A Modified DBSCAN Clustering Method to Estimate Retail Center Extent

Abstract

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.

Beyond retail: New ways of classifying UK shopping and consumption spaces

“…We used the boundaries of the 3110 retail centres located in Great Britain (Pavlis et al, 2018). These boundaries were a preferred option to the official DCLG town centre boundaries developed in 2004, for two reasons.…”

Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data

“…The traditional hierarchical clustering (see Section 1.3) method is used to compare how the proposed space-time hierarchical clustering approach performs on spatiotemporal data to a naïve approach. ST-DBSCAN is a density-based algorithm for clustering spatiotemporal data based on certain parameters [22,52]. The approach for the ST-DBSCAN are different from hierarchical clustering in at least two ways: it reduces the noise in the data, and it automatically detects the number of clusters within the dataset.…”

Building Hierarchies of Retail Centers Using Bayesian Multilevel Models

“…Town centers are complex urban economic systems that are characterized by the clustering of socioeconomic activity (Thurstain-Goodwin and Unwin 2000). Embedded within the urban fabric of town centers are retail centers that are agglomerations of consumer spaces and shopping destinations that are central to economic and civic life (Pavlis, Dolega, and Singleton 2017). Town centers are typically composed of a retail center but in some cases have more expansive functional areas that include office spaces in addition to retail and services.…”