Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang

Authors

  • Henny Pramoedyo Brawijaya University
  • Mudjiono Mudjiono Brawijaya University
  • Adji Achmad Fernandes Brawijaya University
  • Deby Ardianti Brawijaya University
  • Kurniawati Septiani

DOI:

https://doi.org/10.18196/mm.200250

Keywords:

GWR, Kriging, Stunting

Abstract

Stunting is the condition toddlers have Stunting is the condition toddlers have less length or height if compared to age. The high percentage of stunting is influenced by several factors, namely access to healthy latrines, quality drinking water, hand washing behavior with soap, coverage of posyandu access and coverage of breast milk 1-6 months, and there are indications that if an area has a high stunting percentage, then there is a possibility that the nearest area has the same condition. So, the statistic method for this research use the spatial interpolation Geographically Weighted Regression Kriging. Geographically Weighted Regression (GWR) is a weighted regression in which the weighting function is used to describe the closeness of relations between regions. The weight used is distance based weight dan weighting by area (contiguity). Ordinary kriging method calculated with semivariogram which is one function to describe and model the spatial autocorrelation between data of a variable and function as a measure of variance. The results showed that based on value GWR model with weight Fixed Gaussian Kernel better to use then the weighted GWR model Rook Contiguity. The Predicted of prevelensi stunting in the form of map based on interpolation GWR Kriging.
Keywords: Stunting, GWR, and Kriging.

References

Anselin, L. 1988. Spatial Econometrics: Methods and Model. Netherland: Kluwer Academic Publisher.

Fotheringham, A.S., Brundson, C. And Charlton, M. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. UK

Chasco, C., Garcia, I., dan Vicens, J. 2007. Modeling Spatial Variations in Household Disposable Income with Geographically Weighted Regression. Munich Personal RePEc Archive Paper No. 1682.

Anselin, L., Syabri, I., dan Youngihn, K. 2004. GeoDa: An Introduction to Spatial Data Analysis. Urbana: University of Illinois

Walter J., Carsten R. Dan Jeremy W. Lichstein. 2005. Local and global approaches to spatial data analysis in ecology. Global Ecology and Biogeography 14, 97-98.

Armstrong, M. 1998. Basic linear geostatistics. Springer Sciences & Business Media

Isaaks, E.H. & Srivastava, R.M. 1989. Applied Geostatistics. Oxford University: Press, New York.

Setyadi, B. 2005. Variogram Models. http://geodesy.gd.itb.ac.id/bsetyadji/wpcontent/uploads/2007/09/gd4113-4b.pdf. Accessed29 February 2020.

Ministry of Health RI. 2019. Short Toddler Situation (Stunting) di Indonesia. Jakarta: Ministry of Health RI

Sulistiyani, Ratnawati, L.Y., & Priyono, D.I. 2015. Determinant of Stunting in Children under the age of 12-36 months in Puskesmas Randuagung Lumajang Regency. E-Journal Health Reader, 3 No. 2.

Jihad, J., Ahmad, L. A., & Ainurafiq. 2016. Analysis of Determinant Stunting in Toddlers Age 12-24 Monts . Kendari: Haluoleo University

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Published

2020-07-12

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Section

Research