Stochastic rainfall models are commonly used in practice for long-term flood risk management. One of the most widely model types is based on point processes. Despite widespread use such models, whether their known simplifications describing space–time structure will affect accuracy estimation has not been quantified. In this study, we quantify biases introduced by limitations to estimates two m...

Spatial–temporal Gaussian process regression is a popular method for spatial–temporal data modeling. Its state-of-art implementation based on the state-space model realization of and its corresponding Kalman filter smoother, has computational complexity O ( N M 3 ) , where are number time instants spatial input locations, respectively, thus can only be applied to with large but relatively small...

A different type of spatial-temporal correlation in the process approaching the self-organized criticality is investigated for the two simple models for biological evolution. The changed behaviors of the position with minimum barrier are shown to be quantitatively different in the two models. Different results of the correlation are given for the two models. We argue that the correlation can be...

Social network data is generally incomplete with missing information about nodes and their interactions. Here we propose a spatialtemporal latent point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches, we assume that interactions are not fully observable, and certain interaction events lack information about ...

Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time serie...

Spatio-temporal data usually records the states over time of an object, an event or a position in space. Spatio-temporal data can be found in several application fields, such as traffic management, environment monitoring, weather forecast, etc. In the past, huge effort was devoted to spatial data representation and manipulation with particular focus on its visualisation. More recently, the inte...