A Kernel Density Estimation Method for Linear Features in Network Space

doi:10.11947/j.AGCS.2017.20150158

Abstract

Abstract: Kernel density estimation(KDE) is an important method for analyzing spatial distributions of point features or linear features. So far the KDE methods for linear features analyze the features' spatial distributions by producing a smooth density surface over 2D homogeneous planar space, However, the planar KDE methods are not suited for analyzing the distribution characteristics of certain kinds of linear events, such as traffic jams, queue at intersections and taxi carrying passenger events, which usually occur in inhomogeneous 1D network space. This article presents a KDE method for linear features in network space, which first confirms the density distribution of each single linear feature, then computes the density distributions of all linear features in terms of distance and topology relationship in network space. This article extracts "pick-up" linear events from taxi GPS trajectory data and analyzes their distribution patterns in network space. By comparison with existing methods, experiment results show that the proposed method is able to represent the distribution patterns of linear events in network space more accurately.

Key words: linear events, network space, kernel density estimation(KDE), spatial-temporal GPS trajectory

CLC Number:

P208

TANG Luliang, KAN Zihan, LIU Huihui, SUN Fei, WU Huayi. A Kernel Density Estimation Method for Linear Features in Network Space[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(1): 107-113.

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