This map uses smart mapping to show the density of transit stops in Los Angeles, CA. The stops provide information about the surrounding population of each stop.The LA Transit Stops layer displays transit stops with the following information about the population within a 5 minute walk of each transit stop:PopulationPercent employedPercent of seniorsPercent minoritiesNumber of householdsPercent below the poverty linePoverty indexDiversity indexRaceThe layer was created using Esri's Enrich Layer tool in ArcGIS Online.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

This population dataset complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria. This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing. These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities. Creation of this population dataset, derived from a 1961 census, was created to compare modern population density patterns with the distribution of ancient settlement patterns to ascertain if patterns are shared. There is a similarity between these patterns with higher concentrations of settlements and population along the banks of rivers until reaching the northern area of the Jazira where both extend across the wider landscape and away from rivers. Derived from 1:1 million scale map produced for the following report: Food and Agriculture Organization (FAO), United Nations. Etude des Ressources en Eaux Souterraines de la Jezireh Syrienne. Rome: FAO, 1966.Population map was copied to mylar and scanned to create a polygon coverage of the soil classes, which include land-use attribute information. Each polygon was labelled and attributed with population count. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-05 and migrated to Edinburgh DataShare on 2017-02-21.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

Population density 2023 (inhabitants per km²), Lorraine: 2021 Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, INSEE, Statbel, STATEC. Harmonization: IBA / OIE 2024 Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2024 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2418&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/3ed89eb1-9a37-4b86-b793-126411751345 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Pop_density_WMS/guest with layer name(s): -Pop_density_2023

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are 2,644 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. This file includes revisions made to the 2020 Census New Orleans, LA Urban Area where the territory originally delineated as the 2020 Census Laplace--Lutcher--Gramercy, LA Urban Area was combined with the 2020 Census New Orleans, LA Urban Area to form the current New Orleans, LA Urban Area. This file includes revisions made to the 2020 Census Atlanta, GA Urban Area and Gainesville, GA Urban Area, where some urban territory originally designated to the Gainesville, GA Urban Area was reassigned to the Atlanta, GA Urban Area.

Indicador de vulnerabilidad. La densidad de población por municipio se obtiene de forma sencilla a partir de la población y el área de cada municipio. A partir de ésta se puede graduar la exposición en niveles. Datos desde 1998Fuente: NASTATPeriodicidad: Anual

In 2025, the Ile-de-France region, sometimes called the Paris region, was the most populous in France. It is located in the northern part of France, divided into eight departments and crossed by the Seine River. The region contains Paris, its large suburbs, and several rural areas. The total population in metropolitan France was estimated at around ** million inhabitants. In the DOM (Overseas Department), France had more than *** million citizens spread over the islands of Guadeloupe, Martinique, Reunion, Mayotte, and the South American territory of French Guiana. Ile-de-France: the most populous region in France According to the source, more than ** million French citizens lived in the Ile-de-France region. Ile-de-France was followed by Auvergne-Rhône-Alpes and Occitanie region which is in the Southern part of the country. Ile-de-France is not only the most populated region in France, it is also the French region with the highest population density. In 2020, there were ******* residents per square kilometer in Ile-de-France compared to ***** for Auvergne-Rhône-Alpes, the second most populated region in France. More than two million people were living in the city of Paris in 2025. Thus, the metropolitan area outside the city of Paris, called the suburbs or banlieue in French, had more than ten million inhabitants. Ile-de-France concentrates the majority of the country’s economic and political activities. An urban population In 2024, the total population of France amounted to over 68 million. The population in the country has increased since the mid-2000s. As well as the other European countries, France is experiencing urbanization. In 2023, more than ** percent of the French population lived in cities. This phenomenon shapes France’s geography.

Aim: Protected areas are frequently defined on the basis of biological importance. Ecosystem services are expected to be under protection when biodiversity is preserved; however, new approaches are needed to confirm this statement. We evaluated how spatial associations between ecosystem services and plant biodiversity on a large spatial scale influence their representativeness in current protected areas. […]

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WMS Population Mesh Service. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WMS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the demographic statistical information of the population data, corresponding to January 1, 2018, is presented. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

Node of the Institute of Statistics and Cartography of Andalusia. Regional Government of Andalusia. WMS Service of the Pensioners Mesh. Integrated in the Spatial Data Infrastructure of Andalusia following the guidelines of the Statistical and Cartographic System of Andalusia. WMS map service of spatial distribution of the population of Andalusia in cells of 250m x 250m. The information represented in these maps has been georeferenced from the location of the postal address where each of the inhabitants of Andalusia resides. To facilitate the representation of the information and to preserve statistical confidentiality, a regular mesh has been drawn with cells of 250 meters on the side, where all the information that corresponds in each case has been added. Information that could not be georeferenced has been estimated using spatial analysis techniques. On December 23, 2019, the statistical information on contributory pension data, corresponding to January 1, 2018, is presented, according to the type of Social Security pensioner and their income. The website of the Institute of Statistics and Cartography of Andalusia offers a visualization service: "Spatial distribution of the population of Andalusia" for interactive consultation https://www.juntadeandalucia.es/institutodeestadisticaycartografia/distributionpob/index.htm

