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.

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.

Japan data available from WorldPop here.

Japan: Population density, people per square km: The latest value from 2021 is 345 people per square km, a decline from 346 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Japan from 1961 to 2021 is 325 people per square km. The minimum value, 256 people per square km, was reached in 1961 while the maximum of 351 people per square km was recorded in 2004.

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 total population in Japan was estimated at 123.6 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Japan Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

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.

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

Population density (people per sq. km of land area) in Japan was reported at 343 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

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.

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

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.

Over the last decade, Japan’s population has aged more and more, to the point where more than a quarter of Japanese were 65 years and older in 2022. Population growth has stopped and even reversed, since it’s been in the red for several years now.

It’s getting old

With almost 30 percent of its population being elderly inhabitants, Japan is considered the “oldest” country in the world today. Japan boasts a high life expectancy, in fact, the Japanese tend to live longer than the average human worldwide. The increase of the aging population is accompanied by a decrease of the total population caused by a sinking birth rate. Japan’s fertility rate has been below the replacement rate for many decades now, mostly due to economic uncertainty and thus a decreasing number of marriages.

Are the Japanese invincible?

There is no real mystery surrounding the ripe old age of so many Japanese. Their high average age is very likely due to high healthcare standards, nutrition, and an overall high standard of living – all of which could be adopted by other industrial nations as well. But with high age comes less capacity, and Japan’s future enemy might not be an early death, but rather a struggling social network.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

Urban population (% of total population) in Japan was reported at 92.13 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Urban population (% of total) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

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')

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

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)上に生産年齢人口に占める割合（15～64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生産年齢人口に占める割合（15～64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。

In 1938, the year before the outbreak of the Second world War, the countries with the largest populations were China, the Soviet Union, and the United States, although the United Kingdom had the largest overall population when it's colonies, dominions, and metropole are combined. Alongside France, these were the five Allied "Great Powers" that emerged victorious from the Second World War. The Axis Powers in the war were led by Germany and Japan in their respective theaters, and their smaller populations were decisive factors in their defeat. Manpower as a resource In the context of the Second World War, a country or territory's population played a vital role in its ability to wage war on such a large scale. Not only were armies able to call upon their people to fight in the war and replenish their forces, but war economies were also dependent on their workforce being able to meet the agricultural, manufacturing, and logistical demands of the war. For the Axis powers, invasions and the annexation of territories were often motivated by the fact that it granted access to valuable resources that would further their own war effort - millions of people living in occupied territories were then forced to gather these resources, or forcibly transported to work in manufacturing in other Axis territories. Similarly, colonial powers were able to use resources taken from their territories to supply their armies, however this often had devastating consequences for the regions from which food was redirected, contributing to numerous food shortages and famines across Africa, Asia, and Europe. Men from annexed or colonized territories were also used in the armies of the war's Great Powers, and in the Axis armies especially. This meant that soldiers often fought alongside their former-enemies. Aftermath The Second World War was the costliest in human history, resulting in the deaths of between 70 and 85 million people. Due to the turmoil and destruction of the war, accurate records for death tolls generally do not exist, therefore pre-war populations (in combination with other statistics), are used to estimate death tolls. The Soviet Union is believed to have lost the largest amount of people during the war, suffering approximately 24 million fatalities by 1945, followed by China at around 20 million people. The Soviet death toll is equal to approximately 14 percent of its pre-war population - the countries with the highest relative death tolls in the war are found in Eastern Europe, due to the intensity of the conflict and the systematic genocide committed in the region during the war.