“Data Science Tools for Crime Analysis” with Jorge Poco, Fundação Getúlio Vargas (FGV), Brazil
NYU CUSP's Research Seminar Series features leading voices in the growing field of urban informatics. Check out upcoming seminars: https://bit.ly/3MHidOH
The extraction and analysis of crime patterns in large cities is a challenging spatiotemporal problem. The number and type of crimes vary considerably across cities, assuming different patterns depending on each particular location’s urban and social characteristics. Urban factors such as population density, the flow of people, parking lots, and socioeconomic conditions strongly influence the patterns and dynamics of crime in each city location.
In this talk, Dr. Jorge Poco will present data science, visualization, and machine learning tools to identify and understand spatially and temporally crime dynamics. In addition, he will describe some tools capable of exploring specific city locations, which are essential for domain experts to perform their analysis in a bottom-up manner. In this way, we can reveal how urban characteristics related to mobility, pedestrian behavior, and the presence of public infrastructures can influence the amount and type of crime.