Geosemantic Surveillance and Profiling of Abduction Locations and Risk Hotspots Using Print Media Reports

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dc.contributor.authorOgunremi, T.
dc.contributor.authorAdekanmbi, O.
dc.contributor.authorSoronnadi, A.
dc.contributor.authorAkanji, D.
dc.date.accessioned2024-11-05T12:26:05Z
dc.date.available2024-11-05T12:26:05Z
dc.date.issued2024-10-28
dc.description.abstractKidnapping poses a significant social risk in Nigeria, often exacerbated by the lack of local crime data, underreporting of cases, and potential involvement of security operatives. Our research aims to combat this menace by developing a data-driven solution that offers comprehensive insights into crime locations and entities. We have generated a reliable dataset by geoparsing newspaper-reported crime locations and entities using Natural Language Processing (NLP) techniques and Google geocoder. Additionally, we implemented clustering and geospatial analysis to identify social risk hotspots. Our method involves designing an algorithm that can geoparse locations in unstructured raw text. The results of our research provide crucial insights and solutions for addressing the threat of kidnapping in Nigeria. We recommend the implementation of our data-driven approach as an intervention strategy to aid law enforcement and policy makers. Our study contributes to the understanding of the spatiotemporal dynamics of kidnapping cases in Nigeria.
dc.identifier.citationOgunremi T., Adekanmbi O., Soronnadi A. & Akanji D. (2023). “Geosemantic Surveillance and Profiling of Abduction Locations and Risk Hotspots Using Print Media Reports". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 77-82, MIRG
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13036
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleGeosemantic Surveillance and Profiling of Abduction Locations and Risk Hotspots Using Print Media Reports
dc.typeArticle
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