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This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of 'Least Absolute Shrinkage and Selection Operator' (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into explanatory variable groups were developed for seven types of property crime. Then, the geographically weighted regression (GWR) model was used to reveal the spatial associations between distinct explanatory variables and the specific type of crime. The findings demonstrated that human activity dynamics impose a substantially stronger influence on specific types of property crimes than other static variables. In terms of crime type, theft obtained particularly high relationships with dynamic human activity compared to other property crimes. Further analysis revealed important nuances in the spatial associations between property crimes and human activity across different contexts during the pandemic. The result provides support for crime risk prediction that considers the impact of dynamic human activity variables and their varying influences in distinct situations. [ABSTRACT FROM AUTHOR]

Abstract As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.

This study aimed to evaluate the relationships between different groups of explanatory variables (i.e., dynamic human activity variables, static variables of social disorganisation and crime generators, and combinations of both sets of variables) and property crime patterns across neighbourhood areas of London during the pandemic (from 2020 to 2021). Using the dynamic human activity variables sensed from mobile phone GPS big data sets, three types of ‘Least Absolute Shrinkage and Selection Operator’ (LASSO) regression models (i.e., static, dynamic, and static and dynamic) differentiated into explanatory variable groups were developed for seven types of property crime. Then, the geographically weighted regression (GWR) model was used to reveal the spatial associations between distinct explanatory variables and the specific type of crime. The findings demonstrated that human activity dynamics impose a substantially stronger influence on specific types of property crimes than other static variables. In terms of crime type, theft obtained particularly high relationships with dynamic human activity compared to other property crimes. Further analysis revealed important nuances in the spatial associations between property crimes and human activity across different contexts during the pandemic. The result provides support for crime risk prediction that considers the impact of dynamic human activity variables and their varying influences in distinct situations.

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets' functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets. [ABSTRACT FROM AUTHOR]

With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers' behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities. [ABSTRACT FROM AUTHOR]