Although bicycle theft is a common issue across many urban cities, the empirical evidence of bicycle theft patterns is sparse, in particular within Canada. Existing studies have primarily focused on the built environment, while largely overlooking the potential influence of topography and street centrality. Drawing on principles from environmental criminology, this study explores the spatial distribution of reported bicycle theft in Toronto, Canada, between 2014 and 2024 (n = 37,318) across three spatial scales. Measures of spatial access were used to capture both the proximity and availability of select built environment features, alongside street centrality and topographical elements. Findings indicate that both street elevation and hilliness were negatively associated with bicycle theft, suggesting that streets at higher elevation and in more hilly areas experience fewer theft. Several infrastructure-related features, including public transportation stops, bikeshare stations, and bicycle lanes, also emerged as consistent predictors of theft, while street centrality, slope, and parks were not significant. Bicycle parking facilities and universities were only predictive at the smallest spatial scale. These findings highlight the importance of incorporating topography into bicycle theft research, as these factors may shape offender decision making, target accessibility, and perceived effort.

This study investigates how the collective mobility (including movement and visiting) of residents and non-residents affects neighbourhood burglary levels. While past research has linked mobility to urban crime, this study explores how these relationships vary across population groups and social contexts at the neighbourhood level. Using mobile phone GPS data, we distinguished between residents and non-residents based on daily movement patterns. We then measured their mobility within defined spatial and temporal units. An explainable machine learning method (XGBoost and SHAP) was used to assess how mobility patterns influence burglary in London’s LSOAs from 2020 to 2021. Results show that increased collective mobility is generally associated with higher burglary levels. Specifically, non-resident footfall and residents’ stay-at-home time have a stronger influence than other variables like residents’ travelled distance. The impact also varies across neighbourhoods and shifts during periods of COVID-19 restrictions and relaxations. These findings confirm the dynamic link between mobility and crime, highlighting the value of understanding population-specific patterns to inform more targeted policing strategies.

Urban emergencies significantly disrupt the subjective well-being (SWB) of urban population, while limited research has explored how time-use patterns interact with personality traits to shape SWB in crisis contexts. Understanding these mechanisms is essential for effective urban management and community resilience. This study investigates the influence of time use and personality profiles on SWB during urban emergencies, using data from the Shanghai lockdown. We identify three distinct personality profiles (i.e., Positive, Introverted, and Sensitive) and examine their heterogeneous responses. Our findings reveal that key quality-of-life factors, including health perception, social connection, and community liveability, directly influence SWB. Furthermore, time-use patterns, such as outdoor activities, paid work, sleep, online socialising, entertainment, and offline leisure, significantly affect residents’ life quality and SWB. In addition, personality traits moderate these effects: Positive individuals are particularly sensitive to sleep duration, while Sensitive individuals experience greater well-being variations due to outdoor activities. By revisiting the interactions between time use, personality traits, and SWB, our findings offer evidence-based guidance for policymakers and urban planners. This knowledge advances the understanding of psychological adaptation during urban emergencies and provides a foundation for more targeted approaches to community welfare, thereby strengthening community resilience during future crises.

Urban agglomeration, a product of advanced urbanization and industrial transformation, plays a critical role in national spatial and economic systems. Urban agglomeration resilience remains underexplored in existing literature, particularly regarding how multi-scale interactions between macro-level network structures and micro-level public perceptions jointly shape regional adaptive capacities during crises. To address this gap, this study proposes a novel dual-perspective framework integrating population mobility network analysis (macro) and public sentiment analytics (micro) to evaluate the resilience of eight major Chinese urban agglomerations during the COVID-19 epidemic. The results indicate the agglomerations robust intercity connectivity and stable structural networks, such as polycentricity structures or breaking the inter-provincial effect, exhibit higher resilience. Based on the experimental results, we propose multi-scale development strategies. It is argued that the development of urban agglomerations should focus on breaking down urban barriers, enhancing network hierarchies and complexity, and mitigate long-term urban vulnerabilities of unexpected events.

Delineating the boundary or impact area of an economic, cultural, or lifestyle region has been a long-lasting problem in urban and regional geography. The fundamental difficulty lies in the exact definition of a region, and what criteria need to be considered. Existing methods either use criterion-based definitions or network-based measures to evaluate the affiliation of a city to a region. However, both types of methods only give static and definitive results but ignore the dynamism and graduality between regions. In this paper, we propose a Singular Value Decomposition (SVD)-based method to depict the impact areas of regions in China using individual connections among cities. Using the individual mobility data from an online map service, we decompose the mobility patterns of China into a series of eigen-mobility-patterns—each corresponds to the impact area of a city, or a mobility-based region. The overlay of multiple eigen-mobility patterns depicts the “blurred” boundary between the respective regions—or their competing hinterlands. We hope the method could be used to help understand the complexity of drawing regional boundaries and help policymakers to identify the non-confined but blurred economic and cultural landscape of various contexts in regional governance.

Redlining is a discriminatory practice of systematically denying loans or mortgages to residents in specific neighborhoods based on racial or ethnical composition. In current literature research, there is a lack of understanding of the public perceptions of impacts of historical redlining practices at large geographic scales. Although some social groups and organizations conducted surveys or interviews to obtain public perceptions of it on small groups of people in certain areas, our knowledge of the impacts of redlining is limited and may reflect bias. This study used geotagged tweets from 2011 to 2023 to investigate public perceptions of redlining practices in U.S. counties. Multiscale geographically weighted regression (MGWR) was performed to explore both spatial heterogeneity and varying scales of associations between percentage of redlining-related geotagged tweets with negative sentiment and potential explanatory shaping factors in U.S. counties. Counties with a higher average household size, a higher percentage of people aged 45+, a lower homeownership rate, and a higher mobile home percentage have a significant association nationwide with more negative-sentiment expression in redlining-related tweets. However, counties with a lower insurance coverage are less likely to express negative sentiment in redlining-related tweets in some eastern U.S. counties, indicating a local significant association. The findings help people better understand the relationship between public perceptions of redlining practices and potential shaping factors. This study’s methodology can also be applied to investigate public perspectives or perceptions on other controversial social topics.

Urban centers, as core zones for development, are often defined more by functional concepts than explicit boundaries. This ambiguity complicates policy formulation and results in a gap between planning objectives and actual development. Existing studies primarily utilize multi-source data to delineate the actual functional scope of urban centers. However, relying solely on current situation analysis lacks the forward-looking ‘spatial potential’ dimension, thus limiting the effective evaluation and optimization of planning, especially for TOD-based urban centers. Therefore, spatial potential is introduced as a critical intermediary to link and compare planning intentions with actual outcomes. Taking Wujiaochang subcenter in Shanghai as a case study, this study proposes a ‘Planned-Actual-Potential’ (P-A-P) multi-dimensional analysis framework. The differences between these three scopes are compared to derive mismatches between planned supply and actual demand. It is found that due to market forces, the Actual Scope is partially beyond the Planned Scope, while some potential parcels lack corresponding planning support, leading to resource and functional mismatches. Finally, practical suggestions are proposed for planning and policy optimization, including detecting and supporting the potential, yet unplanned, parcels.