All Burglaries Are Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi

The evidence that burglaries cluster spatio-temporally is strong. However, research unclear on whether clustered (repeats/near-repeats) should be treated as qualitatively different crimes compared to unrelated (non-repeats). This study, therefore, investigated if there were differences in modus operandi-signatures (MOs, the habits and methods employed by criminals) between near-repeat non-repeat across 10 Swedish cities, well MO-signatures can aid predicting a burglary classified or crime. Data consisted of 5744 residential burglaries, with 137 MO features characterizing each case. Descriptive data repeats/non-repeats provided together Wilcoxon tests MO-differences crime pairs, while logistic regressions used train models predict scene was Near-repeat rather stylized, showing heterogeneity MOs but homogeneity within cities at same time, significant including subgroups features, such mode entering, target selection, types goods stolen, traces left scene. Furthermore, using regression models, it possible mean F1-score 0.8155 (0.0866) based MO. Potential policy implications are discussed terms how data-driven procedures facilitate analysis spatio-temporal phenomena offenders, law enforcement agencies provide differentiated advice response when suspicion part series opposed an isolated event.