Spatial Statistics and Spatio-temporal Data. Covariance Functions and Directional Properties. M. Sherman (2011). Chichester: John Wiley and Sons. ISBN 978-470-69958-4

The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes specified by different drift parameters, separable nonlinear covariance functions and nonstationary label field. The performance of proposed classification rule is validated by the values of local Bayes and empirical error rates realized by leave one out procedure. A simulation study for spatial covariance functions belonging to powered-exponential family and temporal covariance functions of AR(1) models is carried out. The influence of the values of spatial and temporal covariance parameters to error rates for several label field models are studied. The results showed that the proposed classification methodology can be applied successfully in practice with small error rates and can be a useful tool for discriminant analysis of spatio-temporal data.

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Statistical Analysis of Spatio-Temporal Data Based on Poisson Conditional Autoregressive Model

Informatica ◽

10.15388/informatica.2015.39 ◽

2015 ◽

Vol 26 (1) ◽

pp. 67-87

Author(s):

Yuriy Kharin ◽

Maryna Zhurak

Keyword(s):

Statistical Analysis ◽

Autoregressive Model ◽

Temporal Data ◽

Conditional Autoregressive Model ◽

Conditional Autoregressive ◽

Spatio Temporal

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Intelligent transport systems. Spatio-temporal data dictionary for cooperative ITS and automated driving systems

10.3403/30354876 ◽

2018 ◽

Cited By ~ 1

Keyword(s):

Transport Systems ◽

Data Dictionary ◽

Temporal Data ◽

Automated Driving ◽

Intelligent Transport Systems ◽

Intelligent Transport ◽

Spatio Temporal

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Spatial-temporal data collection process models using mobile sinks

Geodesy and Cartography ◽

10.22389/0016-7126-2018-942-12-22-28 ◽

2019 ◽

Vol 942 (12) ◽

pp. 22-28

Author(s):

A.V. Materuhin ◽

V.V. Shakhov ◽

O.D. Sokolova

Keyword(s):

Energy Consumption ◽

Operation Time ◽

Point Of View ◽

Process Models ◽

Temporal Data ◽

Network Nodes ◽

Mobile Sinks ◽

Autonomous Operation ◽

Data Collection Process ◽

Spatio Temporal

Optimization of energy consumption in geosensor networks is a very important factor in ensuring stability, since geosensors used for environmental monitoring have limited possibilities for recharging batteries. The article is a concise presentation of the research results in the area of increasing the energy consumption efficiency for the process of collecting spatio-temporal data with wireless geosensor networks. It is shown that in the currently used configurations of geosensor networks there is a predominant direction of the transmitted traffic, which leads to the fact that through the routing nodes that are close to the sinks, a much more traffic passes than through other network nodes. Thus, an imbalance of energy consumption arises in the network, which leads to a decrease in the autonomous operation time of the entire wireless geosensor networks. It is proposed to use the possible mobility of sinks as an optimization resource. A mathematical model for the analysis of the lifetime of a wireless geosensor network using mobile sinks is proposed. The model is analyzed from the point of view of optimization energy consumption by sensors. The proposed approach allows increasing the lifetime of wireless geosensor networks by optimizing the relocation of mobile sinks.

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