 14, domain: yDomain }, marks: [ Plot.barX(rsi_survey_count, { x: "rsi_count", y: "vehicle_type_full_name", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(rsi_survey_count, { x: "rsi_count", y: "vehicle_type_full_name", text: d => `${d.rsi_count}`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11 }), Plot.ruleX([0]) ] }); }5b:T68a,{ const freight_sample = aq .from(freight_only) .groupby("vehicle_type_full_name") .rollup({ 'freight_count': d => aq.op.sum(d.count) }).objects(); const rsi_survey_count = aq .from(rsiCleaned) .groupby("vehicle_type_full_name") .rollup({ 'rsi_count': d => aq.op.count() }).objects(); const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] return Plot.plot({ title: "Sample Rate by Vehicle Type (RSI vs Freight)", width:width/1.5, height: height/1.5, marginLeft: 170, marginRight:80, marginBottom: 30, color: { ...globalStyles, range: discreteScheme }, x: { label: "Count", grid: true, domain: [0, Math.max(...freight_sample.map(d => d.freight_count)) * 1.1], tickSize: "14px", labelAnchor: "center" }, y: { label: null, tickSize: 14, domain: yDomain }, marks: [ Plot.barX(freight_sample, { x: "freight_count", y: "vehicle_type_full_name", fill: "#b7d7cc", opacity: 0.8, }), Plot.text(freight_sample, { x: "freight_count", y: "vehicle_type_full_name", text: d => `${d.freight_count}`, dx: 10, textAnchor: "start", fill: "black", fontSize: 11 }), Plot.ruleX([0]) ] }); }5c:T66f,{ const tmp = rsiCleaned.filter(d => d.vehicle_type_full_name === selected_vehicle_type2 && d.distance_category_industry !== null); const rawData = aq.from(tmp) .groupby("survey_location", "distance_category_industry") .count({ as: "value" }) .objects(); const fyDomain = [... new Set(tmp.map(d => d.survey_location))].sort(); const distanceOrder = [ '0-10km', '10-50km', '50-100km', '100-150km', '150-250km', '250-500km', '500-1000km', '1000-2000km', '2000-2500km' ]; let data, yLabel, yDomain; data = rawData.map(d => ({ ...d, displayValue: d.value })); yLabel = "Number of Surveys"; yDomain = [0, Math.max(...data.map(d => d.displayValue)) * 1.1]; return Plot.plot({ width: 1200, height: 800, marginBottom: 50, marginRight: 100, marginTop: 50, style: {...globalStyles, fontSize: "14px"}, color: { range: discreteScheme, legend: true, domain: fyDomain }, x: { label: null, domain: distanceOrder, grid: true }, y: { label: null, ticks: 0 }, fy: { label:null, }, marks: [ Plot.barY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'survey_location', fill: 'survey_location' }), Plot.textY(data, { x: 'distance_category_industry', y: 'displayValue', fy: 'survey_location', dy: -8, text: d => d.displayValue, fill:'black', fontSize: 10 }), Plot.ruleY([0]) ] }); }5d:Tb42,{ const tmp = rsiCleaned.filter(d => d.origin_country_new !== null && d.destination_country_new !== null && d.daily_distance > 0 && d.vehicle_type_full_name === selected_vehicle_type2 ); const avgDistanceData = aq.from(tmp) .groupby('origin_country_new', 'destination_country_new') .rollup({ avg_distance: d => aq.op.mean(d.daily_distance), trip_count: aq.op.count(), min_distance: d => aq.op.min(d.daily_distance), max_distance: d => aq.op.max(d.daily_distance), total_distance: d => aq.op.sum(d.daily_distance) }) .filter(d => d.trip_count >= 1) // Show all routes with distance data .objects(); // Domain ordered from north to south const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; const allDistances = avgDistanceData.map(d => d.avg_distance); const minDistance = Math.min(...allDistances); const maxDistance = Math.max(...allDistances); const avgOverallDistance = allDistances.reduce((sum, dist) => sum + dist, 0) / allDistances.length; return Plot.plot({ title: "Origin-Destination Average Distance Matrix", subtitle: `Average trip distance in kilometers`, width: Math.max(800, geographicOrder.length * 60), height: Math.max(600, geographicOrder.length * 40), marginLeft: 100, marginBottom: 100, color: { type: "sequential", scheme: "Reds", domain: [minDistance, maxDistance], legend: true, label: "Average Distance (km)" }, x: { label: null, domain: geographicOrder, tickRotate: -45 }, y: { label: null, domain: geographicOrder }, marks: [ // Background grid Plot.frame({stroke: "#e0e0e0", strokeWidth: 1}), // Heatmap cells Plot.cell(avgDistanceData, { x: "destination_country_new", y: "origin_country_new", fill: "avg_distance", stroke: "white", strokeWidth: 2, inset: 0.5 }), // Text labels showing average distance Plot.text(avgDistanceData, { x: "destination_country_new", y: "origin_country_new", text: d => `${d.avg_distance.toFixed(0)}`, fill: d => d.avg_distance > avgOverallDistance ? "white" : "black", fontSize: 11, }), // Trip count labels (smaller text) Plot.text(avgDistanceData, { x: "destination_country_new", y: "origin_country_new", text: d => d.trip_count > 1 ? `${d.trip_count} trips`: `${d.trip_count} trip`, fill: d => d.avg_distance > avgOverallDistance ? "white" : "black", fontSize: 8, dy: 10 // Offset below the distance value }) ] }); }5e:T624,{ const tmp_ = rsiCleaned.filter(d => d.cargo_type_agg !== null && d.distance_category_industry !== null); const tmp = tmp_.filter(d => !