NXS-GRX-JPN · Eastern Asia · Regions

Hokkaido

First-level unit, Japan: people, area, the units inside and neighbours, with risk values when released.

Page GRX-JPN-REdition Baseline Edition 2026Evidence close 2026-09-30ISO JPN / JPStatus UN member stateValues not yet publishedMethod v1.0.0-draft
5,009,016
people (GHSL 2025)
78,290 km²
area
4.3%
of Japan
179
units inside
01Map

Inside Hokkaido

The 179 ADM2 units of Hokkaido.

FIG 1 · 179 units inside Hokkaido
Loading the map
Units rise with people per km² · GHSL 2025 raster · terrain · ADM1 ADM2Risk values when released
Lowest fifthLow fifthMiddle fifthHigh fifthHighest fifthNo data
Column height and colour show people per km² by unit, in quantile fifths among the units shown (the key on the map gives the breaks); units with no people on the grid are grey, not the lowest class; the raster beneath is the GHSL 2025 population grid. Source observations, not index values. Select a unit to drill into the units inside it; the path above the map climbs back.
02Figures

People, area and density

Source observations from open grids summed inside the boundary; not index values.

People · GHSL 2025

5,009,016

4.3% of Japan.

People · WorldPop 2025

4,836,379

-3% against GHSL; the two grids differ in method.

Area

78,290 km²

64 people per km².

Code

JP-01

Subdivision code as carried by the boundary source.

Low coastal zone

1,060,045

people on land at or below 10 m, 21.2% of the unit (GEBCO 2024 and GHSL 2025).

03Units

179 units inside Hokkaido

Each unit has its own page.

Boundary licences: Open Data Commons Open Database License 1.0 · Creative Commons Attribution-ShareAlike 2.0.

