  Natural disasters can significantly disrupt human mobility in urban areas.
Studies have attempted to understand and quantify such disruptions using
crowdsourced mobility data sets. However, limited research has studied the
justice issues of mobility data in the context of natural disasters. The lack
of research leaves us without an empirical foundation to quantify and control
the possible biases in the data. This study, using 2017 Hurricane Harvey as a
case study, explores three aspects of mobility data that could potentially
cause injustice: representativeness, quality, and precision. We find
representativeness being a major factor contributing to mobility data
injustice. There is a persistent disparity of representativeness across
neighborhoods of different socioeconomic characteristics before, during, and
after the hurricane's landfall. Additionally, we observed significant drops of
data precision during the hurricane, adding uncertainty to locate people and
understand their movements during extreme weather events. The findings
highlight the necessity in understanding and controlling the possible bias of
mobility data as well as developing practical tools through data justice lenses
in collecting and analyzing data during disasters.