((d.vehicle_type == 15) && (d.cargo_quantity > 1))); const data = aq.from(tmp) .groupby('cargo_type_agg','vehicle_type_full_name') .rollup({ count: aq.op.count() }).objects() const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] return Plot.plot({ title: "Vehicle Distribution by Cargo Type", width: width * 1.1, height: height * 1.1, marginLeft: 40, marginRight:10, marginBottom: 180, fx: { label: null, }, y: { label: "Count", ticks:5, }, x: { label: null, tickRotate: 90, tickSize: 14, grid:true, domain: yDomain }, color: { ...globalStyles, range: ["#7bb3a0"] }, marks: [ Plot.barY(data, { fx: "cargo_type_agg", x: "vehicle_type_full_name", y: "count", fill: "#7bb3a0", opacity: 0.8 }), Plot.text(data, { fx: "cargo_type_agg", x: "vehicle_type_full_name", y: "count", text: d => d.count, dy: -5, fontSize: 9 }), Plot.ruleY([0]) ] }); }5f:T8a4,{ const tmp_ = rsiCleaned.filter(d => d.cargo_type_agg !== null && d.distance_category_industry !== null); const tmp = tmp_.filter(d => !((d.vehicle_type == 15) && (d.cargo_quantity > 1))); const data = aq.from(tmp) .groupby('cargo_type_agg','vehicle_type_full_name') .rollup({ count: aq.op.count() }).objects(); // Calculate totals for each cargo type const cargoTotals = aq.from(data) .groupby('cargo_type_agg') .rollup({ total: d => aq.op.sum(d.count) }) .objects(); // Create a lookup map for totals const totalMap = Object.fromEntries( cargoTotals.map(d => [d.cargo_type_agg, d.total]) ); // Add percentage calculation to the data const dataWithPercentages = data.map(d => ({ ...d, percentage: (d.count / totalMap[d.cargo_type_agg]) * 100 })); const yDomain = [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] return Plot.plot({ title: "Vehicle Distribution by Cargo Type (Percentage within each cargo type)", width: width * 1.1, height: height * 1.1, marginLeft: 40, marginRight: 10, marginBottom: 180, fx: { label: null, }, y: { label: "Percentage (%)", ticks: 5, domain: [0, 80] }, x: { label: null, tickRotate: 90, tickSize: 14, grid: true, domain: yDomain, }, color: { ...globalStyles, range: ["#7bb3a0"] }, marks: [ Plot.barY(dataWithPercentages, { fx: "cargo_type_agg", x: "vehicle_type_full_name", y: "percentage", fill: "#7bb3a0", opacity: 0.8 }), Plot.text(dataWithPercentages, { fx: "cargo_type_agg", x: "vehicle_type_full_name", y: "percentage", text: d => `${d.percentage.toFixed(0)}%`, dy: -5, dx: 3, fontSize: 9 }), Plot.ruleY([0]) ] }); }60:Tcaa,map = { const container = html`

`; yield container; const map = (container.value = new maplibregl.Map({ container, zoom: 6, center: [54.67284648670687, 24.408562218383533], style: "https://basemaps.cartocdn.com/gl/positron-gl-style/style.json", scrollZoom: true })); map.on("load", function () { const flowData = createFlowGeoJSON; const counts = flowData.features.map(f => f.properties.count); const minCount = Math.min(...counts); const maxCount = Math.max(...counts); map.addSource("origin_destination_flows", { type: "geojson", data: flowData }); map.addSource('site_layer', { type:'geojson', data: sitesData }), map.addLayer({ id: "flow_lines", type: "line", source: "origin_destination_flows", layout: { "line-join": "round", "line-cap": "round" }, paint: { "line-color": "#000000", "line-opacity": 0.2, "line-blur": 0.5 } }); map.addLayer({ id: "site", type: "circle", // Changed from "point" to "circle" source: 'site_layer', filter: ['==', ['get', 'Description'], selected_site2.toString()], paint: { 'circle-radius': 6, 'circle-color': '#7bb3a0' } }); }); // Create tooltip const tooltip = document.createElement('div'); tooltip.style.position = 'absolute'; tooltip.style.backgroundColor = 'rgba(0,0,0,0.8)'; tooltip.style.color = 'white'; tooltip.style.padding = '10px'; tooltip.style.borderRadius = '5px'; tooltip.style.pointerEvents = 'none'; tooltip.style.boxShadow = '0 2px 4px rgba(0,0,0,0.3)'; tooltip.style.fontFamily = 'Arial, sans-serif'; tooltip.style.fontSize = '12px'; tooltip.style.maxWidth = '300px'; tooltip.style.zIndex = '1000'; tooltip.style.display = 'none'; container.appendChild(tooltip); // Mousemove event for tooltip map.on("mousemove", 'flow_lines', e => { const features = map.queryRenderedFeatures(e.point, { layers: ['flow_lines'] }); if (features.length > 0) { const feature = features[0]; const props = feature.properties; tooltip.innerHTML = ` Flow Information
Count: ${props.count} trips
From: ${props.origin_country}
To: ${props.destination_country}
Origin: ${props.origin_coords}
Destination: ${props.destination_coords} `; tooltip.style.display = 'block'; tooltip.style.left = (e.point.x + 10) + 'px'; tooltip.style.top = (e.point.y - 10) + 'px'; map.getCanvas().style.cursor = 'pointer'; } else { tooltip.style.display = 'none'; map.getCanvas().style.cursor = ''; } }); map.on("mouseclick", 'flow_lines', () => { tooltip.style.display = 'none'; map.getCanvas().style.cursor = ''; }); map.on("mouseleave", 'flow_lines', () => { tooltip.style.display = 'none'; map.getCanvas().style.cursor = ''; }); invalidation.then(() => map.remove()); return map; }61:T687,{ let data = aq.from(rsiCleaned) .filter(d => d.cargo_type_agg !== null) .groupby("cargo_type_agg") .rollup({ 'average_travel_distance': aq.op.mean("daily_distance"), 'total_travel_distance': aq.op.sum('daily_distance'), 'count': aq.op.count() }).objects(); let domain = [... new Set(data.map(d => d.cargo_type_agg))].sort(); return Plot.plot({ title: "Average Trip Distance vs Frequency by Cargo Type (bubble size: total distance)", width: width/1.3, height: height/1.5, marginLeft: 50, marginRight: 10, marginBottom: 30, colors: { domain: discreteScheme, legend: null, }, style: { ...globalStyles }, x: { ticks:5, label: "Average Travel Distance (km)", domain: [0,1250], labelAnchor: 'center', grid:true }, y: { ticks: 5, domain: [0,580], label: "Frequency", grid:true, labelAnchor: 'center', }, r: { range: [10, 40], label: "Total Distance", legend: true }, marks: [ Plot.frame(), Plot.dot(data, { x: "average_travel_distance", y: "count", r: "total_travel_distance", fill: "cargo_type_agg", fillOpacity: 0.8, stroke: "white", strokeWidth: 1 }), Plot.text(data, { x: "average_travel_distance", y: "count", text: "cargo_type_agg", fontSize: 9, textAnchor: "middle" }), Plot.ruleX([0]), Plot.ruleY([0]), ], }) }62:T6cd,{ let data = aq.from(rsiCleaned) .filter(d => d.cargo_type_agg !