The Evaluation of Trade Area Models and Analysis Methods for Site Selection from International Quick Service Restaurants' (QSR) Perspective


Bas H. K. , Zeren Gülersoy N.

ICONARP INTERNATIONAL JOURNAL OF ARCHITECTURE AND PLANNING, vol.6, no.1, pp.1-28, 2018 (Journal Indexed in ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 6 Issue: 1
  • Publication Date: 2018
  • Doi Number: 10.15320/iconarp.2018.36
  • Title of Journal : ICONARP INTERNATIONAL JOURNAL OF ARCHITECTURE AND PLANNING
  • Page Numbers: pp.1-28

Abstract

International quick service restaurants (QSRs) have become a standalone sector due to their significant market share throughout the world instead of being considered under the food and beverage sector. The success of QSR site selection is directly related to land use and market potential estimation. This relationship has a significant influence on urban texture, identity and cities' development processes, given the high number of QSRs in urban spaces. Diverging from the current retail sector dynamics, the QSR sector brings to the table different needs in terms of trade area characteristics and spatial characteristics. In this respect, the aim of this research is to deliberate a methodological framework investigating site selection decisions of international QSRs and to establish a conceptual framework for an applicable model. Accordingly, first, the relationship between trade area analysis and site selection of international QSRs is examined. After that, trade area models of The Proximal Area Model, Reilly's Law of Retail Gravitation Model, Central Place Theory, Huff Model, Analog Model and Geographic Interdependence Model are discussed according to their competence of QSRs' site selection. Then they are analytically evaluated within the framework of today's economic, social and spatial development variables. Finally, Regression Analysis Methods and Geographical Information Systems (GIS), which are encountered in literature and used in practice are examined, and a new theoretical framework for a site selection model integrating Regression Analysis Methods and GIS is proposed.