The study explores the features of spatiotemporal circles of residents’ daily walking. Through a survey of residents’ walking activity in 16 residential communities, the walking purpose, distance, time, and speed of different residents were analyzed, and the circles of residents’ walking activities in historic and modern districts were identified. It is found that residents’ walking activities showed obvious spatiotemporal and individual differences. Walking activities on weekdays mainly focus on short distances (0.5–1 km) and short duration (5–15 min) for commuting and basic needs, while walking activities on weekends tend to be longer distances (more than 2 km) and longer duration (15–40 min) for leisure purposes. There are significant differences in distance and speed between walking activities in the historic and modern districts, with residents of the historic districts walking a smaller range but more diverse destinations, and residents of the modern districts walking to a wider range but fewer types of destinations. The study provides a scientific basis for multi-circle planning strategies of community life units, and it contributes to the localized adaptation of the “15-minute city” concept by revealing how historical and modern districts shape distinct spatiotemporal circles for walkability in Chinese cities.

Commercial spaces are essential urban environments where consumer movement is inherently sequential, involving multiple destinations and complex spatial interactions. However, existing studies mainly rely on aggregated flow data and provide a limited understanding of how consumers organize their movements into complete spatial sequences. To address this limitation, this study proposes a framework based on typical routes to investigate sequential movement patterns in commercial spaces. Typical routes are conceptualized in two complementary ways: as representative routes extracted from observed traces and as highly probable routes generated from sequential destination choices. Accordingly, two approaches are integrated. First, route clustering is conducted using Levenshtein-ratio-based similarity measurement and affinity propagation (AP) clustering to identify representative route exemplars with distinct sequential structures. Second, route prediction is performed by combining discrete choice models (DCM), convolutional neural networks (CNN), and beam search to generate high-probability movement sequences. The framework is applied to 323 observed consumer routes collected from a large commercial complex in Shanghai, China. The results reveal several recurring movement patterns, such as task-oriented, fashion-oriented, dining-oriented, and family-oriented routes. Among them, the most dominant pattern is characterized by repeated movement between anchor stores through sequences of intermediate, smaller shops, highlighting the organizing role of anchor destinations in structuring consumer circulation. Both clustering and prediction demonstrate that consumer movement in commercial spaces is not random, but follows relatively stable sequential logics. In the prediction task, CNN achieves substantially higher accuracy than DCM, thereby providing stronger support for route simulation. Overall, the study demonstrates that integrating route clustering and route prediction provides a useful framework for understanding, interpreting, and simulating sequential consumer movement in commercial environments, with practical implications for circulation organization, anchor-store layout, and commercial space design.

Cultural heritage conservation planning in China advocates for differentiated planning measures tailored to different heritage elements with diverse values, functions, and locations. However, limited research has focused on the spatial heterogeneity of these multi-dimensional planning measures and the subjective well-being (SWB) of residents within these protected historic districts. This study investigates the spatial differentiation pattern and mechanism of residents’ SWB in three Historic Conservation Areas of Yangzhou, China, by employing a combination of spatial data digitization and data spatialization methods. The findings reveal: (1) All three cases demonstrate notable spatial differentiation in terms of residents’ SWB; (2) A common feature across the three cases is that strengthening community participation, tourism traffic control, and housing improvement projects can significantly improve residents’ SWB. But proximity to historic buildings has little explanatory power for the spatial differentiation of residents’ SWB; (3) Planning factors of traffic accessibility, public facility accessibility, park service areas, and NIMBY areas of public toilets, have significant spatial heterogeneous effects on residents’ SWB across the three cases, which are closely correlated to the varying degrees of tourism development within each district. The findings of this study provide targeted planning strategies for historic districts with different functional orientations and heritage conservation duties, aimed at more effectively enhancing the well-being of heritage site residents by utilizing limited public resources.