Main Article Content
Abstract
Water quality is a key indicator of a community’s health and welfare, yet it has deteriorated significantly due to pollution caused by human activities. This study aimed to evaluate Geographically Weighted Logistic Regression’s (GWLR) ability to handle spatial nonstationarity in the relationship between explanatory factors and water quality status in Pontianak City, and to compare its performance with logistic regression. Three modelling approaches were applied to classify water as polluted or non-polluted: (i) logistic regression with spatially invariant) parameters; (ii) GWLR with a fixed Gaussian kernel, producing spatially varying parameters using a fixed bandwidth; and (iii) GWLR with an adaptive Gaussian kernel, producing spatially varying parameters using an adaptive bandwidth. Model performance was compared using Akaike’s Information Criterion (AIC) and classification accuracy. The GWLR model with a fixed Gaussian kernel produced an AIC of 22.52, whereas the logistic regression model produced a slightly lower AIC of 22.39; both models achieved a classification accuracy of 92.86%, with the adaptive-kernel GWLR showing comparable classification performance. These results indicate that, for the parameter settings considered, GWLR offered performance comparable to, but not substantially better than logistic regression for modelling the factors affecting water quality, despite its capacity to address spatial nonstationarity.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
- Backhaus, K., Erichson, B., Gensler, S., Weiber, R., & Weiber, T. (2023). Multivariate Analysis An Application-Oriented introduction (2nd ed). Springer Gabler.
- Debataraja, N. N., & Kusnandar, D. (2023). Pengantar Analisis Data Spasial. UNTAN Press.
- Debataraja, N. N., Kusnandar, D., Imro’ah, N., & Rachmadiar, M. (2019). Penerapan Metode Cokriging Untuk Mengestimasi Jumlah Zat Padat Terlarut Pada Air Di Permukiman Kota Pontianak. Jurnal Matematika Sains Dan Teknologi, 20(2), 142–148. https://doi.org/10.33830/jmst.v20i2.208.2019
- Fathurahman, M., Purhadi, Sutikno, & Ratnasari, V. (2016). Pemodelan Geographically Weighted Logistic Regression pada Indeks Pembangunan Kesehatan Masyarakat di Provinsi Papua. In Prosiding Seminar Nasional MIPA, 34–42.
- Fikri, M., Debataraja, N. N., & Kusnandar, D. (2019). Penentuan Sebaran Spasial Pencemaran Air Di Kota Pontianak Menggunakan Analisis Diskriminan Dua Kelompok. Media Statistika, 12(2), 226. https://doi.org/10.14710/medstat.12.2.226-235
- Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationship. Wiley.
- Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (1st ed.). Wiley. https://doi.org/10.1002/9781118548387
- Isazade, V., Qasimi, A. B., & Dong, P. (2023). Integration of Moran’s I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran, Iran. Modeling Earth Systems and Environment, 9, 3923–3937. https://doi.org/10.1007/s40808-023-01729-y
- Kusnandar, D., Debataraja, N. N., & Dewi, P. R. (2019). Classification of water quality in Pontianak city using multivariate statistical techniques. Applied Mathematical Sciences, 1069–1075. https://doi.org/10.12988/ams.2019.99130
- Kusnandar, D., Debataraja, N. N., & Fitriani, S. (2021). Pemodelan Sebaran Total Dissolved Solid Menggunakan Metode Mixed Geographically Weighted Regression. Jorunal of Statistical Application and Computational Statistics, 13(1), 9–16. https://doi.org/10.34123/jurnalasks.v13i1.257
- Kusnandar, D., Debataraja, N. N., & Nusantara, R. W. (2022). An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia. Cauchy J. Mat Murni Dan Apl, 7(2), 185–194.
- Kusnandar, D., Debataraja, N. N., Rizki, S. W., & Saputri, E. (2020). Water quality mapping in Pontianak City using multiple discriminant analysis. AIP Conference Proceedings, 2268(1), 020006. https://doi.org/10.1063/5.0016809
- Kusnandar, D., Debataraja, N. N., & Utari, S. (2021). Pemodelan Tingkat Kualitas Air di Kota Pontianak dengan Menggunakan Multivariate Geographically Weighted Regression. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 15(3), 493–502. https://doi.org/10.30598/barekengvol15iss3pp493-502
- Lessani, M. N., & Li, Z. (2024). SGWR: Similarity and Geographically Weighted Regression. International Journal of Geographical Information Science, 38(1232–1255). https://doi.org/10.1080/13658816.2024.2294076
- Lestari, F. D., Kusnandar, D., & Debataraja, N. N. (2020). Estimasi Parameter Model Geographically Weighted. Buletin Ilmiah Matematika Statistika Dan Terapannya (Bimaster), 9(1), 159–164.
- Minister of Environment. (2003). Peraturan Menteri Lingkungan Hidup Nomor 115 Tahun 2003 Tentang Pedoman Penentuan Status Mata Air.
- Minister of Health of the Republic of Indonesia. (2017). Regulation of the Minister of Health of the Republic of Indonesia Number 32 of 2017 concerning Environmental Health Quality Standards and Water Health Requirements for Sanitation Hygiene Needs, Swimming Pools, Solus Per Aqua and Public Baths.
- Omrani, F., Shad, R., & Ziaee, S. A. (2025). A Multiscale geographically weighted regression approach to emphasize the effects of traffic characteristics on vehicular emissions. Science of the Total Environment, 25, 100315. https://doi.org/10.1016/j.aeaoa.2025.100315
- Panggabean, M., & Debataraja, N. N. (2025). Quantitative Analysis of Land Transportation Facilities and Infrastructure (SDG 9) With Labor Absorption Related to GDRP (SDG 8) in West Kalimantan Province, Indonesia. Journal of Lifestyle & SDG’s Review, 5(7), e07252. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n07.pe07252
- Pardoe, I. (2021). Applied Regression Modeling. John Wiley & Sons. Inc.