THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL
DOI:
https://doi.org/10.29244/ijsa.v3i3.564Keywords:
global and local spatial-temporal variable, mixed geographically and temporally weighted regression, povertyAbstract
Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
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References
Anselin, L. (1988). Spatial Econometrics: Methods and Models. New York (US): Springer Science & Business Media.
[BPS] Badan Pusat Statistik Sumatera Utara. (2015a). Provinsi Sumatera Utara dalam angka 2010-2015. Medan (ID): Badan Pusat Statistik Sumatera Utara.
[BPS] Badan Pusat Statistik Sumatera Utara. (2015b). Statistik kesejahteraan rakyat provinsi Sumatera Utara 2010-2015. Medan (ID): Badan Pusat Statistik Sumatera Utara.
Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica: Journal of the Econometric Society, 47(5): 1287–1294.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1999). Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 39(3): 497–524.
Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. Chichester, UK: John Wiley & Sons.
Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3): 383–401.
Leung, Y., Mei, C.-L., & Zhang, W.-X. (2000). Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32(1): 9–32.
Liu, J., Zhao, Y., Yang, Y., Xu, S., Zhang, F., Zhang, X., … Qiu, A. (2017). A mixed geographically and temporally weighted regression: Exploring spatial-temporal variations from global and local perspectives. Entropy, 19(2): 53.
Ma, X., Zhang, J., Ding, C., & Wang, Y. (2018). A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. Computers, Environment and Urban Systems, 70: 113–124.
Rahmawati, R., & Djuraidah, A. (2010). Regresi Terboboti Geografis dengan Pembobot Kernel Kuadrat Ganda untuk Data Kemiskinan di Kabupaten Jember. Forum Statistika Dan Komputasi, 15(2): 32–37.
Utama, N. (2015). Pemodelan mixed geographically temporally weighted regression (mgtwr) pada kasus kemiskinan di provinsi jawa timur (Skripsi). Universitas Brawijaya, Malang (ID).
Winarso, K., Notobroto, H. B., & Fatmawati. (2014). Development of air polluter model for the carbon monoxide (CO) element based on mixed geographically temporal weighted regression (MGTWR) Kriging. Applied Mathematical Sciences, 8(118): 5863–5873.
Yasin, H., Sugito, & Prahutama, A. (2015). Analisis data kemiskinan di Jawa Tengah menggunakan metode mixed geographically and temporally weighted regressions (MGTWR). BIAStatistics, 9(1): 15–23.