UnitCodePeople (GHSL)People (WorldPop)Area km²Per km²ShareCoast ≤ 10 m
Aashikawa–325,831328,866745.854376.5%0
Abashiri–19,53815,774470.97410.4%2,399
Abira–8,3536,478236.42350.2%421
Aibetsu–2,6531,665249.79110.1%0
Akabira–12,4137,384129.28960.2%0
Akaigawa–1,3001,139279.7250%0
Akkeshi–7,7396,096709.75110.2%1,396
Ashibetsu–16,0739,492863.56190.3%0
Ashoro–4,9443,7031,405.4140.1%0
Assabu–5,5254,051460.21120.1%874
Atsuma–5,0644,175404.63130.1%2,260
Betsukai–14,78112,4691,341.57110.3%691
Bibai–27,75817,705277.421000.6%897
Biei–9,9838,369675.7150.2%0
Bifuka–5,2323,621670.1680.1%0
Bihoro–20,68816,558437.48470.4%1,982
Biratori–4,6693,679741.9860.1%0
Chippubetsu–2,8172,16347.1600.1%0
Chitose–94,920104,321594.611601.9%7,542
Date–29,13526,074446.35650.6%3,352
Ebetsu–151,442153,130187.268093%76,226
Embetsu–967711589.2520%81
Engaru–21,81216,4591,332.93160.4%0
Eniwa–64,33868,237293.622191.3%2,929
Erimo–1,9501,597283.1470%93
Esashi–8,5336,3931,115.4180.2%947
Esashi–2,5371,856109.41230.1%403
Fukagawa–24,80217,828528.8470.5%0
Fukushima–78663518740%9
Furano–19,79416,300599.55330.4%0
Furubira–3,8852,136188.11210.1%173
Haboro–4,8753,667471.88100.1%873
Hakodate–272,929236,605676.424035.5%60,779
Hamanaka–4,1123,146426.44100.1%619
Hamatonbetsu–4,0613,111399.3100.1%1,823
Hidaka–13,2129,718990.9130.3%1,423
Higashikagura–5,7016,85368.44830.1%0
Higashikawa–3,0543,743246.52120.1%0
Hiroo–2,8571,865595.5450.1%35
Hokuryu–1,6381,020158.26100%0
Hokuto–28,47425,180396.94720.6%10,537
Honbetsu–10,1187,368390.84260.2%0
Horokanai–1,6631,135765.6720%0
Horonobe–2,4422,091573.6540%445
Ikeda–7,2885,397371.91200.1%504
Imakane–6,1114,283567.12110.1%20
Ishikari–57,32857,494721.13791.1%53,719
Iwamizawa–84,96570,313480.571771.7%18,348
Iwanai–15,11710,55370.482140.3%2,424
Kamifurano–11,5619,631236.65490.2%0
Kamikawa–4,4162,7461,047.740.1%0
Kaminokuni–2,4921,744547.1150%177
Kamishihoro–4,9214,692695.4770.1%0
Kamisunagawa–4,8212,33039.841210.1%0
Kamoenai–236170147.2920%1
Kembuchi–3,5222,489130.62270.1%0
Kikonai–4,7502,554221.41210.1%699
Kimobetsu–2,3001,670188.36120%0
Kitahiroshima–78,17979,967118.666591.6%11,011
Kitami–118,004106,5941,424.02832.4%1,434
Kiyosato–4,6173,485402.23110.1%115
Koshimizu–4,5723,707286.36160.1%533
Kunneppu–7,1225,987190.43370.1%0
Kuriyama–12,7249,348203.27630.3%0
Kuromatsunai–3,0962,493345.1690.1%15
Kushiro–151,085148,3671,360.831113%88,110
Kushiro–23,34522,436276.76840.5%10,640
Kutchan–14,87214,538260.62570.3%0
Kyogoku–3,8282,313231.48170.1%0
Kyowa–3,9893,606304.5130.1%948
Makkari–1,8161,786114.48160%0
Makubetsu–20,47720,316477.03430.4%263
Mashike–814845368.8920%0
Matsumae–2,6511,384292.2890.1%9
Memuro–18,86118,430513370.4%0
Mikasa–10,3947,360302.06340.2%0
Minamifurano–2,8652,224664.0840.1%0
Mombetsu–20,70216,173834.75250.4%2,247
Mori–7,6225,604367.89210.2%126
Moseushi–1,9081,52948.43390%0
Mukawa–9,7656,476710.8140.2%3,343
Muroran–40,29144,26880.615000.8%5,033
Naganuma–12,03110,236168.05720.2%7,221
Naie–5,5203,98488.2630.1%0
Nakafurano–8,7196,393108.32800.2%0
Nakagawa–1,8451,191593.4530%43
Nakasatsunai–4,0864,114292.13140.1%0
Nakashibetsu–23,28822,569683.32340.5%163
Nakatonbetsu–1,8071,324397.8250%0
Nanae–35,87133,693216.711660.7%5,419
Nanporo–13,1299,36381.131620.3%10,064
Nayoro–29,85324,884534.23560.6%0
Nemuro–23,80018,647445.39530.5%1,303
Niikappu–8,6696,647584.56150.2%905
Niki–1,9101,534168.14110%0
Niseko–4,7444,757196.61240.1%0
Nishiokoppe–1,1131,016307.2440%0
Noboribetsu–20,79018,165212.05980.4%2,651
Numata–4,3283,052282.73150.1%0
Obihiro–173,772180,753618.142813.5%0
Obira–3,9302,674626.660.1%174
Oketo–2,9211,953526.8460.1%0
Okoppe–4,0663,075361.95110.1%587
Okushiri–2,1701,406142.36150%11
Oshamambe–4,5213,049311.51150.1%1,540
Otaru–131,921104,406243.45422.6%17,557
Otobe–1,127904162.470%20
Otofuke–36,61535,810465.16790.7%0
Otoineppu–989473274.9740%0
Oumu–1,8541,435635.5630%479
Ozora–7,8206,168342.82230.2%831
Pippu–3,8643,09186.72450.1%0
Rankoshi–5,5584,594449.09120.1%304
Rausu–3,4942,520397.5190.1%41
Rebun–1,7431,36580.85220%37
Rikubetsu–2,7062,080607.7440.1%0
Rishiri–53236776.1170%3
Rishirifuji–2,0901,458105.48200%4
Rumoi–11,4107,894297.04380.2%1,272
Rusutsu–1,8061,672119.85150%0
Samani–983796363.9630%419
Sapporo–1,764,6391,928,7291,119.21,57735.2%490,988
Sarabetsu–3,7883,400175.98220.1%0
Saroma–5,5344,113403.99140.1%177
Sarufutsu–2,4152,151588.5640%723
Setana–7,9605,218637.59120.2%1,379
Shakotan–1,536905237.2160%8
Shari–4,1023,324736.1460.1%1,734
Shibecha–8,2216,7031,097.5870.2%156
Shibetsu–20,37614,7521,117.19180.4%0
Shibetsu–4,9944,233624.180.1%383
Shihoro–6,6885,781258.37260.1%0
Shikabe–3,9612,568110.57360.1%372
Shikaoi–5,3074,886403.36130.1%0
Shimamaki–1,516965436.5130%3
Shimizu–9,8248,465401.75240.2%0
Shimokawa–3,6392,633643.0460.1%0
Shimukappu–1,3501,174570.9520%0
Shinhidaka–19,80315,4331,146.1170.4%2,909
Shinshinotsu–2,9352,27377.97380.1%2,615
Shintoku–6,4545,2001,061.8260.1%0
Shintotsukawa–11,3549,489494.37230.2%0
Shiranuka–7,5385,149772.43100.2%1,477
Shiraoi–19,20014,512425.18450.4%3,813
Shiriuchi–4,8103,405196.69240.1%1,014
Shosanbetsu–1,070690279.2540%29
Sobetsu–2,6601,946201.55130.1%0
Sunagawa–21,26315,81678.52710.4%0
Suttu–2,6651,82594.75280.1%37
Taiki–5,5905,054814.4270.1%63
Takasu–6,7176,076139.39480.1%0
Takikawa–36,00031,036115.873110.7%0
Takinoue–2,8681,968765.2440.1%0
Teshikaga–8,1125,896772.72100.2%0
Teshio–2,0811,407352.7960%652
Tobetsu–30,88725,024422.45730.6%19,651
Tohma–8,6067,894204.42420.2%0
Tomakomai–135,247140,425561.132412.7%63,666
Tomamae–3,5362,480453.5580.1%331
Tomari–2703918230%1
Toyako–11,1168,358180.74620.2%1,047
Toyokoro–3,2772,678535.860.1%1,178
Toyotomi–4,2893,594519.6580.1%939
Toyoura–4,0782,946233.23170.1%134
Tsubetsu–5,2373,185715.3370.1%0
Tsukigata–4,6452,804149.77310.1%982
Tsurui–2,5102,583570.640.1%193
Urahoro–5,1743,514728.3770.1%605
Urakawa–12,3749,224693.26180.2%778
Urausu–2,2501,456101.5220%0
Uryu–3,0101,898191.08160.1%0
Utashinai–2,6581,34255.68480.1%0
Wakkanai–26,06421,640760.32340.5%5,368
Wassamu–3,6432,749224.56160.1%0
Yakumo–16,63712,093954.11170.3%5,414
Yoichi–22,42016,527140.181600.4%7,140
Yubari–10,6485,019761.61140.2%0
Yubetsu–9,4746,947496.79190.2%2,165
Yuni–5,4564,137133.52410.1%0
04Exposure

