Curious human. Interested in the mechanics of how things work.
ldn = computedPopns.filter(row => row.city == 'London')[0];
ldn['urban_2050_a'] > ldn['urban_2019']
computedPopns
allCities = {
//worldMap.attr("style", "background-color:#23548A;");
worldMap.select("#cities").remove()
worldMap.select("#names").remove()
// worldMap.select("#land").attr("fill", "#d8d8d8");
const simulation = d3
.forceSimulation(computedPopns)
.force(
"x",
d3.forceX((d) => mapProjection([d.Longitude, d.Latitude])[0])
)
.force(
"y",
d3.forceY((d) => mapProjection([d.Longitude, d.Latitude])[1])
)
// .force(
// "collide", d =>
// d3.forceCollide(radius*repulsionFactor).strength(0.5)
// )
.force(
"collide",
d3.forceCollide(d => {
if (show === 'GDP') {
if (d[gdp[0]] < d[gdp[1]]) {
return gdpScale(d[gdp[0]])+repulsionFactor;
} else {
return gdpScale(d[gdp[1]])+repulsionFactor;
}
} else {
if (d[popn[0]] < d[popn[1]]) {
return popnScale(d[popn[0]])+repulsionFactor;
} else {
return popnScale(d[popn[1]])+repulsionFactor;
}
}
}).strength(1)
);
const nodeG = worldMap.append("g")
.attr("id", "cities")
.selectAll("g")
.data(computedPopns)
.join("g")
.call(drag(simulation));
// nodeG.append('circle')
// .attr("r", d => radiusScale(d[d["plotOrder"][2]]));
// nodeG.append("g")
// .each(function(node) {
// for (let i in node["plotOrder"]) {
// let year = node["plotOrder"][i];
// d3.select(this).append("g")
// .append("circle")
// .attr("r", d => radiusScale(d[year]))
// .attr("fill", yearScale(year));
// }
// })
if (show==="Comparison"){
nodeG.append('circle')
.attr("r", radius)//(d) => radiusScale(d[d["plotOrder"][0]])
.attr("fill", "none");
nodeG.append("g")
.each(function(node, i) {
let symbol = d3.select(this).append("g");
let pPlotOrder = node['popnPlotOrder'];
let gPlotOrder = node['gdpPlotOrder'];
symbol.append("clipPath")
.attr("id", "left-clip_" + i)
.append("rect")
.attr("width", radius)
.attr("height", radius * 2)
.attr("x", -radius)
.attr("y", -radius);
for (let i = 0; i < pPlotOrder.length; i+=1) {
let prop = pPlotOrder[i];
symbol
.append("circle")
.attr("cx", 0)
.attr("cy", 0)
.attr("r", d => popnScale(d[prop]))
.attr("clip-path", "url(#left-clip_" + i + ")")
.style("fill", (d) => pColor[prop]);
}
symbol.append("clipPath")
.attr("id", "right-clip_" + i)
.append("rect")
.attr("width", radius)
.attr("height", radius * 2)
.attr("x", 0)
.attr("y", -radius);
for (let i = 0; i < gPlotOrder.length; i+=1) {
let prop = gPlotOrder[i];
symbol
.append("circle")
.attr("cx", 0)
.attr("cy", 0)
.attr("r", d => gdpScale(d[prop]))
.attr("clip-path", "url(#right-clip_" + i + ")")
.style("fill", gColor[prop]);
}
})
} else {
let nodeProp;
let nodeScale;
let nodeColor;
if (show==="GDP") {
nodeProp = 'gdpPlotOrder';
nodeScale = gdpScale;
nodeColor = gColor;
} else {
nodeProp = 'popnPlotOrder';
nodeScale = popnScale;
nodeColor = pColor;
}
nodeG.append("g")
.each(function(node, i) {
let symbol = d3.select(this).append("g");
// let pPlotOrder = node['popnPlotOrder'];
if (showType === "Both") {
let plotOrder = node[nodeProp];
for (let i = 0; i < plotOrder.length; i+=1) {
let prop = plotOrder[i];
symbol
.append("circle")
.attr("cx", 0)
.attr("cy", 0)
.attr("r", d => nodeScale(d[prop]))
.attr("clip-path", "url(#right-clip_" + i + ")")
.style("fill", nodeColor[prop]);
}
} else {
if (show === "GDP") {
let prop = showType === 2019 ? "gdp_urban_2019" : "gdp_urban_2050_a";
symbol
.append("circle")
.attr("cx", 0)
.attr("cy", 0)
.attr("r", d => nodeScale(d[prop]))
.attr("clip-path", "url(#right-clip_" + i + ")")
.attr("stroke", "white")
.attr("stroke-width", "0.5px")
.style("fill", nodeColor[prop]);
} else {
let prop = showType === 2019 ? "urban_2019" : "urban_2050_a";
symbol
.append("circle")
.attr("cx", 0)
.attr("cy", 0)
.attr("r", d => nodeScale(d[prop]))
.attr("clip-path", "url(#right-clip_" + i + ")")
.attr("stroke", "white")
.attr("stroke-width", "0.5px")
.style("fill", nodeColor[prop]);
}
}
})
}
simulation.on("tick", () => {
