Geographically and Temporally Weighted Autoregressive to Modeling the Levels of Poverty Population in Java in 2012-2018
Keywords:
poverty, spatial autoregressive, GTWARAbstract
Geographically and temporally weighted regression (GTWR) is a method applied when there is spatial and temporal diversity in the observation. GTWR model just considers local influences of spatial-temporal response variable on the explanatory variables. The GTWR model can add an autoregressive component of response variable, the resulting model is known as a geographically and temporally weighted autoregressive model (GTWAR). This study aims to perform GTWAR modeling which is applied to the data on the proportion of poor people by districts/cities in Java in 2012-2018. The results showed that GTWAR produced Akaike Information Criterion (AIC) smaller than GTWR, and the coefficient of determination (R2) is higher than GTWR.
References
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[14] H. Sugi, M.N. Aidi, A. Djuraidah. Analysis of the Geographically and Temporally Weighted Regression (GTWR) of the GRDP the Construction Sector in Java Island. Forum Geografi, 33(1), 2019.
[2] A.S. Fotheringham, C. Brunsdon, M. Charlton. Geographically Weighted Regression: The Analysis of Spatially Varying Relationship. England: Jhon Wiley & Sons Ltd. 2002.
[3] Bo Wu, Rongrong Li, Bo Huang. A Geographically and Temporally Weighted Autoregressive Model with Application to Housing Prices. International Journal of Geographical Information Science, 28(5): 1186-1204, 2014.
[4] Liu J, Yang Y, Xu S, Zhao Y, Wang Y, Zhang F. A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation. Article Entropy MDPI. 303 (18) : 1-13, 2016.
[5] M Sholihin, A.M. Soleh, A. Djuraidah. Geographically and Temporally Weighted Regression (GTWR) for Modeling Economic Growth Using R. International Journal of Computer Science and Network. 6(6): 800-805, 2017.
[6] G. Erda, Indahwati, A. Djuraidah. "A Comparison of GWTR and Robust GTWR Modelling". International Journal of Scientific Research in Science, Engineering, and Technology, vol. 4 (9), pp 453-457, 2018.
[7] A.H. Asianingrum, A. Djuraidah, Indahwati. robust mixed geographically and temporally weighted regression to modeling the percentage of poverty population in java in 2012-2018. International Journal of Sciences: Basic and Applied Research (IJSBAR), vol. 53 (2), pp 186-197, 2020.
[8] H. Yasin, Sugito, A. Prakutama. "Analisis Data Kemiskinan di Jawa Tengah Menggunakan Metode Mixed Geographically and Temporally Weighted Regressions (MGTWR)". Biostatistics, vol. 1(1), pp. 15-23, 2015.
[9] R. Affifah, Y. Andriyana, I.M. Jaya. "Robust Geographically Weighted Regression with Least Absolute Deviation Method". American Institute of Physics, pp. 1-9, 2017.
[10] Nuramaliyah, A. Saefuddin, M.N. Aidi. "The Best Global and Local Variables of the Mixed Geographically and Temporally Weighted Regression Model. Indonesian Journal of Statistics and Its Applications, vol. 3(3), pp. 320-330, 2019.
[11] K.Y. Widiyanti, H. Yasin, Sugito. "Pemodelan Proporsi Penduduk Miskin Kabupaten dan Kota di Provinsi Jawa Tengah Menggunakan Geographically and Temporally Weighted Regression". Jurnal Gaussian, vol. 3(4), pp. 691-700, 2014.
[12] Anselin L. A Note on Small Sample Properties of Estimators in a FirstOrder Spatial Autoregressive Model. Environ. Plan. A Econ. Sp. 14(8) 1023–1030, 1982.
[13] Anselin L. Spatial Econometrics: Methods and Models. Dordrecht: Academic Publishers. 1988.
[14] H. Sugi, M.N. Aidi, A. Djuraidah. Analysis of the Geographically and Temporally Weighted Regression (GTWR) of the GRDP the Construction Sector in Java Island. Forum Geografi, 33(1), 2019.
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Published
2020-11-06
How to Cite
Djalihu, H. ., Djuraidah, A. ., & Soleh, A. M. . (2020). Geographically and Temporally Weighted Autoregressive to Modeling the Levels of Poverty Population in Java in 2012-2018. International Journal of Sciences: Basic and Applied Research (IJSBAR), 54(4), 55–67. Retrieved from https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/11776
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