The Social Cost of Work From Home / leonardo Nicoletti

uSUpd.filter(d=>d.sub_region_2.match("Alameda"))

Plot.plot({

marks: [

Plot.line(uSUpdGroupMonth.filter(d=>d.area_code=== "Los Angeles, California")[0].data, {

x: "date",

y: "workplaces_percent_change_from_baseline",

curve:"basis"

})

]

})

uSUpd.filter(d=>d.sub_region_2==="Calhoun County")

uSUpd_tot.filter(d=>(d["rural"]===null))

// Plot.plot({

// marks: [

// Plot.line(ukUpdGroupMonth.filter(d=>d.area_code==="E06000050")[0].data, {

// x: "date",

// y: "workplaces_percent_change_from_baseline",

// curve:"basis"})

// ]

// })

// Plot.plot({

// x: {type: "log"},

// y: {domain: [-50, -10]},

// marks: [

// Plot.dot(ukUpd_tot, {

// x: d=>d["rural"],

// y: "workplaces_percent_change_from_baseline",

// })

// ]

// })

// Plot.plot({

// marks: [

// Plot.barY(urbRural_raw, {x: "RUC11", y: "column2"})

// ]

// })

// Plot.plot({

// marks: [

// Plot.rectY(urbRural_raw, Plot.binX({y: "sum"}, {x: "RUC11"}))

// ]

// })

hexes_UK.filter(d=>d.leCat==="London Commuter Belt"&&d.category==="#47a45b").length/hexes_UK.filter(d=>d.category==="#47a45b").length

// hexes_UK.filter(d=>d.leCat==="London's Larger Urban Zone"&&d.category==="#47a45b").length/hexes_UK.filter(d=>d.category==="#47a45b").length

hexesClean_US = makeCleanData("US", ruralDataUS, clusterDataUS)

hexesClean_UK = makeCleanData("UK", ruralDataUK, clusterDataUK)

uSUpdGroupMonth = uSUpdGroup.map(d=> {return {

area_code: d.area_code,

data:

d3.rollups(d.data, v => //[

// d3.median(v, d => d.retail_and_recreation_percent_change_from_baseline),

// d3.median(v, d => d.grocery_and_pharmacy_percent_change_from_baseline),

// d3.median(v, d => d.parks_percent_change_from_baseline),

// d3.median(v, d => d.transit_stations_percent_change_from_baseline),

d3.median(v, d => d.workplaces_percent_change_from_baseline),

// d3.median(v, d => d.residential_percent_change_from_baseline)],

d => d.dateMonth).map(d=> { return {

date: d[0],

workplaces_percent_change_from_baseline: d[1]

}

}).sort((a,b)=>d3.ascending(a.date,b.date))

}

})

uSUpdGroup = d3.groups(

uSUpd.map(d=>({...d,

dateMonth:new Date(d.date.split("-")[0]+"-"+d.date.split("-")[1]),

dateWeek: new Date(d.date.split("-")[0]+"-"+d.date.split("-")[1]+ new Date(d.date).getWeekOfMonth()),

area_code: d.sub_region_2===null||d.sub_region_2===""?d.sub_region_2:

d.sub_region_2.replace(' County','')+", "+d.sub_region_1,

}))

,

v=>v.area_code)

.map(d=> { return {

area_code: d[0],

data: d[1]

}

})

uSUpd_tot = makeAggDataUs(uSUpd)

ukUpd_tot = makeAggData(ukUpd)

function makeCleanData(country, ruralData, clusterData) {

let hexesClean = country==="UK"?

hexes_UK.map(d=>({...d, mobilityWork: d.mobilityWork,

income: d.income,

vertices: null,

points: null,

x: d.x,

y: d.y,

category: d.category,

urbCategory: d.urbCategory,

leCat: d.leCat,

urbRow: d.urbCategory!==null &&

ruralData.filter(c=>c.urbCategory===d.urbCategory)!==undefined &&

ruralData.filter(c=>c.urbCategory===d.urbCategory)[0].data.filter(e=>e.LAD11CD===d.key)[0]!==undefined?

