Population density 2024 (inhabitants per km²). Reference date: 01.01.2024 (Luxembourg and Wallonia), 31.12.2023 (Rhineland-Palatinate and Saarland), 01.01.2022 (Lorraine) Territorial entities: municipalities (Saarland, Wallonie), cantons (Luxembourg), EPCI (Lorraine), Verbandsgemeinden and verbandsfreie Städte und Gemeinden (Rheinland-Pfalz) Statistical data sources: DATer, INSEE Grand Est, IWEPS, Région Grand Est, STATEC, Statistisches Landesamt Rheinland-Pfalz, Statistisches Landesamt Saarland. Harmonization: SIG-GR / GIS-GR 2025 Geodata sources: GeoBasis-DE / BKG, IGN France, NGI-Belgium, ACT Luxembourg. Harmonization: SIG-GR / GIS-GR 2025 Link to interactive map: https://map.gis-gr.eu/theme/main?version=3&zoom=8&X=708580&Y=6429642&lang=fr&rotation=0&layers=2435&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/93569c33-a975-4885-9896-626cff07cfa0 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_2024_infra

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

[Metadata] - 2015 Census Blocks for Hawaii. Source: U.S. Census Bureau, 2016. There is no population data associated with 2015 census block geography - for years between the decennial census, population data is collected via the American Community Survey (ACS) program. The ACS is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. Population data for the ACS is only collected down to the census block level. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/blocks15.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Change in total population 2000-2023, Lorraine: 1999-2021 Territorial entities: arrondissements (Lorraine, Wallonie), cantons (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: Destatis, INSEE, Statbel, STATEC. Calculations. IBA / OIE 2024 Geodata sources: ACT Luxembourg, IGN France, GeoBasis-DE / BKG, NGI-Belgium. 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=2420&opacities=1&bgLayer=basemap_2015_global Link to Geocatalog: https://geocatalogue.gis-gr.eu/geonetwork/srv/eng/catalog.search#/metadata/952d6822-53a3-4f98-b2cf-9eda46462838 This dataset is published in the view service (WMS) available at: https://ws.geoportail.lu/wss/service/GR_Population_change_WMS/guest with layer name(s): -Pop_change_2000_2023

Description

This dataset provide information about population density all over the world.
Data have been compiled from Kontur as a GeoPackage (gpkg) file format [1], 22km global hexagon population grid. Values represent number of people in cell.

GeoPackage format have been converted to Comma Separated Values format (GPKG to CSV) using by Geopandas Python library.

It contains 3 columns; H3 code, population and geometry.
- H3 is a hierarchical geospatial index that refers to cells within a spatial hierarchy.. - Population refers a group of organisms of the same species who inhabit the same particular geographical area and are capable of interbreeding [2]. - Geometry column contains polygons that store their geographic representation.

The dataset is of interest to GIS researchers, social surveyors, and geospatial data enthusiasts.

All the best!

[1] This format was published in 2014; defined by the OGC (Open Geospatial Consortium). Various governments, commercial, and open source organizations widely support the GeoPackage.
[2] "Definition of population (biology)". Oxford Dictionaries. Oxford University Press.

Share of the working age population (20-64 years) in total population 2014 (Lorraine: 2013) Territorial entities: arrondissements (Wallonie), zones d'emploi (Lorraine), Grand Duchy (Luxembourg), Kreise (Saarland, Rheinland-Pfalz) Statistical data sources: INSEE Grand Est; SPF Economie; Statistisches Landesamt Rheinland-Pfalz; Statistisches Amt Saarland; STATEC. Calculations: OIE/IBA 2016 Geodata sources: EuroGeographics EuroRegionalMap v9.1 - 2016. Harmonization: SIG-GR / GIS-GR 2016

World Cities provides a basemap layer for the cities of the world. The cities include national capitals, provincial capitals, major population centers, and landmark cities. Population estimates are provided for those cities listed in open source data from the United Nations Statistics Division, United Nations Human Settlements Programme, and U.S. Census Bureau.

[Metadata] 2010 Census Public Use Microdata Areas (PUMA). Source: US Census Bureau.

[Metadata] - 2015 Census Tracts with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016.

The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.

