21,814 research outputs found
Discovering private trajectories using background information
Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds
Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area
Spatio-temporal interaction is inherent to cases of infectious diseases and
occurrences of earthquakes, whereas the spread of other events, such as cancer
or crime, is less evident. Statistical significance tests of space-time
clustering usually assess the correlation between the spatial and temporal
(transformed) distances of the events. Although appealing through simplicity,
these classical tests do not adjust for the underlying population nor can they
account for a distance decay of interaction. We propose to use the framework of
an endemic-epidemic point process model to jointly estimate a background event
rate explained by seasonal and areal characteristics, as well as a superposed
epidemic component representing the hypothesis of interest. We illustrate this
new model-based test for space-time interaction by analysing psychiatric
inpatient admissions in Zurich, Switzerland (2007-2012). Several socio-economic
factors were found to be associated with the admission rate, but there was no
evidence of general clustering of the cases.Comment: 21 pages including 4 figures and 5 tables; methods are implemented in
the R package surveillance (https://CRAN.R-project.org/package=surveillance
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets
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