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Exploratory Chronotopic Data Analysis

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Book cover Geographic Information Science (GIScience 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9927))

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Abstract

The intrinsic connection between place, space, and time in narrative texts is the subject of chronotopic literary analysis. We take the notion of the chronotope and apply it to exploratory analysis of unstructured big data. Exploratory chronotopic data analysis provides a data-driven perspective on how place, space, and time are connected in large, crowdsourced text collections. In this study, we processed the English Wikipedia text to find all co-occurrences of named places and dates and discovered that times are linked to places in a large majority of cases. We analyzed these millions of connections between places and dates and discovered a number of interesting trends. Because of the scale of the data involved, we suggest that chronotopic data analysis will lead to the development of new data models and methods for geographic information science and related fields, such as digital humanities.

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Notes

  1. 1.

    Or alternatively, we have increasing time and energy to devote to minutiae!.

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Authors and Affiliations

  1. Centre for eResearch, The University of Auckland, Auckland, New Zealand

    Benjamin Adams & Mark Gahegan

Authors
  1. Benjamin Adams
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  2. Mark Gahegan
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Correspondence to Benjamin Adams .

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Editors and Affiliations

  1. University of Texas at Austin , Austin, Texas, USA

    Jennifer A. Miller

  2. University of California at Berkeley , Berkeley, California, USA

    David O'Sullivan

  3. University of Wisconsin-Madison , Madison, Wisconsin, USA

    Nancy Wiegand

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Adams, B., Gahegan, M. (2016). Exploratory Chronotopic Data Analysis. In: Miller, J., O'Sullivan, D., Wiegand, N. (eds) Geographic Information Science. GIScience 2016. Lecture Notes in Computer Science(), vol 9927. Springer, Cham. https://doi.org/10.1007/978-3-319-45738-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-45738-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45737-6

  • Online ISBN: 978-3-319-45738-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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