SANET: A Toolbox for Spatial Analysis on a Network

Okabe, Atsuyuki; Okunuki, Kei-ichi; Shiode, Shino

doi:10.1111/j.0016-7363.2005.00674.x

Cited by 168 publications

(99 citation statements)

References 18 publications

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“…In traditional analysis methods, it is generally assumed that the spatial events can be located stochastically on a plane, and the spatial association between event locations or sub-areas is analysed using the Euclidean (or planar) distance [1][2][3], in which the inherent spatial processes are quantified based the assumption of Euclidean geometry [4]. However, this assumption is not appropriate when a spatial phenomenon is apparently constrained to a subset of geographical space, such as a street network.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Wang

Yue

et al. 2017

IJGI

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Abstract:The spatial variation of geographical phenomena is a classical problem in spatial data analysis and can provide insight into underlying processes. Traditional exploratory methods mostly depend on the planar distance assumption, but many spatial phenomena are constrained to a subset of Euclidean space. In this study, we apply a method based on a hierarchical Bayesian model to analyse the spatial variation of network-constrained phenomena represented by a link attribute in conjunction with two experiments based on a simplified hypothetical network and a complex road network in Shenzhen that includes 4212 urban facility points of interest (POIs) for leisure activities. Then, the methods named local indicators of network-constrained clusters (LINCS) are applied to explore local spatial patterns in the given network space. The proposed method is designed for phenomena that are represented by attribute values of network links and is capable of removing part of random variability resulting from small-sample estimation. The effects of spatial dependence and the base distribution are also considered in the proposed method, which could be applied in the fields of urban planning and safety research.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Wang

Yue

et al. 2017

IJGI

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“…A network version of K-function and its computational implementation are described in Okabe and Yamada (2001). To examine the advantages of network K-function, Yamada & Thill (2004) (Okabe et al, 2006). SANET offers network version of both global and local K-functions as well as some additional utility tools for data processing.…”

Section: Introductionmentioning

confidence: 99%

Kernel Density Estimation of traffic accidents in a network space

Xie

Yan

2008

Computers, Environment and Urban Systems

528

307

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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 ESRI ArcGIS 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 3 lead the way to developing a suite of other network related spatial analysis and modeling methods.

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“…[18], that is, phenomena which do happen in points along edges of the network, such as, for instance, traffic accidents, or close to such edges, as happens in urban settings, where edges model the city streets, close to the buildings where demand happens. See [18,19] for further discussion on the advantages on continuous network models against traditional approaches (discrete or planar location models).…”

Section: Introductionmentioning

confidence: 99%

“…[18] suggests the use of general density distributions, from which random samples are generated, yielding a discrete approximation to the problem, which is the one which is later analyzed. Statistical kernel methods have also been recently proposed to model the demand, [20,23], though, as far as the authors know, no optimization has been carried out, excepting, as said above, discretization via simulation.…”

Section: Introductionmentioning

confidence: 99%

Solving the median problem with continuous demand on a network

Blanquero

Carrizosa

2013

Comput Optim Appl

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Where to locate one or several facilities on a network so as to minimize the expected users-closest facility transportation cost is a problem well studied in the OR literature under the name of median problem.In the median problem users are usually identified with nodes of the network. In many situations, however, such assumption is unrealistic, since users should be better considered to be distributed also along the edges of the transportation network. In this paper we address the median problem with demand distributed along edges and nodes. This leads to a globaloptimization problem, which can be solved to optimality by means of a branch-and-bound with DC bounds. Our computational experience shows that the problem is solved in short time even for large instances.

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SANET: A Toolbox for Spatial Analysis on a Network

Cited by 168 publications

References 18 publications

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Analysis of the Spatial Variation of Network-Constrained Phenomena Represented by a Link Attribute Using a Hierarchical Bayesian Model

Kernel Density Estimation of traffic accidents in a network space

Solving the median problem with continuous demand on a network

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