Spatial analysis of outdoor indecent assault risk: a study using ambient population data

Abstract

Abstract Spatiotemporal data on ambient populations have recently become widely available. Although previous studies have indicated a link between the spatial patterns of crime occurrence and ambient population distribution, more detailed information, such as the population most likely to be victims by gender and age group, could better predict the risk of crime occurrence. Therefore, this study aimed to analyze the risk of indecent assault, a typical crime with a high number of young female victims, in southern Kyoto Prefecture. We utilized population distribution by gender and age group at different times of the day. After extracting daily patterns (factors) of the population using non-negative matrix factorization, we statistically modeled the risk of indecent assault using a spatial conditional autoregressive model. The results showed that the model, which considered a spatiotemporal ambient population, demonstrated superior performance during nighttime hours. Furthermore, by interpreting the factors significantly associated with the risk of crime occurrence, the findings provided valuable insights into local crime prevention measures that consider daily temporal changes in the gender and age-group composition of individuals present in a specific area.

Keywords

• Street crime
• Ambient population
• Mobile spatial statistics
• Non-negative matrix factorization
• Conditional autoregressive model