Detection of multi-scale clusters in network space

Shiode, Shino and Shiode, Narushige 2009. Detection of multi-scale clusters in network space. International Journal of Geographical Information Science 23 (1) , pp. 75-92. 10.1080/13658810801949843

Full text not available from this repository.

Abstract

This paper proposes a new type of point‐pattern analytical method, Network‐Based Variable‐Distance Clumping Method (NT‐VCM), to analyse the distribution pattern of point objects and phenomena observed on a network. It is an extension of Planar Variable‐Distance Clumping Method (PL‐VCM) that was previously defined for point pattern analysis in Euclidian space. The purpose for developing NT‐VCM is to identify point agglomerations across different scales called multi‐scale network‐based clumps among distributed points along a network. The paper first defines a network‐based clump as a set of points where all its elements are found within a certain shortest‐path distance from at least one other element of the same set. It then proposes NT‐VCM as a technique to extract statistically significant multi‐scale clumps on a network. The paper also proposes an efficient algorithm for computing NT‐VCM, which involves the use of the Voronoi diagram, the Delaunay diagram and the minimum spanning tree that are adapted and newly extended for the purpose of analysis on a network. A comparative study of NT‐VCM and PL‐VCM using commercial facility data reveals a notable difference in the location as well as the size of the significant multi‐scale clumps detected in the both cases. Results from the empirical study confirm that NT‐VCM accounts for the actual network distance between the points, thus providing a more accurate description of point agglomerations along the network than PL‐VCM does.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Geography and Planning (GEOPL)
Subjects:	H Social Sciences > HT Communities. Classes. Races
Uncontrolled Keywords:	Clump, Cluster detection, Network, Point pattern analysis
Publisher:	Taylor & Francis
ISSN:	13658816
Last Modified:	07 Nov 2019 09:07
URI:	https://orca.cardiff.ac.uk/id/eprint/10693

Citation Data

Cited 35 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item