30:T5e3,md` ## Impact of Covid on US aviation Commercial aviation has a direct impact on our nation’s economy, creating more than 10 million well-paying American jobs and driving 5 percent of the U.S. gross domestic product and nearly $1.7 trillion in annual economic activity. Healthy U.S. airlines stimulate the commercial aviation industry, as well as the broader economy through increased connectivity, trade and enhanced mobility of people, cultures, goods and ideas. The COVID-19 pandemic has had a significant impact on the aviation industry due to travel restrictions and a slump in demand among travellers. Significant reductions in passenger numbers have resulted in flights being cancelled or planes flying empty between airports, which in turn massively reduced revenues for airlines and forced many airlines to lay off employees. Some have attempted to avoid refunding cancelled trips in order to diminish their losses. Multiple airlines waived fees for flight booking changes and cancellations during the coronavirus outbreak. Flight fares for domestic flights also dropped. On 25 March, the United States Senate passed a bill that would allocate $58 billion in loans and guarantees to aviation-related companies, including $25 billion for passenger carriers and $4 billion for cargo carriers, plus $17 billion for companies "critical to maintaining national security", such as Boeing. Data Sources: https://data.bts.gov/
https://www.tsa.gov/
https://www.iata.org/ `31:Tae5,{ const myDiv = html`

`; const traceLine = [ { x: [...TSASumbyMonth20.keys()], y: dropPercent.map(d => d), type: 'bar', // text: (dropPercent.map(d => d)), // textposition: 'inside', // orientation: 'h', marker: { color: [ 'steelblue', 'steelblue', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red', 'red' ] } } ]; let layout = { title: "Passengers drop in 2020 compared to 2019 (by month)", yaxis: { showline: false, showgrid: false, showticklabels: false }, xaxis: { showgrid: false, tickangle: -45 }, annotations: [ { text: dropPercent[0] + '%', showarrow: false, x: 0.02, y: 1.02, xref: 'paper', yref: 'paper' }, { text: dropPercent[1] + '%', showarrow: false, x: 0.1, y: 1.02, xref: 'paper', yref: 'paper' }, { text: dropPercent[2] + '%', showarrow: false, x: 0.17, y: 0.38, xref: 'paper', yref: 'paper' }, { text: dropPercent[3] + '%', showarrow: false, x: 0.25, y: -0.01, xref: 'paper', yref: 'paper' }, { text: dropPercent[4] + '%', showarrow: false, x: 0.37, y: 0.03, xref: 'paper', yref: 'paper' }, { text: dropPercent[5] + '%', showarrow: false, x: 0.455, y: 0.115, xref: 'paper', yref: 'paper' }, { text: dropPercent[6] + '%', showarrow: false, x: 0.54, y: 0.175, xref: 'paper', yref: 'paper' }, { text: dropPercent[7] + '%', showarrow: false, x: 0.625, y: 0.2, xref: 'paper', yref: 'paper' }, { text: dropPercent[8] + '%', showarrow: false, x: 0.745, y: 0.235, xref: 'paper', yref: 'paper' }, { text: dropPercent[9] + '%', showarrow: false, x: 0.825, y: 0.26, xref: 'paper', yref: 'paper' }, { text: dropPercent[10] + '%', showarrow: false, x: 0.91, y: 0.27, xref: 'paper', yref: 'paper' }, { text: dropPercent[11] + '%', showarrow: false, x: 0.995, y: 0.28, xref: 'paper', yref: 'paper' } ] }; Plotly.newPlot(myDiv, traceLine, layout); return myDiv; }32:T58f,{ const myDiv = html`

`; const traceLine = [ { x: psgLoadFactor.map(d => d['Year']), y: psgLoadFactor.map(d => d['RPM_TOTAL']), type: "scatter", mode: 'lines', name: 'Revenue Passenger Miles' }, { x: psgLoadFactor.map(d => d['Year']), y: psgLoadFactor.map(d => d['ASM_TOTAL']), type: "scatter", mode: 'lines', name: 'Available Seat Miles' } ]; let layout = { title: "Available Seat Miles Vs Revenue Passenger Miles", width: 1000, height: 450, xaxis: { showgrid: false, tickmode: "array", tickvals: [ "Dec-05", "Dec-06", "Dec-07", "Dec-08", "Dec-09", "Dec-10", "Dec-11", "Dec-12", "Dec-13", "Dec-14", "Dec-15", "Dec-16", "Dec-17", "Dec-18", "Dec-19", "Dec-20" ], ticktext: [ "Dec 05", "Dec 06", "Dec 07", "Dec 08", "Dec 09", "Dec 10", "Dec 11", "Dec 12", "Dec 13", "Dec 14", "Dec 15", "Dec 16", "Dec 17", "Dec 18", "Dec 19", "Dec 20" ], tickangle: -45 } }; const config = { responsive: true }; Plotly.newPlot(myDiv, traceLine, layout, config); return myDiv; }33:T485,{ const myDiv = html`

`; const traceLine = [ { x: psgLoadFactor.map((d) => d["Year"]), y: psgLoadFactor.map((d) => d["LF_TOTAL"]), type: "scatter", mode: "lines", name: "Load Factor" } ]; let layout = { title: "Load Factor", width: 900, height: 450, xaxis: { showline: true, showgrid: false, tickmode: "array", tickvals: [ "Dec-05", "Dec-06", "Dec-07", "Dec-08", "Dec-09", "Dec-10", "Dec-11", "Dec-12", "Dec-13", "Dec-14", "Dec-15", "Dec-16", "Dec-17", "Dec-18", "Dec-19", "Dec-20" ], ticktext: [ "Dec 05", "Dec 06", "Dec 07", "Dec 08", "Dec 09", "Dec 10", "Dec 11", "Dec 12", "Dec 13", "Dec 14", "Dec 15", "Dec 16", "Dec 17", "Dec 18", "Dec 19", "Dec 20" ], tickangle: -45 } }; Plotly.newPlot(myDiv, traceLine, layout); return myDiv; }34:T52d,{ const myDiv = html`

`; const trace1 = { x: TSAMon192021, y: actual, name: 'Flights operated', type: 'bar', marker: { color: 'rgba(55,128,191,0.6)' } }; const trace2 = { x: TSAMon192021, y: cancelled, type: 'bar', name: 'Flights Cancelled', marker: { color: 'red' } }; const trace3 = { x: TSAMon192021, y: planned, type: 'line', name: 'Scheduled', marker: { color: 'gray' } }; let layout = { title: "High Cancellation and lower flight operations", width: 1000, height: 450, barmode: "stack", yaxis: { showline: true, showgrid: false, title: "No of Flights" }, xaxis: { showgrid: false }, annotations: [ { text: "Cancellation rates increased in March", showarrow: true, x: "Mar 20", y: 649000 }, { text: "Flights decreased
with high cancellations", showarrow: true, x: "Apr 20", y: 313000 } ] }; var trace = [trace1, trace2, trace3]; const config = { //staticPlot: true, responsive: true, displayModeBar: false, scrollZoom: false }; Plotly.newPlot(myDiv, trace, layout, config); return myDiv; }35:T5d7,{ const myDiv = html`