ruralData.filter(c=>c.urbCategory===d.urbCategory)[0].data.filter(e=>e.LAD11CD===d.key)[0].row:null,

catRow: d.category!=="#ccc"? clusterData.filter(c=>c.category===d.category)[0].data.filter(e=>e.key===d.key)[0].row:null,

leRow:leData.filter(c=>c.leCat === d.leCat)[0].data.filter(e=>e.key===d.key)[0]!==undefined?leData.filter(c=>c.leCat === d.leCat)[0].data.filter(e=>e.key===d.key)[0].row:null

}))

.map(d=>({...d,

paddingCatX: getPadding("UK","category", d.catRow, d.urbRow).x,

paddingCatY: getPadding("UK", "category", d.catRow, d.urbRow).y,

paddingUrbX: getPadding("UK","urbCategory", d.catRow, d.urbRow).x,

paddingUrbY: getPadding("UK", "urbCategory", d.catRow, d.urbRow).y,

paddingLeX: getPadding("UK","urbCategory", d.catRow, d.leRow).x,

paddingLeY: getPadding("UK", "urbCategory", d.catRow, d.leRow).y

// paddingCat: {x:getPadding("UK","category", d.catRow, d.urbRow, d.urbCategory).x,

// y:getPadding("UK", "category", d.catRow, d.urbRow, d.urbCategory).y},

// paddingUrb: {x:getPadding("UK","urbCategory", d.catRow, d.urbRow, d.urbCategory).x,

// y:getPadding("UK", "urbCategory", d.catRow, d.urbRow, d.urbCategory).y}

}))

:

hexes_US.map(d=>({...d, mobilityWork: d.mobilityWork,

income: d.income,

x: d.x,

y: d.y,

category: d.category,

urbCategory: d.urbCategory,

urbRow: d.urbCategory!==null &&

ruralData.filter(c=>c.urbCategory===d.urbCategory)!==undefined &&

ruralData.filter(c=>c.urbCategory===d.urbCategory)[0].data.filter(e=>e.fullName===d.fullName)[0]!==undefined?

ruralData.filter(c=>c.urbCategory===d.urbCategory)[0].data.filter(e=>e.fullName===d.fullName)[0].row:null,

catRow: d.category!=="#ccc"?clusterData.filter(c=>c.category===d.category)[0].data.filter(e=>e.fullName===d.fullName)[0].row:null,

}))

.map(d=>({...d,

paddingCatX: getPadding("US","category", d.catRow, d.urbRow).x,

paddingCatY: getPadding("US", "category", d.catRow, d.urbRow).y,

paddingUrbX: getPadding("US","urbCategory", d.catRow, d.urbRow).x,

paddingUrbY: getPadding("US", "urbCategory", d.catRow, d.urbRow).y

}))

return hexesClean

}

// function makeLondonEverywhereData()

// let londonEvData = hexes_UK.map(d=>({...d, leCat: codesLondon.includes(d.key)?"Greater London":"Not London"}))

// return londonEvData

// }

function makeLondonEverywhereData() {

let categoriesX = [colors[6], colors[2], colors[4], colors[7], colors[5], colors[3], colors[1], colors[0], colors[8]]

let sortOrder = [categoriesX[1], categoriesX[0], categoriesX[4], categoriesX[2], categoriesX[7], categoriesX[5], categoriesX[6],

categoriesX[3], categoriesX[8]]

// let leMap = hexes_UK.map(d=>({...d, leCat: codesLondon.includes(d.key)?"Greater London":"Not London"}))

let leData =

d3.groups(

hexes_UK

.filter(d=>d.category!=="#ccc")

.filter(d=>hexes_UK.map(d=>d.key).filter(onlyUnique).includes(d.key)), v=>v.leCat)

.map(d=> {

return {leCat: d[0], data: d[1].sort(function(a, b) {

return sortOrder.indexOf(a.category) - sortOrder.indexOf(b.category);

})//.sort((a,b)=> d3.descending(a.category,b.category))

.map((c, i) =>({ ...c, row: i}))

