Spatial Big Data and Business Location Decision-Making: Opportunities and Challenges

  • Joseph AversaEmail author
  • Tony Hernandez
  • Sean Doherty


This chapter examines the current state and trajectory of spatial big data and business location decision-making (BLDM) practices amongst major corporations in Canada. The three objectives of the chapter are: (i) to provide a research context for the study of spatial big data (SBD) and associated data science (DS) approaches in business; (ii) to identify the awareness, availability, use, adoption, integration, and development of SBD and DS within BLDM; and (iii) to explore the opportunities and challenges associated with integrating spatial big data into business organizations. The chapter presents qualitative insights from semi-structured interviews with location decision-makers from 24 major business corporations in Canada.


Business Location decision-making Big data Location analytics Data science 


  1. Atkinson, R., & Flint, J. (2001). Accessing hidden and hard-to-reach populations: Snowball research strategies. Social Research Update, 33(1), 1–4.Google Scholar
  2. Aversa, J., Doherty, S., & Hernandez, T. (2018). Big data analytics: The new boundaries of retail location decision making. Papers in Applied Geography, 1–19.Google Scholar
  3. Bazeley, P. (2009). Integrating data analyses in mixed methods research. Journal of Mixed Methods Research, 3(3), 203–207.CrossRefGoogle Scholar
  4. Biernacki, P., & Waldorf, D. (1981). Snowball sampling: Problems and techniques of chain referral sampling. Sociological Methods & Research, 10(2), 141–163.CrossRefGoogle Scholar
  5. Brosekhan, A. A., Velayutham, C. M., & Phil, M. (1995). Consumer buying behaviour—A literature review. Journal of Business and Management, 8–16.Google Scholar
  6. Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24–35.Google Scholar
  7. Byrom, J. (2001). The role of loyalty card data within local marketing initiatives. International Journal of Retail & Distribution Management, 29(7), 333–342.CrossRefGoogle Scholar
  8. Byrom, J. W., Bennison, D. J., Hernández, T., & Hooper, P. D. (2001). The use of geographical data and information in retail locational planning. Journal of Targeting, Measurement and Analysis for Marketing, 9(3), 219–229.Google Scholar
  9. Cao, L. (2017). Data science: A comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 43.CrossRefGoogle Scholar
  10. Carolan, M. (2018). Big data and food retail: Nudging out citizens by creating dependent consumers. Geoforum, 90, 142–150.CrossRefGoogle Scholar
  11. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.CrossRefGoogle Scholar
  12. Cheng, E. W. L., Li, H., & Yu, L. (2005). The analytic network process (ANP) approach to location selection: A shopping mall illustration. Construction Innovation, 5(2), 83–97.Google Scholar
  13. Cheng, X., Fang, L., Hong, X., & Yang, L. (2017). Exploiting mobile big data: Sources, features, and applications. IEEE Network, 31(1), 72–79.CrossRefGoogle Scholar
  14. DeLyser, D., & Sui, D. (2014). Crossing the qualitative-quantitative chasm III: Enduring methods, open geography, participatory research, and the fourth paradigm. Progress in Human Geography, 38(2), 294–307.CrossRefGoogle Scholar
  15. Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766.CrossRefGoogle Scholar
  16. Emerson, R. W. (2015). Convenience sampling, random sampling, and snowball sampling: How does sampling affect the validity of research? Journal of Visual Impairment & Blindness (Online), 109(2), 164.CrossRefGoogle Scholar
  17. Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.CrossRefGoogle Scholar
  18. Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2007(2012), 1–16.Google Scholar
  19. Ghosh, A., & McLafferty, S. L. (1987). Location strategies for retail and service firms. Lexington, MA: Lexington Books.Google Scholar
  20. Gong, L., Liu, X., Wu, L., & Liu, Y. (2016). Inferring trip purposes and uncovering travel patterns from taxi trajectory data. Cartography and Geographic Information Science, 43(2), 103–114.CrossRefGoogle Scholar
  21. Goodchild, M. F. (2013). The quality of big (geo) data. Dialogues in Human Geography, 3(3), 280–284.CrossRefGoogle Scholar
  22. Hay, I. (2005). Qualitative research methods in human geography. South Melbourne, Vic.: Oxford University Press.Google Scholar
  23. Hernandez, T., Bennison, D., & Cornelius, S. (1998). The organizational context of retail locational planning. GeoJournal, 45(4), 299–308.CrossRefGoogle Scholar
  24. Jones, K., & Simmons, J. W. (1993). Location, location, location. Nelson Canada.Google Scholar
  25. Jadhav, D. K. (2013). Big data: The new challenges in data mining. International Journal of Innovation Research Computer Science & Technology, 1(2), 39–42.Google Scholar