`; const trace1 = { x: psgMajorAirportData.map(d => d["Passenger-Arr 2019"]), y: psgMajorAirportData.map(d => d["Airport_Dcode"]), type: 'bar', name: '2019', text: psgMajorAirportData.map(d => d["Passenger-Arr 2019"].toLocaleString() ), textposition: 'outside', orientation: 'h', transforms: [ { type: 'sort', target: 'x', order: 'ascending' } ] }; const trace2 = { x: psgMajorAirportData.map(d => d["Passenger-Arr 2020"]), y: psgMajorAirportData.map(d => d["Airport_Dcode"]), type: 'bar', name: '2020', text: psgMajorAirportData.map(d => d["Passenger-Arr 2020"].toLocaleString() ), textposition: 'outside', orientation: 'h' }; let layout = { title: "Passengers to TOP 10 destinations
(from " + dropdown + ")", width: 1000, height: 450, showlegend: true, legend: { x: 1, y: 0.5 }, yaxis: { // showline: true, showgrid: false // side: 'right' }, xaxis: { //zeroline: true, // showline: true, // autorange: 'reversed', showgrid: false, showticklabels: false } }; var traceLine = [trace1, trace2]; const config = { staticPlot: false, responsive: true, displayModeBar: false, scrollZoom: false }; Plotly.newPlot(myDiv, traceLine, layout, config); return myDiv; }36:Td67,{ var data = [ { type: 'scattermapbox', locationmode: 'USA-states', lat: a112, lon: b112, mode: 'markers', marker: { size: 15, color: 'rgb(255, 0, 0)' } }, { type: 'scattermapbox', locationmode: 'USA-states', lat: flightpathlats, lon: flightpathlons, mode: 'lines+markers+text', name: 'Airports', hovertext: airportName, hoverinfo: "text", line: { width: width, color: 'blue' }, opacity: 0.3 } ]; var layout = { title: 'Top 10 US Domestic Routes Passengers', mapbox: { center: { lat: 37.0902, lon: -95.7129 }, style: "open-street-map", zoom: 3.2 }, width: 1100, height: 700, showlegend: false, annotations: [ { text: "LAX-JFK" + "
" + w[0]["val"].toLocaleString(), showarrow: false, x: .55, y: .52, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "LAX-SFO" + "
" + w[1]["val"].toLocaleString(), showarrow: false, x: 0.1, y: 0.42, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "LAX-LAS" + "
" + w[2]["val"].toLocaleString(), showarrow: false, x: 0.2, y: 0.5, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "ORD-LGA" + "
" + w[3]["val"].toLocaleString(), showarrow: false, x: 0.72, y: 0.7, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "ATL-MCO" + "
" + w[4]["val"].toLocaleString(), showarrow: false, x: 0.74, y: 0.35, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "LAX-ORD" + "
" + w[5]["val"].toLocaleString(), showarrow: false, x: 0.47, y: 0.62, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "ATT-FLL" + "
" + w[6]["val"].toLocaleString(), showarrow: false, x: 0.68, y: 0.2, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "SEA-LAX" + "
" + w[7]["val"].toLocaleString(), showarrow: false, x: 0.15, y: 0.7, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "DFW-LAX" + "
" + w[8]["val"].toLocaleString(), showarrow: false, x: 0.33, y: 0.33, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 }, { text: "ATL-LGA" + "
" + w[9]["val"].toLocaleString(), showarrow: false, x: 0.78, y: 0.5, xref: 'paper', yref: 'paper', bgcolor: '#ff7f0e', opacity: 0.8 } ] }; const config = { staticPlot: false, responsive: true, displayModeBar: false, scrollZoom: false, showLink: false }; const myDiv = DOM.element("div"); Plotly.newPlot(myDiv, data, layout, config); return myDiv; }37:Tdc7,{ let data = majorPsg; let date_range = Object.keys(data[0], 3).slice(4); let traces = []; let myLat = [], myLong = [], myText = [], mySize = []; for (var i = 0; i < data.length; i++) { myLat.push(data[i]['lat']); myLong.push(data[i]['lon']); myText.push( "Passengers: " + (+data[i][date_range[0]]).toLocaleString() + "
" + data[i]["airport"] + " Airport" + "
" + date_range[0] ); mySize.push(data[0][date_range[0]]); } traces.push({ type: 'scattermapbox', locationmode: 'USA-states', lat: myLat, lon: myLong, text: myText, hoverinfo: 'text', marker: { sizemode: 'area', size: mySize, sizeref: 5000, color: 'blue', opacity: 0.5 } }); function modifyTraces(i) { let myText = [], mySize = [], myTraces = JSON.parse(JSON.stringify(traces)); for (var j = 0; j < data.length; j++) { myText.push( "Passengers: " + (+data[j][date_range[i]]).toLocaleString() + "
" + data[j]["airport"] + " Airport" + "
" + date_range[i] ); mySize.push(data[j][date_range[i]]); } myTraces[0].text = myText; myTraces[0].marker.size = mySize; return myTraces; } var frames = []; for (i = 0; i < date_range.length; i++) { frames.push({ name: date_range[i], data: modifyTraces(i) }); } let sliderSteps = []; for (i = 0; i < date_range.length; i++) { sliderSteps.push({ method: 'animate', label: date_range[i], args: [ [date_range[i]], { mode: 'immediate', transition: { duration: 500 }, frame: { duration: 500, redraw: true } } ] }); } let sliders = [ { pad: { l: 130, t: 10 }, currentvalue: { visible: true, prefix: 'Mon-Year:', xanchor: 'right', font: { size: 20, color: '#666' } }, steps: sliderSteps } ]; let updatemenus = [ { x: 0, y: 0, yanchor: 'top', xanchor: 'left', showactive: true, direction: 'left', type: 'buttons', pad: { t: 30, r: 10 }, buttons: [ { method: 'animate', args: [ null, { mode: 'immediate', fromcurrent: true, transition: { duration: 500 }, frame: { duration: 500, redraw: true } } ], label: 'Play' }, { method: 'animate', args: [ [null], { mode: 'immediate', transition: { duration: 0 }, frame: { duration: 0, redraw: false } } ], label: 'Pause' } ] } ]; let layout = { margin: { t: 40, l: 10 }, title: 'Passengers total from Major Airports (Jan 2019 to Jan 2021)', mapbox: { center: { lat: 37.0902, lon: -95.7129 }, style: "open-street-map", zoom: 3.2 }, width: 1100, height: 700, updatemenus: updatemenus, sliders: sliders //annotations: annotations }; const config = { staticPlot: false, responsive: true, displayModeBar: true, scrollZoom: false }; const myDiv = DOM.element("div"); Plotly.newPlot(myDiv, { data: traces, layout: layout, config: config, frames: frames }); return myDiv; }38:T584,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); //const cSizeScale = d3.scaleSqrt().domain(d3.extent(YearSelect, d => Number(d[totalCh]))).range([5, 50]); //const cSize = YearSelect.map(d=>cSizeScale(Number(d[totalCh]))); const hoverText = airportData.map(d => d.airport); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, //marker: { //size: cSize, //Size specified the diameter //line: { //color: "lightgrey", //width: 1 //} //} } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: 'Most Trafficked US airports', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }39:T5e5,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); const cSizeScale = d3.scaleSqrt().domain(d3.extent(FlightYear, d => Number(d[totalCh]))).range([5, 50]); const cSize = FlightYear.map(d=>cSizeScale(Number(d[totalCh]))); //const hoverText = airportData.map(d => d.airport); const hoverText = FlightYear.map(d => d.Name + " " + totalCh + "Flights:" + d[totalCh]); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, marker: { size: cSize, //Size specified the diameter line: { color: "lightgrey", width: 1 } } } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: '10 Trafficked US airports Flights Data Bubble Map', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }3a:T5e0,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); const cSizeScale = d3.scaleSqrt().domain(d3.extent(PS_Year, d => Number(d[totalCh]))).range([5, 50]); const cSize = PS_Year.map(d=>cSizeScale(Number(d[totalCh]))); //const hoverText = airportData.map(d => d.airport); const hoverText = PS_Year.map(d => d.Name + " " + totalCh +"Passengers:"+ d[totalCh]); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, marker: { size: cSize, //Size specified the diameter line: { color: "lightgrey", width: 1 } } } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: '10 Trafficked US airports Passengers Data Bubble Map', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }3b:T411,CleanedTabledata = TableData.map(d => { const regex = /
|

|\n|%/gi; const all = d; const reg = all['REgion'].replace('', ''); const Dem2020 = all['2020
Demand
vs 2019'].replace('%', ''); const Cap2020 = all['2020 Capacity vs 2019'].replace('%', ''); const Prof2020 = all['2020 profits'].replace('b', '').replaceAll('$', ''); const Dem2021 = all['2021
Demand
vs 2020
(vs 2019)'].replaceAll('

',''); const Cap2021 = all['2021
Capacity
vs 2020
(vs 2019)'].replace(regex,''); const Prof2021 = all['2021 Profits'].replace('b', '').replaceAll('$', ''); return {'Region': reg.replace('', ''), 'Demand Percentage 2020 vs 2019': Dem2020, 'Capacity Percentage 2020 vs 2019': Cap2020, '2020 Profit in Billions': Prof2020, 'Demand Percentage 2021 vs 2020 vs (2019) ': Dem2021.replace(regex, ''), 'Capacity Percentage 2021 vs 2020 vs (2019) ': Cap2021.replace('0',')'), '2021 Profit in Billions': Prof2021 } 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2e:{"title":"CAP 6737 Final Project","cells":[{"id":0,"value":"md`# CAP 6737 Final Project`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3169,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":3371,"value":"md` #### Individual Contribution:
\n Divya Selvaraj:
1. Flight Departures, 2. Passengers, 3. Domestic Air Fare, 4. Flight Operations, 5. Major Airports
\n Ausmita Barman: Revenue \nhttps://observablehq.com/d/a2499ce47ec6a7be
\n Mohammad Arifur Rahman: Air Cargo \nhttps://observablehq.com/d/b1371ce3b8ac74f5
\n\n.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"md` ## 1. Flight Departures`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3240,"value":"md` Flight Departures fell down in March and April 2020 before starting to increase and stablizie in later half of 2020. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":1518,"value":"md ` ### 1.1 Domestic Flight Departures`","pinned":false,"hidden":false,"mode":"ojs"},{"id":56,"value":"viewof selectDom = radio({\n title: 'Select',\n options: [\n { label: 'Departures By Month', value: 'month' },\n { label: 'Departures By Day', value: 'day' }\n ],\n value: 'day'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"{\n const div = html`

`;\n const traceLine = [\n {\n x: xvalDom ,\n y: yvalDom,\n type: plotTypeDom\n }\n ];\n let layout = {\n title: \"US Domestic Flight Depatures - \" + selectDom,\n yaxis: {\n showline: true,\n title: \"Domestic Flight Departures\",\n showgrid: false\n },\n xaxis: {\n zeroline: true,\n showline: true,\n \n \n \n tickmode: \"array\",\n tickvals: tickvalsDom,\n ticktext: ticktext,\n tickangle: -45,\n showgrid: false\n }\n };\n\n \n Plotly.newPlot(div, traceLine, layout);\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":214,"value":"md` ### 1.2 International Flight Departures`","pinned":false,"hidden":false,"mode":"ojs"},{"id":237,"value":"viewof selectIntl = radio({\n title: 'Select',\n options: [\n { label: 'Depatures By Month', value: 'month' },\n { label: 'Departures By Day', value: 'day' }\n ],\n value: 'day'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"{\n const myDiv = html`

