MIRG-ICAIR Conference

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Browsing MIRG-ICAIR Conference by Author "Adekanmbi, O."

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    Open Access
    Geo-parsing Locations during Natural Disasters for Emergency Intervention Management: A Case Study of Media Print Report of the Flood Incidence in Nigeria
    (MIRG, 2024-10-28) Sikiru, R.; Adekanmbi, O.; Soronnadi, A.; Akanji, D.
    In recent years, urban flooding has emerged as a severe and recurrent global natural disaster, leading to substantial human casualties and widespread infrastructure damage. Approximately, 23% of the world's population faces direct exposure to flood depths exceeding 0.15 meters during flood events, disproportionately affecting low- and middle-income countries. These floods result from a complex interplay of factors, including climate changes, urban development in flood-prone areas, sea-level rise, dam operations, and poor governance. The ability to quickly ascertain the specific locations affected by flooding is of paramount importance for alerting the public and enabling effective disaster response. Social media platforms and news outlets play a pivotal role in disseminating this critical information. This research introduces a comprehensive methodology tailored to identifying and visualizing flooded locations mentioned in online articles. By precisely identifying flooded places, emergency response teams will be able to allocate resources and aid those in need more efficiently during floods.
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    Open Access
    Geosemantic Surveillance and Profiling of Abduction Locations and Risk Hotspots Using Print Media Reports
    (MIRG, 2024-10-28) Ogunremi, T.; Adekanmbi, O.; Soronnadi, A.; Akanji, D.
    Kidnapping 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.