Original Articles
29 December 2020
Vol. 15 No. 2 (2020)
https://doi.org/10.4081/gh.2020.768

Simulation of the spatial distribution of urban populations based on first-aid call data

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Demography, Geographic Information Systems, High-resolution population, Grid cell, first-aid calls, Simulation
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Authors

Yinan Zhou
School of Public Health, Fudan University, China.
Qin Zhong Zhu
Shanghai Medical Emergency Center, Shanghai, China.
Li Luo
liluo@fudan.edu.cn
School of Public Health, Fudan University, China.

We examined the feasibility of estimating the spatial distribution of urban populations based on first-aid calls based on one high-density place, the Shanghai urban area and one low-density place, the Nanhai District of Foshan City in Guangdong Province. We aggregated the population and the total number of first-aid calls on digital maps divided by grids based on a Geographic Information System (GIS). Geographically weighted regression was applied to test the correlation between the population distribution simulated by first-aid call data and the actual residency. The impact of different population densities, different grid cell sizes and different types of first-aid calls on simulation correlation were tested. We found that the use of first-aid call data could explain 60-95% of the actual population distribution in Shanghai using a grid with 1000*1000 m cell size, while the Nanhai experience was that first-aid calls could only explain 4-76% of the actual population distribution using a grid with 2000*2000 m cell size. Thus, the higher the population density, the better the simulation effect. For a high-population density area, the overall accuracy of simulation can reach as high as 0.878 at the 1-km2 resolution. However, there are different kinds of first-aid calls and for the best estimation of the population distribution in densely populated areas, we suggest using first-aid calls from people requiring acute medical care rather than all first-aid call data.

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Simulation of the spatial distribution of urban populations based on first-aid call data. (2020). Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.768
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