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The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

Journal of Data Analysis and Information Processing 2024年第3期12卷 370-387页

作者： Clement Odooh Somtobe Olisah Jane Onwuchekwa Omoshola Owolabi sevidzem simo yufenyuy Oluwadare Aderibigbe Echezona Obunadike Oghenekome Efijemue Saheed Akintayo Samson Edozie Department of Computer Science Austin Peay State University Clarkesville TN USA Department of Data Science Carolina University Winston-Salem NC USA

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

关键词： Crime Analysis Chicago Data Visualization Crime Trends Power BI Urban Safety

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