`;\n const traceLine = [\n {\n x: xvalIntl,\n y: yvalIntl,\n type: plotTypeIntl\n }\n ];\n let layout = {\n title: \"International Flight Depatures - \" + selectIntl,\n yaxis: {\n showline: true,\n title: \"International Flight Departures\",\n showgrid: false\n },\n xaxis: {\n zeroline: true,\n showline: true,\n tickmode: \"array\",\n tickvals: tickvalsIntl,\n ticktext: ticktext,\n tickangle: -45,\n showgrid: false\n }\n };\n\n Plotly.newPlot(myDiv, traceLine, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":542,"value":"md` ## 2. Passengers`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3247,"value":"md`U.S. airlines carried fewer scheduled service passengers from March 2020 and the numbers are still less than the pre-pandemic levels`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1535,"value":"md` ### 2.1. Passengers Screened at airports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"{\n const div = html`

`;\n\n const traceLine = [\n {\n x: TSAMon192021,\n y: TSAVal192021,\n type: 'line',\n mode: 'lines+markers',\n line: { color: 'blue', width: 1 },\n marker: { symbol: 'x-thin-open' }\n }\n ];\n let layout = {\n margin: { t: 40 },\n title: \"Passengers Screened by TSA (by month)\",\n width: 1000,\n height: 450,\n xaxis: {\n tickangle: -90,\n showgrid: false,\n //showline: false,\n gridcolor: '#dcdcdc',\n tickangle: -45\n },\n yaxis: {\n //showline: true,\n showgrid: true,\n title: \"Passengers\",\n gridcolor: '#dcdcdc'\n }\n };\n\n Plotly.newPlot(div, traceLine, layout);\n return div;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1498,"value":"md` ### 2.2 Percent drop of Passengers from last year`","pinned":false,"hidden":false,"mode":"ojs"},{"id":758,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1548,"value":"md` ### 2.3 Passenger Volume Comparision`","pinned":false,"hidden":false,"mode":"ojs"},{"id":407,"value":"viewof chkYear = checkbox({\n title: \"Choose Years to compare\",\n options: [\n { value: \"2019\", label: \"2019\" },\n { value: \"2020\", label: \"2020\" },\n { value: \"2021\", label: \"2021\" }\n ],\n value: [\"2020\"],\n submit: true\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":396,"value":"{\n const myDiv = html`

`;\n\n const dataTSA = [];\n for (let i = 0; i < chkYear.length; i++) {\n dataTSA[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: chkYear[i],\n x: TSAFormattedByMon.map(d => d.Date),\n y: TSAFormattedByMon.map(d => d['Traveller' + chkYear[i]]).filter(d => d)\n // line: { color: color[i] }\n };\n }\n\n let layout = {\n title: \"Passengers screened at US airports by TSA \",\n width: 1000,\n height: 450,\n xaxis: {\n tickmode: \"array\",\n tickvals: tickvalsTSA,\n ticktext: ticktextTSA,\n showgrid: false,\n showline: false\n },\n\n yaxis: {\n title: \"No of passengers screened\",\n gridcolor: '#dcdcdc'\n }\n };\n\n Plotly.newPlot(myDiv, dataTSA, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1600,"value":"md `### 2.4 Passenger Load Factor`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3250,"value":"md` Available Seat-miles: the number of seats and the distance flown
\n Revenue Passenger-miles: number of passengers and the distance flown`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1671,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":3263,"value":"md` Load Factor: passenger-miles as a proportion of available seat-miles in percent (%)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1626,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1580,"value":"md`## 3. Domestic Air fare`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2075,"value":"Data = md`The 2020 average domestic itinerary air fare of $245 was the lowest inflation-adjusted annual fare since the Bureau of Transportation Statistics began collecting such records in 1995, down 19% from the previous year of $345 in 2019.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1585,"value":"{\n const myDiv = html`

`;\n const traceLine = [\n {\n x: airFaresData.map(d => d['']),\n y: airFaresData.map(d => d['Unadjusted Fares (current dollars)']),\n type: 'line',\n name: 'Unadjusted Fares'\n },\n {\n x: airFaresData.map(d => d['']),\n y: airFaresData.map(d => d['Inflation-Adjusted (2020$)']),\n type: 'line',\n name: 'Inflation-Adjusted Fares'\n }\n ];\n let layout = {\n title: \"US Average Domestic Fares\",\n width: 1000,\n height: 450,\n yaxis: {\n showline: true,\n showgrid: false,\n title: 'Average Domestic Fares'\n // showticklabels: false\n },\n xaxis: { showline: true, showgrid: false }\n };\n\n const config = {\n //staticPlot: true,\n responsive: true,\n displayModeBar: false,\n scrollZoom: false\n };\n\n Plotly.newPlot(myDiv, traceLine, layout, config);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2431,"value":"md` ## 4. Flight Operations `","pinned":false,"hidden":false,"mode":"ojs"},{"id":2437,"value":"md` ### 4.1 Flight Schedule and Cancellations`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3223,"value":"md`#### Flight Schedule:\nAs the coronavirus reduced air travel in March 2020, airlines anticipated more of the same in April 2020 and began un-scheduling flights accordingly. As the graph below indicates, that resulted in a steep record setting reduction in the number of April 2020 scheduled flights. In fact, the 313k flights scheduled in April 2020 is only about half of the 612K flights scheduled in April 2019 and less than half of the 648K flights scheduled in March of 2020 (for major marketing carriers)\n\n#### Cancellations:\nWhile the March percentage of flights canceled was the second lowest, outdone only by September 2001, the April 2020 cancellation rate exceeds both. The 130k canceled flights shown for April 2020 represents a 41% cancellation rate, more than double the 17% cancellation rate in March. It's also more than double the 20% of scheduled fights canceled after 9/11`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1472,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2442,"value":"md` ### 4.2 On Time arrivals\nSeptember’s 93% on-time arrival rate is up from August’s 91% and September 2019’s 84%.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":568,"value":"{\n const myDiv = html`

`;\n const trace1 = {\n x: TSAMon192021,\n y: OnTimePercent,\n type: 'line',\n name: 'Actual Flights'\n };\n\n let layout = {\n title: \"On Time Arrival Rates\",\n width: 1000,\n height: 450,\n barmode: 'stack',\n xaxis: {\n showgrid: false,\n tickangle: -45\n // showzero: true\n },\n yaxis: {\n title: \"On Time Arrival Rates\"\n },\n annotations: [\n {\n text: \"Lowest On Time rate (55%)\",\n showarrow: true,\n x: \"Apr 20\",\n y: 60\n },\n {\n text: \"Highest On Time rate (93%)\",\n showarrow: true,\n x: \"Sep 20\",\n y: 93\n }\n ]\n };\n\n var trace = [trace1];\n\n Plotly.newPlot(myDiv, trace, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1690,"value":"md` ## 5. Major Airports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1909,"value":"md` ### 5.1 Top 10 US destinations from Major Airports \n(Reduction in passengers 2019 Vs 2020)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1978,"value":"viewof dropdown = Select(majorAirportList, {\n value: \"Hartsfield-Jackson Atlanta International Airport\",\n label: \"Select Airport\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1947,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3071,"value":"md` ### 5.2 Major Airports - Top 10 Domestic Routes\n(Reduction in passengers 2019 Vs 2020)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3029,"value":"viewof selectYear = radio({\n title: 'Select Year',\n options: [\n { label: '2019', value: '2019' },\n { label: '2020', value: '2020' }\n ],\n value: '2019'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2538,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":3074,"value":"md` ### 5.3 Total Domestic Passengers from Major Airports\n(Reduction in total number of passengers by month 2019-2020)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1806,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":3270,"value":"md` ## 6. Revenue \nThe coronavirus pandemic paralyzed the aviation industry throughout 2020. Revenue streams of air carriers were immensely low such that revenue could not cover the costs of operating.The damage to the airline industry from the coronavirus pandemic last year was devastating.The financial hardship has continued into 2021 as airlines try to cope with a Covid 19 situation.The pandemic began to impact air travel in late January 2020, but by the end of March the air transport system virtually came to a halt.Moreover, with strict travel restrictions and quarantine requirements make pasengers are less interested in air travel.But as time passed domestic travel has fared better because people are worried about traveling too far from home and fewer health measures are involved than when crossing borders.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3662,"value":"md`Project overview:
a)US Domestic Airlies Revenue Observation
b)Regional Profit Comparison
c) US Busiest Airports Performance`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3665,"value":"md`### a)US Domestic Airlies Revenue Observation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3676,"value":"md`\n1)Domestic airline revenue plot from year 2011 to 2020. In 2020, the revenue was -25M which is the worst of last 10 years records.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3447,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: years,\n y: revByYear,\n type: 'scatter',\n mode: 'lines'\n }\n ];\n\n var layout = {\n title: 'Domestic airlines revenue by year',\n xaxis: { autorange: true, title: 'Years' },\n yaxis: { autorange: true, title: 'Net Income' }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3669,"value":"md`\n2)Interactively choose one domestic airline by using the selection widget and compare the net income and profit loss from 2011 to 2020.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3488,"value":"viewof airlines = select({\n title: \"Select\",\n options: Object.values(names),\n value: \"Spirit Air Lines\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3489,"value":"viewof dd = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"NET_INCOME\", label: \"NET INCOME\"}, {value:\"OP_PROFIT_LOSS\", label : \"PROFIT LOSS\"}],\n value: \"NET_INCOME\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3491,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: airyears,\n y: revORprofitByYear,\n type: 'scatter'\n \n }\n ];\n var layout = {\n title: `${airlines} ${dd} by year`,\n xaxis:{autorange: true, title: 'Years'},\n //yaxis:{autorange: true, title: 'Net Income'}\n }\n \n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3673,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: airyears,\n y: revORprofitByYear,\n type: 'bar',\n transforms: [\n {\n type: 'sort',\n target: 'y',\n order: 'descending'\n }\n ]\n }\n ];\n const layout = {\n title: `${airlines} ${dd} by year`,\n yaxis: {\n //title: 'Number of Case'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3494,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n labels: airyears,\n values: revORprofitByYear,\n type:'pie',\n //sort: false,\n direction: \"clockwise\",\n //insidetextorientation: \"radial\"\n }\n ];\n const layout = {\n title: `${airlines} ${dd} by year`\n };\n\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3682,"value":"md`\n3) Compare upto three domestic airlines performace in terms of net income and profit loss.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3495,"value":"viewof ch = limitCheckboxes(3, checkbox({\n options: Object.values(names),\n value: [\"Spirit Air Lines\", \"American Airlines Inc.\", \"Continental Air Lines Inc.\"],\n title: \"Select upto three states from the list\",\n multiple: true\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3498,"value":"viewof dd1 = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"NET_INCOME\", label: \"NET INCOME\"}, {value:\"OP_PROFIT_LOSS\", label : \"PROFIT LOSS\"}],\n value: \"NET_INCOME\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3500,"value":"{\n const myDiv = html`

