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Measuring McCities: Landscapes of chain and independent restaurants in the United States

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  • Xiaofan Liang
  • Clio Andris

Abstract

Urban planners have a stake in preserving restaurants that are unique to local areas in order to cultivate a distinctive, authentic landscape. Yet, over time, chain restaurants (i.e. franchises) have largely replaced independently owned restaurants, creating a landscape of placelessness. In this research, we explored which (types of) locales have an independent food culture and which resemble McCities : foodscapes where the food offerings can be found just as easily in one place as in many other (often distant) places. We used a dataset of nearly 800,000 independent and chain restaurants for the Continental United States and defined a chain restaurant using multiple methods. We performed a descriptive analysis of chainness (a value indicating the likelihood of finding the same venue elsewhere) prevalence at the urban area and metropolitan area levels. We identified socioeconomic and infrastructural factors that correlate with high or low chainness using random forest and linear regression models. We found that car-dependency, low walkability, high percentage voters for Donald Trump (2016), concentrations of college-age students, and nearness to highways were associated with high rates of chainness. These high chainness McCities are prevalent in the Midwestern and the Southeastern United States. Independent restaurants were associated with dense pedestrian-friendly environments, highly educated populations, wealthy populations, racially diverse neighborhoods, and tourist areas. Low chainness was also associated with East and West Coast cities. These findings, paired with the contribution of methods that quantify chainness, open new pathways for measuring landscapes through the lens of unique services and retail offerings.

Suggested Citation

  • Xiaofan Liang & Clio Andris, 2022. "Measuring McCities: Landscapes of chain and independent restaurants in the United States," Environment and Planning B, , vol. 49(2), pages 585-602, February.
  • Handle: RePEc:sae:envirb:v:49:y:2022:i:2:p:585-602
    DOI: 10.1177/23998083211014896
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    1. Emily Talen & Hyesun Jeong, 2019. "Does the classic American main street still exist? An exploratory look," Journal of Urban Design, Taylor & Francis Journals, vol. 24(1), pages 78-98, January.
    2. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
    3. Sands, Sean & Oppewal, Harmen & Beverland, Michael, 2015. "How in-store educational and entertaining events influence shopper satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 23(C), pages 9-20.
    4. Colin Jones & Qutaiba Al-Shaheen & Neil Dunse, 2016. "Anatomy of a successful high street shopping centre," Journal of Urban Design, Taylor & Francis Journals, vol. 21(4), pages 495-511, July.
    5. McLean-Meyinsse, Patricia E. & Taylor, Shervia S. & Gager, Janet V., 2015. "Self-Reported Consumption of Fast-Food Meals by University Students," Journal of Food Distribution Research, Food Distribution Research Society, vol. 46(1), pages 1-7, March.
    6. Lei Dong & Carlo Ratti & Siqi Zheng, 2019. "Predicting neighborhoods’ socioeconomic attributes using restaurant data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(31), pages 15447-15452, July.
    7. Lena Mossberg & Dorthe Eide, 2017. "Storytelling and meal experience concepts," European Planning Studies, Taylor & Francis Journals, vol. 25(7), pages 1184-1199, July.
    8. Frederick, David A. & Saguy, Abigail C. & Gruys, Kjerstin, 2016. "Culture, health, and bigotry: How exposure to cultural accounts of fatness shape attitudes about health risk, health policies, and weight-based prejudice," Social Science & Medicine, Elsevier, vol. 165(C), pages 271-279.
    9. Austin, S.B. & Melly, S.J. & Sanchez, B.N. & Patel, A. & Buka, S. & Gortmaker, S.L., 2005. "Clustering of fast-food restaurants around schools: A novel application of spatial statistics to the study of food environments," American Journal of Public Health, American Public Health Association, vol. 95(9), pages 1575-1581.
    10. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    11. repec:ucp:bkecon:9781884829987 is not listed on IDEAS
    12. Guojun Zeng & Yongqiu Zhao & Shuzhi Sun, 2014. "Sustainable Development Mechanism of Food Culture’s Translocal Production Based on Authenticity," Sustainability, MDPI, vol. 6(10), pages 1-18, October.
    13. Anna Onesti, 2017. "Built environment, creativity, social art. The recovery of public space as engine of human development," REGION, European Regional Science Association, vol. 4, pages 87-118.
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