South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique

Abstract

South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. However, the two elements that pushed South Africa high in the crime rank are the rates of social violence and homicide. It was reported by Business Insider that South Africa is among the most top 15 ferocious nations on earth. By 1995, South Africa was rated the second highest in terms of murder. However, the crime rate has reduced for some years and suddenly rose again in recent years. Due to social violence and crime rates in South Africa, foreign investors are no longer interested in continuing or starting a business with the nation, and hence, its economy is declining. South Africa’s government is looking for solutions to the crime issue and to redeem the image of the country in terms of high crime ranking and boost the confidence of the investors. Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. The police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. This research work aimed at offering a solution to the problem by building a model that can predict crime. The machine learning approach shall be used to extract useful information from South Africa's nine provinces' crime data. A crime prediction system that can analyze and predict crime is proposed. To accomplish this, South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm (linear regression) was used to build a predictive model to analyze data and predict future crime. The appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses.

Sexual Crime Prediction in an African Context

“…For the purposes of this study, a Cross-Industry Standard Process for Data Mining (CRISP-DM) (Berwind et al, 2016)-a quantitative research methodology based on the post-positivist research paradigm-was used to collect and analyse data on the trends of sexual crimes in South Africa's nine provinces so that trends in sexual crimes can be predicted see Figure 1. Literature shows that CRISP-DM is widely used in data mining research because of its efficiency and suitability for data mining projects (Chapman et al, 2000;Edoka, 2020;Obagbuwa & Abidoye, 2021). The CRISP-DM processes are described below.…”

“…Crime, in general, has resulted in the downgrading of South Africa's economy and sexual crime is a major contributor to the country's crime statistics. Homicide, sexual crimes, arson, and violent attacks are among the major crimes that lead to social issues (Chauhan & Sehgal, 2017;David & Suruliandi, 2017;McClendon & Meghanathan, 2015;Obagbuwa & Abidoye, 2021;Pednekar et al, 2018;Prabakaran & Mitra, 2018).…”

“…Existing data sets are used to predict the development of new information. A number of researchers have published articles on the application of data mining and machine learning algorithms in crime analysis (Chapman et al, 2000;Edoka, 2020;Obagbuwa & Abidoye, 2021).…”

“…Sukhija et al (2020) performed a linear regression analysis to determine the correlation between traits linked to rape incidents in Haryana and to find pertinent variables that can better assist police personnel in crime prevention. When building predictive models with data analytics, linear regression and decision trees work well (Awal et al, 2016;Gonzalez & Leboulluec, 2019;Obagbuwa & Abidoye, 2021;Yerpude, 2020).…”

Regresyon Yöntemlerine Dayalı Suç Tespit Analizi Karşılaştırması Elazığ İli Örneği

“…Obagbuwa ve Abidoye tarafından Kaggle isimli web sitesinden Güney Afrika'da işlenen 27 farklı kategorideki suç verisi elde edilmiştir. Doğrusal regresyon yöntemi kullanılarak suç tahminine dayalı analiz yapılmıştır ve Güney Afrika makamlarının ve güvenlik kurumlarının suç eğilimleri hakkında fikir sahibi olmaları sağlanmıştır [9]. Awal ve arkadaşları tarafından Bangladeş polisinin web sitesinden alınan veriler lineer regresyon yöntemi ile model oluşturularak eğitilmiştir.…”