`;\n //const date = sortedStateData.map(d => d.submission_date);\n const data_new = [];\n const color = [\"blue\", \"green\", \"red\"];\n for (let i = 0; i < ch.length; i++) {\n //extract[i] = attribute=>sortedStateData[i].map(d=>d[attribute]),\n data_new[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: ch[i],\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[i]),\n line: { color: color[i] }\n };\n};\n \n \n Plotly.newPlot(myDiv, data_new);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3502,"value":"{\n const myDiv = html`

`;\n const trace1 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[0]),\n type:'bar',\n name: ch[0]\n };\n const trace2 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[1]),\n type:'bar',\n name: ch[1]\n };\n \n const trace3 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[2]),\n type:'bar',\n name: ch[2]\n };\n var data = [trace1, trace2, trace3];\n const layout = {\n barmode: 'group',\n title: `${dd} comparisons by year`\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3685,"value":"md `### b)Regional Profit Comparison`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3691,"value":"md`1)Compareing the regional (North America, Asia Pacific, Latin America, Europe, Africa and Middle East) air travel capacity and demand by percentage of year 2020 vs 2019.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3694,"value":"viewof dvc = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"Demand Percentage 2020 vs 2019\", label: \"Demand\"}, {value:\"Capacity Percentage 2020 vs 2019\", label : \"Capacity\"}],\n value: \"Demand Percentage 2020 vs 2019\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3695,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: Regions,\n y: NewDataTable.map(i => i[dvc]),\n type:\n 'bar', \n transforms: [\n {\n type: 'sort',\n target: 'y',\n order: 'descending'\n }\n ],\n }\n ];\n const layout = {\n title: `${dvc} `,\n yaxis: {\n title: 'Percentage'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3698,"value":"md `2)Comparing the regional profit in billions for year 2020 and expected profit for year 2021.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3700,"value":"{\n const myDiv = html`

`;\n const y1 = NewDataTable.map(a => a['2020 Profit in Billions']*1000000000);\n const y2 = NewDataTable.map(a => a['2021 Profit in Billions']*1000000000);\n \n \n const trace1 = {\n x: Regions,\n y: y1,\n type:'bar',\n name: 2020\n };\n \n const trace2 = {\n x: Regions,\n y: y2,\n type:'bar',\n name: 2021\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'Profit comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3702,"value":"md `### c) US Busiest Airports Performance`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3703,"value":"md `1) Orlando International Airport (MCO) psssengers, flights and revenue passenger miles comparision from year 2010 to 2020 for both domestic and international.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3731,"value":"{\n const myDiv = html`

`;\n const y1 = mco1_Total.map(a => a['DOMESTIC']);\n const y2 = mco1_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO Passengers comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3732,"value":"{\n const myDiv = html`

`;\n const y1 = mco_Total.map(a => a['DOMESTIC']);\n const y2 = mco_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO Flights comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3734,"value":"{\n const myDiv = html`

`;\n const y1 = mco2_Total.map(a => a['DOMESTIC']);\n const y2 = mco2_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO RPM comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3736,"value":"md `2) 221 US busiest Airports has been spotted in the map. Then comparing performance based on domestic and internationl passengers and flights number of 10 busiest US airports interactively choosing the years from 2010 to 2020. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":3738,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":3740,"value":"viewof yearCh = radio({\n description: 'Choose any year',\n options: ['2010', '2011','2012', '2013','2014','2015','2016','2017','2018','2019','2020'],\n value: '2010'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3742,"value":"viewof totalCh = radio({\n description: 'Choose one',\n options: ['DOMESTIC', 'INTERNATIONAL', 'TOTAL'],\n value: 'TOTAL'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3744,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":3746,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":3752,"value":"md `## Data Sources`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3748,"value":"md`a)US Domestic Airlies Revenue Observation: https://www.transtats.bts.gov/Data_Elements_Financial.aspx?Qn6n=J
b)Regional Profit Comparison: https://www.iata.org/en/pressroom/pr/2020-11-24-01/
c) US Busiest Airports Performance: https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3385,"value":"md` ## Air Cargo `","pinned":false,"hidden":false,"mode":"ojs"},{"id":3861,"value":"md`Since the COVID-19 crisis began, air cargo has been a vital partner in delivering much-needed medicines, medical equipment (including spare parts/repair components), and in keeping global supply chains functioning for the most time-sensitive materials. This has been done through dedicated cargo freighter operations, utilization of cargo capacity in passenger aircraft, and relief flights to affected areas. For many airlines, with their cash reserves going down from a lack of passenger revenue, freight has been a oportunity to recover. Below we will explore
\n1) Revenue from cargo domestic cargo industry of last ten years
\n2)Revenue from international cargo industry of last ten years`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3584,"value":"{\n const myDiv = html`

`;\n\n const data = [{\n type: \"scatter\",\n mode: \"lines\",\n //name: `${country} cases`,\n x: dates,\n y: CargoDom\n }];\n\nvar layout = {\n title: 'Air Cargo (Domestic) Revenue from 2010',\n xaxis:{\n autorange: true,\n rangeselector: {\n buttons: [\n {\n count: 1,\n label: '1 Month',\n step: 'month',\n stepmode: 'backward'\n },\n {\n count: 6,\n label: '6 Months',\n step: 'month',\n stepmode: 'backward'\n },\n { step: 'all' }\n ]\n },\n rangeslider: { range: d3.extent(dates) },\n type: 'date'\n },\n yaxis:{\n title: 'Revenue'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3865,"value":"md ` Here, we are comparing month by month between 2019 and 2020 of domestic air cargo revenues. Even though other industires strugling we easily notice that domestic air cargo industry bringing more revenues in every month of 2020 then the previous year.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3866,"value":"{\n const myDiv = html`

`;\n const trace1 = {\n x: months,\n y: dataDom2019,\n name: '2019',\n type: \"bar\"\n };\n const trace2 = {\n x: months,\n y: dataDom2020,\n name: '2020',\n type: \"bar\"\n };\n var data = [trace1, trace2];\n var layout = {barmode: 'group', \n title:'Domestic Air Cargo Traffic comparison between 2019 and 2020' };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3868,"value":"md ` Except the first quarter, international cargo industries also earning more profits. Specially at the last quarter where revenue incresed by big margins and increased around 30% in december 2020 compare to december 2019.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3870,"value":"{\n const myDiv = html`

`;\n const trace1 = {\n x: months,\n y: dataIN2019,\n name: '2019',\n type: \"bar\"\n };\n const trace2 = {\n x: months,\n y: dataIN2020,\n name: '2020',\n type: \"bar\"\n };\n var data = [trace1, trace2];\n var layout = {barmode: 'group', \n title:'International Air Cargo Traffic comparison between 2019 and 2020' };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3872,"value":"md `3) In this section we will explore the Domestic and International Air Cargo traffic data also graphically analyze the traffic from year 2010 to 2020. Also, compare the number of monthly cargo flights between 2019 and 2020 for both domestic and international.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3604,"value":"{\n const myDiv = html`

`;\n const date = CargoDomSortedDate.map(a=>a.Period).filter(b=> b> new Date('2010-01-01'));\n const data = [{\n type: \"scatter\",\n mode: \"lines\",\n //name: `${country} cases`,\n x: date,\n y: CargoDomSortedDate.map(d => d.Total)\n }];\n\nvar layout = {\n title: 'Domestic Air Cargo Traffic(Departures Performed) from 2010',\n xaxis:{\n autorange: true,\n rangeselector: {\n buttons: [\n {\n count: 1,\n label: '1 Month',\n step: 'month',\n stepmode: 'backward'\n },\n {\n count: 6,\n label: '6 Months',\n step: 'month',\n stepmode: 'backward'\n },\n { step: 'all' }\n ]\n },\n rangeslider: { range: d3.extent(dates) },\n type: 'date'\n },\n yaxis:{\n title: 'Number of flights'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3606,"value":"{\n const myDiv = html`

