MIRG-ICAIR Conference

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Browsing MIRG-ICAIR Conference by Author "Akanji, D."

Now showing 1 - 4 of 4
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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
    Geo-visualization of Hotspots of Citizens Dissatisfaction on Social Services Using Media Print: A Case Study of Fuel and Cash Scarcity in Nigeria
    (MIRG, 2024-10-28) Akojenu, S.; Adekanmbi,O.; Soronnadi, A.; Akanji, D.
    The recent public dissatisfaction in Nigeria due to cash and fuel scarcity underscores the critical role of these resources in the modern economy, impacting various aspects of society like transportation, commerce, and daily living expenses. Existing research on citizen dissatisfaction relied on surveys, but this study employs geosemantics techniques to extract locations of social service dissatisfaction from social media data for efficient resource allocation. The method involves crawling and classifying social media content into three categories (dissatisfied, satisfied, or neutral). Using deep learning and a rule-based geoparsing approach, the study identifies locations mentioned in dissatisfied text in real time. This real-time insight from unstructured text aids in comprehending the complex economic, social, and spatial effects of resource scarcity, facilitating the government in developing effective resource allocation strategies to improve citizens' quality of life.
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    Open Access
    Geo-Visualization of Road Accident and Traffic Congestion Hotspots for Real-Time Traffic Optimization, Mobility Planning, and Commuters’ Safety Using Traffic Reported Social Media Posts
    (MIRG, 2024-10-28) Lawal, O.; Adekanmbi,O; Soronnadi, A.; Akanji, D.
    Amidst the bustling economic hub of Nigeria, known as Lagos, traffic congestion remains a persistent challenge that plagues the city, impeding the economy, health, and safety of its residents. Professionals spend a staggering 90% of their work time navigating traffic, resulting in stress and anxiety. Some are robbed in the process, even in broad daylight. To address this problem, this work is done to help government agencies optimize traffic management and provide road users with the necessary information to navigate the city safely and efficiently and make informed decisions about which routes to take. In this study, we utilized LASTMA's social media tweets to analyse traffic patterns in Lagos, revealing insights on congestion and road safety. Rigorous data preprocessing and machine learning tools such as TfidfVectorizer aided sentiment analysis, achieving 88.9% accuracy with logistic regression outperforming other models. We also finetuned a name entity recognition (NER) model using Spacy to identify and capture location and road entities within the tweets. Findings highlighted breakdowns near sea ports and major routes (e.g., Orile, Bonny Camp, Apapa wharf) and accidentprone areas like the Third Mainland Bridge and Obalende Bridge. With this analysis, government agencies can develop traffic management strategies that prioritize safety and efficiency, such as rerouting traffic away from congested areas or implementing safety measures at accident-prone or traffic robbery-prone locations. Furthermore, traffic-free routes can be provided in real-time to commuters based on their location. This work has the potential to reduce traffic congestion and improve road safety, leading to economic and health benefits for Lagos residents. The results of this research not only have practical implications for the city but can also be extended to other urban areas that have traffic agencies and are also facing similar challenges.
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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.