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Natural disasters can significantly disrupt human mobility in urban areas.Studies have attempted to understand and quantify such disruptions usingcrowdsourced mobility data sets. However, limited research has studied thejustice issues of mobility data in the context of natural disasters. The lackof research leaves us without an empirical foundation to quantify and controlthe possible biases in the data. This study, using 2017 Hurricane Harvey as acase study, explores three aspects of mobility data that could potentiallycause injustice: representativeness, quality, and precision. We findrepresentativeness being a major factor contributing to mobility datainjustice. There is a persistent disparity of representativeness acrossneighborhoods of different socioeconomic characteristics before, during, andafter the hurricane's landfall. Additionally, we observed significant drops ofdata precision during the hurricane, adding uncertainty to locate people andunderstand their movements during extreme weather events. The findingshighlight the necessity in understanding and controlling the possible bias ofmobility data as well as developing practical tools through data justice lensesin collecting and analyzing data during disasters.

Examining mobility data justice during 2017 Hurricane Harvey