Abstract
Traditional choropleth maps, created on the basis of administrative units, often fail to accurately represent population distribution due to the high spatial heterogeneity and the temporal dynamics of the population within the units. Furthermore, updating the data of spatial population statistics is time-consuming and costly, which underlies the relative lack of high-resolution and high-quality population data for implementing or validating population modeling work, in particular in low- and middle-income countries (LMIC). Dasymetric modeling has become an important technique to produce high-resolution gridded population surfaces. In this study, carried out in Dhaka City, Bangladesh, dasymetric mapping was implemented with the assistance of a combination of an object-based image analysis method (for generating ancillary data) and Geographically Weighted Regression (for improving the accuracy of the dasymetric modeling on the basis of building use). Buildings were extracted from WorldView 2 imagery as ancillary data, and a building-based GWR model was selected as the final model to disaggregate population counts from administrative units onto 5 m raster cells. The overall accuracy of the image classification was 77.75%, but the root mean square error (RMSE) of the building-based GWR model for the population disaggregation was significantly less compared to the RMSE values of GWR based land use, Ordinary Least Square based land use and building modeling. Our model has potential to be adapted to other LMIC countries, where high-quality ground-truth population data are lacking. With increasingly available satellite data, the approach developed in this study can facilitate high-resolution population modeling in a complex urban setting, and hence improve the demographic, social, environmental and health research in LMICs.
Highlights
An accurate spatial representation of population counts is important for e.g., risk assessment, policy-making, disaster management, accessibility modeling, poverty mapping, and adaptive strategies for human health [1,2,3,4]
Mapping the population distribution at a high-resolution became popular for producing high-resolution gridded population surfaces (HGPS), which have large benefits for many [5]
Researchers have developed a wide range of methods for mapping the spatial distribution of the population with the aid of Geographic Information Systems (GIS) technology, which is used to create choropleth maps
Summary
An accurate spatial representation of population counts is important for e.g., risk assessment, policy-making, disaster management, accessibility modeling, poverty mapping, and adaptive strategies for human health [1,2,3,4]. Other studies that have been undertaken were based on the intrinsic features of RS imagery, such as image texture indices [13] Those products, were mainly generated either at country [14] or at regional level [15], with spatial resolutions ranging from 100 m to 1 km. This resolution cannot meet the demands in some specific areas or situations, such as emergency/disaster response and resource allocation. This is especially important in low- and middle-income countries (LMICs) where resources for conducting surveys are usually limited. Given the increasing urbanization experienced worldwide, the demand for mapping and the understanding of population distribution at a high-resolution is increasing as well
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