`;\n const date = CargoINSortedDate.map(a=>a.Period).filter(b=> b> new Date('2010-01-01'));\n const data = [{\n type: \"scatter\",\n mode: \"lines\",\n //name: `${country} cases`,\n x: date,\n y: CargoINSortedDate.map(d => d.Total)\n }];\n\nvar layout = {\n title: 'International Air Cargo Traffic(Departures Performed) from 2010',\n xaxis:{\n autorange: true,\n rangeselector: {\n buttons: [\n {\n count: 1,\n label: '1 Month',\n step: 'month',\n stepmode: 'backward'\n },\n {\n count: 6,\n label: '6 Months',\n step: 'month',\n stepmode: 'backward'\n },\n { step: 'all' }\n ]\n },\n rangeslider: { range: d3.extent(dates) },\n type: 'date'\n },\n yaxis:{\n title: 'Number of flights'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3608,"value":"md `4) This section is about incoming travelers through all US-Canada and US-Mexico Borders and ploting the total travelers number from year 2011 to 2020. Total passengers number inclueds
\n * Personal Vehicle Passengers\n * Train Passengers \n * Pedestrians\n * Bus Passengers\nThen, we will compare passengers number arriving through US-Canada Border and US-Mexico border of the year 2018, 2019 and 2020.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3610,"value":"{\n const myDiv = html`

`;\n const date = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n const data1 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2020,\n x: date,\n y: USCanada2020DataRev\n };\n\n const data2 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2019,\n x: date,\n y: USCanada2019DataRev\n };\n \n const data3 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2018,\n x: date,\n y: USCanada2018DataRev\n };\n \n const data=[data1, data2, data3];\n \n const layout = {\n title: 'Passengers crossing US-Canada Border',\n width: 750,\n height: 600,\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3649,"value":"{\n const myDiv = html`

`;\n const date = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n const data1 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2020,\n x: date,\n y: USMexico2020DataRev\n };\n\n const data2 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2019,\n x: date,\n y: USMexico2019DataRev\n };\n \n const data3 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 2018,\n x: date,\n y: USMexico2018DataRev\n };\n \n const data=[data1, data2, data3];\n \n const layout = {\n title: 'Passengers crossing US-Mexico Border',\n width: 750,\n height: 600,\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3650,"value":"{\n const myDiv = html`

`;\n \n const data1 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 'US-Canada Border',\n x: datesFrom2011,\n y: PassengersData2011\n };\n\n const data2 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 'US-Mexico Border',\n x: datesFrom2011,\n y: USMexicoPassengersData2011\n };\n \n const data = [data1, data2];\n \n const layout = {\n title: 'Last Ten Years US Border Crossing: Passengers arriving by road',\n width: 750,\n height: 600,\n xaxis: {\n type: 'date'\n },\n yaxis: {\n type: 'linear'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3882,"value":"md`5)Just like the passenger crossing through the borders we are also exploring the number of trucks coming to USA throug the same border.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3884,"value":"viewof ch = checkbox({\n options: years,\n value: ['2020', '2019', '2018'],\n title: \"Select years to compare\"\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3886,"value":"{\n const myDiv = html`

`;\n const date = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'];\n const data_new = [];\n \n \n for (let i = 0; i < ch.length; i++) {\n data_new[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: ch[i],\n x: date,\n y: extractCanada(Number(ch[i]))\n };\n};\n \n \n const layout = {\n title: 'Trucks crossing US-Canada Border',\n width: 750,\n height: 600,\n };\n Plotly.newPlot(myDiv, data_new, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3888,"value":"{\n const myDiv = html`

`;\n const date = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'];\n const data_new = [];\n \n \n for (let i = 0; i < ch.length; i++) {\n data_new[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: ch[i],\n x: date,\n y: extractMexico(Number(ch[i]))\n };\n};\n \n \n const layout = {\n title: 'Trucks crossing US-Mexico Border',\n width: 750,\n height: 600,\n };\n Plotly.newPlot(myDiv, data_new, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3890,"value":"{\n const myDiv = html`

`;\n \n const data1 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 'US-Canada Border',\n x: datesFrom2011,\n y: USCanada2011TruckData\n };\n\n const data2 = {\n type: \"scatter\",\n mode: \"lines\",\n name: 'US-Mexico Border',\n x: datesFrom2011,\n y: USMexico2011TruckData\n };\n \n const data = [data1, data2];\n \n const layout = {\n title: 'Last Ten Years US Border Crossing: Trucks',\n width: 750,\n height: 600,\n xaxis: {\n type: 'date'\n },\n yaxis: {\n type: 'linear'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3892,"value":"md `6) Finally, we will focus on the impact on employee numbers. Here we see a comparison of employee numbers of last ten years based on carrier groups. Where
\n * Carrier group one and two are mostly cargo carriers\n * Group two contain all the major domestic airlines\n * Group three have small local airlines`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3894,"value":"viewof CG = checkbox({\n options: ['1','2','3','4'],\n value: ['2', '3'],\n title: \"Select Carrier Group to compare\"\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3896,"value":"{\n const myDiv = html`

`;\n const years = [...EmDataGroupByYear.keys()];\n \n const data_new = [];\n \n \n for (let i = 0; i < CG.length; i++) {\n data_new[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: `Group: ${CG[i]}`,\n x: years,\n y: Extract_yearSumCarrierGroup(Number(CG[i]))\n };\n};\n \n \n const layout = {\n title: 'Last ten years (yearly average) employee numbers by carrier group',\n width: 750,\n height: 500,\n };\n Plotly.newPlot(myDiv, data_new, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3898,"value":"{\n const myDiv = html`

