With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

Services provided by public facilities are essential to people’s lives and are closely associated with human mobility. Traditionally, public facility access characteristics, such as accessibility, equity issues and service areas, are investigated mainly based on static data (census data, travel surveys and particular records, such as medical records). Currently, the advent of big data offers an unprecedented opportunity to obtain large-scale human mobility data, which can be used to study the characteristics of public facilities from the spatial interaction perspective. Intuitively, spatial interaction characteristics and service areas of different types and sizes of public facilities are different, but how different remains an open question, so we, in turn, examine this question. Based on spatial interaction, we classify public facilities and explore the differences in facilities. In the research, based on spatial interaction extracted from taxi data, we introduce an unsupervised classification method to classify 78 hospitals in 6 districts of Beijing, and the results better reflect the type of hospital. The findings are of great significance for optimizing the spatial configuration of medical facilities or other types of public facilities, allocating public resources reasonably and relieving traffic pressure.

One of the key concerns in geographical and social sciences is to analyze and predict population migration due to its close association with urban planning, industrial upgrade, and urban development. Although the most prevailing framework, the gravity model, has been applied in its various versions, there is little information available about how city industry structure functions as the invisible distance in the modeling of intercity population migration. Here, we introduce a family of improved gravity models by considering city industry structure proximity, complementarity, and diversities. The resulting models predict population migration patterns in good agreement with the flows observed. Our best model (GM_COM) outperforms the benchmark model (GM_O) by 24.6 percent in terms of mean absolute percentage error. Further analysis shows the improved models offer several advantages with respect to the base models. They have better prediction accuracies for flows with high intensities and long distances. The best model demonstrates obvious improvement when flows occur in eastern China. Given the significant improvement of the proposed models, this study broadens existing research by absorbing city industry structure features into the gravity model and deepens our understanding in the population migration as a function of distance.