Comparing Spatial Welfare Among Major Cities in Java

Firdha Kusuma Wardani; Evi Gravitiani; Tetuko Rawidyo Putro

doi:10.32424/1.erjpe.2023.18.2.3566

Comparing Spatial Welfare Among Major Cities in Java

Firdha Kusuma Wardani, Evi Gravitiani, Tetuko Rawidyo Putro

Abstract

Education, economy, health, tourism, industry, transportation, and social welfare were greatly affected by the 2021 Covid-19 Pandemic. The benchmark for welfare is properly fulfilling the basic needs of society. This study aims to model the level of social welfare in big cities on the island of Java in 2021 by including spatial effects. The method used is Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). GWR model, the weighting used is the Gaussian kernel function. The OLS model produces an R² of 83.96%, while the GWR model produces an R² of 84.03%. This shows that the GWR model is better at explaining the level of diversity in the welfare of cities on the island of Java, which is 84.03% and the rest is influenced by geographical factors because there is no significant difference between the linear regression model and GWR.

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References

Agustina, M. F., Wasono, R., & Darsyah, Moh. Y. (2015). Pemodelan Geographically Weighted Regression (GWR) Pada Tingkat Kemiskinan di Provinsi Jawa Tengah (Vol. 3, Issue 2).

Ananda, N. M. S., Suyitno, S., & Siringoringo, M. (2023). Geographically Weighted Panel Regression Modelling of Human Development Index Data in East Kalimantan Province in 2017-2020. Jurnal Matematika, Statistika Dan Komputasi, 19(2), 323–341. https://doi.org/10.20956/j.v19i2.23775

Arthayanti, Y., Srinadi, I. G. A. M., & Gandhiadi, G. K. (2017). Geographically Weighted Ridge Regression dalam Kasus Multikolinearitas Pada Indeks Pembangunan Manusia di Kabupaten/Kota Provinsi Jawa Timur. Jurnal Matematika, 7(2), 124. https://doi.org/10.24843/jmat.2017.v07.i02.p89

Badan Pusat Statistik. (2021). Welfare Indicators. www.freepik.com/BPS

Borjas, G. J. (2016). Does welfare reduce poverty? Research in Economics, 70(1), 143–157. https://doi.org/10.1016/j.rie.2015.11.002

Case, K. E., & Fair, R. C. (2003). Prinsip-Prinsip Ekonomi (Erlangga, Ed.; Edisi Ketujuh).

Chakraverty, S., & Gupta, D. (2022). As a pandemic strikes: A study on the impact of mental stress, emotion drifts and activities on community emotional well-being. Measurement: Journal of the International Measurement Confederation, 204(September), 112121. https://doi.org/10.1016/j.measurement.2022.112121

Collins, B. (2017). Results from a Well-Being Survey in the North West of England: Inequalities in EQ-5D–Derived Quality-Adjusted Life Expectancy Are Mainly Driven by Pain and Mental Health. Value in Health, 20(1), 174–177. https://doi.org/10.1016/j.jval.2016.08.004

Cui, X., & Chang, C. Ter. (2020). How life expectancy affects welfare in a Diamond-type overlapping generations model. Physica A: Statistical Mechanics and Its Applications, 555, 124616. https://doi.org/10.1016/j.physa.2020.124616

Djokoto, J. G., & Wongnaa, C. A. (2023). Does the level of development distinguish the impacts of foreign direct investment on the stages of human development? Sustainable Futures, 5(November 2022), 100111. https://doi.org/10.1016/j.sftr.2023.100111

Fahrudin, A. (2012). Pengantar Kesejahteraan Sosial. PT Refika Aditama.

