Supplementary Information Files for The utility of Google Trends as a tool for evaluating flooding in data‐scarce places
dataset
posted on 2021-08-20, 12:45 authored by Josh ThompsonJosh Thompson, Robert WilbyRobert Wilby, Tom Matthews, Conor MurphySupplementary Information Files for The utility of Google Trends as a tool for evaluating flooding in data‐scarce places
Google Trends (GT) offers an historical database of global internet searches with the potential to compliment 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 (rho) between monthly mean search interest for flooding and monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall totals was rho = +0.38 (p < 0.005) for Kenya and rho = +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.
Google Trends (GT) offers an historical database of global internet searches with the potential to compliment 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 (rho) between monthly mean search interest for flooding and monthly Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall totals was rho = +0.38 (p < 0.005) for Kenya and rho = +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.
History
School
- Social Sciences and Humanities
Department
- Geography and Environment
