Estimation of Geographically Weighted Regression Case Study on Wet Land Paddy Productivities in Tulungagung Regency

Danang Ariyanto


Regression is a method connected independent variable and dependent variable with estimation parameter as an output. Principal problem in this method is its application in spatial data. Geographically Weighted Regression (GWR) method used to solve the problem. GWR  is a regression technique that extends the traditional regression framework by allowing the estimation of local rather than global parameters. In other words, GWR runs a regression for each location, instead of a sole regression for the entire study area. The purpose of this research is to analyze the factors influencing wet land paddy productivities in Tulungagung Regency. The methods used in this research is  GWR using cross validation  bandwidth and weighted by adaptive Gaussian kernel fungtion.This research using  4 variables which are presumed affecting the wet land paddy productivities such as:  the rate of rainfall(X1), the average cost of fertilizer per hectare(X2), the average cost of pestisides per hectare(X3) and Allocation of subsidized NPK fertilizer of food crops sub-sector(X4). Based on the result, X1, X2, X3 and X4  has a different effect on each Distric. So, to improve the productivity of wet land paddy in Tulungagung Regency required a special policy based on the GWR model in each distric.


spatial data, geographically weighted regression, GWR, cross validation, adaptive gaussian kernel

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