ltsk-package: Local Time Space Kriging
In ltsk: Local Time Space Kriging
ltsk-package R Documentation
Local Time Space Kriging
Description
ltsk library is a collection of programs for implementing local spatial and local spatiotemporal Kriging.
Unlike global Kriging, ltsk subsets the sample around a given location and time where predicted is needed;
estimates variogram using the subset of sample data.
Product-sum model is implemented and automatically estimated using the data points within the local neighbourhood.
A unique advantage of ltsk is that it addresses non-stationarity,
which is difficult to handle in large spatiotemporal dataset.
Author(s)
Naresh Kumar (NKumar@med.miami.edu)
Dong Liang (dliang@umces.edu)
Jun chen (wdidwlia@gmail.com)
Jin Chen (jc.chenjin@gmail.com)
References
Haas, Timothy C. "Local prediction of a spatio-temporal process with an application to wet sulfate deposition." Journal of the American Statistical Association 90.432 (1995): 1189-1199.
Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
Kumar, N., et al. (2013). "Satellite-based PM concentrations and their application to COPD in Cleveland, OH." Journal of Exposure Science and Environmental Epidemiology 23(6): 637-646.
Liang, D. and N. Kumar (2013). "Time-space Kriging to address the spatiotemporal misalignment in the large datasets." Atmospheric Environment 72: 60-69.
ltsk documentation built on Sept. 3, 2023, 1:06 a.m.
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| ltsk-package | R Documentation |
Local Time Space Kriging
Description
ltsk library is a collection of programs for implementing local spatial and local spatiotemporal Kriging. Unlike global Kriging, ltsk subsets the sample around a given location and time where predicted is needed; estimates variogram using the subset of sample data. Product-sum model is implemented and automatically estimated using the data points within the local neighbourhood. A unique advantage of ltsk is that it addresses non-stationarity, which is difficult to handle in large spatiotemporal dataset.
Author(s)
Naresh Kumar (NKumar@med.miami.edu) Dong Liang (dliang@umces.edu) Jun chen (wdidwlia@gmail.com) Jin Chen (jc.chenjin@gmail.com)
References
Haas, Timothy C. "Local prediction of a spatio-temporal process with an application to wet sulfate deposition." Journal of the American Statistical Association 90.432 (1995): 1189-1199.
Iaco, S. De & Myers, D. E. & Posa, D., 2001. "Space-time analysis using a general product-sum model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 21-28, March.
Kumar, N., et al. (2013). "Satellite-based PM concentrations and their application to COPD in Cleveland, OH." Journal of Exposure Science and Environmental Epidemiology 23(6): 637-646.
Liang, D. and N. Kumar (2013). "Time-space Kriging to address the spatiotemporal misalignment in the large datasets." Atmospheric Environment 72: 60-69.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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