Modelos matemáticos e computacionais de otimização de estratégias de redução dos níveis de violência no Brasil / RP

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    Mirante: a visualization tool for analyzing urban crimes
    (2020) Garcia-Zanabria, Germain; Gomez-Nieto, Erick; Silveira, Jaqueline; Poco, Jorge; Nery, Marcelo Batista; Adorno, Sergio; Nonato, Luis Gustavo
    Visualization assisted crime analysis tools used by public security agencies are usually designed to explore large urban areas, relying on grid-based heatmaps to reveal spatial crime distribution in whole districts, regions, and neighborhoods. Therefore, those tools can hardly identify micro-scale patterns closely related to crime opportunity, whose understanding is fundamental to the planning of preventive actions. Enabling a combined analysis of spatial patterns and their evolution over time is another challenge faced by most crime analysis tools. In this paper, we present Mirante, a crime mapping visualization system that allows spatiotemporal analysis of crime patterns in a street-level scale. In contrast to conventional tools, Mirante builds upon street-level heatmaps and other visualization resources that enable spatial and temporal pattern analysis, uncovering fine-scale crime hotspots, seasonality, and dynamics over time. Mirante has been developed in close collaboration with domain experts, following rigid requirements as scalability and versatile to be implemented in large and medium-sized cities. We demonstrate the usefulness of Mirante throughout case studies run by domain experts using real data sets from cities with different characteristics. With the help of Mirante, the experts were capable of diagnosing how crime evolves in specific regions of the cities while still being able to raise hypotheses about why certain types of crime show up.
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    CriPAV: street-level crime patterns analysis and visualization
    (2015-08) Garcia-Zanabria, Germain; Raimundo, Marcos Medeiros; Poco, Jorge; Nery, Marcelo Batista; Silva, Cláudio T.; Adorno, Sergio; Nonato, Luis Gustavo
    Extracting and analyzing crime patterns in big cities is a challenging spatiotemporal problem. The hardness of the problem is linked to two main factors, the sparse nature of the crime activity and its spread in large spatial areas. Sparseness hampers most time series (crime time series) comparison methods from working properly, while the handling of large urban areas tends to render the computational costs of such methods impractical. Visualizing different patterns hidden in crime time series data is another issue in this context, mainly due to the number of patterns that can show up in the time series analysis. In this paper, we present a new methodology to deal with the issues above, enabling the analysis of spatiotemporal crime patterns in a street-level of detail. Our approach is made up of two main components designed to handle the spatial sparsity and spreading of crimes in large areas of the city. The first component relies on a stochastic mechanism from which one can visually analyze probable×intensive crime hotspots. Such analysis reveals important patterns that can not be observed in the typical intensity-based hotspot visualization. The second component builds upon a deep learning mechanism to embed crime time series in Cartesian space. From the embedding, one can identify spatial locations where the crime time series have similar behavior. The two components have been integrated into a web-based analytical tool called CriPAV (Crime Pattern Analysis and Visualization), which enables global as well as a street-level view of crime patterns. Developed in close collaboration with domain experts, CriPAV has been validated through a set of case studies with real crime data in Sa ̃o Paulo - Brazil. The provided experiments and case studies reveal the effectiveness of CriPAV in identifying patterns such as locations where crimes are not intense but highly probable to occur as well as locations that are far apart from each other but bear similar crime patterns.
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    Exploring counterfactual antecedents to reduce criminality
    (2021-12-22) Raimundo, Marcos Medeiros
    This research project developed a series of methodologies to help identifying urban, socioeconomic and space-temporal factors that lead to crime. Our research had four main pillars: (1) Hotspot analysis was used to investigate possible ways to define what is a crime hotspot, in other words, how to define the size and area of geographical area to designate resources to reduce criminality; (2) Space-temporal analysis was used to understand the space and time correlations on crime; (3) Socioeconomic analysis was used to identify the main social and economical variables that affect crime; (4) Counterfactual analysis was used to understand which variables we should change on which magnitude we should change it to reduce significantly the crime in a certain location. All of these analysis where integrated in distinct visualization tools to help users to understand and have insights about crime in order to plan actions to reduce criminality.
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    Modelos matemáticos e computacionais de otimização de estratégias de redução dos níveis de violência com vítimas no Brasil: uma analogia da dinâmica não linear e caos determinístico aplicado à dengue
    (2020-11) Ximenes, Raphael
    Este trabalho tem por objetivo desenvolver um modelo matem ́atico que represente a dinâmica da atividade criminosa existente em uma população, afim de, através dele, sermos capazes de analisar potenciais intervenções, isto é, estratégias de redução dos níveis de violência. A abordagem escolhida para representar a dinâmica da atividade criminosa neste modelo é inspirada nos modelos de propagação de doenças infecciosas que conferem imunidade temporária/permanente.
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    Exploring spatial data on crime analysis
    (2021-12-22) Souza, Matheus Paes de
    This research project aims to analyse the spatial relation between the distribution of crime and the presence of amenities in the city of São Paulo. To that aim, we employ a spatial-aware regression model, Geographically Weighted Regression (GWR). This model takes into account the spatial distribution of the input data, and describes the manner in which the importance of features for the prediction of a variable varies in space.
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    Exploring counterfactual antecedents to reduce criminality in Rio de Janeiro
    (2021-12-22) Guardieiro, Vitória Aquino
    This research aimed to analyze the impact that socioeconomic and urban vari- ables have on crime rates for Rio de Janeiro. To achieve that, we structured a dataset containing, for each neighborhood, the per capita crime rate for three dif- ferent categories of crimes (against passersby, stores, and vehicles), socioeconomic variables (regarding education, age, income, employment, and others), and urban variables (such as the number of industries, commerces, and public administration establishments). Then, we used those features to identify the hotspot neighbor- hoods for each crime type and studied possible counterfactuals for specific regions. We found that not only do the different crimes happen in different parts of the city (passerby crime hotspots concentrate in the South and North zones, the store ones in the South and Central zones, and the vehicle ones in the North zone) but also that the counterfactuals vary significantly depending on the analyzed neigh- borhood. We found, for example, that economic inequality and unemployment can be relevant factors for the passerby and store crimes in wealthier neighborhoods, while the lack of people movement is relevant for other neighborhoods regarding passerby crime.