South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique
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 m...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/5537902 |
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| author | Ibidun Christiana Obagbuwa Ademola P. Abidoye |
| author_facet | Ibidun Christiana Obagbuwa Ademola P. Abidoye |
| author_sort | Ibidun Christiana Obagbuwa |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c01336e570b94571a188ce50df48240e |
| institution | OA Journals |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-c01336e570b94571a188ce50df48240e2025-08-20T02:19:26ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/55379025537902South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression TechniqueIbidun Christiana Obagbuwa0Ademola P. Abidoye1Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley, South AfricaDepartment of Information Technology, Cape Peninsula University of Technology, Cape Town, South AfricaSouth 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.http://dx.doi.org/10.1155/2021/5537902 |
| spellingShingle | Ibidun Christiana Obagbuwa Ademola P. Abidoye South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique Applied Computational Intelligence and Soft Computing |
| title | South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique |
| title_full | South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique |
| title_fullStr | South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique |
| title_full_unstemmed | South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique |
| title_short | South Africa Crime Visualization, Trends Analysis, and Prediction Using Machine Learning Linear Regression Technique |
| title_sort | south africa crime visualization trends analysis and prediction using machine learning linear regression technique |
| url | http://dx.doi.org/10.1155/2021/5537902 |
| work_keys_str_mv | AT ibidunchristianaobagbuwa southafricacrimevisualizationtrendsanalysisandpredictionusingmachinelearninglinearregressiontechnique AT ademolapabidoye southafricacrimevisualizationtrendsanalysisandpredictionusingmachinelearninglinearregressiontechnique |