`;\n \n const data = [{\n type: \"scatter\",\n mode: \"lines\",\n name: 'US-Canada Border',\n x: datesDataUNem,\n y: dataUnem.map(b=> b.Value)\n }];\n \n const layout = {\n title: 'Number of Employees in transportation sector in Thousand',\n width: 750,\n height: 600,\n xaxis: {\n type: 'date'\n },\n yaxis: {\n type: 'linear'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":3900,"value":"md `On the plot above we can easily see the impact of Covid-19 on the transportation sector employment.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":3284,"value":"md`## Data - Flights, Passengers, Airports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"xvalDom = {\n if (selectDom == 'month') {\n return domDeptSum_month;\n } else {\n return domesticDept.map(d => d.Date);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"yvalDom = {\n if (selectDom == 'month') {\n return domDeptSum_value;\n } else {\n return domesticDept.map(d => d.Current);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":169,"value":"tickvalsDom = {\n if (selectDom == 'day')\n return [\n \"01/15/20\",\n \"02/15/20\",\n \"03/15/20\",\n \"04/15/20\",\n \"05/15/20\",\n \"06/15/20\",\n \"07/15/20\",\n \"08/15/20\",\n \"09/15/20\",\n \"10/15/20\",\n \"11/15/20\",\n \"12/15/20\",\n \"01/15/21\",\n \"02/15/21\",\n \"03/15/21\",\n \"04/15/21\"\n ];\n else {\n return [\n \"Jan20\",\n \"Feb20\",\n \"Mar20\",\n \"Apr20\",\n \"May20\",\n \"Jun20\",\n \"Jul20\",\n \"Aug20\",\n \"Sep20\",\n \"Oct20\",\n \"Nov20\",\n \"Dec20\",\n \"Jan21\",\n \"Feb21\",\n \"Mar21\",\n \"Apr21\"\n ];\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"ticktext = [\n \"Jan20\",\n \"Feb20\",\n \"Mar20\",\n \"Apr20\",\n \"May20\",\n \"Jun20\",\n \"Jul20\",\n \"Aug20\",\n \"Sep20\",\n \"Oct20\",\n \"Nov20\",\n \"Dec20\",\n \"Jan21\",\n \"Feb21\",\n \"Mar21\",\n \"Apr21\"\n ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"plotTypeDom = {\n if (selectDom == 'month') {\n return \"bar\";\n } else {\n return \"line\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":520,"value":"text = d3.extent(TSAFormattedByMon.map(d => d.Traveller2020))","pinned":false,"hidden":false,"mode":"ojs"},{"id":249,"value":"xvalIntl = {\n if (selectIntl == 'month') {\n return intlDeptSum_month;\n } else {\n return internlDept.map(d => d.Date);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":260,"value":"yvalIntl = {\n if (selectIntl == 'month') {\n return intlDeptSum_value;\n } else {\n return internlDept.map(d => d.Current);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":265,"value":"plotTypeIntl = {\n if (selectIntl == 'month') {\n return \"bar\";\n } else {\n return \"line\";\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":268,"value":"tickvalsIntl = {\n if (selectIntl == 'day')\n return [\n \"01/15/20\",\n \"02/15/20\",\n \"03/15/20\",\n \"04/15/20\",\n \"05/15/20\",\n \"06/15/20\",\n \"07/15/20\",\n \"08/15/20\",\n \"09/15/20\",\n \"10/15/20\",\n \"11/15/20\",\n \"12/15/20\",\n \"01/15/21\",\n \"02/15/21\",\n \"03/15/21\",\n \"04/15/21\"\n ];\n else {\n return [\n \"Jan20\",\n \"Feb20\",\n \"Mar20\",\n \"Apr20\",\n \"May20\",\n \"Jun20\",\n \"Jul20\",\n \"Aug20\",\n \"Sep20\",\n \"Oct20\",\n \"Nov20\",\n \"Dec20\",\n \"Jan21\",\n \"Feb21\",\n \"Mar21\",\n \"Apr21\"\n ];\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1542,"value":"dropPercent = {\n const res = [];\n const a = [...TSASumbyMonth19.values()];\n const b = [...TSASumbyMonth20.values()];\n for (let i = 0; i < a.length; i++) {\n const temp = Math.round((((b[i] || 0) - (a[i] || 0)) / (a[i] || 0)) * 100);\n res[i] = temp;\n }\n return res;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":475,"value":"tickvalsTSA = [\n \"31/01\",\n \"28/02\",\n \"31/03\",\n \"30/04\",\n \"31/05\",\n \"30/06\",\n \"31/07\",\n \"31/08\",\n \"30/09\",\n \"31/10\",\n \"30/11\",\n \"31/12\"\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":483,"value":"ticktextTSA = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2650,"value":"w = top10.map(function(obj) {\n return {\n \n val: obj[\"Passenger-Arr \" + selectYear]\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2826,"value":"width = {\n if (selectYear==2019) { return 5 }\n else { return 1 }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2949,"value":"airportName = {\n const air = [];\n top10.forEach(d => {\n air.push(d[\"Source\"]);\n air.push(d[\"Dest\"]);\n air.push(null);\n });\n return air;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2607,"value":"a11 = [...new Set(latitude.map(d => d[0][0]))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2620,"value":"a12 = [...new Set(latitude.map(d => d[1][0]))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2626,"value":"a112 = [...new Set(a11.concat(a12))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2630,"value":"b112 = [...new Set(b11.concat(b12))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2611,"value":"b11 = [...new Set(longitude.map(d => d[0][0]))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2624,"value":"b12 = [...new Set(longitude.map(d => d[1][0]))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2572,"value":"flightpathlats={\n const lats = [];\n latitude.forEach(d=>{\n lats.push(d[0][0]);\n lats.push(d[1][0]);\n lats.push(null);\n })\n return lats;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2574,"value":"flightpathlons={\n const lons = [];\n longitude.forEach(d=>{\n lons.push(d[0][0]);\n lons.push(d[1][0]);\n lons.push(null);\n })\n return lons;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1916,"value":"Majorairports = d3.csv(\n \"https://raw.githubusercontent.com/FlightData/USFlightData/main/MajorAirports.csv\",\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1918,"value":"MajorairportsDest = Majorairports.filter(d => d.Type == \"Destination Airports\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2544,"value":"latitude = top10.map(a => {\n const latitude1 = airports\n .filter(b => b.airportID === a[\"Airport Scode\"])\n .map(b => b.latitude);\n const latitude2 = airports\n .filter(b => b.airportID === a.Airport_Dcode)\n .map(b => b.latitude);\n return Object.assign({}, [latitude1, latitude2]);\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2565,"value":"longitude = top10.map(a => {\n const longitude1 = airports\n .filter(b => b.airportID === a[\"Airport Scode\"])\n .map(b => b.longitude);\n const longitude2 = airports\n .filter(b => b.airportID === a.Airport_Dcode)\n .map(b => b.longitude);\n return Object.assign({}, [longitude1, longitude2]);\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2557,"value":"airports = airports_dat.map(function(obj) {\n return {\n airportID: obj['GKA'],\n airportName: obj['Goroka Airport'],\n latitude: +obj['-6.081689834590001'],\n longitude: +obj['145.391998291']\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2543,"value":"top15 = MajorairportsDest.sort(function(a, b) {\n return a['Passenger-Arr 2019'] < b['Passenger-Arr 2019'] ? 1 : -1;\n}).slice(0, 14)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2633,"value":"top10 = top15.filter(d => d.Airport_Dest != 'MCO' && d.Airport_Source != 'LAS' && d.Airport_Dest != 'SEA' && d.Airport_Dest != 'ORD' )","pinned":false,"hidden":false,"mode":"ojs"},{"id":2659,"value":"w2 = top10.map(d => d[\"Passenger-Arr 2020\"]/1000000)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1921,"value":"MajorairportsDestGroup = d3.group(MajorairportsDest, d => d.Source)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1980,"value":"majorAirportList = [...MajorairportsDestGroup.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1934,"value":"psgMajorAirportData = MajorairportsDestGroup.get(dropdown)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2378,"value":"majorPsg = d3.csv(\n 'https://raw.githubusercontent.com/FlightData/USFlightData/main/MajorAirports_passengers.csv'\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2293,"value":"airports_dat = \n d3.csvParse(await (await soFetch(\n \"https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat\", {headers:false}\n )).text() , d3.autotype\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":1476,"value":"psgLoadFactor = d3.csv(\n 'https://raw.githubusercontent.com/FlightData/USFlightData/main/LoadFactor.csv'\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1409,"value":"airFares = scrapeTableData('https://www.bts.gov/newsroom/third-quarter-2020-air-fare-drops-new-low-passenger-numbers-decline')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1425,"value":"airFaresData = getTableData(airFares[0], 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"// read passenger and flight dataset\ncommercialData = d3.csv(\n 'https://data.bts.gov/api/views/bpqk-hyst/rows.csv?accessType=DOWNLOAD',\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":23,"value":"commercialDatabyMonth = commercialData.map(function(obj) {\n return {\n Mode: obj.Mode,\n Indicator: obj.Indicator,\n // Date1 : (obj.Date).slice(0,5),\n Date: timeformatMonDayYr(parseTimemdy(obj.Date)),\n MonYr: timeformat(parseTimemdy(obj.Date)),\n Week_Num: obj['Week Num'],\n Current: obj.Current,\n Last_Year: obj['Last Year']\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":30,"value":"domesticDept = commercialDatabyMonth.filter(d => d.Indicator == 'U.S. Domestic Commercial Flights (Departures)')","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"domDeptSumbyMonth = d3.rollup(domesticDept, v => d3.sum(v, d => d.Current), d => d.MonYr)","pinned":false,"hidden":false,"mode":"ojs"},{"id":35,"value":"domDeptSum_month = [...domDeptSumbyMonth.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"domDeptSum_value = [...domDeptSumbyMonth.values()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":229,"value":"internlDept = commercialDatabyMonth.filter(d => d.Indicator == 'U.S. International Commercial Flights (Departures)')","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"intlDeptSumbyMonth = d3.rollup(internlDept, v => d3.sum(v, d => d.Current), d => d.MonYr)","pinned":false,"hidden":false,"mode":"ojs"},{"id":232,"value":"intlDeptSum_month = [...intlDeptSumbyMonth.