Neighborhood land use composition influences the geographical patterns of property crime. Few studies, however, have investigated if, and how, the relationships between land use and crime change over time. This research applies a Bayesian spatio-temporal regression model to analyze 12 seasons of property crime at the small-area scale. Time-varying regression coefficients estimate the seasonally varying relationships between land use and crime and distinguish both time-constant and season-specific effects. Seasonal property crime trends are commonly hypothesized to be associated with fluctuating routine activity patterns around specific land uses, but past studies do not quantify the time-varying effects of neighborhood characteristics on small-area crime risk. Results show that, accounting for sociodemographic contexts, parks are more positively associated with property crime during spring and summer seasons, and eating and drinking establishments are more positively associated during autumn and winter seasons. Land use is found to have a more substantial impact on spatial, rather than spatio-temporal, crime patterns. Proposed explanations for results focus on seasonal activity patterns and corresponding spatio-temporal interactions with the built environment. The theoretical and analytical implications of this modeling approach are discussed. This research advances past cross-sectional spatial analyses of crime by identifying built environment characteristics that simultaneously shape both where and when crime occurs.
Built Environment and Property Crime in Seattle, 1998–2000: A Bayesian Analysis