== null) .groupby("cargo_type_agg") .rollup({ 'average_travel_distance': aq.op.mean("daily_distance"), 'total_travel_distance': aq.op.sum('daily_distance'), 'total_cargo_quantity': aq.op.sum('cargo_quantity'), 'count': aq.op.count() }).objects(); let domain = [... new Set(data.map(d => d.cargo_type_agg))].sort(); return Plot.plot({ title: "Average Trip Distance vs Frequency by Cargo Type (bubble size: total cargo quantity)", width: width/1.3, height: height/1.5, marginLeft: 50, marginRight: 10, marginBottom: 30, colors: { domain: discreteScheme, legend: null, }, style: { ...globalStyles }, x: { ticks:5, label: "Average Travel Distance (km)", domain: [0,1250], labelAnchor: 'center', grid:true }, y: { ticks: 5, domain: [0,580], label: "Frequency", grid:true, labelAnchor: 'center', }, r: { range: [10, 40], label: "Total Distance", legend: true }, marks: [ Plot.frame(), Plot.dot(data, { x: "average_travel_distance", y: "count", r: "total_cargo_quantity", fill: "cargo_type_agg", fillOpacity: 0.8, stroke: "white", strokeWidth: 1 }), Plot.text(data, { x: "average_travel_distance", y: "count", text: "cargo_type_agg", fontSize: 9, textAnchor: "middle" }), Plot.ruleX([0]), Plot.ruleY([0]), ], }) }63:T67b,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.vehicle_type_full_name !== null ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.cargo_quantity, 0); const average = total/distanceData.length; return Plot.plot({ title: "Cargo Quantity Distribution by Vehicle Type", subtitle: "Box plots showing cargo quantity outliers across vehicle categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Cargo quantity (kg)", grid: true }, y: { label: null, domain: [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] }, marks: [ Plot.boxX(distanceData, { x: "cargo_quantity", y: "vehicle_type_full_name", // fill: "vehicle_type_full_name", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} kg`], frameAnchor: "bottom-left", dy: -80, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }64:T54e,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.vehicle_type_name !== "Multi-container" ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: "Daily Distance Distribution by Vehicle Type", subtitle: "Box plots showing distance outliers across vehicle categories", width:width/1.5, height: height/1.5, marginLeft: 70, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: ["Goods", "Bulk", "Tractor", "Container"] }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "vehicle_type_name", // fill: "vehicle_type_name", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "middle", dy: 15, dx: 50, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }65:T675,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.vehicle_type_full_name !== null ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: "Daily Distance Distribution by Vehicle Type", subtitle: "Box plots showing distance outliers across vehicle categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "vehicle_type_full_name", // fill: "vehicle_type_full_name", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "bottom-left", dy: -80, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }66:T6b4,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.vehicle_type_full_name !== null && d.survey_location === selected_site3 ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: `Daily Distance Distribution by Vehicle Type ${selected_site3}`, subtitle: "Box plots showing distance outliers across vehicle categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: [ "Light Goods Truck", "Medium Goods Enclosed Truck", "Medium Goods Flatbed Truck", "Heavy Goods Enclosed Truck", "Heavy Goods Flatbed Truck", "Heavy Goods Tanker Truck", "Medium Bulk Truck", "Heavy Bulk Truck", "Tractor", "Container Truck", "Container Truck", "Multi-Container" ] }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "vehicle_type_full_name", // fill: "vehicle_type_full_name", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "bottom-left", dy: -80, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }67:T8de,{ const ylabelMap = { "trip_expenses": "Trip Expenses", "trip_cost": "Trip Cost", "cargo_quantity": "Cargo Quantity" } const label = ylabelMap[select_column]; let filteredData = rsiCleaned.filter(d => (d.cargo_type_agg === selected_commodity3) && (d.vehicle_type != null) ); if (selected_commodity3 === "All") { filteredData = rsiCleaned; } const xValues = filteredData.map(d => d.daily_distance).filter(d => d != null); const yValues = filteredData.map(d => d[select_column]).filter(d => d != null); const xDomain = [ Math.min(...xValues), Math.max(...xValues) ]; const yDomain = [ Math.min(...yValues), Math.max(...yValues) ]; const xPadding = (xDomain[1] - xDomain[0]) * 0.05; // 5% padding const yPadding = (yDomain[1] - yDomain[0]) * 0.05; // 5% padding return Plot.plot({ inset: 8, grid: true, width: width/1.5, height: