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Data-driven geography

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Abstract

The context for geographic research has shifted from a data-scarce to a data-rich environment, in which the most fundamental changes are not just the volume of data, but the variety and the velocity at which we can capture georeferenced data; trends often associated with the concept of Big Data. A data-driven geography may be emerging in response to the wealth of georeferenced data flowing from sensors and people in the environment. Although this may seem revolutionary, in fact it may be better described as evolutionary. Some of the issues raised by data-driven geography have in fact been longstanding issues in geographic research, namely, large data volumes, dealing with populations and messy data, and tensions between idiographic versus nomothetic knowledge. The belief that spatial context matters is a major theme in geographic thought and a major motivation behind approaches such as time geography, disaggregate spatial statistics and GIScience. There is potential to use Big Data to inform both geographic knowledge-discovery and spatial modeling. However, there are challenges, such as how to formalize geographic knowledge to clean data and to ignore spurious patterns, and how to build data-driven models that are both true and understandable.

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Authors and Affiliations

  1. Department of Geography, The Ohio State University, Columbus, OH, USA

    Harvey J. Miller

  2. Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA

    Michael F. Goodchild

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  1. Harvey J. Miller
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  2. Michael F. Goodchild
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Correspondence to Harvey J. Miller.

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Miller, H.J., Goodchild, M.F. Data-driven geography. GeoJournal 80, 449–461 (2015). https://doi.org/10.1007/s10708-014-9602-6

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