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Modelo de risco de terreno: uma estratégia preditiva para a implementação de sistemas de videovigilância
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Abstract(s)
Todos os locais comportam em si características espaciais únicas, que os tornam alvos
mais ou menos disponíveis à ocorrência do crime, levando à formação de padrões
criminais que se refletem na previsibilidade do crime. No ambiente altamente incerto que
caracteriza as sociedades hodiernas, impõe-se a adoção de novas estratégias, capazes de
identificar antecipadamente os riscos emergentes. Neste contexto, desenvolvemos a nossa
investigação em torno de uma técnica prospetiva, que emerge enquanto abordagem
inovadora à gestão do risco – o modelo de risco de terreno (RTM). Capaz de ultrapassar
a análise retrospetiva proporcionada pelas técnicas de mapeamento convencionais, o
RTM recorre aos riscos que provêm dos fatores espaciais para delimitar áreas de risco
onde o crime terá maior probabilidade de ocorrer no futuro, permitindo alterar os fatores
de risco que lhe dão origem, nomeadamente através da adoção de medidas de prevenção
situacional, como a implementação de sistemas de videovigilância. Deste modo, guiámos
o nosso estudo procurando compreender de que forma o RTM permite dimensionar um
sistema de videovigilância, com o intuito de definir os locais que, na freguesia de
Alfragide, carecem da instalação de câmaras de vigilância. Através do software RTMDx,
contruímos um modelo direcionado para o crime de furto em veículo, que resultou na
identificação de seis áreas excecionalmente em risco que permitiram sustentar a
implementação de um sistema de videovigilância na freguesia de Alfragide e identificar
as localizações onde o mesmo deve ser instalado, demonstrando assim o potencial desta
técnica prospetiva para a adoção de estratégias preventivas assentes em previsões futuras
e não em eventos passados.
Every place has unique spatial characteristics that make them more or less available to the occurrence of crime. This leads to the formation of criminal patterns that are reflected on the predictability of crime. In the uncertain environment that characterizes today’s societies, it is essential to have the ability to adopt new strategies capable of identifying in advance the emerging risks. In this context, we have developed our research around a prospective technique, which emerges as an innovative approach to risk management – risk terrain modeling (RTM). This approach is capable of overcoming the backscatter analysis provided by conventional mapping techniques by using the risks that arise from spatial factors to identify locations of risk, where crime is most likely to occur in the future. As a result, it allows to change the risk factors that trigger these situations, namely through situational prevention measures such as the implementation of video surveillance systems. This way, guided our study trying to understand how RTM allows us to develop a video surveillance system, in order to define the places in Alfragide that could benefit from the implementation of surveillance cameras. Through the RTMDx software we built a model for theft from vehicle crime, which resulted in the identification of six exceptionally risky places. Therefore, this study supports the implementation of a video surveillance system in Alfragide and allows to properly determine the locations where the cameras should be installed, showing the potential of this technique for the adoption of preventive strategies based on future forecasts and not on past events.
Every place has unique spatial characteristics that make them more or less available to the occurrence of crime. This leads to the formation of criminal patterns that are reflected on the predictability of crime. In the uncertain environment that characterizes today’s societies, it is essential to have the ability to adopt new strategies capable of identifying in advance the emerging risks. In this context, we have developed our research around a prospective technique, which emerges as an innovative approach to risk management – risk terrain modeling (RTM). This approach is capable of overcoming the backscatter analysis provided by conventional mapping techniques by using the risks that arise from spatial factors to identify locations of risk, where crime is most likely to occur in the future. As a result, it allows to change the risk factors that trigger these situations, namely through situational prevention measures such as the implementation of video surveillance systems. This way, guided our study trying to understand how RTM allows us to develop a video surveillance system, in order to define the places in Alfragide that could benefit from the implementation of surveillance cameras. Through the RTMDx software we built a model for theft from vehicle crime, which resulted in the identification of six exceptionally risky places. Therefore, this study supports the implementation of a video surveillance system in Alfragide and allows to properly determine the locations where the cameras should be installed, showing the potential of this technique for the adoption of preventive strategies based on future forecasts and not on past events.
Description
Keywords
Polícia de Segurança Pública Áreas excecionalmente em risco Criminologia ambiental Modelo de risco de terreno Policiamento preditivo Sistemas de videovigilância