height/1.5, marginLeft: 70, marginBottom: 50, color: { legend: true, domain: ["Goods","Bulk","Tractor","Container"], }, style: { ...globalStyles, fontSize:"14px" }, x: { ticks: 5, label: "Distance (Km)", labelAnchor: "center", labelArrow: false, domain: [xDomain[0] - xPadding, xDomain[1] + xPadding] }, y: { label: label, labelAnchor: "center", labelArrow: false, domain: [yDomain[0] - yPadding, yDomain[1] + yPadding], ticks: 5, tickFormat: "~s" }, marks: [ Plot.dot( rsiCleaned, { x: "daily_distance", y: select_column, stroke: "vehicle_type_name", filter: d => selected_commodity3 === "All" ? d.vehicle_type != null : (d.cargo_type_agg === selected_commodity3 && d.vehicle_type != null), opacity: .7 } ), Plot.linearRegressionY( rsiCleaned, { x: "daily_distance", y: select_column, filter: d => selected_commodity3 === "All" ? d.vehicle_type != null : d.cargo_type_agg === selected_commodity3, stroke: "red", strokeWidth: 2 } ) ] }) }68:T8b3,{ const labelMap = { "daily_distance": "Distance (Km)", "trip_expenses": "Trip Expenses", "trip_cost": "Trip Cost", "cargo_quantity": "Cargo Quantity" } const xlabel = labelMap[x_axis]; const ylabel = labelMap[y_axis]; let filteredData = rsiCleaned.filter(d => (d.origin_country_new === filter_by_origin) && (d.cargo_type_agg === filter_by_commodity) ); if (filter_by_commodity === "All" && filter_by_origin === "All") { filteredData = rsiCleaned }else if(filter_by_commodity === "All") { filteredData = rsiCleaned.filter(d => (d.origin_country_new === filter_by_origin) ); }else if(filter_by_origin === "All") { rsiCleaned.filter(d => (d.cargo_type_agg === filter_by_commodity) ); } const xValues = filteredData.map(d => d[x_axis]).filter(d => d != null); const yValues = filteredData.map(d => d[y_axis]).filter(d => d != null); const xDomain = [ Math.min(...xValues), Math.max(...xValues) ]; const yDomain = [ Math.min(...yValues), Math.max(...yValues) ]; const xPadding = (xDomain[1] - xDomain[0]) * 0.05; // 5% padding const yPadding = (yDomain[1] - yDomain[0]) * 0.05; // 5% padding return Plot.plot({ inset: 8, grid: true, width: width/1.5, height: height/1.5, marginLeft: 70, marginBottom: 50, color: { legend: true, domain: ["Goods","Bulk","Tractor","Container"], }, style: { ...globalStyles, fontSize:"14px" }, x: { label: xlabel, ticks: 5, labelAnchor: "center", labelArrow: false, domain: [xDomain[0] - xPadding, xDomain[1] + xPadding] }, y: { label: ylabel, labelAnchor: "center", labelArrow: false, domain: [yDomain[0] - yPadding, yDomain[1] + yPadding], ticks: 5, tickFormat: "~s" }, marks: [ Plot.dot( filteredData, { x: x_axis, y: y_axis, stroke: "vehicle_type_name", opacity: .7 } ), Plot.linearRegressionY( filteredData, { x: x_axis, y: y_axis, stroke: "red", strokeWidth: 2 } ) ] }) }69:Tf6c,{ const titles = { "cargo_type_agg":"Cargo", "vehicle_type_full_name":"Vehicle" } const y_label = { 'daily_distance':"Distance (km)", 'cargo_quantity':"Cargo Quantity", 'trip_cost':"Trip Cost" } const commodityTypes = [...new Set(rsiCleaned.filter(d => d[x_axis2] !== null).map(d => d[x_axis2]))].sort(); const quartileData = commodityTypes.map(commodity => { const commodityDistances = rsiCleaned .filter(d => (d[x_axis2] === commodity) && (d[`${select_column2}_outlier_percentile`] == "False")) .map(d => d[select_column2]) .sort((a, b) => a - b); if (commodityDistances.length > 0) { return { commodity: commodity, count: commodityDistances.length, min: d3.min(commodityDistances), q1: d3.quantile(commodityDistances, 0.25), q2: d3.quantile(commodityDistances, 0.5), q3: d3.quantile(commodityDistances, 0.75), max: d3.max(commodityDistances), mean: d3.mean(commodityDistances), iqr: d3.quantile(commodityDistances, 0.75) - d3.quantile(commodityDistances, 0.25) }; } return null; }).filter(d => d !== null); let title = titles[x_axis2]; let titl2 = y_label[select_column2]; return Plot.plot({ title: `${titl2} Distribution by ${title} Type with Quartiles`, subtitle: "Box plots with Q1 (blue), Q2/Median (green), Q3 (orange), and Mean (red)", width: width*1.1, height: height, marginLeft: 50, marginRight: 150, marginBottom: 130, style: {...globalStyles,fontSize: "11px"}, color: { range: discreteScheme, legend: true }, x: { label: null, tickRotate: -45, domain: [...new Set(rsiCleaned.filter(d=> d[x_axis2] !=null).map(d => d[x_axis2]))].sort() }, y: { label: y_label[select_column2], grid: true, ticks:5, tickFormat : "~s", labelAnchor: "center", // domain:[0,1000000], }, marks: [ Plot.boxY(rsiCleaned, { x: x_axis2, y: select_column2, filter: d=> d[`${select_column2}_outlier_percentile`] === "False", fillOpacity: 0.3, strokeWidth: 2, }), Plot.dot(quartileData, { x: "commodity", y: "q1", fill: "#3B82F6", stroke: "#1E40AF", strokeWidth: 2, r: 5, tip: true }), Plot.dot(quartileData, { x: "commodity", y: "q2", fill: "#10B981", stroke: "#047857", strokeWidth: 2, r: 5, tip: true }), Plot.dot(quartileData, { x: "commodity", y: "q3", fill: "#F59E0B", stroke: "#D97706", strokeWidth: 2, r: 5, tip: true }), Plot.dot(quartileData, { x: "commodity", y: "mean", fill: "#EF4444", stroke: "#DC2626", strokeWidth: 2, r: 5, tip: true }), Plot.text(quartileData, { x: "commodity", y: "q1", text: d => `Q1: ${Math.round(d.q1)}`, dy: 10, dx: -25, fontSize: 10, fill: "#1E40AF", fontWeight: "bold" }), Plot.text(quartileData, { x: "commodity", y: "q2", text: d => `Q2: ${Math.round(d.q2)}`, dy: -12, dx: 25, fontSize: 10, fill: "#047857", fontWeight: "bold" }), Plot.text(quartileData, { x: "commodity", y: "q3", text: d => `Q3: ${Math.round(d.q3)}`, dy: -15, dx: 25, fontSize: 10, fill: "#D97706", fontWeight: "bold" }), Plot.text(quartileData, { x: "commodity", y: "mean", text: d => `Mean:\n${Math.round(d.mean)}`, // dy: -, dx: -25, fontSize: 10, fill: "#DC2626", fontWeight: "bold" }), ] }); }6a:T615,{ const geographicOrder = [ "Kuwait", "Jordan", "Qatar", "Bahrain", "Saudi Arabia", "Ras Al Khaimah", "Umm Al Quwain", "Ajman", "Sharjah", "Dubai", "Abu Dhabi", "Fujairah" ]; const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.origin_country_new !