Identification of robust retailing location patterns with complex network approaches

  1. Sánchez-Saiz, Rosa María 1
  2. Ahedo, Virginia 1
  3. Santos, José Ignacio 1
  4. Gómez, Sergio 2
  5. Galán, José Manuel 1
  1. 1 Universidad de Burgos

    Universidad de Burgos

    Burgos, España


  2. 2 Universitat Rovira i Virgili

    Universitat Rovira i Virgili

    Tarragona, España


Complex & Intelligent Systems

ISSN: 2199-4536

Year of publication: 2021

Type: Article

DOI: 10.1007/S40747-021-00335-8 GOOGLE SCHOLAR

More publications in: Complex & Intelligent Systems


Cited by

  • Scopus Cited by: 3 (31-08-2023)
  • Web of Science Cited by: 3 (15-09-2023)
  • Dimensions Cited by: 3 (08-03-2023)

JCR (Journal Impact Factor)

  • Year 2021
  • Journal Impact Factor: 6.7
  • Journal Impact Factor without self cites: 5.333
  • Article influence score: 0.993
  • Best Quartile: Q1
  • Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartile: Q1 Rank in area: 35/145 (Ranking edition: SCIE)


  • Social Sciences: A+

Journal Citation Indicator (JCI)

  • Year 2021
  • Journal Citation Indicator (JCI): 1.13
  • Best Quartile: Q1
  • Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartile: Q1 Rank in area: 38/190


(Data updated as of 08-03-2023)
  • Total citations: 3
  • Recent citations: 3


AbstractThe problem of location is the cornerstone of strategic decisions in retail management. This decision is usually complex and multidimensional. One of the most relevant success factors is an adequate balanced tenancy, i.e., a complementary ecosystem of retail stores in the surroundings, both in planned and unplanned areas. In this paper, we use network theory to analyze the commercial spatial interactions in all the cities of Castile and Leon (an autonomous community in north-western Spain), Madrid, and Barcelona. Our approach encompasses different proposals both for the definition of the interaction networks and for their subsequent analyses. These methodologies can be used as pre-processing tools to capture features that formalize the relational dimension for location recommendation systems. Our results unveil the retail structure of different urban areas and enable a meaningful comparison between cities and methodologies. In addition, by means of consensus techniques, we identify a robust core of commercial relationships, independent of the particularities of each city, and thus help to distinguish transferable knowledge between cities. The results also suggest greater specialization of commercial space with city size.

