@InProceedings{QueirozNetoSantVida:2016:UsMaSq,
               author = "Queiroz Neto, Jose Florencio de and Santos, Emanuele Marques dos 
                         and Vidal, Creto Augusto",
          affiliation = "{Federal University of Cear{\'a}} and {Federal University of 
                         Cear{\'a}} and {Federal University of Cear{\'a}}",
                title = "MSKDE - Using Marching Squares to Quickly Make High Quality Crime 
                         Hotspot Maps",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "Hotspot maps, Visualization.",
             abstract = "In recent years, violence has considerably increased in the world. 
                         In a certain state of Brazil, for example, the homicide rate grew 
                         from 16 homicides per 100,000 inhabitants in 2000, to 48 homicides 
                         per 100,000 inhabitants in 2014. Police departments worldwide use 
                         various types of crime maps, which are generated with diverse 
                         techniques, in order to analyze and fight crime. Those types of 
                         maps enable decision makers to identify high-risk areas and to 
                         allocate resources more effectively. Hotspot maps, in particular, 
                         are crime maps often available in visual interactive systems for 
                         crime analysis. In order for hotspot maps to be really useful, 
                         they need to be very accurate - specially for resource allocation 
                         tasks - and to be processed very fast for quick analysis of 
                         different scenarios. In this paper, we propose MSKDE - Marching 
                         Squares Kernel Density Estimation, a solution for generating fast 
                         and accurate hotspot maps. We describe the technique and 
                         demonstrate its superior qualities through a careful comparison 
                         with the standard Kernel Density Estimation technique, which is 
                         widely used for generating hotspot maps.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.049",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.049",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5AD6S",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5AD6S",
           targetfile = "PID4370329.pdf",
        urlaccessdate = "2024, May 03"
}