Published
Edited
Jul 10, 2019
projectedPotentialRange = d3.extent(
Object.values(competitionAdjustedPotential)
.map(sources => sources.map(s => s.potential).reduce(arraySum, 0) || 0)
)
projectedPatients = h => competitionAdjustedPotential[h].map(s => s.potential).reduce(arraySum, 0)
ucLayer = {
const markerstyle = d => {
const isCarbon=d.properties["Network"]=="Carbon Health Care (FKA Direct Urgent Care)";
return ({
radius: isCarbon ? 6 : 4,
//fillColor: isCarbon ? "green" : stickyCategoricalColor(d.properties["Network"]),
fillColor: ucColor(googleRatingScore(d.properties)),
fillOpacity: 1,
stroke: true,//d.properties["Network"]=="Carbon Health Care (FKA Direct Urgent Care)",
color: isCarbon ? "white" : "white",
weight: isCarbon ? 2 : 0.5,
opacity: isCarbon ? 1 : 0.5,
})
};
return L.geoJson(urgent_cares, {
filter: d => "0"+d.properties["Full Census Tract"].substr(0,4) == selected_county,
pointToLayer: (feature, latlng) => L.circleMarker(latlng, markerstyle(feature))
})
.bindTooltip(layer => urgentCareTooltip(layer.feature.properties))
.bindPopup(layer => urgentCareTooltip(layer.feature.properties))
}
hexBlocksCompetitionAdjustedLayer = L.geoJSON(hexBlocks, {
filter: d => d.properties.total,
style: d => ({
weight:1,
opacity:0.15,
color: "gray",//strokeColor(d.properties.highIncome/d.properties.total),
//fill: false,
fillOpacity: 0.5,
fillColor: d3.scaleLinear()
.domain(projectedPotentialRange)
.range(["red" ,"green"])
.interpolate(d3.interpolateLab)
(projectedPatients(d.id))
})
})
.bindPopup(function (layer) {
return popup(layer.feature.properties);
})
//competitionAdjustedPotential
carbonConversionRange=d3.extent(hexBlocks.features.map(d => d.properties.carbonPatients.length/d.properties.total))
carbonConversionLayer={
return L.geoJSON(hexBlocks, {
filter: d => d.properties.carbonPatients.length>10,
style: d => ({
weight:1,
opacity:0.15,
color: "gray",//strokeColor(d.properties.highIncome/d.properties.total),
//fill: false,
fillOpacity: 0.6,
fillColor: d3.scaleLinear()
.domain(carbonConversionRange)
.range(["white" ,"blue"])
.interpolate(d3.interpolateLab)
(d.properties.carbonPatients.length/d.properties.total)
})
})
.bindPopup(function (layer) {
return popup(layer.feature.properties);
})
}
competitionAdjustedPotential = _.groupBy(
hexBlocks
.features
//.slice(0, 10)
.map(d => {
const nearby=nearbyUrgentCares(d.id);
const totalScore=nearby.map(u => u.score).reduce(arraySum, 0);
return h3.kRingDistances(d.id, maxDistance)
.map((hs, dist) => hs.map(h => {
const expectedScore=urgentCareWeightFunc({dist, reviewScore: carbonClinicScore});
const patientsInBlock=estimatedAnnualVisits(d.properties) || 0;
return {
from: d.id,
to: h,
potential: expectedScore/(expectedScore+totalScore)*patientsInBlock
};
}))
.reduce(arrayMerge, []);
})
.reduce(arrayMerge, []),
o => o.to
)
nearbyUrgentCares = h =>
h3.kRingDistances(h, maxDistance)
.map((blocks, dist) =>
blocks.map(h2 => {
const ind = hexBlockIndex.get(h2);
return ind ?
hexBlocks.features[ind].properties.urgentCares :
[];
})
.reduce(arrayMerge, [])
.map(ind => ({
dist,
ind,
reviewScore: googleRatingScore(urgent_cares.features[ind].properties),
score: urgentCareWeightFunc({
dist,
reviewScore: googleRatingScore(urgent_cares.features[ind].properties)
}),
name: urgent_cares.features[ind].properties["Urgent Care Clinic Name"]
}))
)
.reduce(arrayMerge, [])
.sort((a,b) => b.score - a.score)
nearbyUrgentCares("8828308129fffff")
urgentCareWeightFunc = ({dist, reviewScore}) =>
reviewScore/(2+dist);
import {mutableForm} from "@mootari/mutable-forms"
geojson2h3=require('https://bundle.run/geojson2h3@1.0.1')
esri=require("https://unpkg.com/@esri/arcgis-to-geojson-utils")
arraySum = (a,b) => a+b;
proxy="https://observable-cors.glitch.me/";
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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.
