@InProceedings{Garcia-ZanabriaGoSiPoNeAdNo:2020:ViToAn,
               author = "Garcia-Zanabria, Germain and Gomez-Nieto, Erick and Silveira, 
                         Jaqueline Alvarenga and Poco, Jorge and Nery, Marcelo and Adorno, 
                         Sergio and Nonato, Luis G.",
          affiliation = "{Universidade de S{\~a}o Paulo} and {Universidade de S{\~a}o 
                         Paulo} and {Universidade de S{\~a}o Paulo} and 
                         {Funda{\c{c}}{\~a}o Get{\'u}lio Vargas} and {Universidade de 
                         S{\~a}o Paulo} and {Universidade de S{\~a}o Paulo} and 
                         {Universidade de S{\~a}o Paulo}",
                title = "Mirante: A visualization tool for analyzing urban crimes",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Crime Mapping, Crime Data, Spatio-Temporal Data, Visual 
                         Analytics.",
             abstract = "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.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00028",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00028",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/439J8DB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/439J8DB",
           targetfile = "74.pdf",
        urlaccessdate = "2024, Apr. 29"
}