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. Data and Resources TIFF Japan - Population density (2015) DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid...

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

The population density in Japan stood at 343.28 people in 2022. Between 1961 and 2022, the population density rose by 86.79 people, though the increase followed an uneven trajectory rather than a consistent upward trend.

Actual value and historical data chart for Japan Population Density People Per Sq Km

Japan JP: Population Density: People per Square Km data was reported at 347.778 Person/sq km in 2017. This records a decrease from the previous number of 348.350 Person/sq km for 2016. Japan JP: Population Density: People per Square Km data is updated yearly, averaging 337.674 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 351.339 Person/sq km in 2008 and a record low of 258.912 Person/sq km in 1961. Japan JP: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Historical dataset showing Japan population density by year from 1961 to 2022.

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).

View yearly updates and historical trends for Japan Population Density. Source: World Bank. Track economic data with YCharts analytics.

In 2020, the population of Tokyo Metropolis amounted to over ***** inhabitants per square kilometer. The number increased from approximately ***** inhabitants per square kilometer in 2000.

Japan JP: Population Density: Inhabitants per sq km data was reported at 342.790 Person in 2022. This records a decrease from the previous number of 344.310 Person for 2021. Japan JP: Population Density: Inhabitants per sq km data is updated yearly, averaging 348.220 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 351.400 Person in 2008 and a record low of 339.030 Person in 1990. Japan JP: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.GGI: Social: Demography: OECD Member: Annual.

Context

Global Map is a set of basic geospatial information at the scale of 1:1 million, which was developed and verified by National Geospatial Information Authorities (NGIAs) in the world so that it is considered as “authoritative data.” Global Mapping Project is a collaborative international project of developing Global Map for sustainable development, environmental protection and disaster mitigation.

The International Steering Committee for Global Mapping (ISCGM) was established to implement the Project. The Geospatial Information Authority of Japan (GSI) served as the Secretariat of ISCGM for the whole duration of the Committee from February 1996 to March 2017, and supported the Project activities.

Recognizing that the objective of Global Mapping Project was mostly achieved by the collective efforts of ISCGM and the participating NGIAs, the 23rd ISCGM meeting held in August, 2016 adopted the resolution of dissolving ISCGM and transferring the Global Map data to the Geospatial Information Section of the United Nations. Thus, Global Mapping Project came to end.

Content

This dataset contains geospatial vector and raster data across the map of Japan. Each zip file contains a portion (or all) of the data layers for the specific map version.

Filename breakdown:
'gm-jpn-ve_u_1_0.zip'
'GlobalMap - Japan - Layer _ Version _ Version_Num .zip'

Acknowledgements

This data is pulled directly from the Geospatial Information Authority of Japan website (http://www.gsi.go.jp/kankyochiri/gm_japan_e.html). To see more information on licensing, please visit the website's Terms of Use.

From Terms of Use:
Information made available on this website (hereinafter referred to as “Content”) may be freely used, copied, publicly transmitted, translated or otherwise modified on condition that the user complies with provisions 1) to 7) below. Commercial use of Content is also permitted.

Cover photo by David Edelstein on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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

Total population and land area of eight Japanese regions.

Dataset

This dataset was created by leeDataWhiz

Released under Attribution 4.0 International (CC BY 4.0)

