Geo-parsing and Geo-Visualization of Road Traffic Crash Incident Locations from Print Media for Emergency Response and Planning
Keywords: Geo-parsing, Natural Language Processing, Geographic Information Retrieval, Road Traffic Crash, Emergency Response Planning
TL;DR: The study entails creation of Road Traffic Crash (RTC) dataset, RTC Named Entity Recognition models for geo-parsing and geo-visualization of RTC incident locations for speedy emergency response and planning
Abstract: Road traffic crashes (RTC) are a major public health concern across the globe, particularly in Nigeria where road transport is the most common mode of transportation. In this paper, we present an approach to RTC related geographic information retrieval and visualization from news articles utilizing the geo-parsing natural language processing technique for emergency response and planning. To capture RTC-details with a high degree of accuracy and precision, we created a dataset from RTC related Nigerian news articles and developed the RTC-NER Baseline and RTC-NER custom spaCy - based Named Entity Recognition (NER) models using the RTC dataset. We evaluated and compared their performance using standard metrics of precision, recall, and f1-score. The RTC-NER performed better than the RTC-NER baseline model for all three metrics with a precision rating of 93.63, recall of 93.61, and f1-score of 93.62. We further used the models for toponym recognition to extract RTC location details, toponym resolution to retrieve corresponding geographical coordinates, and finally, geo-visualization of the data to display the RTC incident environment for emergency response and planning. Our study showcases the potential of unstructured data for decision-making in RTC emergency responses and planning in Nigeria.
Submission Number: 22
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