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路网结构对痴呆相关走失事件的影响:空间缓冲区方法。

Impact of road network structure on dementia-related missing incidents: a spatial buffer approach.

机构信息

Norwich Medical School, 2.04 Bob Champion Research and Education Building, University of East Anglia, Norwich, NR4 7TJ, UK.

Centre for Advanced Spatial Analysis, University College London, Gower Street, London, WC1E 6BT, UK.

出版信息

Sci Rep. 2020 Oct 29;10(1):18574. doi: 10.1038/s41598-020-74915-y.

Abstract

Dementia-related missing incidents are a highly prevalent issue worldwide. Despite being associated with potentially life-threatening consequences, very little is still known about what environmental risk factors may potentially contribute to these missing incidents. The aim of this study was to conduct a retrospective, observational analysis using a large sample of police case records of missing individuals with dementia (n = 210). Due to the influence that road network structure has on our real world navigation, we aimed to explore the relationship between road intersection density, intersection complexity, and orientation entropy to the dementia-related missing incidents. For each missing incident location, the above three variables were computed at a 1 km radius buffer zone around these locations; these values were then compared to that of a set of random locations. The results showed that higher road intersection density, intersection complexity, and orientation entropy were all significantly associated with dementia-related missing incidents. Our results suggest that these properties of road network structure emerge as significant environmental risk factors for dementia-related missing incidents, informing future prospective studies as well as safeguarding guidelines.

摘要

痴呆相关走失事件是全球范围内一个高发问题。尽管这些走失事件可能带来潜在的危及生命的后果,但对于可能导致这些走失事件的环境风险因素,我们知之甚少。本研究旨在使用大量痴呆症患者走失人员的警察案例记录(n=210)进行回顾性观察分析。由于道路网络结构对我们现实世界导航的影响,我们旨在探索道路交叉口密度、交叉口复杂性和方向熵与痴呆相关走失事件之间的关系。对于每个走失事件地点,在这些地点周围 1 公里半径的缓冲区计算上述三个变量;然后将这些值与一组随机地点的值进行比较。结果表明,较高的道路交叉口密度、交叉口复杂性和方向熵均与痴呆相关走失事件显著相关。我们的研究结果表明,道路网络结构的这些特性是痴呆相关走失事件的重要环境风险因素,为未来的前瞻性研究和安全保障指南提供了信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a47f/7596503/4937cd0d030a/41598_2020_74915_Fig1_HTML.jpg

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