== null ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: `Daily Distance Distribution by Vehicle Type`, subtitle: "Box plots showing distance outliers across vehicle categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: geographicOrder, }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "origin_country_new", // filter: d => d["daily_distance_outlier_percentile"] === "True", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "bottom-left", dy: -70, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }6b:T550,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.cargo_type_agg !== null ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: `Daily Distance Distribution by Cargo Type`, subtitle: "Box plots showing distance outliers across cargo categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: [...new Set(distanceData.map(d => d.cargo_type_agg))].sort(), }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "cargo_type_agg", // fill: "cargo_type_agg", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "bottom-left", dy: -170, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }6c:T579,{ const distanceData = rsiCleaned.filter(d => d.daily_distance > 0 && d.daily_distance !== null && d.cargo_type_agg !== null && d.survey_location === selected_site4 ); const total = distanceData.reduce((accumulator, currentValue) => accumulator + currentValue.daily_distance, 0); const average = total/distanceData.length; return Plot.plot({ title: `Daily Distance Distribution by Cargo Type`, subtitle: "Box plots showing distance outliers across cargo categories", width:width/1.5, height: height/1.5, marginLeft: 160, marginRight:80, marginBottom: 30, style: {...globalStyles}, color: { range: discreteScheme, legend: true }, x: { label: "Daily Distance (km)", grid: true }, y: { label: null, domain: [...new Set(distanceData.map(d => d.cargo_type_agg))].sort(), }, marks: [ Plot.boxX(distanceData, { x: "daily_distance", y: "cargo_type_agg", // fill: "cargo_type_agg", fillOpacity: 0.4 }), Plot.ruleX([average], {stroke: "red", strokeDasharray: "3,3"}), Plot.text([{x: average}], { x: "x", text: [`Average: ${average.toFixed(0)} km`], frameAnchor: "bottom-left", dy: -170, dx: 10, fill: "red", fontSize: 12, fontWeight: "bold" }) ] }); }6d:T59a,createFlowGeoJSON = { const tmp = rsiCleaned.filter(d => d.survey_location === selected_site2); const flows = aq.from(tmp) .filter(d => d.origin_longitude != null && d.origin_latitude != null && d.destination_longitude != null && d.destination_latitude != null) .groupby('origin_longitude', 'origin_latitude', 'destination_longitude', 'destination_latitude') .rollup({ count: aq.op.count(), origin_country: d => aq.op.mode(d.origin_country_new), destination_country: d => aq.op.mode(d.destination_country_new) }) .objects(); const features = flows.map(flow => ({ type: "Feature", geometry: { type: "LineString", coordinates: [ [parseFloat(flow.origin_longitude), parseFloat(flow.origin_latitude)], [parseFloat(flow.destination_longitude), parseFloat(flow.destination_latitude)] ] }, properties: { count: flow.count, origin_country: flow.origin_country || 'Unknown', destination_country: flow.destination_country || 'Unknown', origin_coords: `${flow.origin_longitude}, ${flow.origin_latitude}`, destination_coords: `${flow.destination_longitude}, ${flow.destination_latitude}`, flow_id: `${flow.origin_longitude}_${flow.origin_latitude}_${flow.destination_longitude}_${flow.destination_latitude}` } })); return { type: "FeatureCollection", features: features }; }6e:[{"id":"1becbac2cfd940eae69157bf566aa6a98839bb9ccde9f402df4183b4a805b36209a78fe1319f801f8b3212104a7e267cda2304183ec472263ba651f73d7016ba","href":"https://static.observableusercontent.com/files/1becbac2cfd940eae69157bf566aa6a98839bb9ccde9f402df4183b4a805b36209a78fe1319f801f8b3212104a7e267cda2304183ec472263ba651f73d7016ba","name":"tomtom.csv","type":"text/csv","lastModified":1757034725960.809,"size":681},{"id":"68da1cdecd9acb02a99e14b6a029280137624e4463ace3803eae6636a781762679b4123a66e9b5e014c983c4d1ee3318a6de8295dd10f19f412f3298a59d1f9d","href":"https://static.observableusercontent.com/files/68da1cdecd9acb02a99e14b6a029280137624e4463ace3803eae6636a781762679b4123a66e9b5e014c983c4d1ee3318a6de8295dd10f19f412f3298a59d1f9d","name":"comparison.csv","type":"text/csv","lastModified":1756863268797.332,"size":2219},{"id":"4da4ac31ee3dd8941ccae6c9130042ad423159ac131a1fbb182d429958a9172598d6a7ffadf4fcb53faa29d90c8c2e22d872a05e0262ff9e00f41a223af29e8f","href":"https://static.observableusercontent.com/files/4da4ac31ee3dd8941ccae6c9130042ad423159ac131a1fbb182d429958a9172598d6a7ffadf4fcb53faa29d90c8c2e22d872a05e0262ff9e00f41a223af29e8f","name":"freight_only@1.csv","type":"text/csv","lastModified":1756957221706.177,"size":7650164},{"id":"cb79c864e3ce4f070746cb02cacb46a5fa6dd0aed7a971d904cb434684d2c53fbce3d1fbb13e45f96153d22fb470de38c09d1d66721e565e6f9ee07ae3fa2ad0","