Research on geographically weighted regression based on IGGⅢ

doi:10.13474/j.cnki.11-2246.2019.0212

Abstract

Abstract: Aiming at the problem that the geographically weighted regression model has poor fitting effect when the outliers exist, a geographically weighted regression method based on IGGⅢ is proposed. The core is to use the weight function in the IGGⅢ scheme to calculate the weight matrix, and the weight factor is used in the geo-weighted regression parameter estimation model. The simulation data and the real data are used for the test, compared with GWR and ACV-GWR, and the results were evaluated by MSE, MAE and R². The simulation results show that the performance of IGGⅢ-GWR is increased by 51.14%, 23.77% and 28.4% than GWR, increased by 49.96%, 22.57% and 27.1% than ACV-GWR. The actual experimental results show that IGGⅢ-GWR is 12.65%, 7.44% and 0.37% higher than GWR, respectively, and 11.85%, 6.96% and 0.34% higher than ACV-GWR. The experimental results show that the IGGⅢ-GWR can improve the robustness and fitting effect of GWR.

Key words: IGGⅢ, geographically weighted regression, parameter estimation, robust estimation, air quality analysis

CLC Number:

P208

YU Zhiying, ZHANG Fuhao, QIU Agen, ZHAO Yangyang. Research on geographically weighted regression based on IGGⅢ[J]. 测绘通报, 2019, 0(7): 23-27.

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