number

cartodb_id_

geometry

the_geom_GEO

string

field_1_

string

field_10_

string

field_11_

string

field_12_

string

field_13_

string

field_14_

string

field_15_

string

field_16_

string

field_17_

string

field_18_

string

field_19_

string

field_2_

string

field_20_

string

field_21_

string

field_22_

string

field_23_

string

field_24_

string

field_3_

string

field_4_

string

field_5_

string

field_6_

string

field_7_

string

field_8_

string

field_9_

geometry

the_geom_webmercator_GEO

179
GeoJSON
466
130
158
222
363
489
608
746
910
1,142
1,486
Mali
1,933
2,515
3,270
4,175
5,231
21818
Bamako
12.65
-8
89
111
GeoJSON
788
GeoJSON
156
13
20
30
46
69
104
156
235
354
438
China
534
649
757
840
901
20771
Yongzhou
279
26.44
111.61
6
9
GeoJSON
1289
GeoJSON
156
7
10
15
23
34
51
75
137
250
298
China
344
396
447
493
530
23869
Dafeng
52
33.20
120.46
3
5
GeoJSON
1
GeoJSON
Country Code
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
Country or area
2010
2015
2020
2025
2030
City Code
Urban Agglomeration
Note
Latitude
Longitude
1950
1955
GeoJSON
2
GeoJSON
392
16,679
20,284
23,298
26,615
28,549
30,304
32,530
33,587
34,450
35,622
Japan
36,834
38,001
38,323
37,876
37,190
21671
Tokyo
325
35.6895
139.69171
11,275
13,713
GeoJSON
3
GeoJSON
356
2,283
2,845
3,531
4,426
5,558
7,325
9,726
12,407
15,732
18,670
India
21,935
25,703
29,348
32,727
36,060
21228
Delhi
318
28.66667
77.21667
1,369
1,782
GeoJSON
4
GeoJSON
156
6,820
6,428
6,036
5,627
5,966
6,847
7,823
10,450
13,959
16,763
China
19,980
23,741
27,137
29,442
30,751
20656
Shanghai
202
31.22
121.46
4,301
5,846
GeoJSON
5
GeoJSON
76
3,970
5,494
7,620
9,614
12,089
13,395
14,776
15,913
17,014
18,288
Brazil
19,660
21,066
22,119
22,899
23,444
20287
São Paulo
-23.55
-46.64
2,334
3,044
GeoJSON
6
GeoJSON
356
4,060
4,854
5,811
7,082
8,658
10,391
12,436
14,310
16,367
17,891
India
19,422
21,043
22,838
25,207
27,797
21206
Mumbai (Bombay)
19.073975
72.880838
2,857
3,432
GeoJSON
7
GeoJSON
484
5,479
6,969
8,831
10,734
13,028
14,278
15,642
17,017
18,457
19,276
Mexico
20,132
20,999
21,868
22,916
23,865
21853
Ciudad de México (Mexico City)
330
19.427318
-99.141869
3,365
4,294
GeoJSON
8
GeoJSON
156
3,900
4,582
4,426
4,828
5,366
6,017
6,788
8,305
10,162
12,813
China
16,190
20,384
24,201
26,494
27,706
20464
Beijing
47
39.91
116.40
1,671
2,365
GeoJSON
9
GeoJSON
392
10,615
13,065
15,272
16,298
17,028
17,583
18,389
18,940
18,660
18,762
Japan
19,492
20,238
20,523
20,348
19,976
206459
Kinki M.M.A. (Osaka)
324
34.675834
135.553823
7,005
8,623
GeoJSON
10
GeoJSON
818
3,680
4,738
5,585
6,450
7,349
8,328
9,892
11,962
13,626
15,174
Egypt
16,899
18,772
20,568
22,432
24,502
22812
Al-Qahirah (Cairo)
313
30.04
31.24
2,494
3,029
GeoJSON
11
GeoJSON
840
14,164
15,177
16,191
15,880
15,601
15,827
16,086
16,943
17,813
18,087
United States of America
18,365
18,593
18,793
19,314
19,885
23083
New York-Newark
40.717042
-74.003663
12,338
13,219
GeoJSON
12
GeoJSON
50
508
821
1,374
2,221
3,266
4,660
6,621
8,332
10,285
12,331
Bangladesh
14,731
17,598
20,989
24,331
27,374
20119
Dhaka
21
23.71
90.41
336
409
GeoJSON
13
GeoJSON
586
1,853
2,405
3,119
3,989
5,048
6,033
7,147
8,467
10,032
11,885
Pakistan
14,081
16,618
19,230
22,009
24,838
22044
Karachi
24.9056
67.0822
1,055
1,419
GeoJSON
14
GeoJSON
32
6,598
7,317
8,105
8,745
9,422
9,959
10,513
11,390
12,407
13,330
Argentina
14,246
15,180
15,894
16,479
16,956
20058
Buenos Aires
2
-34.61
-58.40
5,098
5,799
GeoJSON
15
GeoJSON
356
5,652
6,261
6,926
7,888
9,030
9,946
10,890
11,924
13,058
13,702
India
14,283
14,865
15,726
17,285
19,092
21211
Kolkata (Calcutta)
22.533455
88.356045
4,513
5,055
GeoJSON
16
GeoJSON
792
1,453
2,001
2,772
3,600
4,397
5,407
6,552
7,665
8,744
10,513
Turkey
12,703
14,164
15,099
15,959
16,694
22691
Istanbul
41.0138
28.9497
967
1,249
GeoJSON
17
GeoJSON
156
2,275
2,188
2,237
2,545
2,961
3,446
4,011
5,615
7,863
9,454
China
11,244
13,332
15,233
16,571
17,380
20484
Chongqing
68
29.56
106.55
1,567
1,913
GeoJSON
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global_city_population_1

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  • 1,693 rows
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