Estimation of Crime Seasonality: A Cross-Sectional Extension to Time Series Classical Decomposition

Reliable estimates of crime seasonality are valuable for law enforcement and crime prevention. Seasonality affects many police decisions from long-term reallocation of uniformed officers across precincts to short-term targeting of patrols for hot spots and serial criminals. This paper shows that crime seasonality is a small-scale, neighborhood-level phenomenon. In contrast, the vast literature on crime seasonality has almost exclusively examined crime data aggregations at the city or even larger scales. Spatial heterogeneity of crime seasonality, however, often gives rise to opposing seasonal patterns in different kinds of neighborhoods, canceling out seasonality at the city-wide level. Thus past estimates of crime seasonality have vastly underestimated the magnitude and impact of the phenomenon. We present a model for crime seasonality that extends classical decomposition of time series based on a multivariate, cross-sectional, fixedeffects model. The crux of the model is an interaction of monthly seasonal dummy variables with five factor scores representing the urban ecology as viewed from the perspective of major crime theories. The urban ecology factors, interacted with monthly seasonal dummy variables, provide neighborhood-level seasonality estimates. A polynomial in time and fixed effects dummy variables for spatial units control for large temporal and spatial variations in crime data. Our results require crime mapping for implementation by police including thematic mapping of next month’s forecasted crime levels (which are dominated by seasonal variations) by grid cell or neighborhood, thematic mapping of the urban ecology for developing an understanding of underlying causes of crime, and ability to zoom into neighborhoods to study recent crime points.