Kernel Density Estimation of traffic accidents in a network space
Kernel Density Estimation of traffic accidents in a network space
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DOI：
10.1016/j.compenvurbsys.2008.05.001
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发表时间：
2008-09-01
影响因子：
6.8
通讯作者：
Yan, Jun
中科院分区：
文献类型：
--
作者：
Xia, Zhixiao;Yan, Jun
标准平面核密度估计（KDE）旨在生成二维地理空间中空间点事件的平滑密度表面。然而，平面KDE可能不适合描述某些点事件，例如交通事故，这类事件通常发生在一维线性空间（即道路网络）内。本文提出了一种新的网络KDE方法来估计此类空间点事件的密度。这种新方法的一个关键特征是，网络空间由等网络长度的基本线性单元（称为线素（线性像素））以及相关的网络拓扑结构来表示。线素的使用不仅有助于沿着网络系统地选择一组规则间隔的位置进行密度估计，而且通过显著提高计算效率使网络KDE的实际应用变得可行。该方法在ESRI ArcGIS环境中实现，并使用2005年肯塔基州鲍灵格林地区的交通事故数据和道路网络进行了测试。测试结果表明，对于交通事故的密度估计，新的网络KDE比标准平面KDE更合适，因为后者涵盖了事件背景（网络空间）之外的空间，并且可能高估密度值。该研究还探讨了两种核函数、线素长度和搜索带宽对密度计算的影响。研究发现，核函数在构建网络空间上的密度模式时重要性最低，而线素长度对空间密度模式的局部变化细节有关键影响。搜索带宽通过控制空间模式的平滑度产生最大影响，在窄带宽时显示局部效应，在较宽带宽时在更大或全局尺度上揭示“热点”。更重要的是，用等长线素的网络系统表示线性网络的想法可能潜在地为开发一系列其他与网络相关的空间分析和建模方法开辟道路。由爱思唯尔有限公司出版。
A standard planar Kernel Density Estimation (KDE) aims to produce a smooth density surface of spatial point events over a 2-D geographic space. However, the planar KDE may not be suited for characterizing certain point events, such as traffic accidents, which usually occur inside a 1-D linear space, the roadway network. This paper presents a novel network KDE approach to estimating the density of such spatial point events. One key feature of the new approach is that the network space is represented with basic linear units of equal network length, termed lixel (linear pixel), and related network topology. The use of lixel not only facilitates the systematic selection of a set of regularly spaced locations along a network for density estimation, but also makes the practical application of the network KDE feasible by significantly improving the computation efficiency. The approach is implemented in the ESRl ArcGlS environment and tested with the year 2005 traffic accident data and a road network in the Bowling Green, Kentucky area. The test results indicate that the new network KDE is more appropriate than standard planar KDE for density estimation of traffic accidents, since the latter covers space beyond the event context (network space) and is likely to overestimate the density values. The study also investigates the impacts on density calculation from two kernel functions, lixel lengths, and search bandwidths. It is found that the kernel function is least important in structuring the density pattern over network space, whereas the lixel length critically impacts the local variation details of the spatial density pattern. The search bandwidth imposes the highest influence by controlling the smoothness of the spatial pattern, showing local effects at a narrow bandwidth and revealing "hot spots" at larger or global scales with a wider bandwidth. More significantly, the idea of representing a linear network by a network system of equal-length lixels may potentially lead the way to developing a suite of other network related spatial analysis and modeling methods. Published by Elsevier Ltd.