scholarly journals Ensemble Interpolation Methods for Spatio-temporal Data Modelling

2010 ◽  
Author(s):  
Philip John Sallis ◽  
Sergio Hernandez
Keyword(s):  
Temporal Data ◽  
Data Modelling ◽  
Interpolation Methods ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Data Modelling and Prediction of PFOA Drinking Water Contamination

2019 ◽  
Vol 3 ◽  
pp. 327
Author(s):  
Rathjens J ◽  
Becker E ◽  
Kolbe A ◽  
Olthoff K ◽  
Bergmann S ◽  
...  
Keyword(s):  
Drinking Water ◽  
Water Contamination ◽  
Temporal Data ◽  
Data Modelling ◽  
Drinking Water Contamination ◽  
Spatio Temporal

scholarly journals Conceptual spatio-temporal data modelling and land use change

2011 ◽  
Author(s):  
L. A. Beser de Deus ◽  
L. F. C. F. Silva ◽  
M. A. V. Freitas
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Temporal Data ◽  
Data Modelling ◽  
Spatio Temporal

scholarly journals k D-STR: A Method for Spatio-Temporal Data Reduction and Modelling

2021 ◽  
Vol 2 (3) ◽  
pp. 1-31
Author(s):  
Liam Steadman ◽  
Nathan Griffiths ◽  
Stephen Jarvis ◽  
Mark Bell ◽  
Shaun Helman ◽  
...  
Keyword(s):  
Data Reduction ◽  
Temporal Data ◽  
Hierarchical Partitioning ◽  
Data Modelling ◽  
Important Process ◽  
Temporal Characteristics ◽  
Modelling Techniques ◽  
Spatio Temporal

Analysing and learning from spatio-temporal datasets is an important process in many domains, including transportation, healthcare and meteorology. In particular, data collected by sensors in the environment allows us to understand and model the processes acting within the environment. Recently, the volume of spatio-temporal data collected has increased significantly, presenting several challenges for data scientists. Methods are therefore needed to reduce the quantity of data that needs to be processed in order to analyse and learn from spatio-temporal datasets. In this article, we present the - Dimensional Spatio-Temporal Reduction method ( D-STR ) for reducing the quantity of data used to store a dataset whilst enabling multiple types of analysis on the reduced dataset. D-STR uses hierarchical partitioning to find spatio-temporal regions of similar instances, and models the instances within each region to summarise the dataset. We demonstrate the generality of D-STR with three datasets exhibiting different spatio-temporal characteristics and present results for a range of data modelling techniques. Finally, we compare D-STR with other techniques for reducing the volume of spatio-temporal data. Our results demonstrate that D-STR is effective in reducing spatio-temporal data and generalises to datasets that exhibit different properties.

Spatial-temporal data collection process models using mobile sinks

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.

scholarly journals Spatio-Temporal Analysis of Water Quality Parameters in Machángara River with Nonuniform Interpolation Methods

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 507 ◽  
Author(s):  
Iván Vizcaíno ◽  
Enrique Carrera ◽  
Margarita Sanromán-Junquera ◽  
Sergio Muñoz-Romero ◽  
José Luis Rojo-Álvarez ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Analysis ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Interpolation Methods ◽  
Spatio Temporal

scholarly journals From spatio-temporal data to chronological networks

2019 ◽  
Author(s):  
Didier A. Vega-Oliveros ◽  
Moshé Cotacallapa ◽  
Leonardo N. Ferreira ◽  
Marcos G. Quiles ◽  
Liang Zhao ◽  
...  
Keyword(s):  
Temporal Data ◽  
Spatio Temporal
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