An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/7/1184 |
| _version_ | 1827719141887311872 |
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| author | Rezaul Roni Peng Jia |
| author_facet | Rezaul Roni Peng Jia |
| author_sort | Rezaul Roni |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:37:05Z |
| format | Article |
| id | doaj.art-bcf13c7127d4422c93754787610fe19c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:37:05Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-bcf13c7127d4422c93754787610fe19c2023-11-19T20:57:06ZengMDPI AGRemote Sensing2072-42922020-04-01127118410.3390/rs12071184An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, BangladeshRezaul Roni0Peng Jia1Department of Geography and Environment, Jahangirnagar University, Savar, Dhaka-1342, BangladeshFaculty of Geo-information Science and Earth Observation, University of Twente, 7500 Enschede, The NetherlandsTraditional 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.https://www.mdpi.com/2072-4292/12/7/1184populationgeographically weighted regressionGWRdasymetric mappingremote sensingsatellite image |
| spellingShingle | Rezaul Roni Peng Jia 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 population geographically weighted regression GWR dasymetric mapping remote sensing satellite image |
| title | An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh |
| title_full | An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh |
| title_fullStr | An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh |
| title_full_unstemmed | An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh |
| title_short | An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh |
| title_sort | optimal population modeling approach using geographically weighted regression based on high resolution remote sensing data a case study in dhaka city bangladesh |
| topic | population geographically weighted regression GWR dasymetric mapping remote sensing satellite image |
| url | https://www.mdpi.com/2072-4292/12/7/1184 |
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