Data-Driven Analysis: A Comprehensive Study of CPS Case Outcomes in 42 English Counties (2014-2018) with R Analytics

Abstract

Abstract This scholarly work thoroughly examines a dataset of criminal activities, specifically emphasizing the process of data pre-processing, cleansing, and subsequent analytical procedures. The dataset utilized in this study is obtained from the Crown Prosecution Service Case Outcomes by Principal Offense Category (POC), covering the period from 2014 to 2018 and including forty-two counties in England. The initial stage of data pre-processing encompasses a systematic sequence of procedures, which includes deleting superfluous percentage columns, arranging the data in chronological order, aligning the columns appropriately, removing special characters, and converting the data types as necessary. Appropriate measures are taken to address missing data to protect the integrity of the dataset. The descriptive analytics section examines multiple variables, encompassing county, year, month, area, and crime categories such as homicide, sexual offenses, burglary, etc. Clustering techniques, such as K-means and Hierarchical clustering, are utilized to identify underlying patterns within the dataset. Classification models such as Support Vector Machines (SVM) and Random Forest are utilized to forecast case outcomes. This is facilitated by employing thorough reporting techniques and doing Receiver Operating Characteristic (ROC) analysis. Time series analysis, namely using ARIMA modeling, is employed to comprehend the temporal patterns present in crime data. The paper presents a comprehensive analysis of the performance of ARIMA models, offering hypotheses, model descriptions, accuracy matrices, and visualizations as evaluation tools.