Crime dynamics under COVID-19 emergency measures in Nigeria: An exploration of residents' perception.

Uloženo v:
Podrobná bibliografie
Název: Crime dynamics under COVID-19 emergency measures in Nigeria: An exploration of residents' perception.
Autoři: Badiora, Adewumi I., Dada, Olanrewaju T., Odufuwa, Bashir O., Adeniyi, Adeyemi S., Omoniyi, Sunday S.
Zdroj: African Security Review; Jun2023, Vol. 32 Issue 2, p151-165, 15p
Abstrakt: This study examines how the COVID-19 emergency has impacted crime across different locations in Nigeria. Data were collected from a sample of residents from across Nigeria and analysed using mean ratings, percentages and chi-square. Based on the residents' perceptions, certain crime types have decreased (e.g. home break-ins and assaults), some remain unchanged (e.g. stealing and pilfering) and others have increased (e.g. cybercrime and domestic violence). The findings show concentrations of crime in urban centres, states on total lockdown and geographical areas with poor economic indicators. The times that most crimes are perpetrated remain unchanged, except for the night time, where there has been a significant increase. Generally, individual responses to crime remain unchanged, although the use of security guards and special security door locks has changed significantly. Conversely, neighbourhood-level responses have changed significantly, particularly with the use of vigilante groups, police and military patrols as well as restrictions of human and vehicular movement. Although some of the causes of this crime change existed before COVID-19, new crime opportunities are also acknowledged. The findings suggest that Nigerian cities may face a severe battle to recover from crime even after the COVID-19 emergency has passed. Policy and further research implications are discussed. [ABSTRACT FROM AUTHOR]
Copyright of African Security Review is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Buďte první, kdo okomentuje tento záznam!
Nejprve se musíte přihlásit.