Freire, R. C., Pieruccini-Faria, F., & Montero-Odasso, M. (2018). Are Human Development Index dimensions associated with gait performance in older adults? A systematic review. Experimental Gerontology, 102(December 2017), 59–68. https://doi.org/10.1016/j.exger.2017.12.001

Giacalone, M., Mattera, R., & Nissi, E. (2022). Well-being analysis of Italian provinces with spatial principal components. Socio-Economic Planning Sciences, 84. https://doi.org/10.1016/j.seps.2022.101377

Hartanto, W., Islami, N. N., Mardiyana, L. O., Ikhsan, F. A., & Rizal, A. (2019). Analysis of human development index in East Java Province Indonesia. IOP Conference Series: Earth and Environmental Science, 243(1). https://doi.org/10.1088/1755-1315/243/1/012061

Kadri, I. A., Susilawati, M., & Sari, K. (2020). Faktor–Faktor Yang Berpengaruh Signifikan Terhadap Indeks Pembangunan Manusia Di Provinsi Papua. E-Jurnal Matematika, 9(1), 31. https://doi.org/10.24843/mtk.2020.v09.i01.p275

Kartika, S., & Kholijah, G. (2020). Penggunaan Metode Geograhically Weighted Regression (GWR) Untuk Mengestimasi Faktor Dominan yang Mempengaruhi Penduduk Miskin di Provinsi Jambi. JOMTA Journal of Mathematics: Theory and Applications, 2(2).

Karyono, Y., Tusianti, E., Gunawan, I. G. N. A. R., Nugroho, A., & Clarissa, A. (2021). Indeks Pembangunan Manusia .

Kashki, A., Karami, M., Zandi, R., & Roki, Z. (2021). Evaluation of the effect of geographical parameters on the formation of the land surface temperature by applying OLS and GWR, A case study Shiraz City, Iran. Urban Climate, 37(September 2020), 100832. https://doi.org/10.1016/j.uclim.2021.100832

Martí, L., Cervelló-Royo, R., & Puertas, R. (2022). Analysis of the nexus between country risk, environmental policies, and human development. Energy Research and Social Science, 92(February). https://doi.org/10.1016/j.erss.2022.102767

Maulana, A., Meilawati, R., & Widiastuti, V. (2019). Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR).

Maulani, A., Herrhyanto, N., & Suherman, M. (2016). Aplikasi Model Geographically Weighted Regression (Gwr) Untuk Menentukan Faktor-Faktor Yang Mempengaruhi Kasus Gizi Buruk Anak Balita Di Jawa Barat.

Murni, J. M., Terapan, D., Qomariyah, N., Susanti, D. S., & Salam, N. (2018). Estimasi Parameter Model Regresi Terboboti Geografis (Studi Kasus Tingkat Kesejahteraan Penduduk Di Kabupaten Banjar). 12(1), 1–10.

Mursito, T. P. A. P., Berlianto, M. C., An’amtaAdinindra, Y., Edbert, I. S., & Ohyver, M. (2022). Modeling the Amount of Poverty in Central Java using Geographically Weighted Regression. 2022 International Conference on Science and Technology (ICOSTECH), 1–6. https://doi.org/10.1109/ICOSTECH54296.2022.9828815

Nadya, M., Rahayu, W., & Santi, V. M. (2016). Analisis Geographically Weighted Regression (Gwr) Pada Kasus Pneumonia Balita Di Provinsi Jawa Barat.

Nurhalizah, & Sitompul, P. (2022). Analysis of Ordinary Least Square and Geographically Weighted Regression on the Human Development Index of North Sumatra 2021. Formosa Journal of Applied Sciences, 1(6), 981–1000. https://doi.org/10.55927/fjas.v1i6.1718

Permai, S. D., Tanty, H., & Rahayu, A. (2016). Geographically weighted regression analysis for human development index. AIP Conference Proceedings, 1775. https://doi.org/10.1063/1.4965165

Putri, F. E., Abapihi, B., & Ruslan, A. (2022). Pemodelan Indeks Pembangunan Manusia Di Indonesia Dengan Pendekatan Geographically Weighted Regression. www.bps.go.id

Qiu, Q., Sung, J., Davis, W., & Tchernis, R. (2018). Using spatial factor analysis to measure human development. Journal of Development Economics, 132(October 2017), 130–149. https://doi.org/10.1016/j.jdeveco.2017.12.007

Rafi, A., Diastina, N., Handajani, S. S., & Slamet, I. (2019). Analisis Model Geographically Weighted Regression (Gwr) Pada Kasus Jumlah Peserta Kb Aktif Di Provinsi Jawa Tengah. Prosiding Seminar Nasional Geotik 2019. ISSN: 2580-8796364.

Reskia, A. (2022). Analisis Spasial Tingkat Kesejahteraan Di Indonesia Menggunakan Geographically Weighted Logistic Regression.