[Metadata] - 2015 Census Public Use Microdata Areas (PUMA) with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016. The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/puma15.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

This data collection comprises a data library, sample outputs, batch files and accompanying documentation from the ESRC-funded project “Population247NRT: Near real-time spatiotemporal population estimates for health, emergency response and national security”. The data comprise a structured set of input data for use with the authors’ SurfaceBuilder247 software and sample outputs which estimate the population distribution of England at specific times on specific dates, referenced to 2011 census population totals.
The sample output files (provided as GeoTIFFs) contain population estimates in 200m grid cells, based on the British National Grid, for 02:00 (2am) and 14:00 (2pm) on a typical weekday in University and school term-time and out of term-time. The estimates are broken down by seven age/economic activity sub-groups for term-time and six for out of term-time, and include estimates of population activity in residential, workplace, education, healthcare and road transportation domains.
The data library, which has been constructed entirely using open data sources, comprises population estimates, by age/economic activity sub-groups, for point locations (typically population-weighted centroids of census output areas and workplace zones, or postcode centroids of sites such as schools or hospitals); time profiles representing usual patterns of population activity at these sites during a 24-hour period; and background grid layers representing the land surface area and major road network. SurfaceBuilder247 uses the data library to generate time-specific gridded population estimates by redistributing the population of each sub-group across the available locations and background grid in accordance with the reference time profiles. The sample output grids provided in this resource may be used directly in GIS software or, alternatively, the input data library may be reprocessed using SurfaceBuilder247 to generate estimates for specific dates and times of interest to the user. Sample batch and session parameter files are included in the resource.

Decision-making and policy formulation in sectors such as health, emergency/crisis response and national security, ideally require accurate dynamic information on the number of people in specific places at specific times of the day, week, season or year. Traditional census data do not provide this level of detail but are often used for such policy and planning purposes. The ESRC-funded Population247 programme of research (Martin et al, 2015) developed a framework, methodology and software tool (SurfaceBuilder247) for integrating diverse contemporary data sources to produce enhanced time-specific population estimates for small geographical areas. Its usefulness has since been demonstrated for flooding and radiation emergency response/planning, through collaborations with HR Wallingford and Public Health England. These models have primarily involved the integration of open administrative data for activities such as place of residence, work, education and health. Now, new and emerging forms of data, such as sensor data, live and static data feeds provided via the internet, and various commercial datasets which were not previously available, provide exciting opportunities to enhance these population estimates. Such new and emerging datasets are useful because they provide near real-time information on population activity in sectors which are particularly dynamic and have previously been difficult to model, such as retail, leisure and transport. However, extracting useful intelligence from these sources, and integrating and calibrating them with existing data sources, poses significant challenges for researchers and practitioners seeking to employ them in the creation of time-specific population estimates. This project will combine new, emerging and existing datasets in order to produce enhanced time-specific population estimates for more informed decision-making and policy formulation in the health, emergency/crisis response and national security sectors. It is a collaborative project between University of Southampton, Public Health England (PHE), Health and Safety Executive (HSE) and Defence Science and Technology Laboratory (Dstl). The project will enhance existing methods and tools for harvesting, processing, integrating and calibrating new, emerging and existing data sources in order to produce time-specific population estimates. It will deliver two substantive policy demonstrator case studies with the project partners. The first case study will demonstrate the potential for using time-specific population estimates for near real-time response in emergencies; the second will explore their usefulness for modelling variation in 'normal' population distributions through space and time in order to inform longer-term planning and policy formulation. Importantly, the project will also encourage the sharing of knowledge and expertise between academia and the public sector through joint design and implementation of the case studies, internal seminars and a jointly organised stakeholder workshop. Invitees to the workshop will be key stakeholders in policy and practice from within and beyond the partners' sectors. The workshop will showcase the data, methods and tools developed by the project, discuss the opportunities and challenges involved in implementing these for decision-making and policy formulation, and identify how such methods might realistically be scaled up within these sectors. Ultimately, the aim of the project is to help partners such as PHE, HSE and Dstl carry out their remits more effectively and efficiently through the provision of better time-specific population estimates.

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.

[Metadata] - 2015 Census Urban Areas and Urbanized Clusters with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016.

The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.

Population (%) aged 30-34 with tertiary educational attainment in 2019 Territorial entities: NUTS 2 Data source: European Commission, Eurostat/GISCO 2021. Harmonization: GIS-GR 2022

This dataset shows the population data collected for the 2011 Census mapped against Counties, Unitary Authorities, and Local Authority Districts. Fields include, total population, break down by sex, households, population in communal living, school boarders and population density for census areas. This data was sourced from the ONS website. http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/index.html It has been combined with the 2011 census area boundary dataset that can also be found on the ONS website. All re-use of this data should acknowledge the OSN as the source of the data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-11 and migrated to Edinburgh DataShare on 2017-02-21.