This dataset contains human population density for the state of California and a small portion of western Nevada for the year 2000. The population density is based on US Census Bureau data and has a cell size of 990 meters.

The purpose of the dataset is to provide a consistent statewide human density GIS layer for display, analysis and modeling purposes.

The state of California, and a very small portion of western Nevada, was divided into pixels with a cell size 0.98 km2, or 990 meters on each side. For each pixel, the US Census Bureau data was clipped, the total human population was calculated, and that population was divided by the area to get human density (people/km2) for each pixel.

The primary data consist of allele or haplotype frequencies for N=1036 anonymized U.S. population samples. Additional files are supplements to the associated publications. Any changes to spreadsheets are listed in the "Change Log" tab within each spreadsheet. DOI numbers for associated publications are listed below, under "References".

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.

Data from Nationmaster.

This data release includes gridded estimates of population sizes at approximately 100 m resolution with national coverage across Ghana. This includes estimates of total population sizes, populations in 36 different age-sex groups, people per household, people per building, households per building, and statistical measures of uncertainty. These results were produced using census microdata from IPUMS and building footprints from Maxar/Ecopia.

The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

The Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset contains global, city-level population data from 3700 BC - AD 1000 for 154 cities in .csv form.Version 2 removes spaces after some city names which could also be done programmatically after the dataset is combined and corrects the spelling of Istanbul, Turkey in one instance.

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development (OPP1182408). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network in the Earth Institute at Columbia University, and the Flowminder Foundation. These data may be distributed using a Creative Commons Attribution Share-Alike 4.0 License. Contact release@worldpop.org for more information.

This dataset categorizes pixels with estimated zero population based on information provided in the census documents. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) …

The geographic distribution of human population is key to understanding the effects of humans on the natural world and how natural events such as storms, earthquakes, and other natural phenomenon affect humans. Dataset SummaryThis layer was created with a model that combines imagery, road intersection density, populated places, and urban foot prints to create a likelihood surface. The likelihood surface is then used to create a raster of population with a cell size of 0.00221 degrees (approximately 250 meters).The population raster is created usingDasymetriccartographic methods to allocate the population values in over 1.6 million census polygons covering the world.The population of each polygon was normalized to the 2013 United Nations population estimates by country.Each cell in this layer has an integer value depicting the number of people that are likely to reside in that cell. Tabulations based on these values should result in population totals that more accurately reflect the population of areas of several square kilometers.This layer has global coverage and was published by Esri in 2014.More information about this layer is available:Building the Most Detailed Population Map in the World

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

This repository includes census-disaggregated population gridded estimates for Burkina Faso, using a top-down approach based on Random Forest modelling. A breakdown by age and sex groups is joined to the gridded population count. A technical report explains the methodology, the validation procedures, the input data used and the limitations of the modelling. The data used for modelling are also attached.

This dataset contains population input data for ISIMIP2a (https://www.isimip.org, Schewe et al. 2019).

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

The Gridded Population of the World, Version 3 (GPWv3): Centroids consists of estimates of human population counts and densities for the years 1990, 1995, 2000, 2005, 2010, and 2015 by administrative Unit centroid location. The centroids are based on the 399,781 input administrative Units used in GPWv3. In addition to population counts and variables, the centroids have associated administrative Unit names and the land area of contained within the administrative Unit. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

WorldMove is an open-access worldwide human mobility dataset, we follow a generative AI-based approach to create a large-scale mobility dataset for cities worldwide. Our method leverages publicly available multi-source data, including population distribution, points of interest (POIs), and synthetic commuting origin-destination flow datasets, to generate realistic city-scale mobility trajectories.

For decades, biogeographers have sought a better understanding of how organisms are distributed among islands. However, the island biogeography of humans remains largely unknown. Here, we investigate how human population size varies among 486 islands at two spatial scales. At a global scale, we tested whether population size increases with island area and declines with island elevation and nearest mainland, as is common in non-human species, or whether humans escape such biogeographic constraints. At a regional scale, we tested whether population sizes vary among islands within archipelagos according to the positioning of different cultural source pools. Results illustrate that on a global scale, human populations increased in size with island area, similar to non-human species, yet they did not decline in size with elevation and distance to nearest mainland. At a regional scale, human population size often varied among islands within archipelagos relative to the location of different cultural source pools. Despite broad-scale similarities in the geographical distribution of human and non-human species among islands, results from this study indicate that the island biogeography of humans may also be influenced by archipelago-specific social, political and historical circumstances.