ANALYSIS OF CRIME PREVENTION ENHANCING URBAN PUBLIC SAFETY THROUGH DATA-DRIVEN STRATEGIES
Abstract
As cities continue to grow and more people move into urban areas, crime has become a serious problem. This study looks at how data-driven strategies can help prevent crime and improve safety in urban communities. The research is based on the Situational Crime Prevention (SCP) theory, which focuses on making it harder for crimes to happen by changing the environment and using technology. The study uses a quantitative, non-experimental method with a descriptive-correlational design to find out how data-driven strategies like anti- street crime actions, community-based programs, and crime awareness campaigns affect crime prevention. A survey was given to 300 students from the University of Mindanao, who live in urban areas and represent different backgrounds. The data were analyzed using basic and advanced statistical tools, such as Pearson’s r and Spearman’s Rho, to see if there’s a connection between the strategies used and how well they prevent crime. The results are expected to show that these strategies help reduce crime, solve problems more effectively, and make urban areas safer. This study is important because it can help police, local leaders, and communities create better plans to fight crime using evidence and technology. It also shows how crime prevention can lead to better lives, safer neighborhoods, and stronger communities.
SDG #16: Peace, Justice and Strong Institutions
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DOI: http://dx.doi.org/10.46827/ejsss.v12i4.2294
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