🇵🇱 폴란드 English Dataset "Population distribution - demographics 2011. (grid)", represents the distribution of the population in relation to the cells of the kilometre grid. Population data were compiled according to the national definition.

Thailand population density for 2021 was 140.15, a 0.18% increase from 2020.

<ul style='margin-top:20px;'>

<li>Thailand population density for 2020 was <strong>139.90</strong>, a <strong>0.24% increase</strong> from 2019.</li>
<li>Thailand population density for 2019 was <strong>139.58</strong>, a <strong>0.25% increase</strong> from 2018.</li>
<li>Thailand population density for 2018 was <strong>139.22</strong>, a <strong>0.32% increase</strong> from 2017.</li>
</ul>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.

In 2024, Seoul had the highest population density of all provinces in South Korea, with about ****** people per square kilometer. The port city of Busan, which lies 300 kilometers southeast of Seoul, followed with about ***** residents per square kilometer. With 90 people per square kilometer, Gangwon was the province with the lowest population density. Population of Seoul The capital of South Korea, Seoul, is the country's largest city with a population of nearly 9.5 million people, meaning that about 20 percent of South Korea's total population live in Seoul. Together with the surrounding Gyeonggi Province and Incheon Metropolitan Area, the greater Seoul region (or Seoul Capital Area) is home to half of the total population of South Korea. This region also forms one of the largest metropolitan areas in the world. Solving the problem of overpopulation in Seoul One of the major problems stemming from overpopulation in Seoul is the housing shortage, leading to a significant surge in real estate prices. Over the past few years, several efforts have been made to curb the excessive population concentration and to solve the associated economic and social problems. In 2007, for example, former President Roh Moo-hyun attempted to move the country's administrative capital to Sejong, which is located 120 kilometers south of Seoul. Although the grand plan did not fully work out, around 40 central administrative agencies have since been moved from Seoul to Sejong, turning the city into the de facto administrative capital of South Korea.

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

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

Chile population density for 2021 was 26.22, a 1% increase from 2020.

<ul style='margin-top:20px;'>

<li>Chile population density for 2020 was <strong>25.96</strong>, a <strong>1.37% increase</strong> from 2019.</li>
<li>Chile population density for 2019 was <strong>25.61</strong>, a <strong>1.81% increase</strong> from 2018.</li>
<li>Chile population density for 2018 was <strong>25.15</strong>, a <strong>1.81% increase</strong> from 2017.</li>
</ul>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.

United States US: Population Density: People per Square Km data was reported at 35.608 Person/sq km in 2017. This records an increase from the previous number of 35.355 Person/sq km for 2016. United States US: Population Density: People per Square Km data is updated yearly, averaging 26.948 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 35.608 Person/sq km in 2017 and a record low of 20.056 Person/sq km in 1961. United States US: 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 United States – Table US.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;

This map shows the population density and total population in the United States in 2010. This is shown by state, county, tract, and block group. The color shows the population per square mile (population density), while the size of each feature shows the total population living there. This is a valuable way to represent population by understanding the quantity and density of the people living there. Areas with high population density are more tightly packed, while low population density means the population is more spread out.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

Japan population density for 2022 was 343.28, a 0.44% decline from 2021.

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<li>Japan population density for 2021 was <strong>344.81</strong>, a <strong>0.46% decline</strong> from 2020.</li>
<li>Japan population density for 2020 was <strong>346.40</strong>, a <strong>0.29% decline</strong> from 2019.</li>
<li>Japan population density for 2019 was <strong>347.42</strong>, a <strong>0.14% decline</strong> from 2018.</li>
</ul>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.

Thailand TH: Population Density: People per Square Km data was reported at 135.132 Person/sq km in 2017. This records an increase from the previous number of 134.791 Person/sq km for 2016. Thailand TH: Population Density: People per Square Km data is updated yearly, averaging 109.212 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 135.132 Person/sq km in 2017 and a record low of 55.245 Person/sq km in 1961. Thailand TH: 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 Thailand – Table TH.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 chart and dataset showing World population density by year from 1961 to 2022.