href":"https://static.observableusercontent.com/files/cb79c864e3ce4f070746cb02cacb46a5fa6dd0aed7a971d904cb434684d2c53fbce3d1fbb13e45f96153d22fb470de38c09d1d66721e565e6f9ee07ae3fa2ad0","name":"results_df@1.csv","type":"text/csv","lastModified":1756863990923.112,"size":407},{"id":"b5a56a9a6796d463d03e57910f2ec14f3da87c06cd416c5291915d9cca705fc90a73a54307b2f89699eecae31b8c2288b4c188d795b1409884c106a24db8170d","href":"https://static.observableusercontent.com/files/b5a56a9a6796d463d03e57910f2ec14f3da87c06cd416c5291915d9cca705fc90a73a54307b2f89699eecae31b8c2288b4c188d795b1409884c106a24db8170d","name":"rsi_cleaned_outliers@4.csv","type":"text/csv","lastModified":1757897092323.813,"size":933439},{"id":"8a02e89f748930ecd6c92811a67d698cdd070aa3fc80a373f1964ef0c9e0d2b6bb4df5df70b4536900fb40d3c4f6b6cae9d34ebd705dfd41fdc68d413b72f99b","href":"https://static.observableusercontent.com/files/8a02e89f748930ecd6c92811a67d698cdd070aa3fc80a373f1964ef0c9e0d2b6bb4df5df70b4536900fb40d3c4f6b6cae9d34ebd705dfd41fdc68d413b72f99b","name":"sites.geojson","type":"application/geo+json","lastModified":1756967494729.873,"size":2207}] 2f:{"id":"8872652dabcc8130","slug":null,"title":"ER Roadside Survey Data analysis","thumbnail":"cf82b8e4d9abca3494351f725f994e7144cc90dc63d27417a5d3bec275db7f92","access_level":"unlisted","create_time":"$D2025-09-02T00:39:07.367Z","update_time":"$D2025-11-12T20:59:59.452Z","view_time":null,"first_public_time":null,"has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"b8b49653f61623f1","login":"ktringuyen","name":"ktringuyen","avatar_url":"https://avatars-next.observableusercontent.com/d4181af5d64e221c05a65bc1b61eee96abe87d64f0b0839e0757f71641ba33c7","tier":"starter_2024","bio":""},"version":1876,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"eae8b26a5e6eeaf3","login":"kytringuyen","name":"Nguyen Ky Tri","avatar_url":"https://avatars-next.observableusercontent.com/b2a48bc07bad0bedc9d706e8f81618992e650a71116025706a3f296f78c66941","tier":null,"bio":"Engineer specialised in Data Science and Data Visualisation"}],"members":[{"type":"team","id":"b8b49653f61623f1","login":"ktringuyen","name":"ktringuyen","avatar_url":"https://avatars-next.observableusercontent.com/d4181af5d64e221c05a65bc1b61eee96abe87d64f0b0839e0757f71641ba33c7","tier":"starter_2024","role":"editor"},{"type":"individual","id":"eae8b26a5e6eeaf3","login":"kytringuyen","name":"Nguyen Ky Tri","avatar_url":"https://avatars-next.observableusercontent.com/b2a48bc07bad0bedc9d706e8f81618992e650a71116025706a3f296f78c66941","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":3,"collection":null,"type":"notebook","body":{"titling":"auto","title":"ER Roadside Survey Data analysis","cells":[{"id":0,"value":"# ER Roadside Survey Data analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":600,"value":"## Origin and Destination data validation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1423,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1427,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":824,"value":"viewof selected_site = Inputs.select([...new Set(rsiCleaned.map(d => d.survey_location))], {label: \"Select a site\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":820,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":834,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1408,"value":"### OD Sankey flow charts","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1401,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1404,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1416,"value":"viewof selected_distance= Inputs.select([\n 50, \n 100,\n 150,\n 250,\n 500,\n 1000,\n 2000,\n 2500\n ], {label: \"Distance (< Km)\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1413,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1376,"value":"## Data sampling comparison","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1493,"value":"### Compare with ATC distribution","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":666,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":679,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":617,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":700,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1491,"value":"## Compare with TOMTOM distribution","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1387,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1397,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":747,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":615,"value":"# Trip distance","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":630,"value":"viewof showPercentage = Inputs.toggle({label: \"Percentage\", value: false})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"# OD for all trips","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":437,"value":"## Aggregate by UAE","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":472,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":479,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"## Disaggregate by UAE","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":462,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":424,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":516,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"# Commodity types","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1436,"value":"### Trip by distance for different commodity type","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":375,"value":"viewof