| Grant number: | 19/04434-1 |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |
| Start date: | July 28, 2019 |
| End date: | December 19, 2019 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Luis Gustavo Nonato |
| Grantee: | Germain García Zanabria |
| Supervisor: | Jose Claudio Teixeira e Silva Junior |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Institution abroad: | New York University, United States |
| Associated to the scholarship: | 17/05416-1 - Visual Analytics of Machine Learning Methods: a practical essay from crime data in São Paulo, BP.DR |
Abstract São Paulo is the largest city in South America, with criminality rates as large as the city. The number and type of crime varies considerably around the city, assuming different patterns depending on urban, environmental, and social characteristics of each particular location. Previous works have mostly focused on the analysis of crime in the city with the intent of uncovering patterns associated with social factors, time seasonality, and urban routine activities. Therefore, those studies and tools are not designed to analyze specific regions of the city such as particular neighborhoods, avenues or public areas, which is important for domain experts to start their analysis in a bottom-up fashion. Moreover, urban local features related to mobility, passersby behavior, and presence of public infrastructures such as terminals of public transportation and schools, can influence the quantity and type of crime, attesting the importance of analytical tools capable of operating locally. In this work, we propose to develop a visualization assisted analytic tool that allows users to analyze the behavior of crimes in specific regions. The tool allows users to identify local hotspots, their pattern of crimes, and how the hotspots and corresponding crime patterns change over time. This tool has been developed from the demand of a team of experts in criminology and deals with four major challenges in this context, i) flexibility to explore local regions and understand their crime patterns, ii) identification of spatial criminal hotspots, iii) understand the dynamic of crime patterns and types over time, and (iv) understand the influence of different urban factors in the dynamic of crime rate. The effectiveness and usefulness of the proposed system will be demonstrated in case studies involving real data and validated by domain experts and by the capability of identify phenomena described in the literature. | |
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