Abstract
The space matters: beyond a mere background for economic activities placement, it constitutes a crucial element for their overall dynamism. This rationale, however, contravenes more traditional urban and regional economics approaches that interpret space as intangible within their spatial models. In that vein, notable constraints can be found in economic-based methods and spatial units oriented to spatialize the territorial endowments and interpret their role within the urban structure. While economics’ methods are limited in their spatial representation, urban and regional planning has otherwise focused in providing instruments that address spatial characteristics of areas where urbanization is predominant. Methods that highlight the configurational properties and the organization of the cityscape structure, could aid economics in its methodological issues. Still, a divide persists between these two fields of research, as neither consistently incorporates the methods and variables considered by the other. In this paper we showcase a method used to create a spatial unit oriented to territorialize economic-based datasets represented at a regional scale within the confines of urban areas’ industrial agglomerations. In this proof of concept, variables related to labour – average number of firms, employees, and firm-size; and to installed capital – average real estate prices – are spatialized to outline their patterns distribution across Tuscany’s cities. Organized in a GIS-based environment, the representation of such variables within a computerized space and with a proper spatial unit provides a basis that can be associated to the configurational aspects of the territory, being a complementary analysis for urban and regional planning.
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Altafini, D., Cutini, V. (2022). Towards a Spatial Approach to Territorialize Economic Data in Urban Areas’ Industrial Agglomerations. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_25
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