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Data Science na PSP: potencial para a prevenção do crime de roubo no COMETLIS
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Orientador(es)
Resumo(s)
O crime de roubo, no período em análise para o presente estudo, revelou uma tendência de
registo sempre acima dos 50% face ao total da criminalidade violenta e grave registada, a
nível nacional, surgindo Lisboa como um dos distrito com maior incidência, facto esse que
pode impactar com o sentimento de segurança das populações. Com o advento das novas
tecnologias, aliada à capacidade de gerar dados massivamente, a Data Science ganha
especial relevo na vida das organizações um pouco por todo o mundo. Nesse sentido, fomos
perceber o que é que esta ciência já evoluiu e que potencial a mesma apresenta para as
organizações, em concreto para as organizações policiais. O objetivo deste estudo foi o de
perceber qual o potencial que a Data Science apresenta para a prevenção do crime de roubo
no Comando Metropolitano da Polícia de Segurança Pública de Lisboa. Para atingir este
objetivo recolhemos as perceções de Chefes e Oficiais de Polícia que desempenham funções
nas divisões e esquadra genéricas, integradas e destacadas do referido Comando.
Percebemos que a Data Science revela potencial, embora no caso da Polícia de Segurança
Pública não esteja a ser capitalizado. Os resultados também revelaram que os Oficiais de
Polícia se mostram mais recetivos do que os Chefes de Polícia ao desenvolvimento e
implementação de uma ferramenta de Data Science. Foi ainda possível apurar que é do
entendimento dos inquiridos que a análise de dados, com recursos a técnicas de Data
Science, pode tornar a Polícia de Segurança Pública uma Força de Segurança mais eficiente.
Percebeu-se também que, atualmente, os dados que são disponibilizados sobre a evolução
criminal, concretamente sobre o crime de roubo, não são suficientes para auxiliar na
prevenção do mesmo.
The crime of robbery, in the period analysed for this study, showed a trend of always being above 50% of the total violent and serious crime recorded at national level, with Lisbon appearing as one of the districts with the highest incidence, a fact that can have an impact on people's sense of security. With the advent of new technologies, combined with the ability to generate massive amounts of data, Data Science is gaining special importance in the lives of organisations all over the world. With this in mind, we set out to understand how this science has evolved and what potential it has for organisations, specifically police organisations. The aim of this study was to understand the potential of Data Science for preventing the crime of robbery in Lisbon Metropolitan Command of the Public Security Police. In order to achieve this goal, we gathered the perceptions of Police Chiefs and Officials who work in the generic, integrated and detached divisions and police stations of the aforementioned Command. We found that Data Science has potential, although in the case of the Public Security Police it is not being capitalised on. The results also revealed that Police Officials are more receptive than Police Chiefs to developing and implementing a Data Science tool. It was also possible to ascertain that the respondents believe that analysing data using Data Science techniques can make the Public Security Police a more efficient security force. They also realised that the data currently available on crime trends, specifically on the crime of robbery, is not sufficient to help prevent it.
The crime of robbery, in the period analysed for this study, showed a trend of always being above 50% of the total violent and serious crime recorded at national level, with Lisbon appearing as one of the districts with the highest incidence, a fact that can have an impact on people's sense of security. With the advent of new technologies, combined with the ability to generate massive amounts of data, Data Science is gaining special importance in the lives of organisations all over the world. With this in mind, we set out to understand how this science has evolved and what potential it has for organisations, specifically police organisations. The aim of this study was to understand the potential of Data Science for preventing the crime of robbery in Lisbon Metropolitan Command of the Public Security Police. In order to achieve this goal, we gathered the perceptions of Police Chiefs and Officials who work in the generic, integrated and detached divisions and police stations of the aforementioned Command. We found that Data Science has potential, although in the case of the Public Security Police it is not being capitalised on. The results also revealed that Police Officials are more receptive than Police Chiefs to developing and implementing a Data Science tool. It was also possible to ascertain that the respondents believe that analysing data using Data Science techniques can make the Public Security Police a more efficient security force. They also realised that the data currently available on crime trends, specifically on the crime of robbery, is not sufficient to help prevent it.
Descrição
Palavras-chave
análise preditiva/ prescritiva data science PSP prevenção criminal predictive/prescritive analyses datascience crime prevention