  We present a method for analysing mobile phone call detail records to
identify individuals whom we believe to be have been internally displaced as a
result of a sudden-onset disaster. We model each anonymous individual's
movements trajectory as a piecewise-constant time series signal, assume that a
disaster-induced displacement is exhibited as a level shift from an
individual's 'normal' location, and then apply a step detection algorithm to
identify level shifts in the signal. In contrast to typical methods that are
used to analyse mobility patterns from call detail records, where the aggregate
movements of large groups of individuals are analysed, our method offers the
advantage that no assumptions regarding the destination or duration of an
individual's displacement are necessary. We have applied the method to the
datasets from three disasters - the 2010 earthquake in Haiti, the 2015 Gorkha
earthquake in Nepal, and Hurricane Matthew in Haiti in 2016. Our results
demonstrate that this method can facilitate improvements in the analysis and
modelling of the mobility of internally displaced persons in post-disaster
scenarios, using call detail records. Such analyses can be used to complement
traditional survey methods to assess the scale and characteristics of
disaster-induced displacements in a timely manner.