OverviewThese are the Homeless Counts for 2020 as provided by the Los Angeles Homeless Services Authority (LAHSA), and the cities of Glendale, Pasadena, and Long Beach. The majority of this data comes from LAHSA using tract-level counts; the cities of Glendale, Pasadena, and Long Beach did not have tract-level counts available. The purpose of this layer is to depict homeless density at a community scale. Please read the note from LAHSA below regarding the tract level counts. In this layer LAHSA's tract-level population count was rounded to the nearest whole number, and density was determined per square mile of each community. It should be noted that not all of the sub-populations captured from LAHSA (eg. people living in vans, unaccompanied minors, etc.) are not captured here; only sheltered, unsheltered, and total population. Data generated on 12/2/20.Countywide Statistical AreasLos Angeles County's 'Countywide Statistical Areas' layer was used to classify the city / community names. Since this is tract-level data there are several times where a tract is in more than one city/community. Whatever the majority of the coverage of a tract is, that is the community that got coded. The boundaries of these communities follow aggregated tract boundaries and will therefore often deviate from the 'Countywide Statistical Area' boundaries.Note from LAHSALAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels. Due to rounding, the census tract-level data may not add up to the total for Los Angeles City Council District, Supervisorial District, Service Planning Area, or the Los Angeles Continuum of Care.The Los Angeles Continuum of Care does not include the Cities of Long Beach, Glendale, and Pasadena and will not equal the countywide Homeless Count Total.Street Count Data include persons found outside, including persons found living in cars, vans, campers/RVs, tents, and makeshift shelters. A conversion factor list can be found at https://www.lahsa.org/homeless-count/Please visit https://www.lahsa.org/homeless-count/home to view and download data.Last updated 07/16/2020

Maps of the analysis of change between * mapping of heat islands/freshness 2020-2022 * and * mapping of heat/freshness islands using 2013-2014 data * on all major urban centers by two methods, i.e. - The map of the Difference between the differences of temperatures in °C (* 2020-2022 * minus * 2013-2014*)), which is calculated at the pixel level and produced at the scale of the Quebec ecumene (2016 census, 2016 census, 167,764 km2). The temperature difference is the difference in temperature in the city compared to a nearby wooded area. A positive value of the difference in temperature differences represents an increase in the temperature gap in 2020-2022 compared to 2013-2014, a negative value represents a decrease in the temperature difference in 2020-2022 compared to 2013-2014. - The map of _SUHII index variation between 2020-2022 and 2013-2014 (%) _, which represents the percentage of change in the Surface Urban Heat Island Intensity (SUHII) index between the two years. This map covers the extent of * 2021 census population centers * () * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants per km2 to which a 2 km buffer zone is added and the values are calculated at the scale of the * dissemination island * of Statistics Canada. The SUHII index highlights areas with higher heat island intensity, by calculating a weighted average from the temperature difference classes, giving more weight to the hottest classes. Index change values below 100% represent a decrease in the intensity of UHIs in 2020-2022 compared to 2013-2014. Values greater than 100% represent an increase in UHI intensity between 2013-2014 and 2020-2022. Values around 100% correspond to an absence of change. The temperature difference classes were produced by the k-means algorithm, which takes into account the distribution of temperature difference values in a population center in a given year. The limits of temperature difference classes may therefore differ between the two years, which will influence the variation value of the SUHII index. For more details on the creation of the various maps as well as their advantages, limitations and potential uses, consult the * Technote * (simplified version) and/or the * methodological report * (full version). The production of this data was coordinated by the National Institute of Public Health of Quebec (INSPQ) and carried out by the forest remote sensing laboratory of the Center for Forestry Education and Research (CERFO), funded under the * 2013-2020 Climate Change Action Plan * of the Quebec government entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Zoning is a locally regulated law that is used as a guideline for land management control and conformity by establishing specific policy that must be followed in the use of land and buildings. Zoning asserts explicit uses that are permitted under varying circumstances. It dictates reasonable development by protecting property from detrimental uses on nearby properties. Zoning also standardizes the size of lots, the building set backs from roads or adjoining property, maximum height of buildings, the population density, and other land use issues.Zoning is used to designate, regulate and restrict the location and use of buildings, structures and land, for agriculture, residence, commerce, trade, industry or other purposes; to regulate and limit the height, number of stories, and size of buildings and other structures hereafter erected or altered to regulate and determine the size of yards and other open spaces and to regulate and limit the density of population; and for said purposes to divide the City into zones of such number, shape and area as may be deemed best suited to carry out these regulations and provide for their enforcement. These regulations are deemed necessary in order to encourage the most appropriate use of land; to conserve and stabilize the value of property; to provide adequate open spaces for light and air, and to prevent and fight fires; to prevent undue concentration of population; to lessen congestion on streets; to facilitate adequate provisions for community utilities and facilities such as transportation, water, sewerage, schools, parks and other public requirements; and to promote health, safety, and the general welfare all in accordance with the comprehensive plan.For more information, please refer to Section 12.04 of the Los Angeles Planning and Zoning Municipal Code and the Generalized Summary of Zoning Regulations, City of Los Angeles.Refresh Rate: Monthly