Detecting localized homogeneous anomalies over spatio-temporal data

Modeling of spatio-temporal of albedo over Iran

The aim of this study is modeling spatiotemporal variations of albedo. This study was conducted using simultaneous effects of several components, such as wetness of surface layer of soil, cloudiness, topography and vegetation density (NDVI), using MEERA2 model with a resolution of 50 in 50 km during 2000-2010 in Iran. The results of spatial analysis of albedo values in Iran showed that the high...

Temporal and Spatio - Temporal Aggregations over Data Streams usingMultiple Time

Learning Localized Spatio-Temporal Models From Streaming Data

We address the problem of predicting spatiotemporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the training dataset is augmented sequentially. Specifically, we develop a localized spatio-temporal covariance model of the process that can capture spatially varying temporal periodicities in the data. We then apply a covarianc...

Spatio-temporal modeling of localized brain activity.

Functional neuroimaging, including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), plays an important role in identifying specific brain regions associated with experimental stimuli or psychiatric disorders such as schizophrenia. PET and fMRI produce massive data sets that contain both temporal correlations from repeated scans and complex spatial correlation...

Spatio-temporal Range Searching over Compressed Kinetic Sensor Data

Sensor networks and the data they collect have become increasingly prevalent and large. Sensor networks are frequently employed to observe objects in motion and are used to record traffic data, observe wildlife migration patterns, and observe motion from many other settings. In order to perform accurate statistical analyses of this data over arbitrary periods of time, the data must be faithfull...