Spatial statistical models that use flow and stream distance Export

@article{VerHoef06, abstract = {Abstract  We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.}, author = {Ver-Hoef, Jay and Peterson, Erin and Theobald, David}, citeulike-article-id = {975641}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10651-006-0022-8}, citeulike-linkout-1 = {http://www.ingentaconnect.com/content/klu/eest/2006/00000013/00000004/00000022}, citeulike-linkout-2 = {http://www.springerlink.com/content/a36r0kt6570537q0}, day = {1}, doi = {10.1007/s10651-006-0022-8}, issn = {1352-8505}, journal = {Environmental and Ecological Statistics}, keywords = {2006, covariance, geostatistics, kriging, non\_euclidean, tesis, valid\_models, variogram}, month = {December}, number = {4}, pages = {449--464}, posted-at = {2009-10-08 14:52:34}, priority = {2}, publisher = {Springer}, title = {Spatial statistical models that use flow and stream distance}, url = {http://dx.doi.org/10.1007/s10651-006-0022-8}, volume = {13}, year = {2006} }

TY - JOUR ID - VerHoef06 L3 - citeulike-article-id:975641 N2 - Abstract  We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions. IS - 4 JF - Environmental and Ecological Statistics SN - 1352-8505 EP - 464 TI - Spatial statistical models that use flow and stream distance PB - Springer VL - 13 SP - 449 KW - 2006 KW - covariance KW - geostatistics KW - kriging KW - non_euclidean KW - tesis KW - valid_models KW - variogram AU - Ver-Hoef, Jay AU - Peterson, Erin AU - Theobald, David PY - 2006/12/01/ UR - http://dx.doi.org/10.1007/s10651-006-0022-8 ER -