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Article

Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy

by 1,2 and 1,2,*
1
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
2
Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 654; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110654
Received: 26 September 2020 / Revised: 24 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R2) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research. View Full-Text
Keywords: population mapping; point of interest; random forest; zonal model; Guangzhou population mapping; point of interest; random forest; zonal model; Guangzhou
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MDPI and ACS Style

Zhao, G.; Yang, M. Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy. ISPRS Int. J. Geo-Inf. 2020, 9, 654. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110654

AMA Style

Zhao G, Yang M. Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy. ISPRS International Journal of Geo-Information. 2020; 9(11):654. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110654

Chicago/Turabian Style

Zhao, Guanwei; Yang, Muzhuang. 2020. "Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy" ISPRS Int. J. Geo-Inf. 9, no. 11: 654. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110654

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