nodeG.attr("transform", d => `translate(${d.x}, ${d.y})`)});
invalidation.then(() => simulation.stop());
// worldMap.append("g")
// .attr("id", "names")
// .selectAll("text")
// .data(topCities)
// .join("text")
// .attr("transform", d => `translate(${mapProjection([d["Longitude"], d["Latitude"]])})`)
// .attr("font-size", "0.4em")
// .attr("font-family", "Akkurat LL")
// .attr("fill", "white")
// .attr("stroke", "#063249")
// .attr("stroke-width", "0.1px")
// .attr("paint-order", "stroke")
// .text(d => d["Urban Agglomeration"]);
return worldMap.node();
}
function forceClusterCollision() {
let nodes
let radii
let strength = 1
let iterations = 1
let clusterPadding = 0 //addition
function radius(d) { return d.r }
function x(d) { return d.x + d.vx }
function y(d) { return d.y + d.vy }
function constant(x) { return function() { return x } }
function jiggle() { return 1e-6 } //change - PLEASE no Math.random() in there ಥ﹏ಥ
// function jiggle() { return (Math.random() - 0.5) * 1e-6 }
function force() {
let i
let n = nodes.length
let tree
let node
let xi
let yi
let ri
let ri2
for (let k = 0; k < iterations; ++k) {
tree = d3.quadtree(nodes, x, y).visitAfter(prepare)
for (i = 0; i < n; ++i) {
node = nodes[i]
ri = radii[node.index]
ri2 = ri * ri
xi = node.x + node.vx
yi = node.y + node.vy
tree.visit(apply)
}//for i
}//for k
function apply(quad, x0, y0, x1, y1) {
let data = quad.data
let rj = quad.r
let r = ri + rj + clusterPadding //change
if (data) {
if (data.index > node.index) {
let x = xi - data.x - data.vx
let y = yi - data.y - data.vy
let l = x * x + y * y
r = ri + rj + (node.cluster !== quad.data.cluster ? clusterPadding : 0) //addition
if (l < r * r) {
if (x === 0) x = jiggle(), l += x * x
if (y === 0) y = jiggle(), l += y * y
l = (r - (l = Math.sqrt(l))) / l * strength
node.vx += (x *= l) * (r = (rj *= rj) / (ri2 + rj))
node.vy += (y *= l) * r
data.vx -= x * (r = 1 - r)
data.vy -= y * r
}//if
}//if
return
}//if
return x0 > xi + r || x1 < xi - r || y0 > yi + r || y1 < yi - r
}//apply
}//force
function prepare(quad) {
if (quad.data) return quad.r = radii[quad.data.index];
for (let i = quad.r = 0; i < 4; ++i) {
if (quad[i] && quad[i].r > quad.r) {
quad.r = quad[i].r
}//if
}//for i
}
function initialize() {
if (!nodes) return;
let i, n = nodes.length, node
radii = new Array(n)
for (i = 0; i < n; ++i) node = nodes[i], radii[node.index] = +radius(node, i, nodes)
}
force.initialize = function (_) {
nodes = _
initialize()
return force
}
force.iterations = function (_) {
return arguments.length ? (iterations = +_, force) : iterations
}
//I wish strength could be a function of the node as well...
force.strength = function (_) {
return arguments.length ? (strength = +_, force) : strength
}
force.radius = function (_) {
return arguments.length ? (radius = typeof _ === "function" ? _ : constant(+_), force) : radius
}
//addition - the actual pixels of padding
force.clusterPadding = function (_) {
return arguments.length ? (clusterPadding = +_, force) : clusterPadding
}
return force
}//function forceCollision
popn = ['urban_2019', 'urban_2050_c']
gdp = ['gdp_urban_2019', 'gdp_urban_2050_c']
selectedPopns[0]
d3
.select(test)
.select('[aria-description="GDP per capita (USD) →"]')
.enter()
.selectAll("text")
.each(function (d) {
// d is the tick's value (in this case, a number)
d3.select(this)
.attr('font-family', 'Helvetica Neue')
.attr("fill", 'black')
.attr("font-size", 10);
})
test2 = Plot.plot({
inset: 8,
grid: true,
width: width*1.25,
height: 1000,
marginLeft: 50, // space to the left of the chart
marginBottom: 50, // space below the chart
color: {
legend: true,
},
facet: {
data: baseData,
x: "continent"
},
x: {
// label: "GDP per capita (USD) →",
tickFormat: "~s",
type: "log",
ticks: 3
},
y: {
// label: "↑ Population (people)",
type: "log",
tickFormat: "~s",
// textColor: "white",
ticks: 5
},
marks: [
//dots
// Plot.dot(baseData, {x: "gdp_capita_2019", y: "urban_2019", text: "iso3", stroke: "continent", strokeWidth: 1,
