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Leveraging Big (Geo) Data with (Geo) Visual Analytics: Place as the Next Frontier

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Spatial Data Handling in Big Data Era

Part of the book series: Advances in Geographic Information Science ((AGIS))

Abstract

A tension exists in the discipline of Geography between the concepts of space and place. Most research and development in Geographical Information Science (GIScience) has been focused on the former, through methods to formally structure data about the world and to systematically model and analyze aspects of the world as represented through those structured data. People, however, live and behave in socially constructed places; what they care about happens in those places rather than in some abstract, modeled ‘space’. Study of place, by human geographers (and other social scientists and humanist scholars), typically using qualitative methods and seldom relying on digital data, has proceeded largely independently of GIScience research focused on space. There have been calls within GIScience to formalize place to enable application of Geographical Information Systems methods to place-based problems, and some progress in this direction has been made. Here, however, a complementary view is offered for treating ‘place’ as a first class object of attention by capitalizing on the combination of “big data” and new human-centered visual analytical methods to enable understanding of the complexity inherent in place as both a concept and a context for human behavior.

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Notes

  1. 1.

    placial rather than platial since place is from the old French place and medieval Latin placea (place, spot)—source: http://www.etymonline.com (earlier Latin used platea (courtyard, open space; broad way, avenue) and Greek used plateia (broad way); for comparison, spatial is from the Latin spatium + al (room, area, distance, stretch of time + of or relating to)); analytics rather than analysis since the latter is the activity while analytics, from the Ancient Greek ἀναλυτικά ‎(ἀnalytiká, is “science of analysis”)—source: https://en.wiktionary.org.

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Acknowledgements

Examples in Section “Data Variety” were derived as part of the GeoTxt project, in collaboration with Jan Wallgrün, Morteza Karimzadeh, and Scott Pezanowski; A portion of this research was supported by the Visual Analytics for Command, Control, and Interoperability Environments (VACCINE) project, a center of excellence of the Department of Homeland Security, under Award #2009-ST-061-CI0001. See also: Wallgrün, Jan Oliver, Morteza Karimzadeh, Alan M. MacEachren, Frank Hardisty, Scott Pezanowski, and Yiting Ju. 2014. “Construction and First Analysis of a Corpus for the Evaluation and Training of Microblog/Twitter Geoparsers.” GIR’14: 8th ACM SIGSPATIAL Workshop on Geographic Information Retrieval, Dallas, TX.

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Authors and Affiliations

  1. GeoVISTA Center, Department of Geography, The Pennsylvania State University, University Park, PA, 16802, USA

    Alan M. MacEachren

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  1. Alan M. MacEachren
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Correspondence to Alan M. MacEachren .

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Editors and Affiliations

  1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Chenghu Zhou

  2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Fenzhen Su

  3. Leibniz Institute for Regional Geography, Leipzig, Sachsen, Germany

    Francis Harvey

  4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jun Xu

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MacEachren, A.M. (2017). Leveraging Big (Geo) Data with (Geo) Visual Analytics: Place as the Next Frontier. In: Zhou, C., Su, F., Harvey, F., Xu, J. (eds) Spatial Data Handling in Big Data Era. Advances in Geographic Information Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4424-3_10

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