Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

The population density in Japan saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 343.28 inhabitants per square kilometer. But still, the population density reached its lowest value of the observation period in 2022. Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Mongolia.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Japan: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

This map shows population density in Japan in 2015, by Country, Prefecture, Municipality, and Block. Population density is shown by people per square kilometer. The national average population density of Japan is 337 people per square kilometer.The pop-up is configured to show the following information at each geography level:Population densityTotal populationTotal householdsPopulation counts by age groupsPopulation counts by genderThe source of this data is Esri Japan. The vintage is 2015.Additional Esri Resources:Esri DemographicsPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM_popmap10adj_v2.tif = Vietnam (VNM) population count map for 2010 (popmap10) adjusted to match UN national estimates (adj), version 2 (v2). DATE OF PRODUCTION: January 2013

The ongoing multi-wave COVID-19 pandemic has disproportional impacts on people with different demographic and socioeconomic background, and their access to healthcare facilities. Vulnerable neighborhoods with low healthcare access are places most needed for the enhancement of medical resources and services. Measuring vulnerability to COVID-19 and healthcare accessibility at the fine-grained level serves as the foundation for spatially explicit health planning and policy making in response to future public health crisis. Despite of its importance, the evaluation of vulnerability and healthcare accessibility is insufficient in Japan—a nation with high population density and super-aging challenge. Drawing on the latest 2022 census data at the smallest statistical unit, as well as transport network, medical and digital cadastral data, land use maps, and points of interest data, our study reformulates the concept of vulnerability in the context of COVID-19 and constructs the first fine-grained measure of vulnerability and healthcare accessibility in Tokyo Metropolis, Japan—the most popular metropolitan region in the world. We delineate the vulnerable neighborhoods with low healthcare access and further evaluate the disparity in healthcare access and built environment of areas at different levels of vulnerability. Our outcome datasets and findings provide nuanced and timely evidence to government and health authorities to have a holistic and latest understanding of social vulnerability to COVID-19 and healthcare access at a fine-grained level. Our analytical framework can be employed to different geographic contexts, guiding through the place-based health planning and policy making in the post-COVID era and beyond.

地図(マップ)上に高齢者人口の割合の統計データを市区町村別で色分け表示しています。過去から現在までの高齢者人口の割合の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの市区町村が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に年少人口の割合の統計データを都道府県別で色分け表示しています。過去から現在までの年少人口の割合の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に生産年齢人口の割合の統計データを都道府県別で色分け表示しています。過去から現在までの生産年齢人口の割合の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に年少人口に占める割合（０～14歳）の統計データを市区町村別で色分け表示しています。過去から現在までの年少人口に占める割合（０～14歳）の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの市区町村が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に高齢者人口の割合の統計データを都道府県別で色分け表示しています。過去から現在までの高齢者人口の割合の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。