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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]

Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play important roles in the high-quality development of urban spatial environments. However, traditional planning design and study methods, typically based on questionnaires, interviews, and on-site research, are inefficient and make it difficult to objectively and comprehensively grasp the overall construction characteristics and problems of urban street space in a large area, thus making it challenging to meet the needs of practical planning. Therefore, based on street view images, this study combined machine learning with an artificial audit to put forward a methodological framework for diagnosing the quality issues of street space. The Gongshu District of Hangzhou, China, was selected as a case study, and the diagnosis of quality problems for streets at different grades was achieved. The diagnosis results showed the current situation and problems of the selected area. Simultaneously, a series of targeted strategies for street spatial update planning was proposed to solve these problems. This diagnostic method, based on a combination of subjective and objective approaches, can be conducive to the precise and comprehensive identification of urban public spatial problems, which is expected to become an effective tool to assist in urban renewal and other planning decisions. [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]