  Nowadays large amounts of GPS trajectory data is being continuously collected
by GPS-enabled devices such as vehicles navigation systems and mobile phones.
GPS trajectory data is useful for applications such as traffic management,
location forecasting, and itinerary planning. Such applications often need to
extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile
objects. The nearest neighbor query (NNQ) is the most applied method for
labeling the visited locations based on the IDs of the POIs in the process of
SVL generation. NNQ in some scenarios is not accurate enough. To improve the
quality of the extracted SVLs, instead of using NNQ, we label the visited
locations as the IDs of the POIs which geometrically intersect with the GPS
observations. Intersection operator requires the accurate geometry of the
points of interest which we refer to them as the Geometries of Interest (GOIs).
In some application domains (e.g. movement trajectories of animals), adequate
information about the POIs and their GOIs may not be available a priori, or
they may not be publicly accessible and, therefore, they need to be derived
from GPS trajectory data. In this paper we propose a novel method for
estimating the POIs and their GOIs, which consists of three phases: (i)
extracting the geometries of the stay regions; (ii) constructing the geometry
of destination regions based on the extracted stay regions; and (iii)
constructing the GOIs based on the geometries of the destination regions. Using
the geometric similarity to known GOIs as the major evaluation criterion, the
experiments we performed using long-term GPS trajectory data show that our
method outperforms the existing approaches.
