While many automated police data sharing systems have been implemented since the attacks of September 11, 2001, when police data sharing was announced to be a priority for law enforcement, little research is available concerning the impact of such systems on the ability of the police to solve crimes. To remedy this gap, this study evaluates the Federated Integrated Network for Data Exchange and Retrieval (FINDER) – an automated police data sharing system currently used by a large number of Florida and Alabama police agencies with development underway across the US. This research is the first study evaluating a police data sharing system using objective measures. As such, rather than using user perceptions about the data sharing system's effectiveness, as most of the existing studies have done in the past, this study uses Uniform Crime Report (UCR) offense clearance rates as an objective measure of police performance. Based on the existing literature, it is hypothesized in this study that the use of a police data sharing system improves the police agency's offense clearance rates. To test a set of hypotheses, panel data containing information on 99 police agencies collected annually over the course of 30 years – 1990 through 2019 – are analyzed using fixed effects models. The results of this research suggest that the use of a police data sharing system improves police agency total offense clearance rates as well as property offense clearance rates. No evidence is found to support the hypothesis that the use of a data sharing system improves violent offense clearance rates. This dissertation overviews the limitations of this study and implications of its findings and concludes with a set of recommendations for policymakers and police practitioners as well as suggests topics for future research.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1712 |
| Date | 01 January 2021 |
| Creators | Korotchenko, Stanislav |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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