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The use of crowdsourced data has been finding practical use for enhancingsituational awareness during disasters. While recent studies have shownpromising results regarding the potential of crowdsourced data for floodmapping, little attention has been paid to data imbalances issues that couldintroduce biases. We examine biases present in crowdsourced reports to identifydata imbalances with a goal of improving disaster situational awareness. Samplebias, spatial bias, and demographic bias are examined as we analyzed reportedflooding from 3-1-1, Waze reports, and FEMA damage data collected in theaftermaths of Tropical Storm Imelda in 2019 and Hurricane Ida in 2021.Integrating other flooding related topics from 3-1-1 reports into the GlobalMoran's I and Local Indicator of Spatial Association (LISA) test revealed morecommunities that were impacted by floods. To examine spatial bias, we performthe LISA and BI-LISA tests on the three datasets at the census tract and censusblock group level. By looking at two geographical aggregations, we found thatthe larger spatial aggregations, census tracts, show less data imbalance in theresults. Finally, one-way analysis of Variance (ANOVA) test performed on theclusters generated from the BI-LISA shows that data imbalance exists in areaswhere minority populations reside. Through a regression analysis, we found that3-1-1 and Waze reports have data imbalance limitations in areas where minoritypopulations reside. The findings of this study advance understanding of dataimbalances and biases in crowdsourced datasets that are growingly used fordisaster situational awareness.

Examining Data Imbalance in Crowdsourced Reports for Improving Flash Flood Situational Awareness