Open exposure layers

Count of H3 resolution-3 cells whose centre falls in the unit and that carry each Natural Earth layer.

Urban areas
1,471.3 H3 cells
Ports
5 H3 cells
Airports
1 H3 cells
Major roads
1,291.6 H3 cells
Railways
1,760.4 H3 cells
Rivers and lakes
165.1 H3 cells
05Risk

Risk values

Values when released. Subnational risk values for Hokkaido publish with the edition from The Global Risks Index. Until then, run a scenario on open data for Japan.
06Sources

Sources, credits and licences

ADM1 boundaries
geoBoundaries (William & Mary geoLab), gbOpen JPN ADM1, source: OpenStreetMap, Wambacher © OpenStreetMap contributors, licence: Open Data Commons Open Database License 1.0 (share-alike: this boundary layer is offered under the same licence) · source · represents 2017. Offered under the same licence (Open Data Commons Open Database License 1.0), kept as its own layer and not merged into other data.
ADM2 boundaries
geoBoundaries (William & Mary geoLab), gbOpen JPN ADM2, source: OpenStreetMap, Wambacher © OpenStreetMap contributors, licence: Creative Commons Attribution-ShareAlike 2.0 (share-alike: this boundary layer is offered under the same licence) · source · represents 2017. Offered under the same licence (Creative Commons Attribution-ShareAlike 2.0), kept as its own layer and not merged into other data.
Basemap
OpenFreeMap © OpenMapTiles (CC BY 4.0) · data © OpenStreetMap contributors (ODbL 1.0). Vector tiles requested by the reader’s browser, unmodified; the map falls back to plain land and sea when the host does not answer.
Terrain
Terrain Tiles (Mapzen, AWS Open Data), compiled from SRTM and other public elevation sources (NASA, USGS, ETOPO1, GMTED, EU-DEM under the Copernicus programme, Geoscience Australia, Kartverket, INEGI, LINZ, data.gov.uk). Display relief, not survey data.
People
GHSL 2025 population grid, European Commission Joint Research Centre (CC BY 4.0), shown at 0.05° and summed per unit; WorldPop 2025 (CC BY 4.0) as a second estimate. Units smaller than a grid cell take the density of the cell under them.
Countries
Natural Earth (public domain).
Elevation
GEBCO Compilation Group (2024) GEBCO 2024 Grid (public domain), reduced to 0.05°: the share of each cell’s land at or below 10 m, weighted by GHSL 2025 people for the low coastal zone.
Position
Boundaries are shown for location only and take no position on any boundary, name or status.
The Global Risks Index

Indicators · sources · country profiles

Not for profit

Our articles, maps and twins are free to read and free to republish. Readers and partners keep it that way.

Support our work

Who funds us

The Index is a not-for-profit institution. To ask who funds a piece of work, use the funding questions form.

Independence

Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.

Licence

Articles: CC BY 4.0. Figures, maps, twins and data: CC BY 4.0. Other uses: ask through the permissions form.