Commercial Classification and Location Modelling: Integrating Different Perspectives on Commercial Location and Structure

Rui Colaço and João de Abreu e Silva
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Rui Colaço: CERIS—Civil Engineering Research and Innovation for Sustainability, Department of Civil Engineering, Architecture and Georesources (DECivil), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal
João de Abreu e Silva: CERIS—Civil Engineering Research and Innovation for Sustainability, Department of Civil Engineering, Architecture and Georesources (DECivil), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

Land, 2021, vol. 10, issue 6, 1-19

Abstract: Commercial classification is essential to describe and compare the spatial patterns of commercial activity. Most classification systems consider a large set of dimensions that include detailed features such as store ownership or development type. Since new business models are continually being developed, the need to revise classification systems is constant. This makes generalisation hard, thus hindering the comparison of commercial structures in different places and periods. Recent studies have focused on cluster analysis and a smaller number of variables to gain insights into commercial structures, directly addressing this issue. Systematic bottom-up classification generates comparable structures, which is essential to contrast policy results in different situations. Furthermore, since form or accessibility are usually considered in classifications, cluster membership is precluded from most retail location models, often relying on the latter as an explanatory variable. Hence, a new classification system is proposed, based on cluster analysis (k-means) and a minimal set of variables: density, diversity, and clustering. This classification was implemented in 1995, 2002, and 2010 in Lisbon. Cross-sectional analysis of the commercial structures shows the system accurately describes commercial location and change, suggesting it can be generalised as a classification system. Since the minimal dataset also allows for cluster membership to be used on location models, the relationship between commercial classification and location modelling could be strengthened, reinforcing the role of commercial studies in urban planning and policymaking.

Keywords: commercial classification; retail location model; cluster analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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