Clustering in point processes on linear networks using nearest neighbour volumes.

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
Journal of Applied Statistics Pub Date : 2024-11-07 eCollection Date: 2025-01-01 DOI:10.1080/02664763.2024.2411214
Juan F Díaz-Sepúlveda, Nicoletta D'Angelo, Giada Adelfio, Jonatan A González, Francisco J Rodríguez-Cortés
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引用次数: 0

Abstract

This study introduces a novel method specifically designed to detect clusters of points within linear networks. This method extends a classification approach used for point processes in spatial contexts. Unlike traditional methods that operate on planar spaces, our approach adapts to the unique geometric challenges of linear networks, where classical properties of point processes are altered, and intuitive data visualisation becomes more complex. Our method utilises the distribution of the Kth nearest neighbour volumes, extending planar-based clustering techniques to identify regions of increased point density within a network. This approach is particularly effective for distinguishing overlapping Poisson processes within the same linear network. We demonstrate the practical utility of our method through applications to road traffic accident data from two Colombian cities, Bogota and Medellin. Our results reveal distinct clusters of high-density points in road segments where severe traffic accidents (resulting in injuries or fatalities) are most likely to occur, highlighting areas of increased risk. These clusters were primarily located on major arterial roads with high traffic volumes. In contrast, low-density points corresponded to areas with fewer accidents, likely due to lower traffic flow or other mitigating factors. Our findings provide valuable insights for urban planning and road safety management.

基于最近邻体积的线性网络点过程聚类。
本研究介绍了一种专门设计用于检测线性网络中点簇的新方法。该方法扩展了用于空间上下文中的点过程的分类方法。与在平面空间上操作的传统方法不同,我们的方法适应线性网络的独特几何挑战,其中点过程的经典属性被改变,直观的数据可视化变得更加复杂。我们的方法利用第k个最近邻体的分布,扩展基于平面的聚类技术来识别网络中点密度增加的区域。这种方法对于区分同一线性网络中的重叠泊松过程特别有效。我们通过对哥伦比亚两个城市波哥大和麦德林的道路交通事故数据的应用,展示了我们方法的实际效用。我们的研究结果显示,在最可能发生严重交通事故(导致受伤或死亡)的路段中,高密度点的明显集群突出了风险增加的区域。这些集群主要位于交通流量大的主干道上。相比之下,低密度点对应的是事故较少的地区,可能是由于交通流量较低或其他缓解因素。我们的研究结果为城市规划和道路安全管理提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Applied Statistics
Journal of Applied Statistics 数学-统计学与概率论
CiteScore
3.40
自引率
0.00%
发文量
126
审稿时长
6 months
期刊介绍: Journal of Applied Statistics provides a forum for communication between both applied statisticians and users of applied statistical techniques across a wide range of disciplines. These areas include business, computing, economics, ecology, education, management, medicine, operational research and sociology, but papers from other areas are also considered. The editorial policy is to publish rigorous but clear and accessible papers on applied techniques. Purely theoretical papers are avoided but those on theoretical developments which clearly demonstrate significant applied potential are welcomed. Each paper is submitted to at least two independent referees.
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