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    The utility of Google Trends as a tool for evaluating flooding in data‐scarce places

    Thompson, Joshua J. and Wilby, Robert L. and Matthews, Tom and Murphy, Conor (2022) The utility of Google Trends as a tool for evaluating flooding in data‐scarce places. Area, 54 (2). pp. 203-212. ISSN 0004-0894

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    Google Trends (GT) offers a historical database of global internet searches with the potential to complement conventional records of environmental hazards, especially in regions where formal hydrometeorological data are scarce. We evaluate the extent to which GT can discern heavy rainfall and floods in Kenya and Uganda during the period 2014 to 2018. We triangulate counts of flood searches from GT with available rainfall records and media reports to build an inventory of extreme events. The Spearman rank correlation (ρ) between monthly mean search interest for flooding and monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall totals was ρ = +0.38 (p < 0.005) for Kenya and ρ = +0.64 (p < 0.001) for Uganda. Media reports of flooding were used to specify a threshold of detectability to give the same overall frequency of floods based on GT search interest. When the GT search index threshold was set at ≥15 and ≥29, the correct detection rate was 75% and 64% within a five-day window of known flood events in Kenya and Uganda, respectively. From these preliminary explorations we conclude that GT has potential as a proxy data source, but greater skill may emerge in places with larger search volumes and by linking to historical information about environmental hazards at sub-national scales. Wider applicability of the GT platform might be possible if there is greater transparency about how Google algorithms determine topics.

    Item Type: Article
    Additional Information: Cite as:Thompson, J.J., Wilby, R.L., Matthews, T. & Murphy, C. 2022, "The utility of Google Trends as a tool for evaluating flooding in data‐scarce places", Area (London 1969), vol. 54, no. 2, pp. 203-212.
    Keywords: big data; data sparse; flood; Google Trends; Kenya; Uganda;
    Academic Unit: Faculty of Social Sciences > Geography
    Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
    Item ID: 17670
    Identification Number:
    Depositing User: Conor Murphy
    Date Deposited: 12 Oct 2023 13:16
    Journal or Publication Title: Area
    Publisher: Wiley on line
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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