TY - JOUR
T1 - A spatiotemporal regression-kriging model for space-time interpolation
T2 - a case study of chlorophyll-a prediction in the coastal areas of Zhejiang, China
AU - Du, Zhenhong
AU - Wu, Sensen
AU - Kwan, Mei Po
AU - Zhang, Chuanrong
AU - Zhang, Feng
AU - Liu, Renyi
N1 - Funding Information:
This research was funded by the National Key Research and Development Program of China (Grant number 2018YFB0505000), the Public Science and Technology Research Funds’ Projects of Ocean (Grant numbers 201305012 and 201505003) and the Special Program for National Basic Research (Grant number 2012FY112300). We thank the Marine Monitoring and Forecasting Center of Zhejiang Province for data supporting, and we thank all the reviewers for their valuable comments and suggestions.
Funding Information:
This research was funded by the National Key Research and Development Program of China [Grant number 2018YFB0505000], the Public Science and Technology Research Funds´ Projects of Ocean [Grant numbers 201305012 and 201505003] and the Special Program for National Basic Research [Grant number 2012FY112300].
Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2018/10/3
Y1 - 2018/10/3
N2 - Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation.
AB - Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation.
KW - GTWR-STK
KW - Zhejiang coastal areas
KW - spatiotemporal autocorrelation
KW - spatiotemporal kriging
KW - spatiotemporal non-stationarity
UR - http://www.scopus.com/inward/record.url?scp=85049887507&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049887507&partnerID=8YFLogxK
U2 - 10.1080/13658816.2018.1471607
DO - 10.1080/13658816.2018.1471607
M3 - Article
AN - SCOPUS:85049887507
VL - 32
SP - 1927
EP - 1947
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 10
ER -