LandScan 2019 Release Technical Issue
The LandScanTM Team is working to upload the 2019 dataset. We have experienced a technical issue and are working to resolve it. Updates will be posted to the website.
Release of LandScan Global 2019
The LandScan Team is pleased to announce that LandScan Global 2019, which is the latest version of this dataset, will be released to the educational community through this portal this fall.
When this newest dataset is posted to the website, all users will be required to go through the process of confirming their educational institution email in order to access any datasets that were previously licensed. However, once confirmed and approved you will have access to your previously licensed datasets as well as the option to download the LandScan Global 2018 dataset.
ORNL’s LandScan is a community standard for global population distribution data. At approximately 1 km (30″ X 30″) spatial resolution, it represents an ambient population (average over 24 hours) distribution. The database is refreshed annually and released to the broader user community around October.
LandScan is now available at no cost to the educational community. The latest LandScan dataset available is LandScan Global 2018. Older LandScan Global data sets (LandScan 1998, 2000-2017) are available through site. These data sets can be licensed for commercial and other applications through multiple third-party vendors.
LandScan is developed using best available demographic (Census) and geographic data, remote sensing imagery analysis techniques within a multivariate dasymetric modeling framework to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution is essentially a combination of locally adoptive models that are tailored to match the data conditions and geographical nature of each individual country and region.