}

})

return leData

}

function makeRuralData(country) {

let categoriesX = country==="UK"?[colors[6], colors[2], colors[4], colors[7], colors[5], colors[3], colors[1], colors[0], colors[8]]:

[colors[2], colors[6], colors[7], colors[4], colors[3], colors[5], colors[8], colors[1], colors[0]] ;

let sortOrder = country==="UK"?[categoriesX[1], categoriesX[0], categoriesX[4], categoriesX[2], categoriesX[7], categoriesX[5], categoriesX[6],

categoriesX[3], categoriesX[8]]:

[categoriesX[0], categoriesX[1], categoriesX[5], categoriesX[3], categoriesX[8], categoriesX[4], categoriesX[7],

categoriesX[2], categoriesX[6]];

let ruralData = country==="UK"?

d3.groups(

ukUrbRural

.map(d=>({...d, category: hexes_UK.filter(c=>c.key===d.LAD11CD)[0]!==undefined?

hexes_UK.filter(c=>c.key===d.LAD11CD)[0].category:null

}))

.filter(d=>d.category!=="#ccc")

.filter(d=>hexes_UK.map(d=>d.key).filter(onlyUnique).includes(d.LAD11CD)), v=>v.RUC11)

.map(d=> {

return {urbCategory: d[0], data: d[1].sort(function(a, b) {

return sortOrder.indexOf(a.category) - sortOrder.indexOf(b.category);

})//.sort((a,b)=> d3.descending(a.category,b.category))

.map((c, i) =>({ ...c, row: i}))

}

}):

d3.groups(

hexes_US,

// .map(d=>({...d, category: hexes.filter(c=>c.key===d.LAD11CD)[0]!==undefined?

// hexes.filter(c=>c.key===d.LAD11CD)[0].category:null

// }))

// .filter(d=>hexes.map(d=>d.fullName).filter(onlyUnique).includes(d.fullName)),

v=>v.urbCategory

)

.filter(d=>d[0]!==null)

.map(d=> {

return {urbCategory: d[0], data: d[1]

.filter(d=>d.urbCategory!==null&&colors.includes(d.category)&&d.mobilityWork!==null&&d.income!==null).sort(function(a, b) {

return sortOrder.indexOf(a.category) - sortOrder.indexOf(b.category);

})//.sort((a,b)=> d3.descending(a.category,b.category))

.map((c, i) =>({ ...c, row: i}))

}

})

return ruralData

}

function makeAggDataUs(data, lkd=false) {

const data_tot = d3.rollups(

data,

v => [d3.median(v, d => d.retail_and_recreation_percent_change_from_baseline),

d3.median(v, d => d.grocery_and_pharmacy_percent_change_from_baseline),

d3.median(v, d => d.parks_percent_change_from_baseline),

d3.median(v, d => d.transit_stations_percent_change_from_baseline),

d3.median(v, d => d.workplaces_percent_change_from_baseline),

d3.median(v, d => d.residential_percent_change_from_baseline),

v[0].place_id,

v[0].sub_region_2===null?v[0].sub_region_2:

v[0].sub_region_2,//.replace(' County',''),

v[0].sub_region_1,

v[0].census_fips_code.length===4?"0"+v[0].census_fips_code:v[0].census_fips_code

],

d => d.place_id).map(([k, v]) =>

({

local_authority: k,

sub_region_1: v[8],

borough_abbr: v[7],//k===null?k:k.replace(' District','').replace('London Borough of ',''),

place_id: v[6],

area_code: v[9],

fullName: v[7] +", "+v[8],

// retail_and_recreation_percent_change_from_baseline: v[0],

// grocery_and_pharmacy_percent_change_from_baseline: v[1],

// parks_percent_change_from_baseline: v[2],

// transit_stations_percent_change_from_baseline: v[3],

workplaces_percent_change_from_baseline: v[4],

// residential_percent_change_from_baseline: v[5],

// tot_mobility: d3.median([v[0], v[1], v[3], v[4], v[5]]),

"Total annual income (£)":

usCountyIncome.filter(

c=>c.geoid===v[9])[0]===undefined?null:

