Predicting criminal behavior with Levy flights using real data from Bogota

I use residential burglary data from Bogota, Colombia, to fit an agent-based modelfollowing truncated Lévy flights (Pan et al., 2018) elucidating criminal rational behaviorand validating repeat/near-repeat victimization and broken windows effects. The estimatedparameters suggest that if an average house or its neighbors have never been attacked,and it is suddenly burglarized, the probability of a new attack the next day increases, dueto the crime event, in 79 percentage points. Moreover, the following day its neighborswill also face an increment in the probability of crime of 79 percentage points. This effectpersists for a long time span. The model presents an area under the Cumulative AccuracyProfile (CAP) curve, of 0.8 performing similarly or better than state-of-the-art crimeprediction models. Public policies seeking to reduce criminal activity and its negativeconsequences must take into account these mechanisms and the self-exciting nature ofcrime to effectively make criminal hotspots safer