Visualizing a changing planet for The Washington Post
Public
Edited
Sep 13, 2024
4 stars
viewof outdoorTime = Inputs.radio(["Less", "More"], {label: "Time spent outdoors", value: "More"})
heatwaveDaysExtended = heatwaveDays.concat(nonExtremeDays)
getActivityPattern = (age, outdoorWorker, numberOfHours) => {
const outdoor = [ 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
const othersBeforeBlackout = getRandomHoursOutside(numberOfHours)
const othersAfterBlackout = getRandomHoursOutside(1)
return {
before : outdoorWorker ? outdoor : othersBeforeBlackout,
after : outdoorWorker ? (Math.random() <= 0.25 ? outdoor : othersAfterBlackout) : othersAfterBlackout
}
}
indoorBlackout = {
const arr = (await FileAttachment('amir_all.csv').csv({typed : true}))
.map((row, i) => {
const startDate = new Date(Date.UTC(2011, 7, 1, 1, 0, 0, 0))
const date = new Date(startDate.getTime() + i*60*60*1000)
return {
...row, date
}
})
const out = {}
arr.forEach(row => {
out[String(row.date.getTime())] = row
})
return out
}
indoorNoBlackout = {
const arr = (await FileAttachment('amir_no_outage.csv').csv({typed : true}))
.map((row, i) => {
const startDate = new Date(Date.UTC(2011, 7, 1, 1, 0, 0, 0))
const date = new Date(startDate.getTime() + i*60*60*1000)
return {
...row, date
}
})
const out = {}
arr.forEach(row => {
out[String(row.date.getTime())] = row
})
return out
}
indoorSeries = {
return {
'2' : indoorData2,
'5' : indoorData5
}
}
getIndoor = ((day, hour, buildingType, backOnlineAfter) => {
const indoorData = indoorSeries[backOnlineAfter]
if(day === 0 && hour === 0) {
return indoorData.find(row => row.day === day && row.hour === 1)[buildingType]
}
return indoorData.find(row => row.day === day && row.hour === hour)[buildingType]
})
indoorData2 = (await FileAttachment("indoor_amir - 2_days (1).csv").csv())
.map((row, i) => {
const d = new Date(row.time.replace('2024', '2011'))
const startDay = new Date('2011-08-07T00:00Z')
const day = d.getUTCDay() - startDay.getUTCDay()
const out = {};
['new-bad', 'new-good', 'old-bad', 'old-good', 'outdoors'].forEach( k => {
out[k] = Number(row[k])
})
return {
...out, day, hour : d.getUTCHours()
}
})
indoorData5 = (await FileAttachment("indoor_amir - 5_days (1)@1.csv").csv())
.map((row, i) => {
const d = new Date(row.time.replace('2024', '2011'))
const startDay = new Date('2011-08-07T00:00Z')
const day = d.getUTCDay() - startDay.getUTCDay()
const out = {};
['new-bad', 'new-good', 'old-bad', 'old-good', 'outdoors'].forEach( k => {
out[k] = Number(row[k])
})
return {
...out, day, hour : d.getUTCHours()
}
})
reduceArray = (array, segmentSize, sum) => {
let reducedArray = [];
for (let i = 0; i < array.length; i += segmentSize) {
let segment = array.slice(i, i + segmentSize);
let mean = ss.mean(segment);
if(sum) {
reducedArray.push(ss.sum(segment))
}
else {
reducedArray.push(mean)
}
}
return reducedArray;
};
extendedHours = [ ...heatwave, ...nonExtremeHours ]
nonExtremeHours = airport.filter(row => {
const startDate = new Date('2011-01-01T00:00Z')
const endDate = new Date('2011-01-01T00:00Z')
startDate.setUTCDate(startDate.getUTCDate() + nonexDay - 1)
endDate.setUTCDate(endDate.getUTCDate() + nonexDay + 20 - 1)
return row.time >= startDate && row.time <= endDate
}).map( row => {
return { ...row, hour : row.time.getUTCHours() }
})
heatwave = airport.filter(row => {
return row.time >= new Date('2011-08-07T00:00:00Z') && row.time <= new Date('2011-08-11T23:59:00Z')
}).map( row => {
return { ...row, hour : row.time.getUTCHours() }
})
heatwaveDays = reduceArray(heatwave.map(row => row.ta), 24).map((v, i) => {
return { tavg : v }
})
getLinearModel = data => {
const {m, b} = ss.linearRegression(data)
return y => {
return (y-b)/m
}
}
numberWithCommas = x => {
return x.toString().replace(/\B(?=(\d{3})+(?!\d))/g, ",")
}
_(airportDailyRolling).find(row => row.date === '09/08/2011')
ap2011.filter((row, i, arr) => {
const row2 = arr[i + 6]
if(!row2) { return }
return row.smoothed >= 31.5 && row2.smoothed < 30
})
ap2011 = airportDailyRolling.filter(row => row.date.endsWith('2011'))
airportDailyRolling = {
const smoothed = rollingAverage(airportDaily.map(row => row.tavg), 5)
return airportDaily.map((row, i) => {
return { ...row, smoothed : smoothed[i] }
})
}
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
airportDaily = _(airport)
.filter(row => {
const d = new Date(row.time)
return d.getUTCMonth() >= 5 && d.getUTCMonth() <= 8 && d.getYear() <= 2023
})
.groupBy(row => {
return (new Date(row.time)).toLocaleDateString('en-GB', {
day: '2-digit',
month: '2-digit',
year: 'numeric'
})
})
.mapValues((arr, dateStr) => {
return {
date : dateStr,
tavg : ss.mean(arr.map(row => row.ta))
}
})
.values()
.valueOf()
erCurveExp(32)
modelInput = {
const data = (await FileAttachment('hstn.usa8506.csv').csv({typed : true}))
const modelInput = data.filter(row => row.tmean >= 29.6)
return modelInput
}
erCurveExp = {
const data = (await FileAttachment('hstn.usa8506.csv').csv({typed : true}))
const modelInput = data.filter(row => row.tmean >= 29.6)
const model = ss.linearRegressionLine(ss.linearRegression(modelInput.map(row => [row.tmean, row.logRR])))
return t => {
return Math.max(1, Math.E**(model(t)))
}
}
test = (await FileAttachment('hstn.usa8506.csv').csv({typed : true})).reverse()
erCurveExp(40)
out = {
return (await FileAttachment('amir_all.csv').csv({typed : true}))
.map((row, i) => {
const startDate = new Date(Date.UTC(2011, 7, 1, 1, 0, 0, 0))
const dateStr = row.date.trim().split(/\s+/g)[1]
const date = new Date(startDate.getTime() + i*60*60*1000)
return {
..._.pick(row, ['outdoor', 'ac100-5day-old-bad', 'ac100-5day-new-good']), date, dateStr
}
}).slice(100, 330)
}
median = round(ss.quantile(rangeOfResults.map(row => row.totalDeaths), 0.5))
q75 = ss.quantile(rangeOfResults.map(row => row.totalDeaths), 0.75)
q25 = ss.quantile(rangeOfResults.map(row => row.totalDeaths), 0.25)
heatwaveDays.map(row => {
return (erCurveExp(row.tavg) - 1)*dailyDeathsPer100k/100000*totalPop
}).reduce(sum, 0)
dailyDeathsPer100k/100000*totalPop*5
airport.filter(row => row.time >= new Date('2024-07-08'))
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