Abstract
AbstractThe recent proliferation of big data sources has given rise to a data deluge. Network theory has become the standard methodology to frame, develop and analyze such massive datasets. In line with the critique of Schwanen (2016), we argue in this paper that initiatives confronting network-based insights with (qualitative) location- and domain-specific insights are necessary in understanding, discussing and advancing the role network analysis can play in geography. By iterating a community detection algorithm to achieve different levels of communities and quantifying the borders between them through damping values (as proposed in Grauwin et al., 2017), we show how to derive the hierarchical structure within the logistics buyer-supplier network in Belgium. This allows for a richer geography, which has been missing in current big data studies.
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