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Article

An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh

by 1,2 and 2,3,4,*
1
Department of Geography and Environment, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
2
Faculty of Geo-information Science and Earth Observation, University of Twente, 7500 Enschede, The Netherlands
3
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
4
International Initiative on Spatial Lifecourse Epidemiology (ISLE), 7500 Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1184; https://doi.org/10.3390/rs12071184
Received: 23 January 2020 / Revised: 31 March 2020 / Accepted: 31 March 2020 / Published: 7 April 2020
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. View Full-Text
Keywords: population; geographically weighted regression; GWR; dasymetric mapping; remote sensing; satellite image population; geographically weighted regression; GWR; dasymetric mapping; remote sensing; satellite image
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MDPI and ACS Style

Roni, R.; Jia, P. An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh. Remote Sens. 2020, 12, 1184. https://doi.org/10.3390/rs12071184

AMA Style

Roni R, Jia P. An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh. Remote Sensing. 2020; 12(7):1184. https://doi.org/10.3390/rs12071184

Chicago/Turabian Style

Roni, Rezaul, and Peng Jia. 2020. "An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh" Remote Sensing 12, no. 7: 1184. https://doi.org/10.3390/rs12071184

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