// //fill:"white", r: 10}),
// }),
// Plot.dot(data_gdpc, {x: "gdp_capita_2050", y: "urban_2050_a", text: "iso3", stroke: "continent"}),
//links
Plot.link(baseData, {
x1: "gdp_capita_2019",
y1: "urban_2019",
x2: "gdp_capita_2050",
y2: "urban_2050_a",
stroke: "continent",
markerEnd: "arrow",
opacity: 0.4,
strokeWidth: 2,
}),
//text
Plot.text(baseData.slice(0, 150), {x: "gdp_capita_2019", y: "urban_2019", text: "city",
fill: "continent",
stroke: "white",
opacity: 0.8,
fontSize: 12}),
// Plot.text(baseData, {x: "gdp_capita_2050", y: "urban_2050_a", text: "city",
// fill: "continent",
// // stroke: "white",
// opacity: 0.8,
// fontSize: 11}),
]
})
test3 = Plot.plot({
inset: 8,
grid: true,
width: 750,
height: 750,
marginLeft: 50, // space to the left of the chart
marginBottom: 50, // space below the chart
color: {
legend: true,
},
// facet: {
// data: baseData,
// x: "continent"
// },
x: {
// label: "GDP per capita (USD) →",
tickFormat: "~s",
type: "log",
ticks: 3
},
y: {
// label: "↑ Population (people)",
type: "log",
tickFormat: "~s",
// textColor: "white",
ticks: 5
},
marks: [
//dots
Plot.dot(baseData, {x: "gdp_capita_2019", y: "urban_2019", text: "iso3", stroke: "continent"}),
// Plot.dot(data_gdpc, {x: "gdp_capita_2050", y: "urban_2050_a", text: "iso3", stroke: "continent"}),
//text
Plot.text(baseData, {x: "gdp_capita_2019", y: "urban_2019", text: "city", font: "IBM Plex Sans",
fill: "continent",
stroke: "white",
opacity: 1,
fontSize: 12}),
// Plot.text(baseData, {x: "gdp_capita_2050", y: "urban_2050_a", text: "city",
// fill: "continent",
// // stroke: "white",
// opacity: 0.8,
// fontSize: 11}),
//links
Plot.link(baseData, {
x1: "gdp_capita_2019",
y1: "urban_2019",
x2: "gdp_capita_2050",
y2: "urban_2050_a",
stroke: "continent",
markerEnd: "arrow",
strokeWidth: 1.5,
opacity: 0.5
}),
]
})
baseData[0]
test = Plot.plot({
inset: 8,
grid: true,
width: width*1.25,
height: 750,
marginLeft: 50, // space to the left of the chart
marginBottom: 50, // space below the chart
color: {
legend: true,
},
facet: {
data: baseData,
x: "continent"
},
x: {
// label: "GDP per capita (USD) →",
tickFormat: "~s",
type: "log",
ticks: 3
},
y: {
// label: "↑ Population (people)",
type: "log",
tickFormat: "~s",
// textColor: "white",
ticks: 5
},
marks: [
//dots
// Plot.dot(data_gdpc, {x: "gdp_capita_2019", y: "urban_2019", text: "iso3", stroke: "continent"}),
// Plot.dot(data_gdpc, {x: "gdp_capita_2050", y: "urban_2050_a", text: "iso3", stroke: "continent"}),
//text
Plot.text(baseData, {x: "gdp_capita_2019", y: "urban_2019", text: "city",
fill: "continent",
// stroke: "white",
opacity: 0.8,
fontSize: 11}),
Plot.text(baseData, {x: "gdp_capita_2050", y: "urban_2050_a", text: "city",
fill: "continent",
// stroke: "white",
opacity: 0.8,
fontSize: 11}),
//links
Plot.link(baseData, {
x1: "gdp_capita_2019",
y1: "urban_2019",
x2: "gdp_capita_2050",
y2: "urban_2050_a",
stroke: "continent",
markerEnd: "arrow",
opacity: 0.3
}),
]
})
pColor = ({'urban_2019': "#A2D6F9", 'urban_2050_c': "#2D76F0"})
gColor = ({'gdp_urban_2019': "#f6aa1c", 'gdp_urban_2050_c': "#bc3908"})
popnScale = d3.scaleLinear().domain(popnDomain).range(radiusRange);
gdpScale = d3.scaleLinear().domain(gdpDomain).range(radiusRange);
selectedPopns[0]
computedPopns = {
let popns = []
for (let i in selectedPopns){
let row = selectedPopns[i];
let gdpGap = row['gdp_urban_2050_c'] - row['gdp_urban_2019'];
let popnGap = row['urban_2050_c'] - row['urban_2019'];
row['gdpGap'] = gdpGap;
row['popnGap'] = popnGap;
popns.push(row);
}
return popns;
}
cleanData = FileAttachment("data.csv").csv({typed: true})
baseData = FileAttachment("urban-data-growth-gdpc-full@2.csv").csv()
import {slider} from "@jashkenas/inputs"
import {Swatches} from "@d3/color-legend"
import {mapProjection, activeColorScheme, worldMap} from "b3a0a78984d6df2e"
import {rangeSlider} from '@mootari/range-slider'
viewof range = Inputs.range([0, 100], {label: "Amount", step: 1})
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