showPercentage2 = Inputs.toggle({label: \"Percentage\", value: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":204,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1433,"value":"## Commodity flow between UAE","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":240,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":853,"value":"viewof selected_commodity = Inputs.select(commodityTypes.sort(), {label: \"Commodity type\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":483,"value":"## Commodity flow between UAE (Disaggregate by UAE)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":486,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"# Cost and route analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":278,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":806,"value":"viewof selected_commodity2 = Inputs.select(commodityTypes.sort(), {label: \"Commodity type\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":496,"value":"### Further disaggregate version","pinned":true,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":493,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":501,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":503,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":561,"value":"## Vehicle type","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":564,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":575,"value":"viewof showPercentage3 = Inputs.toggle({label: \"Percentage\", value: false})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":580,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":583,"value":"viewof selected_vehicle_type = Inputs.select([\"Goods\", \"Bulk\",\"Tractor\",\"Container\"], {label: \"Commodity type\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":585,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":856,"value":"# Vehicle sample (RSI vs ATC count)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":859,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":884,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":891,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1750,"value":"# Travel distance by vehicle types","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1752,"value":"viewof selected_vehicle_type2 = Inputs.select([... new Set(rsiCleaned.map(d => d.vehicle_type_full_name))].sort() , {label: \"Select vehicle type\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1757,"value":"$5c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1783,"value":"$5d","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"{\n return aq\n .from(freight_only)\n .groupby(\"vehicle_type_full_name\")\n .rollup({\n 'freight_count': d => aq.op.sum(d.count)\n }).objects().map(d =>d.freight_count).reduce((acc, v) => acc +v, 0);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":908,"value":"## Commodity type vs vehicle type","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":911,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":945,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":992,"value":"## Interactive map to check OD sanity","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":967,"value":"viewof selected_site2 = Inputs.select([...new Set(rsiCleaned.map(d => d.survey_location))], {label: \"Select a site\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":975,"value":"$60","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1791,"value":"## Additional charts","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1794,"value":"$61","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1862,"value":"$62","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":963,"value":"maplibregl = require(\"maplibre-gl@2.1.9\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"sitesData = await FileAttachment(\"sites.geojson\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1052,"value":"## Outliers and ambiguous values","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1055,"value":"{\n const costData = rsiCleaned.filter(d => d.trip_cost > 0 && d.trip_cost !== null);\n \n return Plot.plot({\n title: \"Trip Cost Distribution - Potential Outliers Flagged\",\n subtitle: \"Log scale histogram showing cost distribution with extreme values\",\n width: width/1.5,\n height: height/1.5,\n marginLeft: 80,\n marginBottom: 60,\n x: {\n type: \"log\",\n label: \"Trip Cost (AED) - Log Scale\",\n // grid: true\n },\n y: {\n label: \"Frequency\",\n ticks:5,\n },\n marks: [\n Plot.rectY(costData, Plot.binX({y: \"count\"}, {\n x: \"trip_cost\",\n fill: \"steelblue\",\n fillOpacity: 0.7,\n thresholds: 50\n })),\n // Plot.ruleX([500000], {stroke: \"red\", strokeDasharray: \"5,5\", strokeWidth: 2}),\n // Plot.text([{x: 500000, y: 0}], {\n // x: \"x\",\n // y: \"y\", \n // text: [\"Extreme outlier threshold\"],\n // dy: -10,\n // fill: \"red\",\n // fontSize: 10\n // })\n ]\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1443,"value":"$63","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":300,"value":"$64","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1097,"value":"$65","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1455,"value":"## Travel distance by vehicle types (filter by site)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1118,"value":"viewof selected_site3 = Inputs.select([...new Set(rsiCleaned.map(d => d.survey_location))], {label:\"Site\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1120,"value":"$66","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1458,"value":"## Scatter plots for different numerical values vs travel distance for different commodity type","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1287,"value":"viewof select_column = Inputs.select([\"trip_cost\",\"trip_expenses\",\"cargo_quantity\"],{label:\"Select column\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1303,"value":"viewof selected_commodity3 = Inputs.select([...new Set(rsiCleaned.filter(d =>d.cargo_type_agg != null).map(d => d.cargo_type_agg)), \"All\"].sort(), {label:\"Select commodity\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1293,"value":"$67","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1502,"value":"viewof x_axis = Inputs.select([\"daily_distance\",\"trip_cost\", \"trip_expenses\", \"cargo_quantity\"], {label:\"Select x-axis\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1504,"value":"viewof y_axis = Inputs.select([\"daily_distance\",\"trip_cost\", \"trip_expenses\", \"cargo_quantity\"], {label:\"Select y-axis\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1508,"value":"viewof filter_by_origin = Inputs.select([... new Set(rsiCleaned.filter(d => d.origin_country_new !== null).map(d => d.origin_country_new)), \"All\"].sort(), {label: \"Filter by origin\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1513,"value":"viewof filter_by_commodity = Inputs.select([... new Set(rsiCleaned.filter(d => d.cargo_type_agg !== null).map(d => d.cargo_type_agg)) , \"All\"].sort(), {label: \"Filter by commodity\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1517,"value":"$68","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1582,"value":"### Violin chart","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1624,"value":"viewof select_column2 = Inputs.select(['daily_distance', 'cargo_quantity', 'trip_cost'],{label: \"Select column\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1670,"value":"viewof x_axis2 = Inputs.select([\"cargo_type_agg\", \"vehicle_type_full_name\"], {label: \"Select x-axis\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1584,"value":"$69","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1734,"value":"[...new Set(rsiCleaned.map(d => d.cargo_type_agg))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1125,"value":"$6a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1132,"value":"$6b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1470,"value":"### Travel distance by commodity type filter by site","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1138,"value":"viewof selected_site4 = Inputs.select([...new Set (rsiCleaned.map(d => d.survey_location))], {label:\"Site\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1141,"value":"$6c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"### Frequency of number of trips make per day","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1146,"value":"{\n const tripsData = rsiCleaned.filter(d => d.daily_trips > 0 && d.daily_trips !== null);\n \n return Plot.plot({\n \n width: width/1.5,\n height: height/1.5,\n marginLeft: 80,\n marginBottom: 60,\n x: {\n label: \"Daily Trips\",\n grid: true,\n domain: [0.1, 6],\n ticks:5,\n },\n y: {\n label: \"Count\",\n ticks: 4,\n grid: true,\n },\n marks: [\n Plot.rectY(tripsData, Plot.binX({y: \"count\"}, {\n x: \"daily_trips\",\n fillOpacity: 0.7,\n thresholds: [0.5, 1.5, 2.5, 3.5, 4.5, 5.5]\n })),\n ]\n });\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1384,"value":"# Data preparation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1487,"value":"$6d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1394,"value":"freight_only = await FileAttachment(\"freight_only@1.csv\").csv({typed:true, header:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1382,"value":"comparison_df = await FileAttachment(\"comparison.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1373,"value":"results_df = await FileAttachment(\"results_df@1.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1189,"value":"tomtom = await FileAttachment(\"tomtom.csv\").csv({header:true, typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"commodityTypes = [...new Set(rsiCleaned.filter(d=>d.cargo_type_agg !=null).map(d => d.cargo_type_agg))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":28,"value":"globalStyles = ({\n fontFamily: \"Arial\",\n color: \"#131d2b\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"discreteScheme = [\n \"#275b79\", // Deep blue\n \"#cc4e1e\", // Rust orange\n \"#418647\", // Forest green\n \"#fdb220\", // Bright yellow\n \"#8b3683\", // Purple\n \"#00a5a5\", // Teal\n \"#bf2860\", // Pink/red\n \"#c17f59\" // Warm gold\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":136,"value":"width = 1200","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"height = 700","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"rsiCleaned_ = await FileAttachment(\"rsi_cleaned_outliers@4.csv\").csv({typed:true, header:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1254,"value":"rsiCleaned = rsiCleaned_.filter(d => (d.survey_location != \"2D\") && (d.origin_formatted_address != null) && (d.destination_formatted_address != null))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":118,"value":"import { SankeyChart } from \"@d3/sankey-component\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W6e","stdlib":"1"}} 30:"$2f:body"