Abstract
Abstract The current study examines seasonality by modelling crime in small spatial units while accounting for numerous land uses (e.g. hotel, store, school and industrial) and sociodemographic characteristics. We estimate logistic regression models that predict the probability of a crime occurring in our sample of blocks in Orlando, FL over the 52 weeks of the year (i.e. block-weeks). In addition, we examined interaction terms between each land use measure and a set of measures for weeks of the year to assess seasonal effects on neighbourhood crime. Our findings reveal a (nonlinear) seasonal effect in which the risk of neighborhood crime peaks during the summer weeks and the effect of certain land uses systematically varies across weeks of the year.
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