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Streets, as one type of land use, are generally treated as developed or impervious areas in most of the land-use/land-cover studies. This coarse classification substantially understates the value of streets as a type of public space with the most complexity. Street space, being an important arena for urban vitality, is valued by various dimensions, such as transportation, recreation, aesthetics, public health, and social interactions. Traditional remote sensing approaches taking a sky viewpoint cannot capture these dimensions not only due to the resolution issue but also the lack of a citizen viewpoint. The proliferation of street view images provides an unprecedented opportunity to characterize street spaces from a citizen perspective at the human scale for an entire city. This paper aims to characterize and classify street spaces based on features extracted from street view images by a deep learning model of computer vision. A rule-based clustering method is devised to support the empirically generated classification of street spaces. The proposed classification scheme of street spaces can serve as an indirect indicator of place-related functions if not a direct one, once its relationship with urban functions is empirically tested and established. This approach is empirically applied to Beijing city to demonstrate its validity.

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The urban structure of large Chinese cities has been well researched, but a systematic analysis of polycentric urban development and the determinants of subcenter formation across municipal districts in cities at the prefectural level and above (PLACMD) is lacking. Using geospatial big data and spatial analysis methods, we measure the urban spatial structure of all 294 PLACMDs to determine the polycentric urban structure in China and conduct an exploratory regression analysis of 59 PLACMDs (due to data restrictions) to explore the formation of polycentric cities. Our results suggest that using location‐based data allows for a timelier and more accurate center identification of detailed urban structural features than using other data. Each PLACMD in China has at least one center, and polycentricity is currently the most common urban spatial structure. PLACMDs with higher populations are more polycentric. Compared with the results obtained from large American urban areas, our regression results imply that population alone accounts for most of the variation in the polycentric index and that commuting costs provide a weak explanation of the existence of Chinese PLACMDs. Both economic development and agglomeration economics are associated with the polycentric index. In contrast, the topographical features are statistically nonsignificant in the regression model.