Want to live somewhere quiet? Then the Brussels-Capital Region maybe would not be the place for you. In a country where the population density was highly depended on the region, the Brussels-Capital Region far exceeded the others in terms of residents per square kilometer. Whereas in Brussels over 7,500 people lived per a square kilometer, in the Walloon Region this was only 276. In total, roughly 1.22 million inhabitants lived in the Brussels-Capital Region in 2022.

 Flemish Region has the highest number of inhabitants  

Although the Brussels-Capital Region had the highest population density, it was by no means Belgium’s region with the largest number of inhabitants. On the contrary: both the Flemish and the Walloon Regions had more inhabitants than the Brussels Region. In total, just over ten percent of Belgium’s population lived in Brussels, the rest was divided among Flanders (58 percent) and Wallonia (32 percent).

 Comparison to the other Benelux countries   

Belgium’s population density amounted to 375 inhabitants per square kilometer in 2021. This was significantly lower than the population density in neighboring country the Netherlands, where on average 519 inhabitants lived on a square kilometer. It was however higher than Luxembourg’s population density, which amounted to about 245 inhabitants per square kilometer. This was the lowest population density of all three Benelux countries.

In the last century, the global population has increased by billions of people. And it is still growing. Job opportunities in large cities have caused an influx of people to these already packed locations. This has resulted in an increase in population density for these cities, which are now forced to expand in order to accommodate the growing population. Population density is the average number of people per unit, usually miles or kilometers, of land area. Understanding and mapping population density is important. Experts can use this information to inform decisions around resource allocation, natural disaster relief, and new infrastructure projects. Infectious disease scientists use these maps to understand the spread of infectious disease, a topic that has become critical after the COVID-19 global pandemic.While a useful tool for decision and policymakers, it is important to understand the limitations of population density. Population density is most effective in small scale places—cities or neighborhoods—where people are evenly distributed. Whereas at a larger scale, such as the state, region, or province level, population density could vary widely as it includes a mix of urban, suburban, and rural places. All of these areas have a vastly different population density, but they are averaged together. This means urban areas could appear to have fewer people than they really do, while rural areas would seem to have more. Use this map to explore the estimated global population density (people per square kilometer) in 2020. Where do people tend to live? Why might they choose those places? Do you live in a place with a high population density or a low one?

In 2024, the share of the population in Taiwan aged 65 and older accounted for approximately 19.2 percent of the total population. While the share of old people on the island increased gradually over recent years, the percentage of the working-age population and the children have both declined. Taiwan’s aging population With one of the lowest fertility rates in the world and a steadily growing life expectancy, the average age of Taiwan’s population is increasing quickly, and the share of people aged 65 and above is expected to reach around 38.4 percent of the total population in 2050. This development is also reflected in Taiwan’s population pyramid, which shows that the size of the youngest age group is only half of the size of age groups between 40 and 60 years. The rapid aging of the populations puts a heavy burden on the social insurance system. Old-age dependency is expected to reach more than 70 percent by 2050, meaning that by then three people of working age will have to support two elders, compared to only one elder supported by four working people today. Aging societies in East Asia Today, many countries in East Asia have very low fertility rates and face the challenges of aging societies. This is especially true among those countries that experienced high economic growth in the past, which often resulted in quickly receding birth rates. Japan was one of the first East Asian countries witnessing this demographic change, as is reflected in its high median age. South Korea had the lowest fertility rate of all Asian countries in recent years, and with China, one of the largest populations on earth joined the ranks of quickly aging societies.

Samoa population density for 2022 was 77.43, a 0.69% increase from 2021.

<ul style='margin-top:20px;'>

<li>Samoa population density for 2021 was <strong>76.90</strong>, a <strong>0.87% increase</strong> from 2020.</li>
<li>Samoa population density for 2020 was <strong>76.24</strong>, a <strong>1.03% increase</strong> from 2019.</li>
<li>Samoa population density for 2019 was <strong>75.46</strong>, a <strong>1.06% increase</strong> from 2018.</li>
</ul>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.

