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Master's Dissertation
DOI
https://doi.org/10.11606/D.96.2021.tde-24092021-152217
Document
Author
Full name
Ian Shinji Ferreira Fukushima
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Ribeirão Preto, 2021
Supervisor
Committee
Scorzafave, Luiz Guilherme Dacar da Silva (President)
Achcar, Jorge Alberto
Carvalho Junior, José Raimundo de Araújo
Laurini, Marcio Poletti
Title in English
Impact of social distancing due to the COVID-19 pandemic on property crimes in São Paulo: a Bayesian spatiotemporal modelling case study
Keywords in English
Bayesian analysis
Causal impact
COVID-19
Crime
Integrated ested laplace approximations
Abstract in English
With the goal of stopping COVID-19 infections, several countries adopted social distancing regulations, including closure of non essential establishments and mobility restrictions. This paper estimates the impact of these regulations on property crime in the state of São Paulo. The impact is estimated using a Bayesian spatiotemporal model, and is disaggregated by microregions. The space variability of the impact is used to infer which observed variables characterize places with higher probability of impact. We found that most microregions experienced a decrease in property crime, and that these places are characterized by higher isolation indices and less receipt of emergency cash transfers.
Title in Portuguese
Impacto do distanciamento social devido à pandemia de COVID-19 nos crimes contra propriedade em São Paulo: um estudo de caso espaço-temporal Bayesiano
Keywords in Portuguese
Análise bayesiana
Aproximações de laplace aninhadas e integradas
COVID-19
Crime
Impacto causal
Abstract in Portuguese
Com o objetivo de frear o número de infecções por COVID-19, vários países adotaram regulamentações de distanciamento social, incluindo o fechamento de estabelecimentos não essenciais e restrições de mobilidade. Este artigo estima o impacto dessas regulamentações sobre os crimes contra a propriedade no estado de São Paulo. O impacto é estimado usando um modelo espaço-temporal Bayesiano e é desagregado por microrregiões. A variabilidade espacial do impacto é usada para inferir quais variáveis observadas caracterizam os locais com maior probabilidade de terem sido impactados. Os resultados indicam que a maioria das microrregiões sofreu uma redução nos crimes contra a propriedade e que esses locais são caracterizados por índices de isolamento mais altos e menos recebimento de transferências emergenciais de dinheiro.
 
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Publishing Date
2021-10-06
 
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