usCountyIncome.filter(

c=>c.geoid===v[9])[0].income,

rural: urbRuralUs.filter(

c=>c.geoid===v[9])[0]===undefined?null:

urbRuralUs.filter(

c=>c.geoid===v[9])[0].pct_rural_2010,

urbCategory: urbRuralUs.filter(

c=>c.geoid===v[9])[0]===undefined?null:

urbRuralUs.filter(

c=>c.geoid===v[9])[0].urbCategory,

// x: hex_us.filter(c=>c.fullName === v[7] +", "+v[8])[0]===undefined?null:

// hex_us.filter(c=>c.fullName === v[7] +", "+v[8])[0].x,

// y: hex_us.filter(c=>c.fullName === v[7] +", "+v[8])[0]===undefined?null:

// hex_us.filter(c=>c.fullName === v[7] +", "+v[8])[0].y

})

)

return data_tot

}

function makeAggData(data, lkd=false) {

const data_tot = d3.rollups(

data,

v => [d3.median(v, d => d.retail_and_recreation_percent_change_from_baseline),

d3.median(v, d => d.grocery_and_pharmacy_percent_change_from_baseline),

d3.median(v, d => d.parks_percent_change_from_baseline),

d3.median(v, d => d.transit_stations_percent_change_from_baseline),

d3.median(v, d => d.workplaces_percent_change_from_baseline),

d3.median(v, d => d.residential_percent_change_from_baseline),

v[0].place_id,

v[0].sub_region_2===null?v[0].sub_region_2:

v[0].sub_region_2.replace(' District','').replace('London Borough of ','').replace("Royal Borough of ", ""),

v[0].sub_region_1,

],

d => d.place_id).map(([k, v]) =>

({

local_authority: k,

sub_region_1: v[8],

borough_abbr: v[7],//k===null?k:k.replace(' District','').replace('London Borough of ',''),

place_id: v[6],

area_code: loa_google.filter(

c=>c.place_id===v[6])[0]===undefined?null:

loa_google.filter(

c=>c.place_id===v[6])[0].lad19cd,

// retail_and_recreation_percent_change_from_baseline: v[0],

// grocery_and_pharmacy_percent_change_from_baseline: v[1],

// parks_percent_change_from_baseline: v[2],

// transit_stations_percent_change_from_baseline: v[3],

workplaces_percent_change_from_baseline: v[4],

// residential_percent_change_from_baseline: v[5],

// tot_mobility: d3.median([v[0], v[1], v[3], v[4], v[5]]),

"Total annual income (£)":

loa_google.filter(

c=>c.place_id===v[6])[0]===undefined?null:

incomeLOA.get(loa_google.filter(

c=>c.place_id===v[6])[0].lad19cd),

// pop: loa_google.filter(

// c=>c.place_id===v[6])[0]===undefined?null:

// popLOA.get(loa_google.filter(

// c=>c.place_id===v[6])[0].lad19cd),

rural: loa_google.filter(

c=>c.place_id===v[6])[0]===undefined?null:

urbRuralLOA.get(loa_google.filter(

c=>c.place_id===v[6])[0].lad19cd)

})

)

return data_tot

}

uSUpd_2021_1 = d3.csvParse(await FileAttachment("2021_US_Region_Mobility_Report_1@1.csv").text())

uSUpd_2021_2 = d3.csvParse(await FileAttachment("2021_US_Region_Mobility_Report_2.csv").text())

uSUpd_2020_1 = d3.csvParse(await FileAttachment("2020_US_Region_Mobility_Report_1.csv").text())

uSUpd_2020_2 = d3.csvParse(await FileAttachment("2020_US_Region_Mobility_Report_2.csv").text())

uSUpd = d3.merge([uSUpd_2021_1, uSUpd_2021_2, uSUpd_2020_1, uSUpd_2020_2])

usCountyIncome = {

let data = await FileAttachment("county_2019 - us_county_death_rate_2019(1).csv").csv();

let dataFinal = data.map(d=> { return {

geoid:d.fips,

income: +d.income,

}})

return dataFinal

}

ukUpd_2021 = d3.csvParse(await FileAttachment("2021_GB_Region_Mobility_Report_1213 (1).csv").text())

hex_us =

// d3.csv("https://gist.githubusercontent.com/lnicoletti/96e5574ab6d6b0e020487e0d660c24d6/raw/5b8416327fe7fef589de0101165209295e4a830a/usCountiesHex_obs.csv", d3.autoType)

