WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

This repository contains the data used in Mirzadeh et al., 2021. It includes two InSAR time-series datasets from Envisat and Sentinel-1 satellite in both ascending and descending orbits, acquired over Yazd-Ardakan Plain, Iran, as well as, the population density information and weather data for this study area.

Dataset 1: Envisat ascending track 99 and descending track 20

Date: 06 Sep 2004 - 12 Jul 2010 (17 ascending acquisitions) + 26 Mar 2003 - 23 Oct 2010 (23 descending acquisitions)

Processor: ISCE/stripmapStack + MintPy

Displacement time-series (in HDF-EOS5 format): timeseries_LODcor_ERA5_ramp_demErr.h5

Mean LOS Velocity (in HDF-EOS5 format): velocity.h5

Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5

Geometry (in HDF-EOS5 format): geometryRadar.h5

Dataset 2: Sentinel-1 ascending track 130 and descending track 64

Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 10 Oct 2014 - 24 Mar 2020 (119 descending acquisitions)

Processor: ISCE/topsStack + MintPy

Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5

Mean LOS Velocity (in HDF-EOS5 format): velocity.h5

Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5

Geometry (in HDF-EOS5 format): geometryRadar.h5

The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products, and the MintPy commands/functions.

Urban sprawl and urbanization as driving forces of land degradation have direct and indirect impacts on local climate dynamic. In this paper, the hypothesis that urban sprawl and unsustainable land use change cause local climate changes has been studied. Tehran as a megacity has been considered to show the urban sprawl and urbanization impacts on local climate. The methodology is divided into two main parts based on the primary datasets (satellite imagery and local climate data). The Landsat images and digital elevation model maps extracted from Shuttle Radar Topography Mission 1 Arc-Second Global data of Tehran acquired in every 5 years during June and July from 1975 to 2015 have been used for this study. The second dataset that has been used in this study contains daily mean temperature and precipitation (from 1990 to 2010) of eight meteorological synoptic stations in the study area. The results show that the rapid and unsustainable urban growth have significant effects on local climate. Moreover, it has been found that the urbanization and urban sprawl as well as unsustainable land use change caused significant change (P = 0.005) in evaporation rate in the study area (especially in east and center regions of the city with high population density).

Demographic characteristics of the study population.