The Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050, data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in ten-year increments, of population distribution and internal climate-related and other migration. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on crop productivity and water availability from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Three scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, and a more inclusive development scenario combining SSP2 and RCP8.5, and each scenario represents an ensemble of four model runs combining different climate impact models. The modeling work was funded and developed jointly with The World Bank, and covers most World Bank client countries, with reports released in 2018 and 2021 that address different regions and provide full methodological details.

Untangling the spatial and temporal processes that influence population dynamics of migratory species is challenging, because changes in abundance are shaped by variation in vital rates across heterogeneous habitats and throughout the annual cycle. We developed a full-annual-cycle, integrated population model and used demographic data collected between 2011 and 2014 in southern Indiana and Belize to estimate stage-specific vital rates of a declining migratory songbird, the Wood Thrush (Hylocichla mustelina). Our primary objective was to understand how spatial and temporal variation in demography contributes to local and regional population growth. Our full-annual-cycle model allowed us to estimate: 1) age-specific, seasonal survival probabilities, including latent survival during both spring and autumn migration, and 2) how the relative contribution of vital rates to population growth differed among habitats. Wood Thrushes in our study populations experienced the lowest apparent survival rates during migration and apparent survival was lower during spring migration than during fall migration. Both mortality and high dispersal likely contributed to low apparent survival during spring migration. Population growth in high-quality habitat was most sensitive to variation in fecundity and apparent survival of juveniles during spring migration, whereas population growth in low-quality sites was most sensitive to adult apparent breeding-season survival. These results elucidate how full-annual-cycle vital rates, particularly apparent survival during migration, interact with spatial variation in habitat quality to influence population dynamics in migratory species.

This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.

This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.

Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)

Method - demographic fractions

Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.

To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:

\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)

Where:

- \(\delta_{year,\ country,age}^{\text{wpp}}\) is the ratio of change in demographic for a given age and and country from the UN WPP dataset.

- \(f_{year,\ country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country, and year.

- \(f_{2010,country,age}^{\text{wpp}}\) is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.

The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.

For each subset of cells corresponding to a given country c, the fraction of population in a given age band is calculated as:

\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)

Where:

- \(f_{year,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for given year, for the grid cell c.

- \(f_{2010,c,age}^{\text{gpw}}\) is the fraction of the population in a given age band for 2010, for the grid cell c.

The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.

Method - demographic totals

Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.

The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.

Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.

A computerized data set of demographic, economic and social data for 227 countries of the world. Information presented includes population, health, nutrition, mortality, fertility, family planning and contraceptive use, literacy, housing, and economic activity data. Tabular data are broken down by such variables as age, sex, and urban/rural residence. Data are organized as a series of statistical tables identified by country and table number. Each record consists of the data values associated with a single row of a given table. There are 105 tables with data for 208 countries. The second file is a note file, containing text of notes associated with various tables. These notes provide information such as definitions of categories (i.e. urban/rural) and how various values were calculated. The IDB was created in the U.S. Census Bureau''s International Programs Center (IPC) to help IPC staff meet the needs of organizations that sponsor IPC research. The IDB provides quick access to specialized information, with emphasis on demographic measures, for individual countries or groups of countries. The IDB combines data from country sources (typically censuses and surveys) with IPC estimates and projections to provide information dating back as far as 1950 and as far ahead as 2050. Because the IDB is maintained as a research tool for IPC sponsor requirements, the amount of information available may vary by country. As funding and research activity permit, the IPC updates and expands the data base content. Types of data include: * Population by age and sex * Vital rates, infant mortality, and life tables * Fertility and child survivorship * Migration * Marital status * Family planning Data characteristics: * Temporal: Selected years, 1950present, projected demographic data to 2050. * Spatial: 227 countries and areas. * Resolution: National population, selected data by urban/rural * residence, selected data by age and sex. Sources of data include: * U.S. Census Bureau * International projects (e.g., the Demographic and Health Survey) * United Nations agencies Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08490

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

This site is for us to upload the database is used for the analysis in the manuscript titled "Uneven Burdens: The Intersection of Brownfields, Pollution, and Socioeconomic Disparities in New Jersey, USA". The manuscript is currently in review, and we will update the full version of the database upon publication. We apologize if there is any inconvenience. We will update this site as soon as possible.

These are the data and code for the first column in the Spatial Demography journal's Software and Code series

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use Scenarios (ICLUS) project.

