As a hot research topic in urban geography, spatiotemporal interaction analysis has been used to detect the hotspot mobility patterns of crowds and urban structures based on the origin-destination (OD) flow data, which provide useful information for urban planning and traffic management applications. However, existing methods mainly focus on the detection of explicit spatial interaction patterns (such as spatial flow clusters) in OD flow data, with less attention to the discovery of underlying crowd travel demands. Therefore, this paper proposes a framework to discover the crowd travel demands by associating the dynamic spatiotemporal interaction patterns and the contextual semantic features of the geographical environment. With urban functional zones (UFZs) as the basic units of human mobility in urban spaces, this paper gives a case study in Wuhan, China, to detect and interpret the human mobility patterns based on the characteristics of spatiotemporal interaction between UFZs. Firstly, we build the spatiotemporal interaction matrix based on the OD flows of different UFZs and analyze the characteristics of the interaction matrix. Then, hotspot poles, defined as the local areas where people gather significantly, are extracted using the Gi-statistic-based spatial hotspot detection algorithm. Next, we develop a frequent interaction pattern mining method to detect the frequent interaction patterns of the hotspot poles. Finally, based on the detected frequent interaction patterns, we discover the travel demands of crowds with semantic features of corresponding urban functional zones. The characteristics of crowd travel distance and travel time are further discussed. Experiments with floating car data, road networks, and POIs in Wuhan were conducted, and results show that the underlying travel demands can be better discovered and interpreted by the proposed framework and methods in this paper. This study helps to understand the characteristics of human movement and can provide support for applications such as urban planning and facility optimization.

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Rapid urbanization in China has led to an exponential increase in the stocks of metals used in cities. Exploring their amount and growth patterns is an important way to forecast future metal demand and identify the potential for urban mining. Here, we use a combination of bottom-up and GIS tools to estimate the amount of in-use stocks and scrap metal of steel, copper, and aluminum in 366 regions of mainland China from 2010 to 2020. We then downscaled the 2020 metal scrap volume based on a multi-source dataset of socioeconomic factors. Finally, the accessibility of the urban mining pilot base (UMPB) was calculated using the two-step floating catchment area method (2SFCA), and the spatial layout assessment analysis of the UMPB was conducted under the supply–demand balance perspective. The results showed that the total in-use stocks of steel, copper, and aluminum increased from an initial 3186 million tons to 5216 million tons, with a corresponding trend of continued growth in the amount of metal scrap. The high value of scrap metal in 2020 is concentrated in the Beijing–Tianjin–Hebei urban agglomeration, the Yangtze River Delta region, and the Chengdu–Chongqing metropolitan area. The accessibility results show that the road network distance-based accessibility covered a smaller area than the Euclidean distance-based accessibility, but when the UMPB service radius was set to 300 km, the road network distance-based accessibility could also cover most of the eastern part of China. The spatial evaluation results of UMPB show that for service radii of 200 km and 300 km, low-supply and high-demand areas account for 6.32 percent and 5.89 percent, respectively.