  26. Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., Ramakrishnan, R., et al. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86–94.CrossRefGoogle Scholar
  27. Kitchin, R. (2013). Big Data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3(3), 262–267.CrossRefGoogle Scholar
  28. Kohavi, R., Mason, L., Parekh, R., & Zheng, Z. (2004). Lessons and challenges from mining retail e-commerce data. Machine Learning, 57(1–2), 83–113.CrossRefGoogle Scholar
  29. Lavalle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.Google Scholar
  30. Lee, D. H. (2013). Smartphones, mobile social space, and new sociality in Korea. Mobile Media & Communication, 1(3), 269–284.CrossRefGoogle Scholar
  31. Lee, J. G., & Kang, M. (2015). Geospatial big data: Challenges and opportunities. Big Data Research, 2(2), 74–81.CrossRefGoogle Scholar
  32. Lloyd, A., & Cheshire, J. (2017). Deriving retail centre locations and catchments from geo-tagged Twitter data. Computers, Environment and Urban Systems, 61, 108–118.CrossRefGoogle Scholar
  33. Marr, B. (2015). Big data. A Super Simple Explanation For Everyone.Google Scholar
  34. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.Google Scholar
  35. Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544.CrossRefGoogle Scholar
  36. Patton, M. Q. (1990). Qualitative evaluation and research methods. SAGE Publications, Inc.Google Scholar
  37. Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the special issue information technology in retail: Toward omnichannel retailing. International Journal of Electronic Commerce, 18(4), 5–16.CrossRefGoogle Scholar
  38. Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51–59.CrossRefGoogle Scholar
  39. Ramanathan, U., Subramanian, N., & Parrott, G. (2017). Role of social media in retail network operations and marketing to enhance customer satisfaction. International Journal of Operations & Production Management, 37(1), 105–123.CrossRefGoogle Scholar
  40. Rogers, D. (2007). Retail location analysis in practice. Research Review, 12(2), 73–78.Google Scholar
  41. Ruiz-Ruiz, A. J., Blunck, H., Prentow, T. S., Stisen, A., & Kjærgaard, M. B. (2014). Analysis methods for extracting knowledge from large-scale wi-fi monitoring to inform building facility planning. In 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 130–138). IEEE.Google Scholar
  42. Reynolds, J., & Wood, S. (2010). Location decision making in retail firms: Evolution and challenge. International Journal of Retail & Distribution Management, 38(11), 828–845.CrossRefGoogle Scholar
  43. Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing & Health, 18(2), 179–183.CrossRefGoogle Scholar
  44. Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J. A., Demšar, U., & Fotheringham, A. S. (2016). Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, 30(5), 881–906.CrossRefGoogle Scholar
  45. Simkin, L. P., Doyle, P. P., & Saunders, D. J. (1985). How retailers put site location techniques into operation: An assessment of major multiples’ practice. International Journal of Retail & Distribution Management, 13(3), 21–26.CrossRefGoogle Scholar
  46. Stone, M., Bearman, D., Butscher, S. A., Gilbert, D., Crick, P., & Moffett, T. (2003). The effect of retail customer loyalty schemes—Detailed measurement or transforming marketing? Journal of Targeting, Measurement and Analysis for Marketing, 12(3), 305–318.CrossRefGoogle Scholar
  47. Szolnoki, G., & Hoffmann, D. (2013). Online, face-to-face and telephone surveys—Comparing different sampling methods in wine consumer research. Wine Economics and Policy, 2(2), 57–66.CrossRefGoogle Scholar
  48. Thatcher, J. (2014). Big data, big questions—Living on fumes: Digital footprints, data fumes, and the limitations of spatial big data. International Journal of Communication, 8, 19.Google Scholar
  49. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omnichannel retailing: Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174–181.CrossRefGoogle Scholar
  50. Vigneron, F., & Johnson, L. W. (2017). Measuring perceptions of brand luxury. In Advances in luxury brand management (pp. 199–234). Cham: Palgrave Macmillan.Google Scholar
  51. Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287–299.CrossRefGoogle Scholar
  52. Wood, S., & Reynolds, J. (2012). Leveraging locational insights within retail store development? Assessing the use of location planners’ knowledge in retail marketing. Geoforum, 43(6), 1076–1087.Google Scholar
  53. Zerbino, P., Aloini, D., Dulmin, R., & Mininno, V. (2018). Big data-enabled customer relationship management: A holistic approach. Information Processing & Management.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Ryerson UniversityTorontoCanada
  2. 2.Wilfrid Laurier UniversityWaterlooCanada

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