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"intlDeptSum_value = [...intlDeptSumbyMonth.values()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"parseTimemdy = d3.timeParse(\"%m/%d/%Y\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"timeformatMonDayYr = d3.timeFormat(\"%m/%d/%y\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"timeformat = d3.timeFormat(\"%b%y\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":353,"value":"timeformatDayMon = d3.timeFormat(\"%d/%m\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":341,"value":"timeformatMonName = d3.timeFormat(\"%b\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1421,"value":"TSAscreen = scrapeTableData(\n \"https://www.tsa.gov/coronavirus/passenger-throughput\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":323,"value":"TSAscreenData = getTableData(TSAscreen[0], 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":325,"value":"TSAFormatted = TSAscreenData.map(function(obj) {\n return {\n Date: new Date(obj.Date.trim()),\n Traveller2019: obj['2019 Traveler Throughput '].trim(),\n Traveller2020: obj['2020 Traveler Throughput '].trim(),\n Traveller2021: obj['2021 Traveler Throughput '].trim()\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"TSA_1 = sortData(TSAFormatted.filter(d => d.Date >= new Date(\"01/01/2021\")))","pinned":false,"hidden":false,"mode":"ojs"},{"id":331,"value":"TSA_2 = sortData(TSAFormatted.filter(d => d.Date < new Date(\"01/01/2021\")))","pinned":false,"hidden":false,"mode":"ojs"},{"id":332,"value":"TSA_reordered = [...TSA_1,...TSA_2]","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"TSAFormattedByMon = TSA_reordered.map(function(obj) {\n return {\n Date: timeformatDayMon(obj.Date),\n DateMon: timeformatMonName(obj.Date),\n Traveller2019: +obj['Traveller2019'].replaceAll(\",\", \"\"),\n Traveller2020: +obj['Traveller2020'].replaceAll(\",\", \"\"),\n Traveller2021: +obj['Traveller2021'].replaceAll(\",\", \"\")\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":356,"value":"TSAFormattedByMonYear = TSA_reordered.map(function(obj) {\n return {\n Date2019: timeformatDayMon(obj.Date) + \"/\" + 19,\n Data2019: +obj['Traveller2019'].replaceAll(\",\", \"\"),\n Date2020: timeformatDayMon(obj.Date) + \"/\" + 20,\n Data2020: +obj['Traveller2020'].replaceAll(\",\", \"\"),\n Date2021: timeformatDayMon(obj.Date) + \"/\" + 21,\n Data2021: +obj['Traveller2021'].replaceAll(\",\", \"\"),\n Traveller2020: +obj['Traveller2020'].replaceAll(\",\", \"\"),\n Traveller2021: +obj['Traveller2021'].replaceAll(\",\", \"\")\n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":357,"value":"TSA2019 = TSAFormattedByMonYear.map(function(obj) {\n return { Date: obj.Date2019, Val: obj.Data2019 };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":361,"value":"TSA2020 = TSAFormattedByMonYear.map(function(obj) {\n return { Date: obj.Date2020, Val: obj.Data2020 };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":362,"value":"TSA2021 = TSAFormattedByMonYear.map(function(obj) {\n return { Date: obj.Date2021, Val: obj.Data2021 };\n}).filter(d => d.Val)","pinned":false,"hidden":false,"mode":"ojs"},{"id":367,"value":"TSAData192021 = [...TSA2019.map(d => d.Val), ...TSA2020.map(d => d.Val), ...TSA2021.map(d => d.Val), ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":364,"value":"TSADate192021 = [...TSA2019.map(d => d.Date), ...TSA2020.map(d => d.Date), ...TSA2021.map(d => d.Date), ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":379,"value":"TSASumbyMonth19 = d3.rollup(\n TSAFormattedByMon,\n v => d3.sum(v, d => d.Traveller2019),\n d => d.DateMon + \" \" + 19\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":385,"value":"TSASumbyMonth21 = d3\n .rollup(\n TSASumMonth21,\n v => d3.sum(v, d => d.Traveller2021),\n d => d.DateMon + \" \" + 21\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":372,"value":"TSASumMonth21 = TSAFormattedByMon.filter(d => d.Traveller2021 != 0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":716,"value":"TSASumbyMonth20 = d3.rollup(\n TSAFormattedByMon,\n v => d3.sum(v, d => d.Traveller2020),\n d => d.DateMon +\" \" + 20\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":375,"value":"TSAMon192021 = [...TSASumbyMonth19.keys(), ...TSASumbyMonth20.keys(), ...TSASumbyMonth21.keys() ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":376,"value":"TSAVal192021 = [\n ...TSASumbyMonth19.values(),\n ...TSASumbyMonth20.values(),\n ...TSASumbyMonth21.values()\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":329,"value":"function sortData(a) {\n var b = a.slice().sort((a, b) => a.Date - b.Date);\n return b;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":315,"value":"async function scrapeTableData(url) {\n const response = await soFetch(url);\n const text = await response.text();\n //const domparser = new DOMParser();\n //const dom = domparser.parseFromString(text, \"text/html\");\n const dom = html`${text}`;\n const tables = await dom.querySelectorAll('table');\n if (tables.length <= 0) {\n throw new Error('Could not find any tables.');\n }\n const tablesLen = tables.length;\n\n const tablesData = [];\n for (let i = 0; i < tablesLen; i++) {\n let tableEl = tables[i];\n\n tablesData.push(tableEl);\n }\n return tablesData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":317,"value":"getTableData = (data, start = 1) => {\n const d = tableToJson(data, start);\n const t = d.slice(1);\n const columns = d[0];\n return t.map(d =>\n columns.reduce((acc, e, i) => {\n acc[e] = d[i];\n return acc;\n }, {})\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"function tableToJson(table,start=1) {\n //Code from : https://stackoverflow.com/questions/9927126/how-to-convert-the-following-table-to-json-with-javascript\n var data = [];\n for (var i = start; i < table.rows.length; i++) {\n var tableRow = table.rows[i];\n var rowData = [];\n for (var j = 0; j < tableRow.cells.length; j++) {\n rowData.push(tableRow.cells[j].innerHTML);\n }\n data.push(rowData);\n }\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":669,"value":"planned = [...plannedMon.values()];","pinned":false,"hidden":false,"mode":"ojs"},{"id":652,"value":"delays = [...delaysMon.values()];","pinned":false,"hidden":false,"mode":"ojs"},{"id":672,"value":"cancelled = [...cancelledMon.values()];","pinned":false,"hidden":false,"mode":"ojs"},{"id":1145,"value":"diverted = [...divertedMon.values()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":685,"value":"onTime = [...OnTime.values()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1154,"value":"actual = onTimefn(planned,0,cancelled,diverted)","pinned":false,"hidden":false,"mode":"ojs"},{"id":687,"value":"onTimefn = (arr1, arr2, arr3, arr4 ) => {\n const res = [];\n for(let i = 0; i < arr1.length; i++){\n const temp = Math.abs((arr1[i] || 0) - (arr2[i] || 0) - (arr3[i] || 0) - (arr4[i] || 0));\n res[i] = temp;\n };\n return res}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2118,"value":"arrivals20192020 = d3.csv(\n 'https://raw.githubusercontent.com/FlightData/USFlightData/main/Arrivals_20192020.csv'\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2121,"value":"arrivalsMonYr = arrivals20192020.map(function(obj) {\n return {\n MonYr: obj[\"month\"].concat(\"/\", obj.year),\n scheduled_arr: +obj.arr_flights,\n cancelled: +obj.arr_cancelled,\n diverted: +obj.arr_diverted,\n delays: +obj.arr_del15,\n actual: obj.arr_flights - obj.arr_cancelled - obj.arr_diverted,\n onTime: (obj.arr_flights - obj.arr_cancelled - obj.arr_diverted - obj.arr_del15) \n };\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2153,"value":"divertedMon = d3.rollup(\n arrivalsMonYr,\n v => d3.sum(v, d => d['diverted']),\n d => d['MonYr']\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2160,"value":"plannedMon = d3.rollup(\n arrivalsMonYr,\n v => d3.sum(v, d => d['scheduled_arr']),\n d => d['MonYr']\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2176,"value":"delaysMon = d3.rollup(\n arrivalsMonYr,\n v => d3.sum(v, d => d['delays']),\n d => d['MonYr']\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2182,"value":"cancelledMon = d3.rollup(\n arrivalsMonYr,\n v => d3.sum(v, d => d['cancelled']),\n d => d['MonYr']\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2215,"value":"OnTime = d3.rollup(\n arrivalsMonYr,\n v => d3.sum(v, d => d['onTime']),\n d => d['MonYr']\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2221,"value":"OnTimePercent = perct(onTime , planned)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2233,"value":"perct = (arr1, arr2) => {\n const res = [];\n for (let i = 0; i < arr1.length; i++) {\n const temp = ((arr1[i] || 0) / (arr2[i] || 0)) * 100;\n const temp_perc = temp.toFixed(2);\n res[i] = temp_perc;\n }\n return res;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1314,"value":"filterArray = [\"ATL\", \"LAX\", \"ORD\", \"DFW\", \"DEN\", \"LGA\", \"SFO\", \"LAS\",\"IAH\", \"CLT\",\"DTW\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3450,"value":"md` ## Data - Revenue`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3452,"value":"rev2020GroupbyAirlines = d3.group(data19and20, d=> d.CARRIER_NAME)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3454,"value":"data2020= FileAttachment(\"2020.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3456,"value":"data19and20 = FileAttachment(\"Rev20192020.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3459,"value":"data2019 = FileAttachment(\"2019.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3460,"value":"data2018 = FileAttachment(\"2018.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3462,"value":"data2017 = FileAttachment(\"2017.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3465,"value":"data2016 = FileAttachment(\"2016.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3467,"value":"data2015 = FileAttachment(\"2015.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3468,"value":"data2014 = FileAttachment(\"2014.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3470,"value":"data2013 = FileAttachment(\"2013.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3472,"value":"data2012 = FileAttachment(\"2012.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3474,"value":"data2011 = FileAttachment(\"2011.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3476,"value":"combinedData = Promise.all([data2011,data2012,data2013,data2014,data2015,data2016,data2017,data2018,data2019, data2020]).then((big_datas) => d3.merge(big_datas))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3478,"value":"years = [...groupByYears.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3481,"value":"groupByYears = d3.group(combinedData, d=>d.YEAR)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3483,"value":"revByYear = years.map(year =>{\n const all = groupByYears.get(year)\n const rev = all.map(d =>d.NET_INCOME)\n return d3.sum(rev)\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":3485,"value":"groupCombine = d3.group(combinedData, d=>d.CARRIER_NAME)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3504,"value":"function limitCheckboxes(limit, widget) {\n widget.addEventListener('change', function onchange() {\n const checked = widget.querySelectorAll('input[type=\"checkbox\"]:checked');\n const unchecked = widget.querySelectorAll('input[type=\"checkbox\"]:not(:checked)');\n for(const n of unchecked) n.disabled = checked.length >= limit;\n });\n return widget;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3505,"value":"names = [...groupBynames.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3508,"value":"groupBynames = d3.group(combinedData, d=>d.CARRIER_NAME)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3510,"value":"AirByYears = d3.group(AirData, d=>d.YEAR)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3512,"value":"revORprofitByYear = airyears.map(year =>{\n const all = AirByYears.get(year)\n const rev = all.map(d =>d[dd])\n return d3.sum(rev)\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":3513,"value":"airyears = [...AirByYears.