Dynamic Analysis of Vehicle Robberies and Thefts: An Approach with Sliding Windows

Abstract

Objective: This article aims to jointly analyse the time series of the rates of stolen vehicles and stolen vehicles daily in Salvador, Bahia, Brazil, using DFA and DCCA methods, both with the sliding windows approach.   Theoretical Framework: Salvador, the capital of the state of Bahia and the geographic space of the research, has the second largest fleet of motor vehicles in the northeast region of Brazil and the eighth when compared to other municipalities in Brazil.   Method: The DFA and ρDCCA with Sliding Windows were used. The DFA is a statistical method that estimates autocorrelation in non-stationary time series on different time scales.   Results and Discussion: Through exploratory data analysis, some properties were identified, such as positive asymmetry, stationarity, and nonstationarity depending on the year and crime assessed, as well as inverse fluctuation over the years between the average rates of stolen and stolen vehicles. The sliding windows approach identified greater relative variability around the average vehicle theft rate from 2004 to 2015 for w= 365 and from 2004 to 2016 for w = 1000 and a higher frequency of persistent autocorrelation (αDFA >0.50) (w=365 and w=1000).  While the level of cross-correlation varied qualitatively between positive (ρDCCA (n) >0) and negative (ρDCCA (n) <0) depending on the year, time scale, window and crime assessed. Conclusion: Considering the complexity of the modelled series, the methodology used, and our findings, we hope to contribute to research related to the topic and initiatives to monitor, mitigate and plan to combat these crimes.