Bibliographic References

  • Weber A (1909) Uber den Standort der Industrien. Russell & Russell, Tübingen
  • Marshall A (1890) Principle of economics. Macmillan, London
  • Marcon E, Puech F (2003) Evaluating the geographic concentration of industries using distance-based methods. J Econ Geogr 3:409–428.
  • Krugman P (1991) Geography and trade. MIT Press, London
  • Beguin H (1992) Christaller’s central place postulates. Ann Reg Sci 26:209–229.
  • Moses LN (1958) Location and the theory of production. Q J Econ 72:259.
  • Wallsten SJ (2001) An empirical test of geographic knowledge spillovers using geographic information systems and firm-level data. Reg Sci Urban Econ 31:571–599.
  • Bozkaya B, Yanik S, Balcisoy S (2010) A GIS-based optimization framework for competitive multi-facility location-routing problem. Netw Spat Econ 10:297–320.
  • Brache J, Felzensztein C (2019) Geographical co-location on Chilean SME’s export performance. J Bus Res 105:310–321.
  • Fujita M, Krugman P (2003) The new economic geography: Past, present and the future. Pap Reg Sci 83:139–164
  • Duranton G, Puga D (2004) Micro-foundations of urban agglomeration economies. In: Handbook of regional and urban economics. Elsevier, pp 2063–2117
  • Jara-Figueroa C, Jun B, Glaeser EL, Hidalgo CA (2018) The role of industry-specific, occupation-specific, and location-specific knowledge in the growth and survival of new firms. Proc Natl Acad Sci 115:12646–12653.
  • Pablo-Martí F, Arauzo-Carod JM (2018) Spatial distribution of economic activities: a network approach. J Econ Interact Coord.
  • Berman BR, Evans JR, Chatterjee PM (2018) Retail management. A strategic approach. Pearson
  • Ciari F, Löchl M, Axhausen KW (2008) Location decisions of retailers: an agent-based approach. In: 15th International conference on recent advances in retailing and services science, pp 1–34
  • Zentes J, Morschett D, Schramm-Klein H (2012) Strategic retail management. Gabler, Wiesbaden
  • Reynolds J, Wood S (2010) Location decision making in retail firms: evolution and challenge. Int J Retail Distrib Manag 38:828–845.
  • Erbıyık H, Özcan S, Karaboğa K (2012) Retail store location selection problem with multiple analytical hierarchy process of decision making an application in Turkey. Procedia Soc Behav Sci 58:1405–1414.
  • Shaikh SA, Memon MA, Prokop M, Kim KS (2020) An AHP/TOPSIS-based approach for an optimal site selection of a commercial opening utilizing geospatial data. In: Proc 2020 IEEE Int Conf Big Data Smart Comput BigComp 2020, pp 295–302.
  • Konishi H (2005) Concentration of competing retail stores. J Urban Econ 58:488–512.
  • Xiong X, Xiong F, Zhao J et al (2020) Dynamic discovery of favorite locations in spatio-temporal social networks. Inf Process Manag 57:102337.
  • Chen YM, Chen TY, Chen LC (2020) On a method for location and mobility analytics using location-based services: a case study of retail store recommendation. Online Inf Rev.
  • Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing. ACM Trans Intell Syst Technol 5:1–55.
  • Ma Y, Mao J, Ba Z, Li G (2020) Location recommendation by combining geographical, categorical, and social preferences with location popularity. Inf Process Manag 57:102251.
  • Bao J, Zheng Y, Wilkie D, Mokbel M (2015) Recommendations in location-based social networks: a survey. GeoInformatica 19:525–565.
  • Valverde-Rebaza JC, Roche M, Poncelet P, de Lopes A (2018) The role of location and social strength for friendship prediction in location-based social networks. Inf Process Manag 54:475–489.
  • Zhang K, Pelechrinis K, Lappas T (2018) Effects of promotions on location-based social media: evidence from foursquare. Int J Electron Commer 22:36–65.
  • Guo B, Liu Y, Ouyang Y et al (2019) Harnessing the power of the general public for crowdsourced business intelligence: a survey. IEEE Access 7:26606–26630.
  • Monaghan S, Lavelle J, Gunnigle P (2017) Mapping networks: exploring the utility of social network analysis in management research and practice. J Bus Res 76:136–144.
  • Hidalgo CA, Castañer E, Sevtsuk A (2020) The amenity mix of urban neighborhoods. Habitat Int.
  • Goh S, Choi MY, Lee K, Kim K (2016) How complexity emerges in urban systems: theory of urban morphology. Phys Rev E 93:052309.
  • Jensen P (2006) Network-based predictions of retail store commercial categories and optimal locations. Phys Rev E 74:035101.
  • Jensen P (2009) Analyzing the localization of retail stores with complex systems tools. In: Adams NM, Robardet C, Siebes A, Boulicaut J-F (eds) Advances in intelligent data analysis VIII. Springer, Berlin, pp 10–20
  • Reilly WJ (1931) The law of retail gravitation. Knickerbocker Press, New York
  • Huff DL (1964) Defining and estimating a trading area. J Mark 28:34–38.
  • Cliquet G, Baray J (2020) Location-based marketing. Wiley
  • Ladle JK, David SD (2009) Retail site selection: A new, innovative model for retail development. Cornell Real Estate Rev 7:1–27
