Geographically Weighted Regression with The Best Kernel Function on Open Unemployment Rate Data in East Java Province

Robiansyah  Putra; Sischa  Wahyuning Tyas; Muhammad Ghani Fadhlurrahman

doi:10.20885/enthusiastic.vol2.iss1.art4

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Abstract

Unemployment is one of the problems that hinders employment development programs. Based on East Java BPS data, the Open Unemployment Rate in East Java in 2019 is about 3.92 percent. In 2020, unemployment increased by 466.02 thousand people and OUR increased by 2.02 percent to 5.84 percent in August 2020. In addition to the indicators that affect OUR, each observation location has different characteristics, so multiple linear regression modeling is not appropriate. Geographically Weighted Regression is one of the spatial analysis developed from multiple linear regression for data containing spatial heterogeneity effects. The weighting functions used for this GWR model are Kernel Fixed and Adaptive functions (Gaussian, Bi-Square, Tricube, and Exponential). The analytical step carried out in estimating the parameters is to use WLS. In the test, the best weighting was obtained, namely the Adaptive Tricube. Based on the results of the study, the GWR model with Adaptive Tricube weighted resulted in the value of R-Squared = 84.88%. However, the best model is obtained from the GWR model with exponential weighting with the smallest Akaike Information Criterion (AIC) value compared to the others, namely AIC = 86.01264 with R-Squared = 91.67.

Keywords

statistics open unemployment geographically weighted regression spatial analysis bandwidth kernel

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Author Biographies

Robiansyah Putra, Universitas Gadjah Mada, Indonesia

Sischa Wahyuning Tyas , Universitas Gadjah Mada, Indonesia

Muhammad Ghani Fadhlurrahman, Universitas Gadjah Mada, Indonesia

How to Cite

Putra, R., Wahyuning Tyas , S. ., & Fadhlurrahman, M. G. (2022). Geographically Weighted Regression with The Best Kernel Function on Open Unemployment Rate Data in East Java Province. Enthusiastic : International Journal of Applied Statistics and Data Science, 2(1), 26–36. https://doi.org/10.20885/enthusiastic.vol2.iss1.art4

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References

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J. Jiang, L. Luo, P. Xu, and P. Wang, “How Does Social Development Influence Life Expectancy? A Geographically Weighted Regression Analysis in China”, Int J Public Health, Vol. 163, pp. 95-104, October 2018.
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BPS, Badan Pusat Statistika, [Online]. Available: https://jatim.bps.go.id/.
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H. Yasin, “Pemilihan Variabel pada Model Geographically Weighted Regression”, Media Statistika, Universitas Diponegoro, Vol. 4, No. 2, pp. 63-72, 2011.
I. Nurhuda, and I. G. N. M. Jaya, “Pemodelan Kriminal di Jawa Timur dengan Metode Geograhically Weighted Regression (GWR)”, Jurnal Matematika MANTIK, UIN Sunan Ampel Surabaya, Vol. 4, No. 2, pp. 150-158, 2018.

References

Mariana, “Pendekatan Regresi Spasial dalam Pemodelan Tingkat Pengangguran Terbuka”, Jurnal Matematika dan Pembelajarannya, IAIN Ambon, Vol. 1, No. 1, pp. 42-63, 2013.

J. Jiang, L. Luo, P. Xu, and P. Wang, “How Does Social Development Influence Life Expectancy? A Geographically Weighted Regression Analysis in China”, Int J Public Health, Vol. 163, pp. 95-104, October 2018.

N. Lutfiani, S. Sugiman, S. Mariani, “Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-Square”, UNNES Journal of Mathematics, Vol. 8, No. 1, pp. 82-91, 2019.

BPS, Badan Pusat Statistika, [Online]. Available: https://jatim.bps.go.id/.

N. R. Draper, and H. Smith, Applied Regression Analysis, New York: John Wiley and Sons Inc, 1998.

A. R. Tizona, R. Goejantoro, and Wasono, “Pemodelan Geographically Weighted Regression dengan Fungsi Pembobot Adaptive Kernel Bi-Square untuk Angka Kesakitan Demam Berdarah di Kalimantan Timur Tahun 2015”, Jurnal Eksponensial, Vol. 8, No. 1, pp. 87-94, 2017.

A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chicester: John Wiley and Sons, 2002.

C. Brunsdon, A. S. Fotheringham, and M. Charlton, “Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity”, Geographical Analysis, Vol. 28, No. 4, pp. 281-298, 1996.

C. Chasco, I. Garcia, J. Vicens, “Modeling Spastial Variations in Household Disposible Income with Geographically Weighted Regression”, Munich Personal RePEc Archive (MPRA), [Online]. Available: https://mpra.ub.uni-muenchen.de/1682/.

H. Yasin, “Pemilihan Variabel pada Model Geographically Weighted Regression”, Media Statistika, Universitas Diponegoro, Vol. 4, No. 2, pp. 63-72, 2011.

I. Nurhuda, and I. G. N. M. Jaya, “Pemodelan Kriminal di Jawa Timur dengan Metode Geograhically Weighted Regression (GWR)”, Jurnal Matematika MANTIK, UIN Sunan Ampel Surabaya, Vol. 4, No. 2, pp. 150-158, 2018.

Geographically Weighted Regression with The Best Kernel Function on Open Unemployment Rate Data in East Java Province

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Robiansyah Putra, Universitas Gadjah Mada, Indonesia

Sischa Wahyuning Tyas , Universitas Gadjah Mada, Indonesia

Muhammad Ghani Fadhlurrahman, Universitas Gadjah Mada, Indonesia

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