  This paper presents a framework for intrinsic point of interest discovery
from trajectory databases. Intrinsic points of interest are regions of a
geospatial area innately defined by the spatial and temporal aspects of
trajectory data, and can be of varying size, shape, and resolution. Any
trajectory database exhibits such points of interest, and hence are intrinsic,
as compared to most other point of interest definitions which are said to be
extrinsic, as they require trajectory metadata, external knowledge about the
region the trajectories are observed, or other application-specific
information. Spatial and temporal aspects are qualities of any trajectory
database, making the framework applicable to data from any domain and of any
resolution. The framework is developed under recent developments on the
consistency of nonparametric hierarchical density estimators and enables the
possibility of formal statistical inference and evaluation over such intrinsic
points of interest. Comparisons of the POIs uncovered by the framework in
synthetic truth data to thousands of parameter settings for common POI
discovery methods show a marked improvement in fidelity without the need to
tune any parameters by hand.