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3515,"value":"AirData = combinedData.filter(d => d.CARRIER_NAME == airlines)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3518,"value":"AirData3 = ch.flatMap(airline => combinedData.filter(d => d.CARRIER_NAME == airline))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3520,"value":"AirByYears3 = d3.group(AirData3, d=>d.YEAR)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3521,"value":"ThreeRevORprofitByYear = airyears3.map(year =>{\n const all = AirByYears3.get(year)\n const air0 = all.filter(a => a.CARRIER_NAME == ch[0])\n const rev0 = air0.map(d =>d[dd1])\n const air1 = all.filter(a => a.CARRIER_NAME == ch[1])\n const rev1 = air1.map(d =>d[dd1])\n const air2 = all.filter(a => a.CARRIER_NAME == ch[2])\n const rev2 = air2.map(d =>d[dd1])\n return {0:d3.sum(rev0), 1:d3.sum(rev1), 2:d3.sum(rev2)}\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":3523,"value":"ThreeRevORprofitByYear.map(d => d['1'])","pinned":false,"hidden":false,"mode":"ojs"},{"id":3526,"value":"airyears3 = [...AirByYears3.keys()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3711,"value":"Regions = [...groupByRegion.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":3712,"value":"groupByRegion = d3.group(NewDataTable, d=>d.Region)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3714,"value":"NewDataTable = CleanedTabledata.filter(a => a.Region != 'World')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3716,"value":"TableData= getTableData(TableFromWeb[0], 0)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3718,"value":"TableFromWeb = scrapeTableData(\n \"https://www.iata.org/en/pressroom/pr/2020-11-24-01/\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3725,"value":"$3b","pinned":true,"hidden":false,"mode":"ojs"},{"id":3720,"value":"mapLayout = ({\n autosize: false,\n width: 900,\n height: 500,\n margin: {\n l: 20,\n r: 50,\n b: 20,\n t: 50,\n pad: 4\n },\n paper_bgcolor: '#AAAAAA',\n plot_bgcolor: '#8888c7'\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3722,"value":"import { toView } from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":3755,"value":"airportData = d3.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3756,"value":"mco2_Total = data_rpm.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3758,"value":"data_rpm = FileAttachment(\"RPM_MCO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3760,"value":"Flight_Total.filter(a => a.Year == yearCh).map(d=> Number(d[totalCh]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":3762,"value":"FlightYear = Flight_Total.filter(a => a.Year == yearCh)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3764,"value":"FlightbyYear = Flight_Total.filter(a=> a.Year)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3766,"value":"Flight_Total = FlightData.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3768,"value":"FlightData = Promise.all([data_Chi,data_Atl,data_Dfw,data_Phx,data_Den,data_Iah,data_Sfo,data_Lax,data_Mco,data_Clt]).then((big_datas) => d3.merge(big_datas))","pinned":true,"hidden":false,"mode":"ojs"},{"id":3770,"value":"data_Chi = FileAttachment(\"Flights_chicago-2@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3772,"value":"data_Den = FileAttachment(\"Flights_Den-2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3774,"value":"data_Dfw = FileAttachment(\"Flights_DFW-2@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3776,"value":"data_Phx = FileAttachment(\"Flights_PHX-2@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3778,"value":"data_Atl = FileAttachment(\"FlightsATL-2@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3780,"value":"data_Sfo = FileAttachment(\"Flights_SFO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3782,"value":"mco_Total = data_Mco.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3784,"value":"data_Mco = FileAttachment(\"Flights_MCO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3786,"value":"data_Lax = FileAttachment(\"Flights_IAH.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3788,"value":"data_Clt = FileAttachment(\"Flights_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3790,"value":"data_Iah = FileAttachment(\"Flights_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3792,"value":"PS_Year = PS_Total.filter(a => a.Year == yearCh)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3794,"value":"PS_Total = PSData.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3796,"value":"PSgroupByName = d3.group(PSData, d => d.Name)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3798,"value":"PSData = Promise.all([data_Chi1,data_Atl1,data_Dfw1,data_Phx1,data_Den1,data_Iah1,data_Sfo1,data_Lax1,data_Mco1,data_Clt1]).then((big_datas) => d3.merge(big_datas))","pinned":true,"hidden":false,"mode":"ojs"},{"id":3800,"value":"data_Atl1 = FileAttachment(\"Passengers_ATL-1@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3802,"value":"data_Phx1 = FileAttachment(\"Passengers_PHX-1@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3804,"value":"data_Den1 = FileAttachment(\"Passengers_Denxls-1@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3806,"value":"data_Chi1 = FileAttachment(\"Passengers_chicago-1@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3808,"value":"data_Dfw1 = FileAttachment(\"Passengers_DFW-1@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3810,"value":"data_Lax1 = FileAttachment(\"Passengers_LAX.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3812,"value":"data_Clt1 = FileAttachment(\"Passengers_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3814,"value":"data_Iah1 = FileAttachment(\"Passengers_IAH.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3816,"value":"data_Sfo1 = FileAttachment(\"Passengers_LAX.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3818,"value":"data_Mco1 = FileAttachment(\"Passengers_MCOxls.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3820,"value":"mco1_Total = data_Mco1.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3822,"value":"MCOyear = [...data_Mco1byyear.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":3824,"value":"data_Mco1byyear = d3.group(data_Mco1, d=>d.Year)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3826,"value":"usa = datasets['us-10m.json']()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3828,"value":"datasets = require('vega-datasets@1.25')","pinned":true,"hidden":false,"mode":"ojs"},{"id":3578,"value":"md`## Air Cargo `","pinned":false,"hidden":false,"mode":"ojs"},{"id":3588,"value":"dates = dataMonthlyTrans.map(d => new Date(d.date)).filter(a => a>startDate)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3590,"value":"startDate = new Date(\"2010-01-01\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":3592,"value":"dataMonthlyTrans = d3.csv(\"https://data.bts.gov/resource/crem-w557.csv\", d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3594,"value":"dataMonthlyTrans.columns","pinned":true,"hidden":false,"mode":"ojs"},{"id":3596,"value":"CargoIN = dataMonthlyTrans.map(a => a.u_s_air_carrier_cargo_millions_of_revenue_ton_miles_international).filter(obj => obj)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3598,"value":"CargoDom = dataMonthlyTrans.map(a => a.u_s_air_carrier_cargo_millions_of_revenue_ton_miles_domestic).filter(obj => obj)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3612,"value":"months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']","pinned":true,"hidden":false,"mode":"ojs"},{"id":3614,"value":"dataIN2019 = CargoINSortedDate.filter(a => a.Period > new Date('2018-12-01') & a.Period d.Total)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3616,"value":"dataDom2019 = CargoDomSortedDate.filter(a => a.Period > new Date('2018-12-01') & a.Period d.Total)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3618,"value":"dataIN2020 = CargoINSortedDate.filter(a => a.Period > new Date('2019-12-01') & a.Period d.Total)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3620,"value":"dataDom2020 = CargoDomSortedDate.filter(a => a.Period > new Date('2019-12-01') & a.Period d.Total)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3622,"value":"dates2020 = CargoINSortedDate.map(a=>a.Period).filter(b=> b> new Date('2019-12-01')& ba.Period).filter(b=> b> new Date('2018-12-01')& ba.Period).filter(b=> b> new Date('2010-01-01')& b {\n const newRecord = { ...date };\n newRecord.Period = new Date(d3.timeFormat('%Y-%m-%d')(\n parseTime(date.Period)\n ));\n return newRecord;\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3632,"value":"CargoNumberDom = d3.csvParse(await FileAttachment(\"Traffic_Dom.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3634,"value":"CargoNumberInter = d3.csvParse(await FileAttachment(\"Traffic_IN.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3636,"value":"CargoINSortedDate = CargoNumberInter.map(date => {\n const newRecord = { ...date };\n newRecord.Period = new Date(d3.timeFormat(\"%Y-%m-%d\")(\n parseTime(date.Period)\n ));\n return newRecord;\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3653,"value":"USCanada2020Data = [...USCanada2020GroupByDate.keys()].map(date=>{\n const all0 = USCanada2020GroupByDate.get(date).filter(a => a.measure ==p[0]).map(v => v.value);\n const all1 = USCanada2020GroupByDate.get(date).filter(a => a.measure ==p[1]).map(v => v.value);\n const all2 = USCanada2020GroupByDate.get(date).filter(a => a.measure ==p[2]).map(v => v.value);\n const all3 = USCanada2020GroupByDate.get(date).filter(a => a.measure ==p[3]).map(v => v.value);\n return d3.sum(all0)+d3.sum(all1)+d3.sum(all2)+d3.sum(all3)\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":3655,"value":"USCanada2020DataRev = USCanada2020Data.reverse()","pinned":true,"hidden":false,"mode":"ojs"},{"id":3656,"value":"USCanada2020GroupByDate = d3.group(USCanada2020, d=>d.date)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3658,"value":"USCanada2020 = TableFromJson.filter(a => a.border == \"US-Canada Border\" & new Date(a.date)> new Date('2019-12-02'))","pinned":true,"hidden":false,"mode":"ojs"},{"id":6,"value":"md` ### Libraries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"Plotly = require(\"plotly.js-dist\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":321,"value":"import { soFetch } from '@alecglassford/so-fetch'","pinned":false,"hidden":false,"mode":"ojs"},{"id":1970,"value":"import { checkbox, radio } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2494,"value":"import { Select } from \"@observablehq/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3529,"value":"import { select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3544,"value":"import { Table } from \"@observablehq/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3830,"value":"import { vl } from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3c","stdlib":"1","titling":"auto"}