// d3.csv("https://gist.githubusercontent.com/lnicoletti/6287798c66d526c134e91da1d289f1f3/raw/951a889c43268b4793f7ca5e9125cd76a07c2240/usCountiesHex_obs_2.csv", d3.autoType)

// d3.csv("https://gist.githubusercontent.com/lnicoletti/62ee1b2144ad7dbcd6a66eddbc1b0544/raw/6cd8ed474e719767f40e7de88e02b6815b1e0d03/usCountiesHex.csv", d3.autoType)

d3.csv("https://gist.githubusercontent.com/lnicoletti/a01622da3e106830fe1c51ab96403234/raw/2b0d35c4efff50a65beb29759ae1a55eb99f73ac/usCountiesHex_tilemaps_albers.csv")

hex_la = d3.json("https://raw.githubusercontent.com/odileeds/hexmaps/gh-pages/maps/uk-local-authority-districts-2021.hexjson")

loa_google = d3.csv("https://gist.githubusercontent.com/lnicoletti/75225f0d73379a2e655adba5c7ab2e73/raw/a9fc2eafef3ce111898f527933ee6a60de7c0e8c/loa_to_google.csv")

incomeLOA = new Map([...incomeLOA_map, ...incomeScot_map, ...incomeNI_map])

incomeNI_map = d3.rollup(incomeNI_raw, v => d3.mean(v, d => +d["Total annual income (£)"]), d => d["Local authority code"])

incomeLOA_map = d3.rollup(incomeLOA_raw, v => d3.mean(v, d => +d["Total annual income (£)"].trim().replace(",","")), d => d["Local authority code"])

incomeScot_map = d3.rollup(incomeScot_raw, v => d3.mean(v, d => +d["Total annual income (£)"]), d => d["Local authority code"])

incomeScot_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/0362b4f47cda3e21abfade570db5517c/raw/7328051a65edd71691cc6a129cbabe4586c97286/scotlandIncome.csv", d3.autoType)

incomeNI_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/47978c96e59de67c9bb263d54c746e76/raw/2f360795eb339debff580ec4d6d816a7ce8103cb/nIrelandIncome.csv")

incomeLOA_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/fb38b11e3ee566f5fc2dd4a8ee4a7fc4/raw/a37b54dc60d5a8f3297a9c83d6a5baa172193dc2/incomeestimatesforsmallareas.csv")

popLOA = d3.rollup(popLOA_raw, v => d3.mean(v, d => +d["Population density"]), d => d["LA code"])

engUrbRural = d3.csv("https://gist.githubusercontent.com/lnicoletti/9b5df10403fd89cc6af0ee8cd26b7925/raw/cacd508ed040a1d190401e9b16247b970181aac1/ukUrbRural_2class.csv", d3.autoType)

ukUrbRural = d3.merge([engUrbRural, scotUrbRural])

urbRuralLOA = d3.rollup(urbRural_raw, v => d3.mean(v, d => +d["Rural including hub towns (rural & rural related) population1 (%)"]), d => d["LAD11CD"])

// popLOA_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/80acb6ca345efd71a49f1e9a7e3855ad/raw/6d40d0f36f3d98eae534dca6cf100eb228317686/ukpopestimatesmid2020on2021geography.csv", d3.autoType)

popLOA_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/78a02c631de8767e69c672ce790a3b17/raw/7f44d1501c10a70e7362934193a9d6799d7d93d5/ukPopDen.csv", d3.autoType)

urbRural_raw = d3.csv("https://gist.githubusercontent.com/lnicoletti/7baf3e11996edb30a5fb590d3f76f7ef/raw/11a7c8f3ff3525e565776f9406facc2f793bede4/ukUrbRural.csv", d3.autoType)

incomeIt =

//https://lab24.ilsole24ore.com/mappaRedditi/redditiTabelle.html

d3.csv("https://gist.githubusercontent.com/lnicoletti/db9c74083db65ad219c9951a2a2709cf/raw/b55cb1e293f70d2467d9b5c7c8ac66a6c0a3cc1c/IT_income.csv", d3.autoType)

