Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns

Alegre-Mondragón, Ana J.; Silva-Arias, Cristian

doi:10.1007/978-3-030-98096-2_14

Lecture Notes in Geoinformation and Cartography

2022

DOI: 10.1007/978-3-030-98096-2_14

|View full text |Cite

Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns

Ana J. Alegre-Mondragón,

Cristian Silva-Arias

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Silva Filho,

Ribas Junior,

Zebende

et al. 2024

RGSA

View full text Add to dashboard Cite

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.

show abstract

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

Silva Filho,

Ribas Junior,

Zebende

et al. 2024

RGSA

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Effects of COVID-19 in Mexico City: Street Robbery and Vehicle Theft Spatio-Temporal Patterns

Cited by 1 publication

References 23 publications

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

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

Contact Info

Product

Resources

About