Caracterização temporal e espacial de roubos de rua contrastando contextos pré-pandêmico e pandêmico

Abstract

A multidisciplinary study conducted in Minas Gerais investigated the temporal dynamics of street robberies, analyzing both pre-pandemic and pandemic periods. Utilizing data from the Military Police, time series data were examined at various scales, including hourly, daily, 10-day intervals, and monthly, employing advanced statistical methods such as spectral frequency analysis, autocorrelations, and decomposition techniques. The results were surprising, revealing an average decrease of 64% in robberies during the pandemic and identifying a stationary pattern in pandemic occurrences, indicating increased randomness in the events. Contrary to expectations, no seasonality was observed in robberies concerning weekdays, the beginning or end of the month, or months of the year. These findings offer valuable insights into understanding the temporal patterns of crimes and contribute to the development of more effective public safety policies. Additionally, the study underscored the usefulness of the geocoder (Geo) in robbery analysis, enabling the conversion of addresses into geographic coordinates, facilitating the visualization of the spatial distribution of crimes. The integration of this data with additional geographic information and the creation of interactive maps using the Leaflet library and the SF package in the R environment were highlighted as crucial tools for identifying high-crime areas, regions with reduced police presence, and planning effective public safety strategies, emphasizing the importance of geospatial data analysis in decision-making in this field.