  • Önüt S, Efendigil T, Soner Kara S (2010) A combined fuzzy MCDM approach for selecting shopping center site: an example from Istanbul, Turkey. Expert Syst Appl 37:1973–1980.
  • García JL, Alvarado A, Blanco J et al (2014) Multi-attribute evaluation and selection of sites for agricultural product warehouses based on an analytic hierarchy process. Comput Electron Agric 100:60–69.
  • Wang S-P, Lee H-C, Hsieh Y-K (2016) A Multicriteria approach for the optimal location of gasoline stations being transformed as self-service in Taiwan. Math Probl Eng 2016:1–10.
  • Roig-Tierno N, Baviera-Puig A, Buitrago-Vera J, Mas-Verdu F (2013) The retail site location decision process using GIS and the analytical hierarchy process. Appl Geogr 40:191–198.
  • Choudhury S, Howladar P, Majumder M, Saha AK (2019) Application of novel MCDM for location selection of surface water treatment plant. IEEE Trans Eng Manag.
  • Çoban V (2020) Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation. Complex Intell Syst 6:507–529.
  • Hidalgo CA et al (2018) The principle of relatedness. In: Morales A, Gershenson C, Braha D, Minai A, Bar-Yam Y (eds) Unifying themes in complex systems IX. ICCS 2018. Springer proceedings in complexity. Springer, Cham.
  • Tobler WR (1979) Cellular geography. Philosophy in geography. Springer Netherlands, Dordrecht, pp 379–386
  • Karamshuk D, Noulas A, Scellato S et al (2013) Geo-spotting: mining online location-based services for optimal retail store placement. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 793–801
  • Lin J, Oentaryo R, Lim E-P et al (2016) Where is the goldmine? In: Proceedings of the 27th ACM conference on hypertext and social media—HT ’16. ACM Press, New York, pp 93–102
  • Xu M, Wang T, Wu Z et al (2016) Store location selection via mining search query logs of baidu maps. arXiv:1606.03662
  • Chen L, Zhang D, Pan G et al (2015) Bike sharing station placement leveraging heterogeneous urban open data. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing—UbiComp ’15. ACM Press, New York, pp 571–575
  • Chen L, Zhang D, Wang L et al (2016) Dynamic cluster-based over-demand prediction in bike sharing systems. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. ACM, New York, pp 841–852
  • Chen J, Yu C, Jin H (2019) Evaluation model for business sites planning based on online and offline datasets. Futur Gener Comput Syst 91:465–474.
  • Rohani AMBM, Chua F-F (2018) Location analytics for optimal business retail site selection. In: Computational science and its applications—ICCSA 2014 and its applications—ICCSA 2018. Springer International Publishing, pp 392–405
  • Guo B, Li J, Zheng VW et al (2018) CityTransfer. Proc ACM Interact Mobile Wearable Ubiquit Technol 1:1–23.
  • Marcon E, Puech F (2010) Measures of the geographic concentration of industries: improving distance-based methods. J Econ Geogr 10:745–762.
  • Reichardt J, Bornholdt S (2004) Detecting fuzzy community structures in complex networks with a Potts model. Phys Rev Lett 93:218701.
  • Bollobás B (1980) A probabilistic proof of an asymptotic formula for the number of labelled regular graphs. Eur J Combin 1:311–316.
  • Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113.
  • Sarzynska M, Leicht EA, Chowell G, Porter MA (2016) Null models for community detection in spatially embedded, temporal networks. J Complex Netw 4:363–406.
  • Fagiolo G, Squartini T, Garlaschelli D (2013) Null models of economic networks: the case of the world trade web. J Econ Interact Coord 8:75–107.
  • Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44.
  • Porter M, Onnela J-P, Mucha PJ (2009) Communities in networks. Am Math Soc.
  • Danon L, Díaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 2005:P09008–P09008.
  • Gómez S, Jensen P, Arenas A (2009) Analysis of community structure in networks of correlated data. Phys Rev E Stat Nonlinear Soft Matter Phys 80:16114.
  • Vinh NX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11:2837–2854
  • Meilă M (2007) Comparing clusterings—an information based distance. J Multivar Anal 98:873–895.
  • Lancichinetti A, Fortunato S (2012) Consensus clustering in complex networks. Sci Rep 2:336.
  • Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S (2011) Finding statistically significant communities in networks. PLoS ONE 6:e18961.
  • Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008.
  • Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA 105:1118–1123.
  • Gregory S (2010) Finding overlapping communities in networks by label propagation. New J Phys 12:103018.
  • Newman MEJ (2004) Analysis of weighted networks. Phys Rev E 70:056131.
  • Arenas A, Duch J, Fernández A, Gómez S (2007) Size reduction of complex networks preserving modularity. New J Phys 9:176–176.
  • Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103:8577–8582.
  • Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization. Phys Rev E 72:027104.
  • Arenas A, Fernández A, Gómez S (2008) Analysis of the structure of complex networks at different resolution levels. New J Phys 10:053039.
  • Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133.