In the context of population aging and increasing global extreme climate events, this study focuses on evaluating the urban resilience and sustainable development capacity of two cities (Changchun and Hohhot). By integrating data from multiple sources and applying techniques such as big data statistical analysis, random forest, and decision tree, the research addresses several key aspects. Firstly, housing prices in different areas of the two cities are predicted, and the total existing housing stock is estimated. Secondly, the service levels of various departments in the two cities are quantitatively analyzed to identify commonalities and differences. Thirdly, the resilience of the two cities in coping with extreme weather and emergencies is evaluated, along with their sustainable development capacity. Based on these analyses, future development plans are formulated for each city, including investment areas, amounts, and expected improvements in smart city development. The results show differences in housing prices, service levels, and resilience between the two cities, with Changchun generally showing more balanced development and higher resilience in some aspects. The study also acknowledges limitations in data and models and suggests future research directions for further optimization.
Wang et al. (Tue,) studied this question.
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