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Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

Journal of Data Analysis and Information Processing 2024年第1期12卷 49-75页

作者： Somtobe Olisah clement odooh Oghenekome Efijemue Echezona Obunadike Jane Onwuchekwa Omoshola Owolabi Saheed Akintayo Callistus Obunadike 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 comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

关键词： Human Trafficking Global Trends Data Analysis Victim Demographics Policy Implications Technological Advancements Socioeconomic Factors Forecasting Regional Disparities Transnational Crime

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

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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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