Computer Science ›› 2017, Vol. 44 ›› Issue (3): 274-277, 312.doi: 10.11896/j.issn.1002-137X.2017.03.056

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Mixed Geographically and Temporally Weighted Regression and Two-step Estimation

ZHAO Yang-yang, LIU Ji-ping, YANG Yi, ZHANG Fu-hao and QIU A-gen   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In response to a phenomenon that both global stationary characteristics and spatial-temporal non-stationary characteristics exist at the same time,an approach named mixed geographically and temporally weighted regression (MGTWR) was proposed.This paper showed mathematical definition of MGTWR and gave the formula of regression parameters by using two-step estimation method.Besides,the weight calculation method and the parameter optimization method based on Akaike information criterion (AIC) were introduced.Some simulated data with different degrees of complexity were adopted to test the performance of method.Result shows that R2 are more than 0.8 when MGTWR and GTWR are used.Both MGTWR and GTWR can deal with the phenomenon that both global stationary characteristics and spatial-temporal non-stationary characteristics have.What’s more,MGTWR is better than GTWR.As MGWR cannot detect temporal non-stationary characteristics,the results of MGWR are bad.In addition,the complexity of the data affects the performance of MGTWR,GTWR and MGWR.The simpler the data are,the better the results will be.

Key words: MGTWR,GTWR,Two-step estimation

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