A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression

被引:3
作者
Yang, Zhijia [1 ,2 ]
Dai, Wujiao [1 ,2 ]
Santerre, Rock [3 ]
Kuang, Cuilin [1 ,2 ]
Shi, Qiang [1 ,2 ]
机构
[1] Cent South Univ, Dept Surveying Engn & Remote Sensing Sci, Changsha 410083, Peoples R China
[2] Key Lab Precise Engn Surveying & Deformat Disaste, Changsha 410083, Peoples R China
[3] Laval Univ, Ctr Res Geomat, Quebec City, PQ G1V 0A6, Canada
基金
中国国家自然科学基金;
关键词
TESTS;
D O I
10.1155/2019/4352396
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The geographically and temporally weighted regression (GTWR) model is a dynamic model which considers the spatiotemporal correlation and the spatiotemporal nonstationarity. Taking into account these advantages, we proposed a spatiotemporal deformation modelling method based on GTWR. In order to further improve the modelling accuracy and efficiency and considering the application characteristics of deformation modelling, the inverse window transformation method is used to search the optimal fitting window width and furthermore the local linear estimation method is used in the fitting coefficient function. Moreover, a comprehensive model for the statistical tests method is proposed in GTWR. The results of a dam deformation modelling application show that the GTWR model can establish a unified spatiotemporal model which can represent the whole deformation trend of the dam and furthermore can predict the deformation of any point in time and space, with stronger flexibility and applicability. Finally, the GTWR model improves the overall temporal prediction accuracy by 43.6% compared to the single-point time-weighted regression (TWR) model.
引用
收藏
页数:11
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共 24 条
  • [1] A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD
    Bai, Yang
    Wu, Lixin
    Qin, Kai
    Zhang, Yufeng
    Shen, Yangyang
    Zhou, Yuan
    [J]. REMOTE SENSING, 2016, 8 (03)
  • [2] Geographically weighted regression - modelling spatial non-stationarity
    Brunsdon, C
    Fotheringham, S
    Charlton, M
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES D-THE STATISTICIAN, 1998, 47 : 431 - 443
  • [3] Some notes on parametric significance tests for geographically weighted regression
    Brunsdon, C
    Fotheringham, AS
    Charlton, M
    [J]. JOURNAL OF REGIONAL SCIENCE, 1999, 39 (03) : 497 - 524
  • [4] Tetramethylpyrazine attenuates spinal cord ischemic injury due to aortic cross-clamping in rabbits
    Chen, Shaoyang
    Xiong, Lize
    Wang, Qiang
    Sang, Hanfei
    Zhu, Zhenhua
    Dong, Hailong
    Lu, Zhihong
    [J]. BMC NEUROLOGY, 2002, 2 (1)
  • [5] Modeling the spatio-temporal heterogeneity in the PM10-PM2.5 relationship
    Chu, Hone-Jay
    Huang, Bo
    Lin, Chuan-Yao
    [J]. ATMOSPHERIC ENVIRONMENT, 2015, 102 : 176 - 182
  • [6] Spatio-temporal modelling of dam deformation using independent component analysis
    Dai, W.
    Liu, B.
    Meng, X.
    Huang, D.
    [J]. SURVEY REVIEW, 2014, 46 (339) : 437 - 443
  • [7] Modeling dam deformation using independent component regression method
    Dai, Wu-jiao
    Liu, Bin
    Ding, Xiao-li
    Huang, Da-wei
    [J]. TRANSACTIONS OF NONFERROUS METALS SOCIETY OF CHINA, 2013, 23 (07) : 2194 - 2200
  • [8] Geographical and Temporal Weighted Regression (GTWR)
    Fotheringham, A. Stewart
    Crespo, Ricardo
    Yao, Jing
    [J]. GEOGRAPHICAL ANALYSIS, 2015, 47 (04) : 431 - 452
  • [9] Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis
    Fotheringham, AS
    Charlton, ME
    Brunsdon, C
    [J]. ENVIRONMENT AND PLANNING A, 1998, 30 (11) : 1905 - 1927
  • [10] Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices
    Huang, Bo
    Wu, Bo
    Barry, Michael
    [J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2010, 24 (03) : 383 - 401