Date of Conferral
10-5-2026
Date of Award
October 2026
Degree
Ph.D.
School
Criminal Justice
Advisor
Dearis Hoard
Abstract
Motor vehicle accidents (MVAs) pose a persistent public safety concern in Grand Cayman, yet limited local evidence existed on when and where crashes occurred or whether police enforcement predicted daily accident counts. The purpose of this quantitative archival study was to examine temporal and spatial patterns in MVA patterns and assess whether daily traffic ticket and driving under the influence (DUI) arrest counts predicted daily MVAs during 2023 and 2024. Routine activity and deterrence theories guided the study. The Royal Cayman Islands Police Service archival data included 6,299 accident records 731 daily observations. Analyses included descriptive statistics, chi-square tests, Spearman correlations, negative binomial regression, lagged regression, and time-series analyses. Results showed significant spatial and temporal patterning, with crashes concentrated in George Town, along major roadways, on Fridays, and during afternoons. The same-day negative binomial model was significant; traffic tickets negatively predicted daily MVAs, while DUI arrests positively predicted them. Prior-day DUI arrests predicted lower next-day MVA counts, whereas 7-day lagged effects were not significant. The findings support targeted, data-driven enforcement and improved crash-cause and traffic-exposure data collection. The study’s potential contribution to positive social impact includes informing evidence-based prevention strategies intended to reduce preventable crashes, injuries, fatalities, and institutional burdens in Grand Cayman.
Recommended Citation
Stephenson, Sheldon, "Motor Vehicle Accidents in Grand Cayman: Assessing Trends and Police Enforcement and Prevention Strategies" (2026). Walden Dissertations and Doctoral Studies. 20878.
https://scholarworks.waldenu.edu/dissertations/20878
