Historical chart and dataset showing China population density by year from 1961 to 2022.

Population density (people per sq. km of land area) in China was reported at 150 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

China: Population density, in people per sq. mile: The latest value from is people per sq. mile, unavailable from people per sq. mile in . In comparison, the world average is 0 people per sq. mile, based on data from countries. Historically, the average for China from to is people per sq. mile. The minimum value, people per sq. mile, was reached in while the maximum of people per sq. mile was recorded in .

This layer shows the Hong Kong population density in 2021 Population Census. It is a subset of the census data 2021 made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This layer shows the Mid Year Population Density within the 18 districts of Hong Kong. It is a subset of data made available by the Census and Statistics Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in CSV format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran’s I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of ‘martial arts boxings’, ‘Wushu hometowns’, ‘intangible cultural heritage boxings of Wushu’, ‘population education level’, ‘Per capita disposable income’, and ‘population density’ exhibited a descending trajectory, whilst ‘educational level of the populace’ inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

The urban spatial structure in this study refers to the combination of different categories of land use, and the purpose of the study is to reveal the intrinsic correlation characteristics between urban land use structural combination forms and urban functions. Through the integration of land and population maps and other multi-source data, with the help of exploratory spatial data analysis and other models, this research deals with the land use spatial structure characteristics of Changchun city and its coordination relationship with urban functions. Main conclusions of the study are as follows. The overall density of the land use in the central urban area of Changchun shows patterns of the core being higher than the periphery, the large-scale agglomeration being significant and the small-scale relatively scattered, and the pattern of the mixed land use function index has obvious differentiation characteristics. The study shows that, in the context of the spatial pattern, the overall coupling coordination degree of the land use structure index and the urban function index shows a trend of a gradual decrease, from the core to the periphery. In the context of category differences, the coupling coordination of the land use structure with the population distribution and the Baidu thermal distribution is relatively high, and the coupling coordination with various service facilities is relatively low. Finally, in the context of scale differences, all types of coupling coordination degrees have significant sensitivity to the spatial scales. A large scale significantly reflects the overall decrease in the coupling coordination degrees from the core to the periphery, while a small scale shows the polycentric pattern characteristics of the urban spatial structure.

Data for: Decision-making Analysis for Green Roof Layout and Functional Types with Optimal Expected Ecosystem Services Benefits in High-Density Cities: A Case Study in the Central City of Guangzhou, China. We use multi-source spatial data from various websites to evaluate ecosystem services demand in the main city of Guangzhou, China. The data includes: (1)Carbon dioxide (CO₂) emissions. Source: https://edgar.jrc.ec.europa.eu/. Accessed July 25, 2024. (2)Precipitation. Source: http://www.geodata.cn. Accessed July 19, 2024. (3)Air Quality Index (AQI). Source: http://www.cnemc.cn/. Accessed July 28, 2024. (4)Landsat 8 and 9 satellite imagery (30m resolution). Source: https://www.usgs.gov/. Accessed July 24, 2024. (5)Digital Elevation Model (DEM). Source: https://www.gscloud.cn/. Accessed July 20, 2024. (6)Land cover (30m resolution). Source: http://globeland30.org/home.html. Accessed July 19, 2024. (7)Road vector data. Source: https://ditu.amap.com/. Accessed July 12, 2024. (8)Building vector data. Source: https://lbsyun.baidu.com/products/map. Accessed January 8, 2024. (9)Population Density (100m resolution). Source: https://www.worldpop.org/. Accessed July 10, 2024. (10)POl vector data. Source: https://ditu.amap.com/. Accessed July 12, 2024.

Table S1. Phenotypic analysis of MFSL in two parents and RIL population of Chinese cabbage. (XLSX 95 kb)

Urban land structure, urban function measurement index selection, and data sources.

When we were first informed about doing a project with ArcGIS, I immediately thought of a project about the Chinese Mystery Snail. I had become mildly interested in these snails in the summer of 2019 after an invasives program, and I thought that it would be a great topic to research and collect data on. I knew that they were an invasive in Squam Lake, and I had seen them in the Squam River, so I was interested in finding out how common they were throughout the Squam area. Procedure:The first step in my procedure was doing some research on the Chinese Mystery Snail in New Hampshire. I wanted to find out the basic details about this snail as well as where it has been found. To my surprise, there didn't seem to be too much research on the extent of the spread. Since I had seen the snails in the Squam River, I initially thought to do my data collection in Little Squam Lake. However, as I checked the shores of public access points around the lake, I could not find any evidence of these snails. This caused me to move my study to the Squam River where I knew I would find snails. I then decided what sort of information I wanted to collect along with tracking the number and location of snails. I knew that I wanted to collect the depth where they were found, and I also decided to track the type of substrate they were found in. I also thought that I might be able to collect water samples from each location to test what pH, temperature, and other qualities of the water the snails may prefer, but I did not have the materials available to do so. I created a survey that included the depth, substrate, if they were alive or dead, the location, and a place to take photos of my findings. As I started to record data, I realized that some of the features on my survey were not best for the data I was trying to collect. As a result, I had to type the number of snails I found in the comments section rather than in a specific spot. I also did not have enough variation in my type of substrate, so I had to type in the different substrates I found in the "other" category. I only had time to primarily record data for one side of the river, so there are probably other points along the river on the other side where the snails are located. Also, the weather and light conditions were not the best on the day where I collected data, so there could also be spots where I missed snails due to my lack of visibility in the area. I recorded the data by using the Survey123 app on my phone, imputing my data there along with taking the pictures with each point. When I was out recording data, I used a kayak to get around, going slowly along the banks of the river to see if I could see any snails. When I found a snail, I would stop my kayak, take a picture of the snail(s) that I found, and then have my mom, who was kayaking with me, measure the depth of the snails while I inputted the other data into the survey. After I collected all of my data, I uploaded it into a map on ArcGIS Online, creating all of the points where I had found snails. Since I was kayaking and therefore did not have any wifi or service, there were some points that I had to manually shift a small amount because the GPS had been a few feet off and placed the points on land instead of in the water. I then searched for layers that showed the outline of the river and included that in my map. I also wanted to show the bathymetry of the Squam River, but there were no layers where this was recorded in the river, only in the lakes. After adjusting my points and starting to make the map, I realized that there were too many categories for just one map. This caused me to create three maps in order to show the data I wanted to include in my project. I chose to show the population density, the depth, and the type of substrate the snails were found in. For the maps showing the depth and the type of substrate, I also included the population density in order to show which depths and substrates the snails tended to prefer. I then created my Story Map, putting all of the maps in, along with quickly creating a few extra maps to help visualize the information. I included all of the research I had previously done, and did a little more research in areas where I felt I needed a bit more information. I included photos from my day collecting data into the Story Map and formatted it in order to make the most sense with clear transitions between subjects.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

The urban spatial structure in this study refers to the combination of different categories of land use, and the purpose of the study is to reveal the intrinsic correlation characteristics between urban land use structural combination forms and urban functions. Through the integration of land and population maps and other multi-source data, with the help of exploratory spatial data analysis and other models, this research deals with the land use spatial structure characteristics of Changchun city and its coordination relationship with urban functions. Main conclusions of the study are as follows. The overall density of the land use in the central urban area of Changchun shows patterns of the core being higher than the periphery, the large-scale agglomeration being significant and the small-scale relatively scattered, and the pattern of the mixed land use function index has obvious differentiation characteristics. The study shows that, in the context of the spatial pattern, the overall coupling coordination degree of the land use structure index and the urban function index shows a trend of a gradual decrease, from the core to the periphery. In the context of category differences, the coupling coordination of the land use structure with the population distribution and the Baidu thermal distribution is relatively high, and the coupling coordination with various service facilities is relatively low. Finally, in the context of scale differences, all types of coupling coordination degrees have significant sensitivity to the spatial scales. A large scale significantly reflects the overall decrease in the coupling coordination degrees from the core to the periphery, while a small scale shows the polycentric pattern characteristics of the urban spatial structure.

Distribution of markers and marker density across chromosomes in the common wheat map developed in Yanda1817 × Beinong6 RILs population.

The research on the vulnerability dataset of disaster bearing bodies in the China Pakistan Economic Corridor (domestic section) is based on multi-source data fusion, and a vulnerability evaluation system covering natural disasters and socio-economic systems has been constructed. This dataset integrates field survey data (infrastructure distribution, population density), satellite remote sensing data (surface deformation monitoring, vegetation coverage), and statistical yearbook data (GDP, disaster prevention investment), and forms a multidimensional vulnerability database through GIS spatial analysis, remote sensing interpretation, and data standardization processing. The research team has developed a three-dimensional evaluation index system that includes exposure, sensitivity, and adaptability. The exposure index covers physical elements such as the proportion of geological hazard prone areas and the density of transportation arteries; Sensitivity indicators involve socio-economic factors such as ecological vulnerability index and poverty incidence rate; The indicators of adaptability include emergency response capability, medical resource density, and other elements of disaster prevention and reduction capability. To improve the evaluation accuracy, the traditional vulnerability index model was improved by introducing the random forest algorithm for weight optimization, and the stability of the model was verified through Monte Carlo simulation. The analysis results show that there is significant spatial heterogeneity in the domestic section of the corridor: high vulnerability areas are concentrated in the Karakoram Pamir geologically active zone, driven by a combination of frequent extreme weather events, insufficient infrastructure disaster resistance standards, and weak regional economic resilience. The future research can be further extended to the high-altitude mountains along the "the Belt and Road". In combination with multi-scale remote sensing monitoring and socio-economic big data, we can deepen the research on the formation mechanism of cross-border disaster risk in the context of climate change, and provide scientific support for building a resilient Silk Road.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Niger, the Democratic Republic of Congo, and Chad, the population increase peaks at over three percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. However, African cities are currently growing at larger rates. Indeed, most of the fastest-growing cities in the world are located in Sub-Saharan Africa. Gwagwalada, in Nigeria, and Kabinda, in the Democratic Republic of the Congo, ranked first worldwide. By 2035, instead, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria.

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.