Geo-parsing and analysis of road traffic crash incidents for data-driven emergency response planning
Road traffic crashes (RTCs) are a major public health concern worldwide, particularly in Nigeria, where road transport is the most common mode of transportation. This study presents the geo-parsing approach for geographic information extraction (IE) of RTC incidents from news articles. We developed...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Heliyon |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024170983 |
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| Summary: | Road traffic crashes (RTCs) are a major public health concern worldwide, particularly in Nigeria, where road transport is the most common mode of transportation. This study presents the geo-parsing approach for geographic information extraction (IE) of RTC incidents from news articles. We developed two custom, spaCy-based, RTC domain-specific named entity recognition (NER) models: RTC NER Baseline and RTC NER. These models were trained on a dataset of Nigerian RTC news articles. Evaluation of the models’ performances shows that the RTC NER model outperforms the RTC NER Baseline model on both Nigerian and international test data across all three standard metrics of precision, recall and F1-score. The RTC NER model exhibits precision, recall and F1-score values of 93.63, 93.61 and 93.62, respectively, on the Nigerian test data, and 91.9, 87.88 and 89.84, respectively, on the international test data, thus showing its versatility in IE from RTC reports irrespective of country. We further applied the RTC NER model for feature extraction using geo-parsing techniques to extract RTC location details and retrieve corresponding geographical coordinates, creating a structured Nigeria RTC dataset for exploratory data analysis. Our study showcases the use of the RTC NER model in IE from RTC-related reports for analysis aimed at identifying RTC risk areas for data-driven emergency response planning. |
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| ISSN: | 2405-8440 |