Modelagem de predição de crimes na região metropolitana de São Paulo

Abstract

The issue of public security is a challenge for Brazilian society, and crime is a major concern for the most populous state in the country, São Paulo. It is always desirable for the public administration to model and predict criminal trends, taking into account historical dates and the georeferencing of each municipality, meaning latitude and longitude. In this context, the use of spatiotemporal models to explain the relationship between predictors and crimes, as well as considering location, can be of great importance. One possible model is Spatial Autoregressive (SAR) modeling, which takes into account covariates and implicit spatial dependencies. In this work, based on the number of crimes, SAR modeling is used to describe and model the cities in the metropolitan region of São Paulo, Brazil, including the annual seasonality observed in the data. To visualize the data and develop modeling with the spatial neighborhood matrix, R packages such as spatialreg are used. The Lasso method is used to pre-select variables with greater significance, such as the number of inhabitants per household, the dropout rate, and the public elementary school dropout rate in the early years. Then, the SAR model is applied to include spatial information and enhance crime modeling. In general, this work focuses on developing spatiotemporal modeling for crimes in the state of São Paulo, identifying predictor variables that influence the quantity of crimes in a given municipality. In addition to the SAR model, artificial neural networks, such as multilayer models and Long Short-Term Memory (LSTM), are also used in the research, and compared with the SAR model. The goal of this dissertation is to develop predictive modeling considering spatiotemporal data for crimes in the metropolitan region of São Paulo, using predictor variables that influence the occurrence and quantity of crimes in a particular municipality. It is expected that the obtained results are useful for decision making by public administration, since the work creates a method to analyze crime patterns in a specific municipality, and also helps the city improve security issues through various social factors.