The transfer space realizes the connectivity of subway intersections. Passengers generally express that they have a poor experience in the use of this space, so improving the environmental suitability of transfer spaces at subway stations is a top priority. Based on a literature review and field research, this study established an environmental suitability evaluation system for transfer spaces and used the fuzzy comprehensive evaluation method to evaluate the environmental suitability of eight samples in Shanghai. The results showed that the evaluation results of the eight samples were ranked as follows: Hanzhong Road Station > People’s Square Station > East Nanjing Road Station > Century Avenue Station > Xujiahui Station > Laoximen Station > Jiangsu Road Station > Shanghai Railway Station. Through the analysis of the relationship between the indicators, it was found that the environmental suitability of a transfer space is greatly affected by safety and convenience, while practicality, comfort, and aesthetics were found to have a weak influence on the suitability of transfer spaces. These evaluation methods and results provide a reference for the improvement of the environmental quality of subway transfer spaces in other cities.

Big data has provided new opportunities, directions, and methods for research on urban commercial center systems. Based on a quantitative assessment of big data and public participation, the “big data + public feedback” evaluation model can objectively and scientifically quantify the scale and structural characteristics of urban commercial center systems. In this paper, socioeconomic and material spatial attributes were considered in the selection of four indexes, including commercial agglomeration centrality, commercial facility service level, commercial industry status, and industry attraction. Specifically, we based our selection on the big data of the point-of-interest network, housing price, and population. ArcGIS, SPSS, and other analytical tools were employed to conduct a comparative analysis, cluster analysis, spatial network analysis, and correlation analysis. Using these data, we constructed an assessment index system, which was then utilized to comprehensively evaluate the current commercial land use in Nanjing’s main urban area and measure the degree of commercialization. The commercial center system in the main urban area of Nanjing was found to be consistent with the spatial structure system of “one main core, five secondary cores, multiple district cores, three horizontal axes, and one vertical axis.” Meanwhile, a public questionnaire was used to evaluate the public’s perception of the commercialization level in Nanjing. Finally, the results obtained were used for comparison with the structure of the commercial center system of Nanjing commercial network planning. We discovered that the results of the public’s perception of the commercialization level in Nanjing were similar to those of the big data analysis, which confirmed the credibility of big data analysis results. In conclusion, the findings of this study provide a basis for developing urban commercial center-level positioning and propose a method for data-assisted planning research.

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities.