网络空间中线要素的核密度估计方法

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。     

A network Kernel Density Estimation for linear features in space–time analysis of big trace data

ABSTRACT

Kernel Density Estimation (KDE) is an important approach to analyse spatial distribution of point features and linear features over 2-D planar space. Some network-based KDE methods have been developed in recent years, which focus on estimating density distribution of point events over 1-D network space. However, the existing KDE methods are not appropriate for analysing the distribution characteristics of certain kind of features or events, such as traffic jams, queue at intersections and taxi carrying passenger events. These events occur and distribute in 1-D road network space, and present a continuous linear distribution along network. This paper presents a novel Network Kernel Density Estimation method for Linear features (NKDE-L) to analyse the space–time distribution characteristics of linear features over 1-D network space. We first analyse the density distribution of each linear feature along networks, then estimate the density distribution for the whole network space in terms of the network distance and network topology. In the case study, we apply the NKDE-L to analyse the space–time dynamics of taxis’ pick-up events, with real road network and taxi trace data in Wuhan. Taxis’ pick-up events are defined and extracted as linear events (LE) in this paper. We first conduct a space–time statistics of pick-up LE in different temporal granularities. Then we analyse the space–time density distribution of the pick-up events in the road network using the NKDE-L, and uncover some dynamic patterns of people’s activities and traffic condition. In addition, we compare the NKDE-L with quadrat method and planar KDE. The comparison results prove the advantages of the NKDE-L in analysing spatial distribution patterns of linear features in network space.     

Network-constrained and category-based point pattern analysis for Suguo retail stores in Nanjing,China

The distribution of many geographical objects and events is affected by the road network; thus, network-constrained point pattern analysis methods are helpful to understand their space structures and distribution patterns. In this study, network kernel density estimation and network K-function are used to study retail service hot-spot areas and the spatial clustering patterns of a local retail giant (Suguo), respectively, in Nanjing city. Stores and roads are categorized to investigate the influence of weighting different categories of point events and network on the analysis. In addition, the competitive relation between Suguo and foreign-brand retail chains was revealed. The comprehensive analysis results derived from the combination of the first-order and second-order properties can be further used to examine the reasonability of the existing store distribution and optimize the locational choice of new stores.     

Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation

Road density (i.e., km/km²) is a useful broad index of the road network in a landscape and has been linked to several ecological effects of roads. However, previous studies have shown that road density, estimated by grid computing, has weak correlation with landscape fragmentation. In this article, we propose a new measure of road density, namely, kernel density estimation function (KDE) and quantify the relation between road density and landscape fragmentation. The results show that road density estimated by KDE (km/km²) elucidates the spatial pattern of the road network in the region. Areas with higher road density are dominated by a larger proportion of built-up landscape and less possession of forest and vice versa. Road networks segregated the landscape into smaller pieces and a greater number of patches. Furthermore, Spearman rank correlation model indicates that road density (km/km²) is positively related to landscape fragmentation. Our results suggest that road density, estimated by KDE, may be a better correlate with effects of the road on landscape fragmentation. Through KDE, the regional spatial pattern of road density and the prediction of the impact of the road on landscape fragmentation could be effectively acquired.