Acta Geodaetica et Cartographica Sinica ›› 2023, Vol. 52 ›› Issue (2): 307-317.doi: 10.11947/j.AGCS.2023.20210336

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

Uncertainty measuring and constraining method for geographic weighted regression model results

LIU Ning1, ZOU Bin1, ZHANG Honghui2,3   

  1. 1. School of Geosciences and Info-physic, Central South University, Changsha 410083, China;
    2. Guangdong Guodi Planning Science Technology Co., Ltd., Guangzhou 510075, China;
    3. College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410012, China
  • Received:2021-06-11 Revised:2022-09-12 Published:2023-03-07
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41871317;41871318)

Abstract: As a classical local weighting least-square method, the geographic weighted regression (GWR) model always suffers from the space sparsity of samples and the local multicollinearity of predictors, which results in the uncertainties of the model results show spatial heterogeneity. By constructing accuracy evaluation metric of posterior standard error based on the covariance propagation law, this study proposed an uncertainty measuring and constraining method for geographic weighted regression model and validated this method using the instance of ground PM2.5 concentration remote sensing mapping. After uncertainty constraint, the results show the fitted accuracy and sample-based/site-based/regional-based cross validation accuracy for GWR model with different parameters are all improved; the sign error of regression coefficients caused by local multicollinearity are also corrected; the outlier and negative values in the GWR predicted values can also be effectively detected which improve the reliability of the ground PM2.5 concentration mapping results. The proposed method can effectively guarantee the stability and effectiveness of GWR results.

Key words: geographic weighted regression, uncertainty measuring, PM2.5 concentration, remote sensing mapping

CLC Number: