Charting Disaster Recovery via Google Street View: A Social Science Perspective on Challenges Raised by the Fukushima Nuclear Disaster

Leslie Mabon

International Journal of Disaster Risk Science ›› 2016, Vol. 7 ›› Issue (2) : 175 -185.

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International Journal of Disaster Risk Science ›› 2016, Vol. 7 ›› Issue (2) : 175 -185. DOI: 10.1007/s13753-016-0087-4
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Charting Disaster Recovery via Google Street View: A Social Science Perspective on Challenges Raised by the Fukushima Nuclear Disaster

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Abstract

There is increasing interest in using Google Street View (GSV) for research purposes, particularly with regard to “virtually auditing” the built environment to assess environmental quality. Research in this field to date generally suggests GSV is a reliable means of understanding the “real world” environment. But limitations around the dates and resolution of images have been identified. An emerging strand within this literature is also concerned with the potential of GSV to understand recovery post-disaster. Using the GSV data set for the evacuated area around the Fukushima Dai’ichi nuclear power plant as a case study, this article evaluates GSV as a means of assessing disaster recovery in a dynamic situation with remaining uncertainty and a significant value and emotive dimension. The article suggests that GSV does have value in giving a high-level overview of the post-disaster situation and has potential to track recovery and resettlement over time. Drawing on social science literature relating to Fukushima, and disasters more widely, the article also argues it is imperative for researchers using GSV to reflect carefully on the wider socio-cultural contexts that are often not represented in the photo montage.

Keywords

Digital representation of place / Fukushima nuclear disaster / Google Street View / Post-disaster recovery / Social dimensions of energy

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Leslie Mabon. Charting Disaster Recovery via Google Street View: A Social Science Perspective on Challenges Raised by the Fukushima Nuclear Disaster. International Journal of Disaster Risk Science, 2016, 7(2): 175-185 DOI:10.1007/s13753-016-0087-4

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