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Do Physicists Have Geography Envy? And What Can Geographers Learn from It?

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  • David O’Sullivan
  • Steven M. Manson

Abstract

Recent years have seen an increasing amount of work by physicists on topics outside their traditional research domain, including geography. We explore the scope of this development, place it in a historical context dating back at least to statistical physics in the nineteenth century and trace the origins of more recent developments to the roots of computational science after World War II. Our primary purpose is not historical, however. Instead, we are concerned with understanding what geographers can learn from the many recent contributions by physicists to understanding spatiotemporal systems. Drawing on examples of work in this tradition by physicists, we argue that two apparently different modes of investigation are common: model-driven and data-driven approaches. The former is associated with complexity science, whereas the latter is more commonly associated with the fourth paradigm, more recently known as “big data.” Both modes share technical strengths and, more important, a capacity for generalization, which is absent from much work in geography. We argue that although some of this research lacks an appreciation of previous geographical contributions, when assessed critically, it nevertheless brings useful new perspectives, new methods, and new ideas to bear on topics central to geography, yet neglected in the discipline. We conclude with some suggestions for how geographers can build on these new approaches, both inside and outside the discipline.

Suggested Citation

  • David O’Sullivan & Steven M. Manson, 2015. "Do Physicists Have Geography Envy? And What Can Geographers Learn from It?," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(4), pages 704-722, July.
  • Handle: RePEc:taf:raagxx:v:105:y:2015:i:4:p:704-722
    DOI: 10.1080/00045608.2015.1039105
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    Cited by:

    1. Ben Derudder & Zachary Neal, 2018. "Uncovering Links Between Urban Studies and Network Science," Networks and Spatial Economics, Springer, vol. 18(3), pages 441-446, September.
    2. Boeing, Geoff, 2018. "Measuring the Complexity of Urban Form and Design," SocArXiv bxhrz, Center for Open Science.
    3. Jonathan Reades, 2020. "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development," REGION, European Regional Science Association, vol. 7, pages 21-34.
    4. Justus Uitermark & Michiel van Meeteren, 2021. "Geographical Network Analysis," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 112(4), pages 337-350, September.
    5. Yue Dou & Guolin Yao & Anna Herzberger & Ramon Felipe Bicudo da Silva & Qian Song & Ciara Hovis & Mateus Batistella & Emilio Moran & Wenbin Wu & Jianguo Liu, 2020. "Land-Use Changes in Distant Places: Implementation of a Telecoupled Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(1), pages 1-11.
    6. Boeing, Geoff, 2017. "Methods and Measures for Analyzing Complex Street Networks and Urban Form," SocArXiv 93h82, Center for Open Science.
    7. Ben Derudder, 2021. "Network Analysis of ‘Urban Systems’: Potential, Challenges, and Pitfalls," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 112(4), pages 404-420, September.
    8. An, Li & Grimm, Volker & Sullivan, Abigail & Turner II, B.L. & Malleson, Nicolas & Heppenstall, Alison & Vincenot, Christian & Robinson, Derek & Ye, Xinyue & Liu, Jianguo & Lindkvist, Emilie & Tang, W, 2021. "Challenges, tasks, and opportunities in modeling agent-based complex systems," Ecological Modelling, Elsevier, vol. 457(C).

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