Geographical Network Analysis

Uitermark, Justus; Meeteren, Michiel van

doi:10.1111/tesg.12480

Cited by 21 publications

(22 citation statements)

References 97 publications

(117 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, a network analysis can be only as good as the network model it is based on (Butts, 2009). This point is illuminated in domains such as biology, neuroscience, geography, and cosmology (Bassett & Sporns, 2017;Krioukov et al, 2012;Uitermark & van Meeteren, 2021), and needs to be recognized wherever network science is applied.…”

Section: What Is Network Science?mentioning

confidence: 99%

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Chen

Poquet

2022

Learning Analytics

View full text Add to dashboard Cite

Network analysis has contributed to the emergence of learning analytics. In this editorial, we briefly introduce network science as a field and situate it within learning analytics. Drawing on the Learning Analytics Cycle, we highlight that effective application of network science methods in learning analytics involves critical considerations of learning processes, data, methods and metrics, and interventions, as well as ethics and value systems surrounding these areas. Careful work must meaningfully situate network methods and interventions within the theoretical assumptions explaining learning, as well as within pedagogical and technological factors shaping learning processes. The five empirical papers in the special section demonstrate diverse applications of network analysis, and the invited commentaries from cognitive network science and physics education research further discuss potential synergies between learning analytics and other sister fields with a shared interest in leveraging network science. We conclude by discussing opportunities to strengthen the rigour of network-based learning analytics projects, expand current work into nascent areas, and achieve more impact by holistically addressing the full cycle of learning analytics.

show abstract

Section: What Is Network Science?mentioning

confidence: 99%

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Chen

Poquet

2022

Learning Analytics

View full text Add to dashboard Cite

show abstract

“…Similar conceptual theorizing is evident in recent reflections on spatial scale (Oshan et al, 2022; Poon, 2004; Poorthuis and Zook, 2020) and in time geography (Miller, 2005). On the subject of geographic network analysis, there has also been discussion about the meaning of edges and nodes and what is captured or missed with various definitions (Gibadullina, Bergmann, and O’Sullivan, 2021; Uitermark and Van Meeteren, 2021). In the same special issue, Derudder (2021), still on the subject of networks, reflects on the implications of conceptualizing cities as discrete units.…”

Section: Big Theorymentioning

confidence: 99%

Quantitative methods II: Big theory

Franklin

2022

Progress in Human Geography

View full text Add to dashboard Cite

In this second report on quantitative methods, I consider the foundational triumvirate that underpins the sub-discipline: data, methods, and theory. I argue that, although ‘big data’ and, more recently, ‘big code’ may have captured the limelight, theory is invaluable and should not be disregarded. Showcasing the integral role of theory in quantitative human geography, I identify three kinds of useful theory (old school, enduring and conceptual) and, along the way, highlight beneficial ways in which they contribute to our thinking about data and methods. Throughout, I emphasize that theory is a crucial component of contemporary and future quantitative methods development.

show abstract

“…Critical realism suggests that rather than viewing individuals as the foundation of social systems and structures as emergent, it is more fruitful to think of them as mutually implicated (Bourdieu, 1979; Byrne and Callaghan, 2013). Social mechanisms – including such widespread mechanisms as homophily or preferential attachment – are always shaped, facilitated, or activated by the context in which they occur, meaning that they are not the ground zero of social life but rather contingent and conditional (Uitermark and Van Meeteren, 2021). As Fuchs (2007: 27) puts it, ‘the self-organization of society is not something that happens only blindly and unconsciously but depends on conscious, knowledgeable agents and creative social relationships’.…”

Section: A Heterodox Computational Social Sciencementioning

confidence: 99%

For a heterodox computational social science

Törnberg

Uitermark

2021

Big Data & Society

Self Cite

View full text Add to dashboard Cite

The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life.

show abstract

Geographical Network Analysis

Cited by 21 publications

References 97 publications

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Quantitative methods II: Big theory

For a heterodox computational social science

Contact Info

Product

Resources

About