We propose a new copula model for spatial data that are observed repeatedly in time. The model is based on the assumption that there exists a common factor that affects the measurements of a process in space and in time. Unlike models based on multivariate normality, our model can handle data with tail dependence and asymmetry. The likelihood for the proposed model can be obtained in a simple f...

This paper discusses the role of granularity in judgments about spatio-temporal location and relations. It distinguishes three aspects of spatio-temporal granularity: the granularity with which location is approximated, the granularity which arises as a function of the degree to which we recognize or trace over changes of location, and granularity as it affects our apprehension of spatio-tempor...

Spatio-temporal events are collected at high levels of detail (LoDs) in many phenomena. Both spatial and temporal characteristics of data can be expressed at different LoDs. Depending on the level of detail, different spatiotemporal patterns can be detected, and in some specific cases spatio-temporal patterns are just detected in some LoDs [1]. It is crucial to model spatio-temporal phenomena h...

The particle system is widely used in vector field feature visualization due to its dynamics and simulation. However, there are some defects of the method based on Euler fields, such as unclear expression discontinuous temporal expression, so cannot effectively express characteristics wind scale. We propose a Lagrangian spatio-temporal interpolation solve these problems, which realizes fusion t...

A query optimizer requires cost models to calculate the costs of various access plans for a query. An effective method to estimate the number of disk (or page) accesses for spatio-temporal queries has not yet been proposed. The TPR-tree is an efficient index that supports spatio-temporal queries for moving objects. Existing cost models for the spatial index such as the R-tree do not accurately ...

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differs from relational data for which computational approaches are developed in the data mining community for multiple decades, in that both spatial and te...

Functional groups with diverse responses to environmental factors sum to produce communities with less temporal variability in their biomass than those lacking this diversity. The detection of these compensatory dynamics can be complicated by a spatio-temporal alternation in the environmental factors limiting growth (both abiotic and biotic), which restricts the occurrence of compensatory dynam...