CTO and head of research chez @heavenagency - also MagicMorning Founder -
Film Maker & Photographer based in #lille, #politics, #oceanlife + #velorution & #skateanywhere
Public
Edited
Dec 13, 2022
viewof annee = Inputs.radio(["2022", "2021", "2020", "2019", "2018","2017"], {label: "Année"})
{
const Geo_lille =
vl.markGeoshape({color:'#2A2A2A',stroke: '#FFF', strokeWidth:1, strokeDash: [3, 3]})
.data(vl.topojson(Lille_Geo).feature('poly') )
//.encode({"color": {"field": "valeur_fonciere","type": "quantitative"}})
// MAISONS
const plotALl =
vl.markSquare(
{color:'#FF6600',opacity: '1', filled: false, size:1, strokeWidth: 0.5 , strokeOpacity:'1'}
)
.title("Lille, mutations > "+ + " euros")
.data(Computed)
.encode({
"longitude": { "field": "longitude", "type": "quantitative" },
"latitude": { "field": "latitude", "type": "quantitative" },
"size": { "field": "valeur_fonciere", "type": "quantitative", "title":"Valeur Foncière"},
"color": { "field": "type_local", "type": "nominal", "title":"Nature du bien"},
"tooltip" : {"field": "valeur_fonciere"},
})
//.transform(vl.filter({"and": [{ "field": "valeur_fonciere","gt": n}, {"field": "date_mutation", "range": [{"year": annee, "month": "jan", "date": 1}, {"year": annee, "month": "dec", "date": 1}]}]}))
.transform(vl.filter({ "field": "surface_reelle_bati","gt": n}))
// .encode(
// // vl.color().fieldN('type_local').scale({domain: [50, 300], clamp: true}).legend({format: '%'}),
// vl.size().fieldQ('valeur_fonciere').scale({domain: [200000, 3000000], clamp: true}).legend({}),
// // vl.tooltip().fieldQ('valeur_fonciere').format('.0%')
// )
// RENDER
return vl.layer(Geo_lille,plotALl)
.project(vl.projection('mercator'))
//.width(700)
//.height(400)
.width(900).height(500)
.height(Math.floor(0.5 * width))
.padding(100)
//.autosize({ "type": "none","resize":false, "contains": "padding"})
//.background('#000')
.config({view: {stroke: null}})
.render();
}
// To use copied code replace "data" with your own variable
viewof rr = aq
.from(Lille_Mutations)
// incohérences à gérer
.filter(d => d["valeur_fonciere"] < 3000000)
//.filter(d => d["valeur_fonciere"] > 10000)
.filter(d => d["surface_reelle_bati"] > 100)
.filter(d => d["surface_reelle_bati"] < 150)
.filter(d => d["type_local"] == "Appartement")
.derive({ prixdum2: d => d["valeur_fonciere"]/d["surface_reelle_bati"] })
.rename({ prixdum2: 'Prix du m2' })
// .rollup({
// min:d => op.min(d["valeur_fonciere"]),
// max:d => op.max(d["valeur_fonciere"]),
// mean:d => op.mean(d["valeur_fonciere"]),
// median:d => op.median(d["valeur_fonciere"]),
// count:d => op.count()
// })
.orderby(aq.desc("surface_reelle_bati"))
.view(200000)
import { aq, op } from '@uwdata/arquero'
// import {Wrangler, op} from "@observablehq/data-wrangler"
import {columns} from "@bcardiff/observable-columns"
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
