网络空间中线要素的核密度估计方法

doi:10.11947/j.AGCS.2017.20150158

测绘学报 ›› 2017, Vol. 46 ›› Issue (1): 107-113.doi: 10.11947/j.AGCS.2017.20150158

网络空间中线要素的核密度估计方法

唐炉亮¹, 阚子涵¹, 刘汇慧^1,2, 孙飞¹, 吴华意¹

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 武汉大学测绘学院, 湖北武汉 430079

收稿日期:2015-03-25 修回日期:2016-10-08 出版日期:2017-01-20 发布日期:2017-02-06
通讯作者: 阚子涵 E-mail:kzh@whu.edu.cn
作者简介:唐炉亮(1973-),男,教授,博士,博士生导师,研究方向为时空GIS、轨迹大数据分析与挖掘。E-mail:tll@whu.edu.cn
基金资助:
国家自然科学基金（41671442；41571430；41271442）

A Kernel Density Estimation Method for Linear Features in Network Space

TANG Luliang¹, KAN Zihan¹, LIU Huihui^1,2, SUN Fei¹, WU Huayi¹

1. Department of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2015-03-25 Revised:2016-10-08 Online:2017-01-20 Published:2017-02-06
Supported by:
The National Natural Science Foundation of China (Nos. 41671442;41571430;41271442)

摘要/Abstract

摘要： 核密度估计（KDE）方法是分析点要素或线要素空间分布模式的一种重要方法，但目前线要素核密度方法只能分析线要素在二维均质平面空间的密度分布，不能正确分析交通拥堵、交叉口排队、出租车载客等线事件在一维非均质道路网络空间中的密度分布。本文提出了一种网络空间中线要素的核密度估计方法（网络线要素KDE方法），首先确定每个线要素在网络空间上的密度分布，然后根据网络空间距离和拓扑关系确定网络空间的线要素核密度与时空分布。以出租车GPS轨迹数据中提取的“上客”线事件为例，分析出租车“上客”线事件在网络空间中的密度分布，通过与现有方法比较的试验结果表明，本文提出的方法更能准确反映路网空间中线事件的分布特征。

关键词: 线事件, 网络空间, 核密度, 时空GPS轨迹

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

中图分类号:

P208

唐炉亮, 阚子涵, 刘汇慧, 孙飞, 吴华意. 网络空间中线要素的核密度估计方法[J]. 测绘学报, 2017, 46(1): 107-113.

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.

