Examining data imbalance in crowdsourced reports for improving flash flood situational awareness

Abstract

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 (such as user-generated flood reports) for flash flood mapping and situational awareness, little attention has been paid to data imbalance issues that could introduce biases in data and assessment. To address this gap, in this study, we examine biases present in crowdsourced reports to identify data imbalance with a goal of improving disaster situational awareness. Three biases are examined: sample bias, spatial bias, and demographic bias. To examine these biases, we analyzed reported flooding from 3-1-1 reports (which is a citizen hotline allowing the community to report problems such as flooding) and Waze reports (which is a GPS navigation app that allows drivers to report flooded roads) with respect to FEMA damage data collected in the aftermaths of Tropical Storm Imelda in Harris County, Texas, in 2019 and Hurricane Ida in New York City in 2021. First, sample bias is assessed by expanding the flood-related categories in 3-1-1 reports. Integrating other flooding related topics into the Global Moran's I and Local Indicator of Spatial Association (LISA) revealed more communities that were impacted by floods. To examine spatial bias, we perform the LISA and BI-LISA tests on the data sets—FEMA damage, 3-1-1 reports, and Waze reports—at the census tract level 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. Through a regression analysis, we found that 3-1-1 reports and Waze reports have data imbalance limitations in areas where minority populations and single parent households reside. The findings of this study advance understanding of data imbalance and biases in crowdsourced datasets that are growingly used for disaster situational awareness. Through addressing data imbalance issues, researchers and practitioners can proactively mitigate biases in crowdsourced data and prevent biased and inequitable decisions and actions.