%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2020/09.18.21.33
%2 sid.inpe.br/sibgrapi/2020/09.18.21.33.39
%@doi 10.1109/SIBGRAPI51738.2020.00028
%T Mirante: A visualization tool for analyzing urban crimes
%D 2020
%A Garcia-Zanabria, Germain,
%A Gomez-Nieto, Erick,
%A Silveira, Jaqueline Alvarenga,
%A Poco, Jorge,
%A Nery, Marcelo,
%A Adorno, Sergio,
%A Nonato, Luis G.,
%@affiliation Universidade de São Paulo
%@affiliation Universidade de São Paulo
%@affiliation Universidade de São Paulo
%@affiliation Fundação Getúlio Vargas
%@affiliation Universidade de São Paulo
%@affiliation Universidade de São Paulo
%@affiliation Universidade de São Paulo
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Porto de Galinhas (virtual)
%8 7-10 Nov. 2020
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Crime Mapping, Crime Data, Spatio-Temporal Data, Visual Analytics.
%X 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 \emph{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.
%@language en
%3 74.pdf