Title
Crimanalyzer: Understanding crime patterns in São Paulo
Date Issued
01 August 2015
Access level
open access
Resource Type
journal article
Author(s)
Garcia G.
Silveira J.
Paiva A.
Nery M.B.
Silva C.T.
Adorno S.
Nonato L.G.
Publisher(s)
IEEE Computer Society
Abstract
São Paulo is the largest city in South America, with crime rates that reflect its size. The number and type of crimes vary considerably around the city, assuming different patterns depending on urban and social characteristics of each particular location. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban routine activities. Therefore, those studies and tools are more global in the sense that they are not designed to investigate specific regions of the city such as particular neighborhoods, avenues, or public areas. Tools able to explore specific locations of the city are essential for domain experts to accomplish their analysis in a bottom-up fashion, Revealing how urban features related to mobility, passersby behavior, and presence of public infrastructures (e.g., terminals of public transportation and schools) can influence the quantity and type of crimes. In this paper, we present CrimAnalyzer, a visual analytic tool that allows users to study the behavior of crimes in specific regions of a city. The system allows users to identify local hotspots and the pattern of crimes associated to them, while still showing how hotspots and corresponding crime patterns change over time. CrimAnalyzer has been developed from the needs of a team of experts in criminology and deals with three major challenges: i) flexibility to explore local regions and understand their crime patterns, ii) identification of spatial crime hotspots that might not be the most prevalent ones in terms of the number of crimes but that are important enough to be investigated, and iii) understand the dynamic of crime patterns over time. The effectiveness and usefulness of the proposed system are demonstrated by qualitative and quantitative comparisons as well as by case studies run by domain experts involving real data. The experiments show the capability of CrimAnalyzer in identifying crime-related phenomena.
Volume
14
Issue
8
Language
English
OCDE Knowledge area
Criminología
Scopus EID
2-s2.0-85095546838
PubMed ID
Source
IEEE Transactions on Visualization and Computer Graphics
ISSN of the container
10772626
DOI of the container
10.1109/TVCG.2019.2947515
Sponsor(s)
This work was supported by CNPq-Brazil (grants #302643/2013-3 and #301642/2017-63), CAPES-Brazil (grants #10242771), and São Paulo Research Foundation (FAPESP)-Brazil (grant#2014/12236-1, #2016/04391-2 and #2017/05416-1). The views expressed are those of the authors and do not reflect the official policy or position of the São Paulo Research Foundation. We also thanks Intel for making available part of the computational resources we use in the development of this work. Silva is funded in part by: the Moore-Sloan Data Science Environment at NYU; NASA; NSF awards CNS-1229185, CCF-1533564, CNS-1544753, CNS-1730396, CNS-1828576, and DARPA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA.
Sources of information: Directorio de Producción Científica Scopus