Spatial data sets do not only contain true information, there is also a certain amount of ‘noise’ associated with the data. The use of these data in spatio-temporal analyses, often results in a sub-optimal representation of reality. Generalising spatial data sets collected at different times may serve the purpose of filtering noise so that spatio-temporal change can be better elucidated. In thi...

On the route to a spatio-temporal geoscience information system, an appropriate data model for geo-objects in space and time has been developed. In this model, geo-objects are represented as sequences of geometries and properties with continuous evolution in each time interval. Because geomodeling software systems usually model objects at specific time instances, we want to interpolate the geom...

This study examined the relation of dual-task performance to individual differences on neuropsychological tests. Neuropsychological test scores from 16 young and 16 older participants were simultaneously submitted to a factor analysis that yielded 2 factors (Attention/Executive and Memory) that differed by age and 2 (Motor Speed and Cognitive Status) that did not. Regression analyses revealed t...

The spatio-temporal variogram is an important factor in prediction through kriging, especially fields such as environmental sustainability or climate change, where data analysis based on this concept. However, the traditional estimator, which commonly employed for these purposes, extremely sensitive to outliers. We approach problem two ways paper. First, new robust estimators are introduced, de...

Recently, a fast increment of spatio-temporal data volume has been achieved and more importantly the data might distribute everywhere. So, there is a need for spatio-temporal data mining systems that are able to support such distributed spatio-temporal query and analysis operations. Distributed spatiotemporal data mining technologies were discussed in this paper. After discussing the process of...