Predicting criminal behavior with Levy flights using real data from Bogota

Mateo Dulce Rubio ()

No 17347, Documentos de Trabajo from Quantil

Abstract: 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

Keywords: Criminal behavior; Crime prediction model; Machine learning; Agent-basedmodel (search for similar items in EconPapers)
JEL-codes: C53 C63 H39 K42 (search for similar items in EconPapers)
Pages: 22
Date: 2019-04-30
New Economics Papers: this item is included in nep-big, nep-cmp, nep-law and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:col:000508:017347

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