Contents

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

Spatial capture–recapture models (SCRs) provide an integrative statistical tool for analyzing animal movement and population patterns. Although incorporating home range formation with a theoretical basis of animal movement into SCRs can improve the prediction of animal space use in a heterogeneous landscape, this approach is challenging owing to the sparseness of recapture events. In this study, we developed an advection–diffusion capture–recapture model (ADCR), which is an extension of SCRs incorporating home range formation with advection–diffusion formalism, providing a new framework to estimate population density and landscape permeability. we tested the unbiasedness of the estimator using simulated capture–recapture data generated by a step selection function. We also compared accuracy of population density estimates and home range shapes with those from an SCR incorporating the least-cost path. In addition, ADCR was applied to real dataset of Asiatic black bear in Japan to demonstrate the capacity of the ADCR to detect geographical barriers that constrain animal movements. Population density, permeability, and home range estimates of ADCR were unbiased over randomly determined sets of true parameters. Although the accuracy of density estimates by ADCR was nearly identical to those of existing models, the home range shape could be predicted more accurately by ADCR than by an SCR incorporating the least-cost path. For the application to bear dataset, ADCR could detect the effect of water body as a barrier of movement which is consistent with previous population genetic studies. ADCR provides unique opportunities to elucidate both individual- and population-level ecological processes from capture–recapture data. By offering a formal link with step selection functions to estimate animal movement, it is suitable for simultaneously modeling with capture–recapture data and animal movement data. This study provides a basis for studies of the interplay between animal movement processes and population patterns. Methods Study site Our survey was conducted in the eastern Toyama prefecture, Japan. Our study site locates at the western foot of Tateyama mountains and partly overlapped to the Chubusangaku National Park. It contains a wide range of topography from lowland, hill to mountains. In the hilly area, agricultural lands along the rivers divide the forest landscape. The deciduous coniferous trees (Fagus crenata, Quercus crispula and Q. serrata) which offer food for bears in autumn are dominant species of the forest (Arimoto et al. 2011). As in other parts of Japan, a hard crop of acorns causes behavioral changes in black bears that increase conflicts with human (Ohnishi et al. 2011). Survey design From 2013 to 2015, we conducted a camera trap capture-recapture survey at 86 locations in the forest (Fig. S1). The survey were conducted from May to October, which is active season for bears. In each location, we set a camera trap (Trophycam ; Bushnell Outdoor Products, Overland Park) with video-recording mode. The duration of video was 30 seconds, and lag time after a trigger was set to minimal value. For efficient photographing of a chest mark as a key to individual recognition, we used an odor stimulant (mixture of honey and red wine) to encourage bears to stand up in front of camera by the protocol shown by Higashide et al. (2013). The odor stimulant was filled in a plastic bottle covered by a robust polyvinyl chloride tubing and fixed to the surrounding trees for protection from bear attachs. We visited each location every one to two months to replace batteries and SD cards and to refill the odor stimulant. The records of the same individual at a location within 60 minutes were grouped into a detection event. An image library of chest marks was developed from the video footage taken, and identical individuals were matched manually (Higashide et al. 2012). For fitting the capture recapture models, we aggregated the numbers of detections for each camera trap, individual and year.

This dataset shows the population of Japanese descent, by county, in 1945. The data was manually digitized from Table 13: Number of Evacuees Known to Have Returned to West Coast States Compared with 1940 Population of Japanese Descent by County, and Post Office Address: California, Washington and Oregon in The Evacuated People: A Quantitative Description, a report published by the War Relocation Authority (the civilian agency that oversaw the forced relocation/internment program) in 1946.The original scanned tables are available on InternmentArchives.org.Featured in Justice Deferred.

In the past decade, Japan’s degree of urbanization has leveled off at around 92.04 percent. This means that less than 10 percent of Japan’s population of 126 million inhabitants do not live in an urban setting. Japan is well above the degree of urbanization worldwide, which is 55 percent. Japan is also known for its high population density: In 2017, it amounted to an eye-watering 347.78 inhabitants per square kilometer - however, it is not even among the top twenty countries with the highest population density worldwide. That ranking is lead by Monaco, followed by China, and Singapore. Japan’s aging population The main demographic challenge that Japan currently faces is an aging population, as the number of inhabitants over 65 years old is an increasing percentage of the population. As of 2018, Japan is the country with the largest percentage of total population over 65 years, and life expectancy at birth there is about 84 years. Simultaneously, the birth rate in Japan is declining, resulting in negative population growth in recent years. One method Japan is using to address these demographic shifts is by investing in automated work processes; it's one of the top countries interested in collaborative robots.

Content

The data provides the population of Japan as collected by the official Japanese government from 1920 to 2015. It is given by year, prefecture, age range, and gender.

Inspiration

Can the data be used to answer questions such as the following?

• When and how did mass movement of population occurred from rural to urban areas?
• Which prefectures are rural? Which are urban?
• Were there gender and/or age differences in the movement of population?
• In what manner is the population shifting toward aged people?
• And much more.

Method of Editing and Translation

The following script written by the dataset owner was used:

import pandas as pd
import numpy as np
import re

japan_census = pd.read_csv('~/Downloads/c03.csv', encoding = 'SJIS')
# Eliminate a note
japan_census = japan_census.iloc[:-1]
# Eliminate the sums across prefectures
japan_census = japan_census[japan_census['年齢5歳階級'] != '総数']