THese are extracts from American Factfinder 2, and are the data for the column in the Spatial Demography Journal

Estimated population density per grid-cell. The dataset is available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per square kilometre based on country totals adjusted to match the corresponding official United Nations population estimates that have been prepared by the Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat (2019 Revision of World Population Prospects). The mapping approach is Random Forest-based dasymetric redistribution.

Two data sets including the group size analysis (All_Sheep_Groups_GroupSizeAnalysis.csv) and the case study in Wrangell St. Elias National Park and Preserve (WRST_CaseStudy.csv), both of which also have associated metadata files. Both data sets were used in Delisle et al., "Linking behavioral ecology and population monitoring: The importance of group size for spatial population models".

Abstract Challenges in the field of demographic projections include, among others, the volatility of the migration component - critical for the projection of small areas; the compatibility between projections of small and large areas; and the measurement and inclusion of uncertainty in future scenarios of population growth. This article presents a new probabilistic method to conduct interregional population forecasting dealing with these three challenges. The proposed method has the following advantages: 1) it only requires information about the last place of residence and the population distributions of the last two Censuses; 2) it generates confidence intervals for the projected populations; 3) it makes the role of migration flows in the growth dynamics explicit and; 4) it facilitates the elaboration of counterfactual scenarios and sensitivity analysis using matrices of interregional population growth and distribution. We describe the patterns and trends in migration flows in the state of São Paulo applying spatial visualization tools and identifying areas in which migration is responsible for considerable shares of the demographic dynamics. About 95% of the 572 municipal projected populations of São Paulo had good precision and were within expected confidence intervals. We used data from the 1980, 1991 and 2000 Brazilian Censuses.

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Spatial Features is a dataset curated by CARTO providing access to a set of location-based features with global coverage that have been unified in common geographic supports (eg. Quadgrid). This product has been specially designed to facilitate spatial modeling at scale. Spatial Features includes core demographic data and POI aggregations by category that have been generated by processing and unifying globally available sources such as Worldpop and OpenStreetMap. The current version of this product is available in two different spatial aggregations: Quadgrid level 15 (with cells of approximately 1x1km) and Quadgrid level 18 (with cells of approximately 100x100m).

This web map contains ZIP Code points, ZIP Code boundaries, and 2020 U.S. Census Tract boundaries for the United States, including all 50 states and the District of Columbia. The data is maintained by Esri Demographics (Esri DM) and sourced from ArcGIS Data and Maps, providing a comprehensive view of geographic and demographic distributions across the country. Data Layers Included:

Census Tracts (2020) – Defined polygon boundaries containing 2020 Census population data, Census codes, and Esri Updated Demographics. These tracts serve as small, stable geographic areas used for demographic analysis. ZIP Code Points – Point-based locations representing U.S. ZIP Codes, including postal names, ZIP Code types, population estimates, and area size. The points are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics. This layer is updated annually. ZIP Code Boundaries – Polygon representations of ZIP Code areas, useful for mapping service areas, jurisdictional boundaries, and demographic studies. This layer is updated annually. The boundaries are sourced from TomTom (March 2023) and the population estimates are from Esri Demographics.

Data Source and Usage Rights: The data used in this hosted feature layer is provided via ArcGIS Data and Maps. The datasets within ArcGIS Data and Maps are subject to varying redistribution rights granted by Esri’s third-party data suppliers. Users must consult the Redistribution Rights document to determine permitted uses, applicable disclaimers, attribution requirements, and other conditions of use. This dataset may be used and redistributed with proper metadata and attribution in accordance with the Esri Master License Agreement. A downloadable layer package for 2020 Census Tracts is available here: 🔗 2020 Census Tract Layer Package Public Information Access: The Texas Public Information Act grants the public the right to access government records, except where certain exceptions apply. The public information officer may not ask why the records are requested. 🔗 Request public records online: Austin Public Information Requests This work is licensed under the Esri Master License Agreement.

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

The Groundswell Africa Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050 data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in five-year increments, of population distribution and internal climate-related and other migration for West Africa and the Lake Victoria Basin. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on water availability, crop productivity, and pasturelands (where cropping does not occur), as well as flood impacts, from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Four scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, a more inclusive development scenario combining SSP2 and RCP8.5, and an optimistic scenario combining SSP2 and RCP2.6. Each scenario provides an ensemble average of four model runs combining different climate impact models as well as confidence intervals to better capture uncertainties. The modeling work was funded and developed jointly with The World Bank.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

This record contains the base datasets used in the research to create the maps of distribution of population.