Panel Data Spatial Regression Modeling with a Rook Contiguity Weighting Function on the Human Development Index in West Sumatera Province
DOI:
https://doi.org/10.31764/jtam.v8i1.16675Keywords:
HDI, Rook Contiguity, Random Effect Spatial Autoregrresive.Abstract
The achievement of the level of welfare of a region or country can be seen from the level of human development as measured by the Human Development Index (HDI). West Sumatra is one of the provinces with HDI achievements above the national average. However, there are still regencies/cities in West Sumatra Province that have HDI achievements below the national average and HDI achievements in West Sumatra Province Regencies/Cities have changed in 2017-2021. Therefore, in this study, spatial analysis of panel data was used. The aim of this research is to find out the general description of the HDI of West Sumatra Province, obtain a panel data spatial regression model and obtain variables that significantly influence on HDI in West Sumatra Province 2017─2021because differences in HDI achievement were suspected to have influences from areas that were side by side and the area was observed more than once. The model formed from this analysis using the rook contigutiy weighting function is Random Effect Spatial Autoregressive because the spatial interactions formed in human development index data in West Sumatra Province are real at lag. This model is a suitable model based on panel spatial model selection and has an R2 value of 92.94%. Analysis of human development index data in regencies/cities in West Sumatra Province using spatial regression panel data obtained results that expectations of school length (X1), average length of schooling (X2), and population density (X3) significantly directly influenced the human development index in regencies/cities in West Sumatra Province.
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References
Alfiani, S., & Arum, P. R. (2022). Pemodelan Pertumbuhan Ekonomi di Jawa Barat Menggunakan Metode Geographically Weighted Panel Regression. In Universitas Muhammadiyah Semarang Jl. Kedungmundu (Vol. 15, Issue 2). www.unipasby.ac.iJurnal Ilmiah Teori Dan Aplikasi Statistika (Vol. 15, Issue 2). https://doi.org/10.36456/jstat.vol15.no2.a5506d
Anselin, L. (2010). Spatial Econometrics Methods and Models (Vol. 4). Dordrecht, Springer Netherlands.
Ayuwida, C. A., Arum, P. R., & Al Haris, M. (2021). Model Seemingly Unrelated Regression Pada Data Kemiskinan Jawa Timur Menggunakan Matriks Pembobot Queen Contiguity Dan Rook Contiguity. Jurnal Statistika Universitas Muhammadiyah Semarang, 9 (1). https://doi.org/doi.org/10.26714/jsunimus.9.1.2021.64-68
Chi, G., & Zhu, J. (2020). Spatial Regression Models for the Social Sciences. https://doi.org/10.4135/9781544302096
Elhorst, J. (2014). Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. https://doi.org/10.1007/978-3-642-40340-8
Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (2010). Handbook of Spatial Statistics (1st ed., Vol. 1). CRC Press. https://doi.org/10.1201/9781420072884
Golgher, A., & Voss, P. (2015). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography, 4, 1–31. https://doi.org/10.1007/s40980-015-0016-y
Guliyev, H. (2020). Determining the spatial effects of COVID-19 using the spatial panel data model. Spatial Statistics, 38. https://doi.org/10.1016/j.spasta.2020.100443
Kim, I. (2021). Spatial distribution of neighborhood-level housing prices and its association with all-cause mortality in Seoul, Korea (2013–2018): A spatial panel data analysis. SSM - Population Health, 16. https://doi.org/10.1016/j.ssmph.2021.100963
Lee, L., & Yu, J. (2010). Estimation of spatial autoregressive panel data models with fixed effects. Journal of Econometrics, 154(2), 165–185. https://doi.org/10.1016/j.jeconom.2009.08.001
Lesage, J., & Pace, R. K. (2009). Introduction to Spatial Econometrics (1st ed., Vol. 1). Taylor & Francis. https://doi.org/https://doi.org/10.1201/9781420064254
Majeed, M., & Mazhar, M. (2021). An Empirical Analysis of Output Volatility and Environmental Degradation: A Spatial Panel Data Approach. Environmental and Sustainability Indicators, 10, 100104. https://doi.org/10.1016/j.indic.2021.100104
Masruroh, M., & Subekti, R. (2016). Aplikasi Regresi Partial Least Square Untuk Analisis Hubungan Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia Di Kota Yogyakarta. Media Statistika, 9(2), 75–84. https://doi.org/10.14710/medstat.9.2.75-85
Qi, Z., Lin, H., & Junya, B. (2021). Spatial Autoregressive Analysis of Nationwide Street Network Patterns with Global Open Data. Environment and Planning B Urban Analytics and City Science, 0, 1–18. https://doi.org/10.1177/2399808320987846
Raiher, A., Carmo, A., & Stege, A. (2017). The effect of technological intensity of exports on the economic growth of Brazilian microregions: A spatial analysis with panel data. EconomiA, 18. https://doi.org/10.1016/j.econ.2017.03.001
Saputri, W. A. K., & Suryowati, K. (2018). Analisis Faktor-Faktor yang Mempengaruhi Gini Ratio Di Provinsi Papua Dengan Model Spasial Data Panel. Jurnal Statistika Industri Dan Komputasi, 03(2), 1–11. https://doi.org/10.34151/statistika.v3i02.1060
Siswoyo, E. (2014). Kesenjangan dan Ketimpangan Akibat Pembangunan Tidak Merata. https://tubasmedia.com/
SluÄiaková, S. (2021). Effects of the unit-based pricing of waste in Slovakia: Spatial panel data models and matching approach. Environmental Challenges, 2, 100022. https://doi.org/10.1016/j.envc.2021.100022
Tobler, W. (2010). Geographical Filters and Their Inverses. Geographical Analysis, 1, 234–253. https://doi.org/10.1111/j.1538-4632.1969.tb00621.x
Ukra, M. G., Nusrang, M., & Poerwanto, B. (2022). Regresi Panel Spasial Untuk Pemodelan Indeks Pembangunan Manusia Di Kabupate/Kota Se-Kalimantan. VARIANSI: Journal of Statistics and Its Application on Teaching and Research, 4(2), 70–78. https://doi.org/10.35580/variansiunm34
Wang, H., Du, Z., Wang, X., Liu, Y., Yuan, Z., Liu, Y., & Xue, F. (2015). Detecting the association between meteorological factors and hand, foot, and mouth disease using spatial panel data models. International Journal of Infectious Diseases, 34, 66–70. https://doi.org/10.1016/j.ijid.2015.03.007
Wang, N., Zhang, X., Wang, Z., Chen, Y., & Li, S. (2023). Can financial development improve environmental quality? New findings from spatial measures of Chinese urban panel data. Heliyon, 9(7), e17954. https://doi.org/10.1016/j.heliyon.2023.e17954
Wang, Y., Wang, M., Wu, Y., & Sun, G. (2023). Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China. Environment International, 174, 107889. https://doi.org/10.1016/j.envint.2023.107889
Widyastuti, M. N., Srinadi, I. G. A. M., & Susilawati, M. (2019). Pemodelan Jumlah Kasus Pneumonia Balita Di Jawa Timur Menggunakan Regresi Spatial Autoregressive Moving Average. E-Jurnal Matematika, 8(3), 236. https://doi.org/10.24843/mtk.2019.v08.i03.p259
Yulianti, S., Widyanigsih, Y., & Nurrohmah, S. (2021). Spatial panel data model on human development index at Central Java. Journal of Physics: Conference Series, 1722, 012090. https://doi.org/10.1088/1742-6596/1722/1/012090
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