Spatializing Social Networking Analysis to Capture Local Innovation Flows towards Inclusive Transition

Carmelina Bevilacqua, Poya Sohrabi and Nourhan Hamdy
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Carmelina Bevilacqua: CLUDsLab, PAU Department, Università degli Studi Mediterranea of Reggio Calabria, 89124 Reggio Calabria, RC, Italy
Poya Sohrabi: CLUDsLab, PAU Department, Università degli Studi Mediterranea of Reggio Calabria, 89124 Reggio Calabria, RC, Italy
Nourhan Hamdy: CLUDsLab, PAU Department, Università degli Studi Mediterranea of Reggio Calabria, 89124 Reggio Calabria, RC, Italy

Sustainability, 2022, vol. 14, issue 5, 1-19

Abstract: The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.

Keywords: spatial analysis; innovation flows; urban transition; inclusive; clusters; lagging regions; network analysis; data city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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