//https://www.infodata.ilsole24ore.com/2019/11/16/scopri-pil-pro-capite-delle-tua-provincia-misura-la-tua-ricchezza/

incomeFR = d3.csv("https://gist.githubusercontent.com/lnicoletti/6ce6ea0f0e834c1474229d979ceccba9/raw/2bb1c632e47834546ea357fb0aa8324853a03c46/FR_income.csv", d3.autoType)

//https://www.insee.fr/fr/statistiques/4508514#figure1_radio2

// https://opendata.cbs.nl/statline/#/CBS/nl/dataset/84639NED/table

incomeNL = d3.csv("https://gist.githubusercontent.com/lnicoletti/d186d92c1c186d525c45fa344daea662/raw/6f4b88b7ba98a73270c35f130eef24a0b6570a5f/NL_income.csv", d3.autoType)

incomeCA = d3.csv("https://gist.githubusercontent.com/lnicoletti/451413e33229d7f9b9c882a6a5eb129a/raw/c5af6d251e8582d068e8f83ce3d2b5cb2565541d/CA_income.csv", d3.autoType)

//https://www12.statcan.gc.ca/census-recensement/2016/geo/map-carte/ref/thematic-thematiques/inc-rev/map-eng.cfm?TYPE=1

incomeAU = d3.csv("https://gist.githubusercontent.com/lnicoletti/f3790a08faaea687f35bdb1bf83247f4/raw/1562aaa28cec29c8fe39cc5d12a69d1df254ce4d/AU_income.csv", d3.autoType)

//https://www.abs.gov.au/statistics/labour/earnings-and-work-hours/personal-income-australia/latest-release#data-download

// LGA TABLE 1.5

incomeDK = d3.csv("https://gist.githubusercontent.com/lnicoletti/780606be32f8e9d2a18afee9b2fe8589/raw/3b7d26afb49e93a10a736afc71aeaa7802a6eaa7/DK_income.csv", d3.autoType)

//https://www.statistikbanken.dk/statbank5a/SelectVarVal/saveselections.asp

incomeSE = d3.csv("https://gist.githubusercontent.com/lnicoletti/8f896198771fba0b3df1ba3e6177c3bc/raw/c13945b4709b56a1f59b031c1d38fe38a89bd41d/SE_income.csv", d3.autoType)

// https://www.scb.se/en/finding-statistics/statistics-by-subject-area/household-finances/income-and-income-distribution/income-and-tax-statistics/pong/tables-and-graphs/income--persons-counties-and-municipalities/total-income-from-employment-and-business-2000-and-20182020-by-municipality.-median-values-in-2020-prices/

incomeNO = d3.csv("https://gist.githubusercontent.com/lnicoletti/b666273df142d7ce3dbe72552be2952b/raw/94df2806ef4389cef0b793e1f9c56609c44504ce/NO_income.csv", d3.autoType)

// https://www.ssb.no/en/statbank/table/06944/tableViewLayout1/

incomeES = d3.csv("https://gist.githubusercontent.com/lnicoletti/58a2f55641c850ac4a0792681479b3df/raw/01d0cb14d2c0f961fdd6f77cd0390f9c3ce922ca/ES_income.csv", d3.autoType)

//https://es.wikipedia.org/wiki/Anexo:Provincias_de_Espa%C3%B1a_por_producto_interno_bruto_per_c%C3%A1pita

incomePT = d3.csv("https://gist.githubusercontent.com/lnicoletti/a137af23df1a49a3d48f4a6f0a26ee00/raw/c6a4b85213d63d1bd177e70371c6ec70600fd41f/PT_income.csv", d3.autoType)