参考文献

[1] SILVERMAN B W. Density Estimation for Statistics and Data Analysis[M]. New York:Chapman & Hall, 1986.
[2] BAILEY T C, GATRELL A C. Interactive Spatial Data Analysis[M]. Harlow Essex, England:Longman, 1995.
[3] TIMOTHÉE P, NICOLAS L B, EMANUELE S, et al. A Network Based Kernel Density Estimator Applied to Barcelona Economic Activities[C]//Proceedings of International Conference on Computational Science and Its Applications-ICCSA 2010. Berlin Heidelberg:Springer, 2010:32-45.
[4] BORRUSO G. Network Density Estimation:A GIS Approach for Analyzing Point Patterns in Network Space[J]. Transactions in GIS, 2008, 12(3):377-402.
[5] BORRUSO G. Network Density Estimation:Analysis of Point Patterns over a Network[C]//Proceedings of International Conference on Computational Science and Its Applications-ICCSA 2005. Berlin Heidelberg:Springer, 2005:126-132.
[6] 吕安民, 李成名, 林宗坚, 等. 人口密度的空间连续分布模型[J]. 测绘学报, 2003, 32(4):344-348. LÜ Anmin, LI Chengming, LIN Zongjian, et al. Spatial Continuous Surface Model of Population Density[J]. Acta Geodaetica et Cartographica Sinica, 2003, 32(4):344-348.
[7] 杨红磊, 彭军还. 基于马尔可夫随机场的模糊c-均值遥感影像分类[J]. 测绘学报, 2012, 41(2):213-218. YANG Honglei, PENG Junhuan. Remote Sensing Classification Based on Markov Random Field and Fuzzy c-means Clustering[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2):213-218.
[8] 周恩策, 刘纯平, 张玲燕, 等. 基于时间窗的自适应核密度估计运动检测方法[J]. 通信学报, 2011, 32(3):106-114, 124. ZHOU Ence, LIU Chunping, ZHANG Lingyan, et al. Foreground Object Detection Based on Time Information Window Adaptive Kernel Density Estimation[J]. Journal of Communications, 2011, 32(3):106-114, 124.
[9] MILLER H J. Potential Contributions of Spatial Analysis to Geographic Information Systems for Transportation (GIS-T)[J]. Geographical Analysis, 1999, 31(4):373-399.
[10] XIE Zhixiao, YAN Jun. Kernel Density Estimation of Traffic Accidents in a Network Space[J]. Computers, Environment and Urban Systems, 2008, 32(5):396-406.
[11] OKABE A, SATOH T, SUGIHARA K. A Kernel Density Estimation Method for Networks,Its Computational Method and a GIS-based Tool[J]. International Journal of Geographical Information Science, 2009, 23(1):7-32.
[12] LI Qingquan, ZHANG Tong, WANG Handong, et al. Dynamic Accessibility Mapping Using Floating Car Data:A Network-constrained Density Estimation Approach[J]. Journal of Transport Geography, 2011, 19(3):379-393.
[13] 禹文豪, 艾廷华. 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报, 2015, 44(1):82-90. DOI:10.11947/j.AGCS.2015.20130538. YU Wenhao, AI Tinghua. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90. DOI:10.11947/j.AGCS.2015.20130538.
[14] BORRUSO G. Network Density and the Delimitation of Urban Areas[J]. Transactions in GIS, 2003, 7(2):177-191.
[15] WORTON B J. Kernel Methods for Estimating the Utilization Distribution in Home-range Studies[J]. Ecology, 1989, 70(1):164-168.
[16] CAI Xuejiao, WU Zhifeng, CHENG Jiong. Using Kernel Density Estimation to Assess the Spatial Pattern of Road Density and Its Impact on Landscape Fragmentation[J]. International Journal of Geographical Information Science, 2013, 27(2):222-230.
[17] YING Lingxiao, SHEN Zehao, CHEN Jiding, et al. Spatiotemporal Patterns of Road Network and Road Development Priority in Three Parallel Rivers Region in Yunnan, China:An Evaluation Based on Modified Kernel Distance Estimate[J]. Chinese Geographical Science, 2014, 24(1):39-49.
[18] SCHEEPENS R, WILLEMS N, VAN DE WETERING H, et al.Composite Density Maps for Multivariate Trajectories[J]. IEEE Transactions on Visualization and Computer Graphics, 2011, 17(12):2518-2527.
[19] LEE D, HAHN M. A Study on Density Map Based Crash Analysis[C]//Proceedings of 2014 International Conference on Information Science and Applications (ICISA). Seoul, South Korea:IEEE, 2014:1-3.

网络空间中线要素的核密度估计方法

A Kernel Density Estimation Method for Linear Features in Network Space

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	艾廷华, 周梦杰, 李晓明. 网络空间同位模式的加色混合可视化挖掘方法[J]. 测绘学报, 2017, 46(6): 753-759.
[2]	向隆刚, 邵晓天. 载体轨迹停留信息提取的核密度法及其可视化[J]. 测绘学报, 2016, 45(9): 1122-1131.
[3]	禹文豪, 艾廷华, 刘鹏程, 何亚坤. 设施POI分布热点分析的网络核密度估计方法[J]. 测绘学报, 2015, 44(12): 1378-1383.
[4]	唐炉亮, 刘章, 杨雪, 阚子涵, 李清泉, 董坤. 符合认知规律的时空轨迹融合与路网生成方法[J]. 测绘学报, 2015, 44(11): 1271-1276.
[5]	禹文豪, 艾廷华. 核密度估计法支持下的网络空间POI点可视化与分析[J]. 测绘学报, 2015, 44(1): 82-90.
[6]	杨红磊,彭军还. 基于马尔科夫随机场与模糊C均值的遥感影像分类[J]. 测绘学报, 2012, 41(2): 213-218.