Accurate and up-to-date population data is a prerequisite for many different applications, including risk and vulnerability management. There is, however, a shortage of data with a high spatial resolution, particularly in developing countries. Population densities are determined either by population census, typically conducted every ten years, or from global grid-based (raster) population datasets with relatively low resolution. Global population datasets are designed for global modeling studies, including climate change research, and their resolution is generally too low for local or community purposes. This paper presents a methodology for transforming population census data into grid-based (raster) population data with a relatively high resolution (100 m). Population census data, land cover, rural settlement data, and other geospatial datasets were utilized for a study area in the Khulna district of Bangladesh. Local experts validated the geographic information system (GIS)-derived population dataset as realistic and reasonably accurate. Our derived gridded population data was compared with the available LandScan global dataset. The overall difference between the population for 2010, which was projected from the 2001 census data, and our gridded population data was about 2.4% whereas the LandScan data overestimated the population in the study area by 49%.
Key words: Population density, spatial interpolation, geographic information system, bangladesh, Land Scan.
Copyright © 2019 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0