In 2021, there were 5,333 people living in Amsterdam per square kilometer of land. In comparison, in 2020 there were 5,352 people per square kilometer of land, meaning that there was a small decrease.

Ascertaining the precise and accurate spatial distribution of population is essential in conducting effective urban planning, resource allocation, and emergency rescue planning. The random forest (RF) model is widely used in population spatialization studies. However, the complexity of population distribution characteristics and the limitations of the RF model in processing unbalanced datasets affect population prediction accuracy. To address these issues, a population spatialization model that integrates feature selection with an improved random forest is proposed herein. Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. The random forest was constructed using feature subsets that were selected via different feature selection methods, namely MIC-RF, RFECV-RF and MDA-RF. Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. Based on this model, a spatial population distribution dataset of the Southern Sichuan Economic Zone at a 500m resolution was generated. Finally, the population dataset generated in this study was compared and validated with the WorldPop dataset. The results showed that utilizing feature selection methods improves model accuracy to varying degrees compared with RF based on all factors, and the MDA-RF had the lowest MAPE of 0.174 and the highest R2 of 0.913 among them. Therefore, feature factors selection using the MDA method was considered the optimal feature subset. Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. Compared with the WorldPop dataset, the accuracy of the results predicted using the proposed method was enhanced. The MRE and RMSE of the WorldPop dataset were 57.24 and 23174.98, respectively, while the MRE and RMSE of the proposed method were 25.00 and 15776.50, respectively. This implies that the method proposed in this paper could simulate population spatial distribution more accurately.

Ascertaining the precise and accurate spatial distribution of population is essential in conducting effective urban planning, resource allocation, and emergency rescue planning. The random forest (RF) model is widely used in population spatialization studies. However, the complexity of population distribution characteristics and the limitations of the RF model in processing unbalanced datasets affect population prediction accuracy. To address these issues, a population spatialization model that integrates feature selection with an improved random forest is proposed herein. Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. The random forest was constructed using feature subsets that were selected via different feature selection methods, namely MIC-RF, RFECV-RF and MDA-RF. Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. Based on this model, a spatial population distribution dataset of the Southern Sichuan Economic Zone at a 500m resolution was generated. Finally, the population dataset generated in this study was compared and validated with the WorldPop dataset. The results showed that utilizing feature selection methods improves model accuracy to varying degrees compared with RF based on all factors, and the MDA-RF had the lowest MAPE of 0.174 and the highest R2 of 0.913 among them. Therefore, feature factors selection using the MDA method was considered the optimal feature subset. Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. Compared with the WorldPop dataset, the accuracy of the results predicted using the proposed method was enhanced. The MRE and RMSE of the WorldPop dataset were 57.24 and 23174.98, respectively, while the MRE and RMSE of the proposed method were 25.00 and 15776.50, respectively. This implies that the method proposed in this paper could simulate population spatial distribution more accurately.

Note: For information on data collection, confidentiality protection, nonsampling error, and definitions, see the 2020 Island Areas Censuses Technical Documentation..Due to COVID-19 restrictions impacting data collection for the 2020 Census of American Samoa, data tables reporting social and economic characteristics do not include the group quarters population in the table universe. As a result, impacted 2020 data tables should not be compared to 2010 and other past census data tables reporting the same characteristics. The Census Bureau advises data users to verify table universes are the same before comparing data across census years. For more information about data collection limitations and the impacts on American Samoa's data products, see the 2020 Island Areas Censuses Technical Documentation..Explanation of Symbols: 1.An "-" means the statistic could not be computed because there were an insufficient number of observations. 2. An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.3. An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.4. An "N" means data are not displayed for the selected geographic area due to concerns with statistical reliability or an insufficient number of cases.5. An "(X)" means not applicable..Source: U.S. Census Bureau, 2020 Census, American Samoa.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68μg/m3, and 8.39μg/m3 elevated the year 2019 compared with 2015. ii) The Moran’s I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.