Using parallel geocoding to analyse the spatial characteristics of road traffic injury occurrences across Lagos, Nigeria.

Using parallel geocoding to analyse the spatial characteristics of road traffic injury occurrences across Lagos, Nigeria.
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DOI:
10.1136/bmjgh-2023-012315
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发表时间:
2023-05
期刊:
影响因子:
8.1
通讯作者:
Nwariaku, Fiemu E.
Nwariaku, Fiemu E.
中科院分区:
医学2区
文献类型:
--
作者:
Mehta, Avirut;Kim, Dohyeong;Allo, Nicholas;Odusola, Aina Olufemi;Malolan, Chenchita;Nwariaku, Fiemu E.

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虽然高收入国家长期以来一直在努力了解和减轻道路交通伤害(RTI)的发生,但低收入/中等收入国家(LMIC)的类似项目经常受到体制和信息障碍的阻碍。地理空间分析的技术进步为克服这些障碍的一部分提供了一条途径,并使研究人员能够在寻求减轻RTI相关的负面健康结果时创造可行的见解。这项分析开发了一个并行的地理编码工作流程,以改善调查低保真数据集常见的LMIC。随后,此工作流程应用于RTI数据集,从拉各斯州,尼日利亚,最大限度地减少位置误差的地理编码,将输出从四个商业上可用的地理编码器。从这些地理编码器的输出之间的一致性进行评估,并生成空间可视化,以提供深入了解RTI发生在分析区域内的分布。这项研究强调了地理空间数据分析的影响,在中低收入国家的卫生资源分配的现代技术,并最终,病人的结果。
While efforts to understand and mitigate road traffic injury (RTI) occurrence have long been underway in high-income countries, similar projects in low/middle-income countries (LMICs) are frequently hindered by institutional and informational obstacles. Technological advances in geospatial analysis provide a pathway to overcome a subset of these barriers, and in doing so enable researchers to create actionable insights in the pursuit of mitigating RTI-associated negative health outcomes. This analysis develops a parallel geocoding workflow to improve investigation of low-fidelity datasets common in LMICs. Subsequently, this workflow is applied to and evaluated on an RTI dataset from Lagos State, Nigeria, minimising positional error in geocoding by incorporating outputs from four commercially available geocoders. The concordance between outputs from these geocoders is evaluated, and spatial visualisations are generated to provide insight into the distribution of RTI occurrence within the analysis region. This study highlights the implications of geospatial data analysis in LMICs facilitated by modern technologies on health resource allocation, and ultimately, patient outcomes.
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