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Recent urban studies have used human mobility data such as taxi trajectories and smartcard data as a complementary way to identify the social functions of land use. However, little work has been conducted to reveal how multi‐modal transportation data impact on this identification process. In our study, we propose a data‐driven approach that addresses the relationships between travel behavior and urban structure: first, multi‐modal transportation data are aggregated to extract explicit statistical features; then, topic modeling methods are applied to transform these explicit statistical features into latent semantic features; and finally, a classification method is used to identify functional zones with similar latent topic distributions. Two 10‐day‐long "big" datasets from the 2,370 bicycle stations of the public bicycle‐sharing system, and up to 9,992 taxi cabs within the core urban area of Hangzhou City, China, as well as point‐of‐interest data are tested to reveal the extent to which different travel modes contribute to the detection and understanding of urban land functions. Our results show that: (1) using latent semantic features delineated from the topic modeling process as the classification input outperforms approaches using explicit statistical features; (2) combining multi‐modal data visibly improves the accuracy and consistency of the identified functional zones; and (3) the proposed data‐driven approach is also capable of identifying mixed land use in the urban space. This work presents a novel attempt to uncover the hidden linkages between urban transportation patterns with urban land use and its functions. [ABSTRACT FROM AUTHOR]

Urban land use is constantly changing via human activities. These changes are recorded by imprint data. Traditionally, urban dynamics studies focus on two-dimensional spatiotemporal analysis. Based on our best knowledge, there is no study in the literature that uses imprint data for better understanding urban dynamics. In this research, we propose a spatial–temporal–semantic triple analytical framework to better understand urban dynamics by making full use of the imprint data, toponyms. The framework includes a text classification method and geographical analysis methods to understand urban dynamics in depth. Based on the inherent temporal and spatial information, we enrich semantic information with street names to explain urban dynamics in multiple dimensions. Taking Hangzhou city as an example, we used street names to reproduce the city changes over the past century. The results obtained through analysis of street names may accurately reflect the real development process of Hangzhou. This research demonstrates that imprint data left by urban development may play a pivotal role in better understanding urban dynamics. [ABSTRACT FROM AUTHOR]

As a major labor force of cities, young people provide a huge driving force for urban innovation and development, and contribute to urban industrial upgrading and restructuring. In addition, with the acceleration of urbanization in China, the young floating population has increased rapidly, causing over-urbanization and creating certain social problems. It is important to analyze the demand of urban youth and promote their social integration. With the development of the mobile Internet and the improvement of the city express system, ordering food delivery has become a popular and convenient way to dine, especially in China. Food delivery data have a significant user attribute where the ages of most delivery customers are under 35 years old. In this paper, we introduce food delivery data as a new data source in urban functional zone detection and propose a time-series-based clustering approach to discover the urban hotspot areas of young people. The work and living areas were effectively identified according to the human behavioral characteristics of ordering food delivery. Furthermore, we analyzed the relationship between young people and the industry structure of Hangzhou and discovered that the geographical distribution of the identified work areas was similar to that of the Internet and e-commerce companies. The characteristics of the identified living areas were also analyzed in combination with the distribution of subway lines and residential communities, and it was found that the living areas were mainly distributed along subway lines and that urban villages appeared in the living hotspot regions, indicating that transportation and living cost were two important factors in the choice of residential location for young people. The findings of this paper can help urban industrial and residential planning and young population management. [ABSTRACT FROM AUTHOR]

Research on commercial activities and their spatial structure is an important topic in urban geography and economic geography. As an online-to-offline business activity, takeout ordering is an important approach for urban residents to solve their catering needs. It can well reflect the working and living conditions of the population and is a source for studying urban life. Based on the takeout data in the city of Hangzhou, China, this study uses the customers' detailed order information to extract three dietary taste patterns: spicy, sweet, and light. We then combine the dietary taste patterns with points of interest and land use data to identify the location attributes of order destination and explore the temporal and spatial distribution characteristics of dietary taste patterns. The results show differences in dietary behavior of people at the community scale, and help us understand the local culture and socio-demographic characteristics from the perspective of diet. Dietary taste pattern is closely related to timing and location, which reflects differences in consumption tendencies, and socio-demographic heterogeneity. This research helps to present the dietary characteristics of different population groups and their spatial distribution, and supplements the content of urban research on foodscape. • Extracts three dietary patterns of Hangzhou people based on take-out order data • Links dietary habits to socio-demographic heterogeneity and sense of place • Utilizes a new type of big data from the food delivery industry into urban analytics on foodscape research and planning [ABSTRACT FROM AUTHOR]