def prefecture(japanese):
 return {
  '北海道': 'Hokkaido',
  '青森県': 'Aomori Prefecture',
  '岩手県': 'Iwate Prefecture',
  '宮城県': 'Miyagi Prefecture',
  '秋田県': 'Akita Prefecture',
  '山形県': 'Yamagata Prefecture',
  '福島県': 'Fukushima Prefecture',
  '茨城県': 'Ibaraki Prefecture',
  '栃木県': 'Tochigi Prefecture',
  '群馬県': 'Gunma Prefecture',
  '埼玉県': 'Saitama Prefecture',
  '千葉県': 'Chiba Prefecture',
  '東京都': 'Tokyo Metropolis',
  '神奈川県': 'Kanagawa Prefecture',
  '新潟県': 'Niigata Prefecture',
  '富山県': 'Toyama Prefecture',
  '石川県': 'Ishikawa Prefecture',
  '福井県': 'Fukui Prefecture',
  '山梨県': 'Yamanashi Prefecture',
  '長野県': 'Nagano Prefecture',
  '岐阜県': 'Gifu Prefecture',
  '静岡県': 'Shizuoka Prefecture',
  '愛知県': 'Aichi Prefecture',
  '三重県': 'Mie Prefecture',
  '滋賀県': 'Shiga Prefecture',
  '京都府': 'Kyoto Prefecture',
  '大阪府': 'Osaka Prefecture',
  '兵庫県': 'Hyogo Prefecture',
  '奈良県': 'Nara Prefecture',
  '和歌山県': 'Wakayama Prefecture',
  '鳥取県': 'Tottori Prefecture',
  '島根県': 'Shimane Prefecture',
  '岡山県': 'Okayama Prefecture',
  '広島県': 'Hiroshima Prefecture',
  '山口県': 'Yamaguchi Prefecture',
  '徳島県': 'Tokushima Prefecture',
  '香川県': 'Kagawa Prefecture',
  '愛媛県': 'Ehime Prefecture',
  '高知県': 'Kochi Prefecture',
  '福岡県': 'Fukui Prefecture',
  '佐賀県': 'Saga Prefecture',
  '長崎県': 'Nagasaki Prefecture',
  '熊本県': 'Kumamoto Prefecture',
  '大分県': 'Oita Prefecture',
  '宮崎県': 'Miyazaki Prefecture',
  '鹿児島県': 'Kagoshima Prefecture',
  '沖縄県': 'Okinawa Prefecture',
 }.get(japanese)

japan_census_translated = pd.DataFrame()
japan_census_translated['Year'] = japan_census['西暦（年）'].astype('int')
japan_census_translated['Prefecture'] = japan_census['都道府県名'].map(lambda x: prefecture(x))
japan_census_translated[['Age Lower Bound', 'Age Upper Bound']] = [
 [m.group(1), m.group(2)] for m in japan_census['年齢5歳階級'].map(lambda x: re.search('(\d+)\D+(\d+)?', x))
]
japan_census_translated = pd.DataFrame(
 np.repeat(japan_census_translated.values, 2, axis = 0),
 columns = japan_census_translated.columns
)
japan_census_translated[['Gender', 'Population']] = [
 x for _, row in japan_census.iterrows() for x in [
  ['Male', int(row.loc['人口（男）'])],
  ['Female', int(row.loc['人口（女）'])],
 ]
]
print(japan_census_translated)
japan_census_translated.to_csv('japanese_census.csv')

BackgroundDespite evidence that neighbourhood conditions affect residents' health, no prospective studies of the association between neighbourhood socio-demographic factors and all-cause mortality have been conducted in non-Western societies. Thus, we examined the effects of areal deprivation and population density on all-cause mortality in Japan.MethodsWe employed census and survival data from the Japan Public Health Center-based Prospective Study, Cohort I (n = 37,455), consisting of middle-aged residents (40 to 59 years at the baseline in 1990) living in four public health centre districts. Data spanned between 1990 and 2010. A multilevel parametric proportional-hazard regression model was applied to estimate the hazard ratios (HRs) of all-cause mortality by two census-based areal variables —areal deprivation index and population density—as well as individualistic variables such as socioeconomic status and various risk factors.ResultsWe found that areal deprivation and population density had moderate associations with all-cause mortality at the neighbourhood level based on the survival data with 21 years of follow-ups. Even when controlling for individualistic socio-economic status and behavioural factors, the HRs of the two areal factors (using quartile categorical variables) significantly predicted mortality. Further, this analysis indicated an interaction effect of the two factors: areal deprivation prominently affects the health of residents in neighbourhoods with high population density.ConclusionsWe confirmed that neighbourhood socio-demographic factors are significant predictors of all-cause death in Japanese non-metropolitan settings. Although further study is needed to clarify the cause-effect relationship of this association, the present findings suggest that health promotion policies should consider health disparities between neighbourhoods and possibly direct interventions towards reducing mortality in densely populated and highly deprived neighbourhoods.