Crime Risks Increase in Areas Proximate to Theme Parks: A Case Study of Crime Concentration in Orlando

Abstract

Research has examined the influence of ecological characteristics of cities on spatial crime distributions. Given the potential economic and human impacts, a subset of this work has focused on special events or specific venues, which attract a significant number of people and represent unique logistics. In this context, the spatial attributes of tourist cities, particularly those near heavily trafficked attractions, may be related to elevated risk for property crime and violence. This study examines crime patterns surrounding Universal Studios Florida theme park by analyzing census block data in Orlando. Various statistical techniques are utilized including geospatial mapping, local indicators of spatial association analysis (LISA), and spatial regression analysis controlling for autocorrelation between neighborhoods. Results indicate that the location of the theme park is associated with uneven crime distribution in Orlando, but those impacts are significantly influenced by the consideration of crime-generating/attracting facilities located within census blocks.

Keywords:

• Crime concentration
• theme parks
• Universal Studios Florida
• Orlando
• tourism

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1 The five types include: (1) tourists as incidental victims; (2) the tourist location as a venue for crime; (3) tourism, the provider of victims; (4) tourists—generators of a demand for criminal activity?; and (5) tourists and tourist resources as specific targets of criminal action.

2 A total 5,469 census blocks are identified as situated in Orlando boundaries. However, after consideration of geographic characteristics of Orlando such as lakes, 4,588 census blocks are included in the analysis.

3 In spite of the attempts to maximize the geocoding hit rate for crime incidents, the hit rate is not considerably higher because some crime incidents either do not have any geospatial information or have limited crime attributes, dropping them from the analysis. However, the success rate of geocoding remains above the minimum acceptable level of 85% (Ratcliffe, 2004).

Additional information

Notes on contributors

Sungil Han

Sungil Han is a PhD student in the Criminology Program at The University of Texas at Dallas. His research interest includes public confidence in the police, fear of crime, immigration and crime, and issues related to communities and crime.

Matt R. Nobles

Matt R. Nobles is Professor of Criminal Justice and Public Affairs at the University of Central Florida (Orlando, FL). His research interests include violent and interpersonal crimes, stalking, spatial analysis, and quantitative methods.

Alex R. Piquero

Alex R. Piquero is Ashbel Smith Professor of Criminology at The University of Texas at Dallas and Professor of Criminology at Monash University, Melbourne, Australia. His research interests include criminal careers, criminological theory, and quantitative research methods. He is Fellow of both the American Society of Criminology and the Academy of Criminal Justice Sciences. In 2014, he received The University of Texas System Regents’ Outstanding Teaching Award and in 2018 he was elected to The University of Texas System Academy of Distinguished Teachers. In 2019, he received the Academy of Criminal Justice Sciences Bruce Smith, Sr. Award for contributions to criminal justice.

Nicole Leeper Piquero

Nicole Leeper Piquero is the Robert Holmes Professor of Criminology and Associate Vice President for Research Development at The University of Texas at Dallas. She received her Ph.D. in Criminology and Criminal Justice from the University of Maryland. Her research focus includes the study of white-collar and corporate crimes, criminological theory, as well as gender and crime.