Statistical Analysis of the Distribution of Points on a Network

Okabe, Atsuyuki; Yomono, Hidehiko; Kitamura, Masayuki

doi:10.1111/j.1538-4632.1995.tb00341.x

Cited by 84 publications

(38 citation statements)

References 8 publications

(7 reference statements)

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“…Statistical properties of random fields on a network are summarized in [78]. Recently, several spatial statistics, such as spatial autocorrelation, K-function, and Kriging, have been generalized to spatial networks [79][80][81]. Little research has been done on spatiotemporal statistics on the network space.…”

Section: Spatial Statistics For Different Types Of Spatial Datamentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

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Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

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Section: Spatial Statistics For Different Types Of Spatial Datamentioning

confidence: 99%

Spatiotemporal Data Mining: A Computational Perspective

Shekhar

Jiang

Ali

et al. 2015

IJGI

174

113

View full text Add to dashboard Cite

show abstract

“…It is an algorithm for decomposing a circuit-type network into a tree starting from a pole, or a root, and is based on the concept of shortest-distance paths. Later, Okabe, Yomono, and Kitamura (1995) and Okabe and Okunuki (2001) proposed a more refined computational algorithm for constructing such trees and defined it the extended shortest-path tree. They use links that extend from end-nodes of the shortest-path tree until they cover the entire links.…”

Section: Flexible Extended Shortest-path Treementioning

confidence: 99%

“…They use links that extend from end-nodes of the shortest-path tree until they cover the entire links. When the entire network is covered by the extended tree, two end-nodes extending from the opposite ends meet at a single point, which was called an indifferent point by Chorley and Haggett (1967) and a collision point by Okabe, Yomono, and Kitamura (1995). In other words, there exist multiple, mostly two, alternative shortest-path distance routes of equidistance from the root to the collision point.…”

Section: Flexible Extended Shortest-path Treementioning

confidence: 99%

Analysis of a Distribution of Point Events Using the Network‐Based Quadrat Method

Shiode

2008

Geographical Analysis

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This study proposes a new quadrat method that can be applied to the study of point distributions in a network space. While the conventional planar quadrat method remains one of the most fundamental spatial analytical methods on a two-dimensional plane, its quadrats are usually identified by regular, square grids. However, assuming that they are observed along a network, points in a single quadrat are not necessarily close to each other in terms of their network distance. Using planar quadrats in such cases may distort the representation of the distribution pattern of points on a network. The network-based units used in this article, on the other hand, consist of subsets of the actual network, providing more accurate aggregation of the data points along the network. The performance of the network-based quadrat method is compared with that of the conventional quadrat method through a case study on a point distribution on a network. The w 2 statistic and Moran's I statistic of the two quadrat types indicate that (1) the conventional planar quadrat method tends to overestimate the overall degree of dispersion and (2) the network-based quadrat method derives a more accurate estimate on the local similarity. The article also performs sensitivity analysis on network and planar quadrats across different scales and with different spatial arrangements, in which the abovementioned statistical tendencies are also confirmed.

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“…However, although the KDE has shown acceptable properties using density values, its homogeneous 2D assumption for events distributed in 1.5D space, such as TC on a road network, seems to be irrelevant [33][34][35][36][37][38]. To overcome this limitation, Okabe proposed the idea of the spatial analysis based on a network, Network-Constrained Kernel Density Estimation (NKDE), which can overcome the shortcomings of the KDE method and reduce the deviation of its results [39][40][41]. Furthermore, research has demonstrated the validity of NKDE to analyze network-based phenomena, such as TC [35,[42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

Nie

Wang

et al. 2015

Sustainability

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Abstract:Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration. OPEN ACCESSSustainability 2015, 7 2663

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Statistical Analysis of the Distribution of Points on a Network

Cited by 84 publications

References 8 publications

Spatiotemporal Data Mining: A Computational Perspective

Spatiotemporal Data Mining: A Computational Perspective

Analysis of a Distribution of Point Events Using the Network‐Based Quadrat Method

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

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