Riccardi, E., Fontana, L., Pacella, D., Fusco, F., Marinaro, I., Costanzo, G., Vassallo, F., Triassi, M., & Iavicoli, I. (2023). Impact of Covid-19 pandemic on psychological well-being of firefighters. Safety and Health at Work, xxxx. https://doi.org/10.1016/j.shaw.2023.06.002

Sadarrudin, M. A. P. A., Rochaida, E., Hasid, Z., & Suharto, R. B. (2022). Causality between Regional Economic Independence and Decentralization on Poverty Alleviation and Community Welfare Mediated by Economic Development. International Journal of Sustainable Development and Planning, 17(2), 623–632. https://doi.org/10.18280/ijsdp.170227

Saputra, H. Y., & Radam, I. F. (2022). Accessibility model of BRT stop locations using Geographically Weighted regression (GWR): A case study in Banjarmasin, Indonesia. International Journal of Transportation Science and Technology, 12(3), 779–792. https://doi.org/10.1016/j.ijtst.2022.07.002

Saputro, D. R. S., Hastutik, R. D., & Widyaningsih, P. (2021). The modeling of human development index (HDI) in Papua - Indonesia using geographically weighted ridge regression (GWRR). AIP Conference Proceedings, 2326. https://doi.org/10.1063/5.0040329

Sarabhai, Vikram. A. (1975). Poverty And Economic Development.

Shintia Dewi, F., & Yasin, H. (2015). Pemodelan Status Kesejahteraan Daerah Kabupaten Atau Kota Di Jawa Tengah Menggunakan Geographically Weighted Logistic Regression Semiparametric. 4(1), 43–52. http://ejournal-s1.undip.ac.id/index.php/gaussian

Tian, M., Wang, X., Wang, Q., Qiao, Y., Wu, H., & Hu, Q. (2023). Geographically weighted regression (GWR) and Prediction-area (P-A) plot to generate enhanced geochemical signatures for mineral exploration targeting. Applied Geochemistry, 150(November 2022), 105590. https://doi.org/10.1016/j.apgeochem.2023.105590

Tribhuwaneswari, A. B., Hapsery, A., & Rahayu, W. K. (2022). Spatial autoregressive quantile regression as a tool for modelling human development index factors in 2020 East Java. AIP Conference Proceedings, 2668(October). https://doi.org/10.1063/5.0112828

Trimanto, Metusala, D., Erlinawati, I., Angio, M. H., Yusuf, H. M., & Budiarto, K. (2023). Study of population and conservation of Dendrobium capra J.J. Smith, an endangered and endemic orchid from Java Island, Indonesia. Journal for Nature Conservation, 75(May), 126476. https://doi.org/10.1016/j.jnc.2023.126476

Virtriana, R., Deanova, M. A., Safitri, S., Anggraini, T. S., Ihsan, K. T. N., Deliar, A., & Riqqi, A. (2023). Identification of land cover change and spatial distribution based on topographic variations in Java Island. Acta Ecologica Sinica, April. https://doi.org/10.1016/j.chnaes.2023.08.002

Walsemann, K. M., Fisk, C. E., & Dues, A. N. (2021). A spatial analysis of county-level education context and population health and wellbeing. Wellbeing, Space and Society, 2. https://doi.org/10.1016/j.wss.2020.100002

Wang, J., Wang, C., Li, S., & Luo, Z. (2021). Measurement of relative welfare poverty and its impact on happiness in China: Evidence from CGSS. China Economic Review, 69(November 2020), 101687. https://doi.org/10.1016/j.chieco.2021.101687

Yamada, E., & Shimizutani, S. (2022). The COVID 19 pandemic, daily mobility and household welfare: Evidence from Tajikistan. Transportation Research Interdisciplinary Perspectives, 15(February), 100641. https://doi.org/10.1016/j.trip.2022.100641

Zhu, C., Zhang, X., Zhou, M., He, S., Gan, M., Yang, L., & Wang, K. (2020). Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological Indicators, 117(July), 106654. https://doi.org/10.1016/j.ecolind.2020.106654

DOI: https://doi.org/10.32424/1.erjpe.2023.18.2.3566