Identification and Analysis of Industrial Land in China Based on the Point of Interest Data and Random Forest Model - He, Qingsong; Tang, Xinyu (Frontiers in Environmental Science, 2022)
The purposes of this study are to provide a new concept and technical method for the large-scale identification of industrial land and analyze the distribution characteristics of industrial land in China. The research following methods are employed: Using point of interest data and random forest model and based on data accessibility, this paper selected 2015 data on Wuhan and Luoyang as training samples to identify the industrial land of China. Then, the proportion of industrial land in all 334 prefecture-level cities on the Chinese mainland was calculated, and the spatial pattern was analyzed. The results showed that: 1) By comparing multiple experiments and robustness analysis, the optimal parameter setting of the random forest model is obtained. According to the test of actual industrial land distribution in Wuhan city and Luoyang city, the identification of industrial land in different scale cities by random forest is accurate and effective. 2) From the perspective of spatial pattern, industrial land showed a “large aggregation and small scattering” distribution. 3) From the perspective of spatial distribution, the proportion of industrial land in these cities showed spatial aggregation. High-high aggregation areas were mainly distributed in North and Northeast China, and low-low aggregation areas were mainly located in West China. 4) From the perspective of associated factors, industrial land was close to rivers, highways, and railway station and had a relatively low correlation with the distribution of airports. Industrial land was located within approximately 10–60 km distance from municipal government office. In terms of the proportion of industrial land, cities with industrial land, which were closer to railway stations, had a higher proportion of industrial land, while cities which were closer to four other types of related factors (waters and lakes, major highways, airports, and municipal government stations)had a lower share of industrial land. In conclusion, the method based on point of interest data and random forest model can accurately and effectively identify large-scale industrial land.