  The use of crowdsourced data has been finding practical use for enhancing
situational awareness during disasters. While recent studies have shown
promising results regarding the potential of crowdsourced data for flood
mapping, little attention has been paid to data imbalances issues that could
introduce biases. We examine biases present in crowdsourced reports to identify
data imbalances with a goal of improving disaster situational awareness. Sample
bias, spatial bias, and demographic bias are examined as we analyzed reported
flooding from 3-1-1, Waze reports, and FEMA damage data collected in the
aftermaths of Tropical Storm Imelda in 2019 and Hurricane Ida in 2021.
Integrating other flooding related topics from 3-1-1 reports into the Global
Moran's I and Local Indicator of Spatial Association (LISA) test revealed more
communities that were impacted by floods. To examine spatial bias, we perform
the LISA and BI-LISA tests on the three datasets at the census tract and census
block group level. By looking at two geographical aggregations, we found that
the larger spatial aggregations, census tracts, show less data imbalance in the
results. Finally, one-way analysis of Variance (ANOVA) test performed on the
clusters generated from the BI-LISA shows that data imbalance exists in areas
where minority populations reside. Through a regression analysis, we found that
3-1-1 and Waze reports have data imbalance limitations in areas where minority
populations reside. The findings of this study advance understanding of data
imbalances and biases in crowdsourced datasets that are growingly used for
disaster situational awareness.
