Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/3749
DC FieldValueLanguage
dc.contributor.advisorNaicker, Nalen-
dc.contributor.advisorAdeliyi, Timothy Temitope-
dc.contributor.authorMonyeki, Phirimeen_US
dc.date.accessioned2021-12-09T15:55:03Z-
dc.date.available2021-12-09T15:55:03Z-
dc.date.issued2021-11-02-
dc.identifier.urihttps://hdl.handle.net/10321/3749-
dc.descriptionA dissertation submitted in fulfilment of the requirement for the degree of Master of Information and Communications Technology, Department of Information Technology, Faculty of Accounting and Informatics, Durban University of Technology, 2021.en_US
dc.description.abstractWhen South Africa is compared to other countries, it has a notably high rate of crime. The country has seen a concomitantly high occurrence of murder, residential burglary, drug-related crime and carjacking (hijacking) crime. The government is desperately seeking solutions that can be implemented to reduce recurrent crime. Several reasons to explicate high crime trends in different areas include alcohol or drug abuse, low standards of education, poor parenting skills and a lack of social and vocational skills. This study aimed to gain better insight into crime trends in South Africa using data mining techniques. Decision-making linked to the data could help the government implement a coherent crime strategy to mitigate crime. The crime dataset chosen for this study was publicly available at kaggle.com. The dataset was prepared using Python programming code. The research design was utilised as an overall strategy to compile all different components of this study with an intention of answering the research questions and attaining the research objectives. To identify the significant changes, ChangePoint Analysis (CPA) was performed to pinpoint the abrupt change in the South African crime dataset. Two methods called Cumulative Sum (CUSUM) and Bootstrap were implemented in this study of CPA. To analyse the trend of data, CUSUM and Bootstrap were performed to measure the occurrence of change points based on the confidence levels. The CPA outcome depicted multiple significant changes and abrupt shifts in several provinces of South Africa. Linear regression (LR) was utilised to predict the future trends of crime in South Africa from 2016 – 2022 based on the erstwhile 2005 – 2015 crime statistics. The results showed that crime has been on the increase in South Africa with certain provinces such as Western Cape, Gauteng and KwaZulu-Natal being identified as crime hotspots. Future studies on crime should focus only on one province to gain insight into the dominating crimes and hotspots within that particular province, with a view to developing highly specific crime-reduction interventions.en_US
dc.format.extent116 pen_US
dc.language.isoenen_US
dc.subject.lcshData mining--South Africaen_US
dc.subject.lcshCrime analysisen_US
dc.subject.lcshChange-point problemsen_US
dc.subject.lcshCriminal behavior, Prediction ofen_US
dc.subject.lcshData mining in law enforcement--South Africaen_US
dc.titleData mining to analyse recurrent crime in South Africaen_US
dc.typeThesisen_US
dc.description.levelMen_US
dc.identifier.doihttps://doi.org/10.51415/10321/3749-
item.openairetypeThesis-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Theses and dissertations (Accounting and Informatics)
Files in This Item:
File Description SizeFormat
MonyekiP_2021.PDFThesis1.75 MBAdobe PDFView/Open
Show simple item record

Page view(s)

123
checked on Jun 28, 2022

Download(s)

163
checked on Jun 28, 2022

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.