Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

Abstract

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

Based on GIS technology and data-driven epidemic risk assessment methodology

“…With the advent of the era of geospatial big data, the correlation between geospatial data and population distribution at a micro-scale has been widely utilized for analyzing population density within administrative divisions [16] . Scholars have used geospatial big data to simulate population distribution [17] and population aggregation characteristics [18] within administrative divisions. As a result, an increasing number of researchers have gradually incorporated geospatial big data as auxiliary data for representing population distribution and aggregation features at a fine-grained level in spatial health research [19,20] , and combined it with data-driven approaches for applications such as disease risk assessment [20,21] .…”

Evaluation and System Coupling of Beautiful Qinghai–Tibet Plateau Construction Based on Point of Interest Data

“…The point of interest (POI) data are highly accurate, diverse in types, rich in attributes, and updated in real time, making them ideal for spatial analysis [40][41][42]. The POI data can be used for urban spatial structure analysis [43][44][45], urban centers [46,47] and functional area identification [48][49][50], land use mapping [51,52], poverty evaluation [53], spatial hotspot analysis of retail [54,55], and population spatialization [56][57][58][59]. Moreover, the POI data are real-time, representative, and comprehensive.…”