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Location and co-location in retail: a probabilistic approach using geo-coded data for metropolitan retail markets

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Abstract

In this paper, we employ geo-coded data at a fine spatial resolution for Sweden’s metropolitan areas to assess retail co-location. Retail clusters and their place in urban space are assessed from several angles. The probability of a specific type of retail unit to be established in a 250 by 250 m square is modelled as a function of (i) the presence of other similar retail establishments, (ii) the presence of stores that belong to other retail sectors and (iii) other characteristics of the square area, and its access to demand in the pertinent urban landscape. The analysis clarifies which types of retail clusters one can expect to find in a metropolitan region, as well as their relationship to the urban landscape. We analyse three distinct types of stores: clothing, household appliances, and specialized stores. Stores with high intensities of interaction are co-located, and predominantly located close to the urban cores, consistent with predictions from bid rent theory and central place theory. We further document negative location tendencies between shops that sell frequently purchased products and shops that sell durables. Moreover, our results highlight the importance of demand in the close surroundings, which is particularly strong for small-scale establishments.

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Notes

  1. “Specialized stores” throughout this paper refers to the stores selling one or few particular products (e.g. optical stores, book stores, flower shops).

  2. In the previous literature, low-order retailing refers to goods that are not purchased frequently, e.g. convenience goods, whereas high-order retailing refers to stores selling somewhat more durable goods (Dicken and Lloyd 1990). Location in Space, Theoretical Perspectives in Economic Geography (Third ed.). New York: HarperCollinsPublishers, Inc.

  3. Also see Öner and Larsson (2014) for the use of the same data.

  4. We use the four-digit aggregation level for retailing activities in our regression analysis.

  5. \(\lambda _\mathrm{im}\), for intra-municipality 0.02, \(\lambda _\mathrm{ir}\) (intra-regional) 0.1, and for \(\lambda _\mathrm{ir}\) (extra-regional) 0.05. The values correspond to an ‘S’-shaped curve, in which the willingness to commute is high within the local municipality, lower in the associated FER, and again quite high for the small subset of actors who commute to the surrounding FERs.

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Author information

Authors and Affiliations

  1. Jönköping International Business School, Jönköping, Sweden

    Johan P. Larsson & Özge Öner

  2. Centre for Entrepreneurship and Spatial Economics, Jönköping, Sweden

    Johan P. Larsson & Özge Öner

Authors
  1. Johan P. Larsson
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  2. Özge Öner
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Corresponding author

Correspondence to Özge Öner.

Appendices

Appendix 1

See Tables 4 and 5.

Table 4 Description and sectoral codes of the retail branches, four-digit
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Table 5 Descriptive statistics for all variables used in the regression analysis
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Appendix 2

See Fig. 2.

Fig. 2
figure 2

Histograms displaying distributions of the three independent variables. Note the graphs include all observations (squares) with retail presence, but excludes squares with more than a total of 15 retail establishments (less than 0.5 % of total observations)

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Larsson, J.P., Öner, Ö. Location and co-location in retail: a probabilistic approach using geo-coded data for metropolitan retail markets. Ann Reg Sci 52, 385–408 (2014). https://doi.org/10.1007/s00168-014-0591-7

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  • DOI: https://doi.org/10.1007/s00168-014-0591-7

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