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brightredchilli/100_most_populated.csv

Forked from edwardloveall/100_most_populated.csv
Last active December 11, 2015 19:39
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100_most_populated.csv
City latitude Country rank population longitude
Tokyo 35.670479 Japan 1 28,025,000 139.740921
Mexico City 19.32792 Mexico 2 18,131,000 -99.19109
Mumbai 19.11105 India 3 18,042,000 72.87093
S·o Paulo 0.3617 Brazil 4 17, 711,000 -52.147511
New York City 40.71455 USA 5 16,626,000 -74.7124
Shanghai 31.247709 China 6 14,173,000 121.472618
Lagos 6.43918 Nigeria 7 13,488,000 3.42348
Los Angeles 34.5329 USA 8 13,129,000 -118.245009
Calcutta 22.52667 India 9 12,900,000 88.34616
Buenos Aires -34.554539 Argentina 10 12,431,000 -58.469082
SeÛul 37.557121 South Korea 11 12,215,000 126.977379
Beijing 39.90601 China 12 12,033,000 116.387909
Karachi 24.88978 Pakistan 13 11,774,000 67.28511
Delhi 24.6353 India 14 11,680,000 81.56905
Dhaka 23.709801 Bangladesh 15 10,979,000 90.407112
Manila 14.60962 Philippines 16 10,818,000 121.589
Cairo 30.8374 Egypt 17 10,772,000 31.25536
’saka 34.677471 Japan 18 10,609,000 135.50325
Rio de Janeiro -22.8901 Brazil 19 10,556,000 -43.216202
Tianjin 39.128399 China 20 10,239,000 117.185112
Jakarta -6.18287 Indonesia 21 9,815,000 106.829109
Paris 48.856925 France 22 9,638,000 2.34121
Istanbul 41.1144 Turkey 23 9,413,000 28.965521
Moscow 30.317137 Russian Fed 24 9,299,000 -97.56554
London 51.506325 United Kingdom 25 7,640,000 0.127144
Lima -12.436 Peru 26 7,443,000 -77.21217
Tehr„n 35.702591 Iran 27 7,380,000 51.408829
Bangkok 13.72635 Thailand 28 7,221,000 100.641418
Chicago 41.88415 USA 29 6,945,000 -87.632409
Bogot· 4.65637 Colombia 30 6,834,000 -74.11779
Hyderabad 17.4376 India 31 6,833,000 78.4706
Chennai 13.6397 India 32 6,639,000 80.24311
Essen 51.45181 Germany 33 6,559,000 7.1063
Ho Chi Minh City 10.75918 Vietnam 34 6,424,519 106.662498
Hangzhou 30.252501 China 35 6,389,000 120.165024
Hong Kong 22.411249 China 36 6,097,000 114.153542
Lahore 31.54991 Pakistan 37 6,030,000 74.327301
Shenyang 41.788509 China 38 5,681,000 123.40612
Changchun 43.88131 China 39 5,566,000 125.312622
Bangalore 12.97092 India 40 5,544,000 77.60482
Harbin 45.755199 China 41 5,475,000 126.62252
Chengdu 30.67 China 42 5,293,000 104.71022
Santiago -33.463039 Chile 43 5,261,000 -70.647942
Guangzhou 23.107389 China 44 5,162,000 113.267616
St ' Petersburg 30.317137 Russian Fed 45 5,132,000 -97.56554
Kinshasa -4.31642 DRC 46 5,068,000 15.29834
Baghd„d 33.328152 Iraq 47 4,796,000 44.386028
Jinan 36.65551 China 48 4,789,000 116.96701
Wuhan 30.572399 China 49 4,750,000 114.279121
Toronto 43.648565 Canada 50 4,657,000 -79.385329
Yangon 16.80389 Myanmar (Burma) 51 4,458,000 96.154694
Alger 36.765808 Algeria 52 4,447,000 3.3193
Philadelphia 39.95227 USA 53 4,398,000 -75.162369
Qingdao 36.87509 China 54 4,376,000 120.34272
Milano 45.468945 Italy 55 4,251,000 9.18103
Pusan 35.170429 South Korea 56 4,239,000 128.999481
Belo Horizonte -19.936501 Brazil 57 4,160,000 -43.9617
Almadabad 23.8539 India 58 4,154,000 72.615692
Madrid 40.4203 Spain 59 4,072,000 -3.705774
San Francisco 37.77916 USA 60 4,051,000 -122.420049
Alexandria 31.19224 Egypt 61 3,995,000 29.88987
Washington DC 38.89037 USA 62 3,927,000 -77.31959
Houston 29.76045 USA 63 3,918,000 -95.369784
Dallas 32.778155 USA 64 3,912,000 -96.795404
Guadalajara 20.68759 Mexico 65 3,908,000 -103.351079
Chongging 29.544001 China 66 3,896,000 106.522621
Medellin 6.23651 Colombia 67 3,831,000 -75.590279
Detroit 42.331685 USA 68 3,785,000 -83.47924
Handan 36.60194 China 69 3,763,000 114.470253
Frankfurt 50.112035 Germany 70 3,700,000 8.6834
Porto Alegre -30.39909 Brazil 71 3,699,000 -51.208
Hanoi 21.3195 Vietnam 72 3,678,000 105.819908
Sydney -33.869629 Australia 73 3,665,000 151.206955
Santo Domingo 39.922985 Domincian Republic 74 3,601,000 -97.820189
Singapore 1.29378 Singapore 75 3,587,000 103.853256
Casablanca 33.605381 Morocco 76 3,535,000 -7.63194
Katowice 50.256055 Poland 77 3,488,000 19.30948
Pune 18.52671 India 78 3,485,000 73.8616
Bangdung -6.91242 Indonesia 79 3,420,000 107.606911
Monterrey 25.630215 Mexico 80 3,416,000 -100.284894
MontrÈal 45.512288 Canada 81 3,401,000 -73.554392
Nagoya 35.14986 Japan 82 3,377,000 136.926224
Nanjing 32.485 China 83 3,375,000 118.778969
Abidjan 5.32339 CÙte d'Ivoire 84 3,359,000 -4.2627
Xi'an 31.644899 China 85 3,352,000 104.414009
Berlin 52.516074 Germany 86 3,337,000 13.376987
Riyadh 24.64039 Saudi Arabia 87 3,328,000 46.7533
Recife -8.775 Brazil 88 3,307,000 -34.9007
Dusseldorf 51.21563 Germany 89 3,251,000 6.776055
Ankara 39.94293 Turkey 90 3,190,000 32.86048
Melbourne -37.817532 Australia 91 3,188,000 144.967148
Salvador -12.996 Brazil 92 3,180,000 -38.494011
Dalian 38.94381 China 93 3,153,000 121.576523
Caracas 10.49605 Venezuela 94 3,153,000 -66.898277
Adis Abeba 9.2273 Ethiopia 95 3,112,000 38.746792
Athina 37.97615 Greece 96 3,103,000 23.736415
Cape Town -33.979012 South Africa 97 3,092,000 18.4823
Koln 50.941655 Germany 98 3.067,000 6.955065
Maputo -25.9681 Mozambique 99 3,017,000 32.58065
Napoli 40.839901 Italy 100 3,012,000 14.251852
Raw
README.md

