Consumption and symbolic capital in the metropolitan space: Integrating 'old' retail data sources with social big data. (November 2020)

• Record Type: Journal Article
• Authors: Carpio-Pinedo, José; Gutiérrez, Javier
• Is Part Of: Cities. Volume 106(2020)
• Publication Date: 2020-11
• Subjects: Commercial space -- Retail -- Retail geography -- Symbolic capital -- Big data -- Foursquare -- Madrid; City planning -- Periodicals; Urban policy -- Periodicals; 711.4
• DOI: 10.1016/j.cities.2020.102859
• Languages: English
• ISSNs: 0264-2751

Abstract

Abstract: While commerce is one of the key activities in cities, its spatial description still requires further attention, especially by considering the different dimensions of commercial space: physical, economic and socio-symbolic. The latter is becoming more and more important in an era where consumption is at the centre of social relations. Further, although data availability has been an enduring obstacle in commercial research, we are witnessing the advent of new data sources, and social-network big data is an opportunity to unveil the places to which consumers attribute prestige or symbolic capital, at the extent of entire metropolitan areas. This paper compares the physical, economic and socio-symbolic dimensions of commercial spaces through the analysis of three different commercial data sources: cadastral micro-data, business register and social-network big data. For the case of Madrid Metropolitan Area, the three databases are compared with correlation analysis and density maps, coming out as partly redundant and partly complementary. Getis-Ord's hotspot statistics integrated into a cluster analysis enable a comprehensive understanding of commercial environments, enriching previous spatial hierarchies. The spatial distribution of symbolic capital unveils a relation with socio-spatial segregation and paves the way to new reflections on the spatiality of consumption as a social practice. Graphical abstract: Unlabelled Image Highlights: 'Old' commercial data and social big data come out as only partly redundant. The integration of old and new databases results in a comprehensive commercial space typology. Social big data enables the analysis of metropolitan symbolic capital spatiality. Consumption-related symbolic capital spatiality is related to socio-spatial segregation.