// Rendimento bruto declarado médio por sujeito passivo

// https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=445098890&PUBLICACOESmodo=2

// europe gdp/pop NUTS 3

//https://ec.europa.eu/eurostat/databrowser/view/NAMA_10R_3GDP/default/table?lang=en&category=na10.nama10.nama_10reg.nama_10r_gdp

//https://ec.europa.eu/eurostat/databrowser/view/NAMA_10R_3POPGDP/default/map?lang=en&category=na10.nama10.nama_10reg.nama_10r_gdp

// NUTS3_pop = d3.csv("https://gist.githubusercontent.com/lnicoletti/c3bdf81d2d007d264697ec34453ea3e9/raw/7151baff60130ecc750acb6191adbda127157925/NUTS3_pop.csv", d3.autoType)

// NUTS3_gdp = d3.csv("https://gist.githubusercontent.com/lnicoletti/c478121e02bcd2a46ab8bae6755dcaca/raw/e97985bd8b2205762af8a6e4af9132d1f8a11ef7/NUTS3_gdp.csv", d3.autoType)

// NUTS3_income = NUTS3_gdp.filter(d=>d.TIME_PERIOD===2018)

// .map(d=>{return{

// province:d.geo,

// income:d.OBS_VALUE/NUTS3_pop.filter(c=>c.TIME_PERIOD===2018&&c.geo===d.geo)[0].OBS_VALUE*1000

// }})

countries_sm = d3.merge([uKUpd_sm, uSUpd_sm, itUpd_sm, fRUpd_sm, nLUpd_sm, cAUpd_sm, AUUpd_sm, dKUpd_sm, sEUpd_sm, nOUpd_sm, eSUpd_sm, pTUpd_sm])

t = dKUpd_sm.filter(d=>d.country==="Denmark")

d3.extent(t, d=>d.income)

codesLondon = "E09000001,E09000033,E09000002,E09000003,E09000004,E09000005,E09000006,E09000007,E09000008,E09000009,E09000010,E09000012,E09000013,E09000014,E09000015,E09000016,E09000017,E09000018,E09000019,E09000022,E09000023,E09000024,E09000025,E09000026,E09000027,E09000028,E09000029,E09000030,E09000031,E09000032,E09000011,E09000020,E09000021".split(",").concat(londonLUZ)

londonLUZ = ["E07000095", "E07000242", "E07000096", "E07000098", "E07000102", "E07000243", "E07000240", "E07000103", "E07000241", "E07000066", "E07000068", "E07000069", "E07000070", "E07000072", "E07000073", "E07000074", "E07000075", "E06000033", "E06000034", "E07000077", "E07000107", "E07000109", "E07000110", "E06000035", "E07000115", "E07000116", "E07000111", "E07000207", "E07000208", "E07000209", "E07000210", "E07000211", "E07000212", "E07000213", "E07000214", "E07000215", "E07000217", "E06000036", "E06000039", "E06000040", ]

//https://en.wikipedia.org/wiki/Larger_urban_zone

//https://en.wikipedia.org/wiki/London_metropolitan_area#cite_note-9

// hexes_UK.filter(d=>d.n.toLowerCase().match("S".toLowerCase()))

londonCommuterBelt = ["E06000038", "E06000033", "E06000035", "E06000032", "E07000209", "E07000216", "E06000036", "E07000089", "E07000214", "E07000226", "E06000039", "E07000066", "E06000060", "E07000070", "E07000084", "E07000110", "E07000243", "E07000242", "E07000099", "E07000116", "E06000040", "E07000241", "E07000211", "E07000068", "E07000227", "E07000115", "E06000056", "E07000111"]

//https://web.archive.org/web/20150924002318/http://www.espon.eu/export/sites/default/Documents/Projects/ESPON2006Projects/StudiesScientificSupportProjects/UrbanFunctions/fr-1.4.3_April2007-final.pdf#page=119

//https://en.wikipedia.org/wiki/London_metropolitan_area#cite_note-9

d3 = require('d3', "d3-hexjson")

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