  With the aim to contribute to humanitarian response to disasters and violent
events, scientists have proposed the development of analytical tools that could
identify emergency events in real-time, using mobile phone data. The assumption
is that dramatic and discrete changes in behavior, measured with mobile phone
data, will indicate extreme events. In this study, we propose an efficient
system for spatiotemporal detection of behavioral anomalies from mobile phone
data and compare sites with behavioral anomalies to an extensive database of
emergency and non-emergency events in Rwanda. Our methodology successfully
captures anomalous behavioral patterns associated with a broad range of events,
from religious and official holidays to earthquakes, floods, violence against
civilians and protests. Our results suggest that human behavioral responses to
extreme events are complex and multi-dimensional, including extreme increases
and decreases in both calling and movement behaviors. We also find significant
temporal and spatial variance in responses to extreme events. Our behavioral
anomaly detection system and extensive discussion of results are a significant
contribution to the long-term project of creating an effective real-time event
detection system with mobile phone data and we discuss the implications of our
findings for future research to this end.
  KEYWORDS: Big data, call detail record, emergency events, human mobility
