Luiz G. A. Alves|Haroldo V. Ribeiro|Francisco A. Rodrigues
Crime prediction through urban metrics and statistical learning
Universidade de São Paulo|Universidade Estadual de Maringá|Universidade de São Paulo
understanding causes crime longstanding issue researchers agenda hard task extract causality data several linear models proposed predict crime existing correlations crime urban metrics however nongaussian distributions multicollinearity urban indicators common find controversial conclusions influence urban indicators crime machine learning ensemblebased algorithms handle well problems use random forest regressor predict crime quantify influence urban indicators homicides approach 97 accuracy crime prediction importance urban indicators ranked clustered groups equal influence robust slightly changes data sample analyzed results determine rank importance urban indicators predict crime unveiling unemployment illiteracy important variables describing homicides brazilian cities believe approach helps producing robust conclusions regarding effects urban indicators crime potential applications guiding public policies crime control