地理分析和道路交通事故事件的分析,用于数据驱动的应急响应计划
Patricia Ojonoka Idakwo1,2, Olubayo Adekanmbi2, Anthony Soronnadi2
1Department of Computer Science, African University of Science and Technology, Abuja, Nigeria.
Heliyon
|March 5, 2025
概括
这项研究开发了一种专门的命名实体识别 (NER) 模型,从新闻中提取道路交通事故 (RTC) 信息. 该模型准确地识别了机地点,有助于风险分析和应急规划.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 道路交通事故 (RTC) 构成了全球重大公共卫生挑战,由于道路运输的广泛使用,尼日利亚的流行率很高.
- 从新闻文章等非结构化来源有效地提取数据,对于理解和减轻RTC至关重要.
研究的目的:
- 开发和评估定制的,基于spaCy的,RTC域特定的命名实体识别 (NER) 模型,用于从RTC新闻文章中提取地理信息.
- 在尼日利亚和国际数据集上评估开发的NER模型的性能.
- 创建一个结构化的RTC事件数据集,用于探索性数据分析和风险区域识别.
主要方法:
- 开发两个定制的基于空间Cy的NER模型:RTC NER基线和RTC NER,基于尼日利亚RTC新闻文章进行培训.
- 地理分析技术用于地理信息提取 (IE) 和地理坐标检索.
- 使用尼日利亚和国际测试数据的精度,回忆和F1得分指标进行性能评估.
主要成果:
- 该RTC NER模型显著优于RTC NER基线模型.
- 在尼日利亚数据上,RTC NER实现了高性能指标:93.63%的精度,93.61%的回忆率和93.62%的F1得分.
- 在国际数据上,RTC NER表现出了强的表现 (91.9%的精度,87.88%的回忆,89.84%的F1得分),表明了多功能性.
结论:
- 开发的RTC NER模型对于从RTC报告中提取信息是有效的,无论地理来源如何.
- 该模型允许创建结构化数据集,用于识别RTC热点,并为应急响应计划提供信息.
- 这种方法促进了以数据为导向的策略,以减轻道路交通事故和提高公共安全.
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