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A Modified DBSCAN Clustering Method to Estimate Retail Center Extent
Author(s) -
Pavlis Michalis,
Dolega Les,
Singleton Alex
Publication year - 2018
Publication title -
geographical analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.773
H-Index - 65
eISSN - 1538-4632
pISSN - 0016-7363
DOI - 10.1111/gean.12138
Subject(s) - dbscan , cluster analysis , computer science , data mining , adjacency list , sample (material) , pattern recognition (psychology) , artificial intelligence , algorithm , correlation clustering , cure data clustering algorithm , chemistry , chromatography
This research introduces a new method for the identification of local retail agglomerations within Great Britain, implementing a modification of the established density based spatial clustering of applications with noise (DBSCAN) method that improves local sensitivity to variable point densities. The variability of retail unit density can be related to both the type and function of retail centers, but also to characteristics such as size and extent of urban areas, population distribution, or property values. The suggested method implements a sparse graph representation of the retail unit locations based on a distance‐constrained k‐nearest neighbor adjacency list that is subsequently decomposed using the Depth First Search algorithm. DBSCAN is iteratively applied to each subgraph to extract the clusters with point density closer to an overall density for each study area. This innovative approach has the advantage of adjusting the radius parameter of DBSCAN at the local scale, thus improving the clustering output. A comparison of the estimated retail clusters against a sample of existing boundaries of retail areas shows that the suggested methodology provides a simple yet accurate and flexible way to automate the process of identifying retail clusters of varying shapes and densities across large areas; and by extension, enables their automated update over time.