| 英文摘要： |
Point of interest (POI) data can provide a clear spatial location and accurate attributes for geoscience research. The traditional assessment of Beautiful China construction (BCC) has relied on statistical materials, which have shortcomings in terms of timeliness, authenticity, efficiency, and accuracy. Referring to the theoretical framework of the Zhongke Beauty Index, we built an evaluation index system and technical process based on POI data. In terms of the Inner Mongolia Autonomous Region (IMAR), 5.09 million POIs were collected using the web crawler technique, and the Beautiful Inner Mongolia construction evaluation and analysis were performed. The results show the following: (1) POI data can be used to establish an evaluation index system for the construction of Beautiful Inner Mongolia on the county scale; in the dimensions of industrial development, social harmony, and institutional improvement, it shows especially good application prospects. (2) The Beautiful Inner Mongolia index in 2020 was 0.22. Among the five dimensions, the industrial development index was the highest, while the cultural heritage index was the lowest. We found significant spatial differences in the dimensions of cultural heritage as well as social harmony. (3) The areas in the IMAR with a low-level construction were mostly industrial and mining areas, agricultural counties, and other economically developing areas, among which the Baiyunebo mining area and Xianghuangqi and Shiguai areas had the lowest comprehensive beauty index values. (4) We also found large numerical disparities in the level of Beautiful Inner Mongolia construction between municipal districts and banners/counties, and the ranking of each region was affected by the population and coverage areas of administrative units. After verification, we found an overall good consistency between the evaluation indexes proposed in this paper and a previous study. Therefore, this paper provides a new perspective and an effective method for the application of Internet big data in economic and social evaluation work. |
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