Map Projection Tests with D3

Raw
index.html
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>100 Most Populated</title>
<script src="http://d3js.org/d3.v3.js"></script>
<script src="http://d3js.org/d3.geo.projection.v0.min.js"></script>
<script src="http://d3js.org/topojson.v0.min.js"></script>
<script src="script.js" type="text/javascript" defer></script>
<style type="text/css" media="screen">
svg {
background: #81C1FF;
}
path {
fill: #eee;
stroke: rgba(0,0,0,0.2);
}
circle {
fill: rgba(0,0,0,0.2);
stroke: rgba(0,0,0,0.5);
}
</style>
</head>
<body>
</body>
</html>
Raw
script.js
var width = 976,
height = 506;
var svg = d3.select("body").append("svg")
.attr("width", width)
.attr("height", height)
d3.json("world-110m.json", function(error, world) {
var countries = topojson.object(world, world.objects.countries);
var projection = d3.geo.naturalEarth()
.scale(180)
.translate([width / 2, height / 2])
var path = d3.geo.path()
.projection(projection)
var map = svg.append("g")
.attr("class", "map")
map.append("path")
.datum(countries)
.attr("d", path);
d3.csv('100_most_populated.csv', function(csv) {
locations = svg.append("g")
.attr("class", "locations");
csv.forEach(function(loc) {
var place_ll = projection([loc.longitude, loc.latitude]);
console.log(place_ll);
locations.append("circle")
.attr("r", 3)
.attr("cx", place_ll[0])
.attr("cy", place_ll[1])
})
})
});
Display the source blob
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Raw
world-110m.json
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