An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia

Naomi Nessyana Debataraja, Dadan Kusnandar, Rossie Wiedya Nusantara


Geographically weighted regression (GWR) is an exploratory analytical tool that creates a set of location-specific parameter estimates. The estimates can be analysed and represented on a map to provide information on spatial relationships between the dependent and the independent variables. A problem that is faced by the GWR users is how best to map these parameter estimates. This paper introduces a simple mapping technique that plots local t-values of the parameters on one map. This study employed GWR to evaluate chemical parameters of water in Pontianak City. The chemical oxygen demand (COD) was used as the dependent variable as an indicator of water polution. Factors used for assessing water pollution were pH (X1), iron (X2), fluoride (X3), water hardness (X4), nitrate (X5), nitrite (X6), detergents (X7) and dissolved oxygen, DO, (X8). Samples were taken from 42 locations. Chemical properties were measured in the laboratory. The parameters of the GWR model from each site were estimated and transformed using Geographic Information Systems (GIS). The results of the analysis show that X1, X2, X3, X5, and X7 influence the amount of COD in water. The resulting map can assist the exploration and interpretation of data.


chemical parameters; geographically weighted regression; modelling; t-value mapping

Full Text:



BPS Kota Pontianak, Pontianak Municipality in Figure, BPS, Pontianak, 2019.

L. M. Jordan, Religion and Demography in the United States: A geographic analysis, Doctoral dissertation, University of Coloradi, 2006.

Republic of Indonesia, 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 Sanitary Hygiene, Swimming Pools, Solus per Aqua and Public Baths, 2017

T. Nakaya, A.S. Fotheringham, C. Brunsdon, and M. Charlton, "Geographically weighted Poisson regression for disease association mapping," Statistics in Medicine, vol. 24, pp. 2965-2717, 2005.

P. Goovaerts, "Geostatistical analysis of disease data: Estimation of cancer mortality risk from empirical frequencies using Poisson kriging," International Journal of Health Geographics, vol.4, 2005.

J.L. Mennis, and L. M. Jordan, "The distribution of environmental equity: exploring spatial nonstationarity in multivariate models of air toxic releases," Annals of the Association of American Geographers, vol. 95, pp. 249-268, 2005.

A.W. Wardhana, Dampak pencemaran lingkungan (Impact of environment pollution), Andi Ofsett, Yogyakarta, 2001.

VY-J. Chen, P-C. Wu, T-C. Yang, and H-J. Su, Examining non-stastionary effects of social determinants on cardiovascular mortality after cold surges in Taiwan, Science of the Total Environment, vol. 408, pp. 2042-2059, 2010 [PubMed: 20138646].

J.L. Menis, "Mapping the result of geographically weighted regression," The Cartographic Journal, vol. 43, pp. 171-179, 2006.

A. S. Fotheringham, M. Charlton, and C. Brunsdon, "Two techniques for exploring non-stationarity in geographical data," Geographical Systems, vol. 4, pp. 59-82, 1997.

D. Yu, Y.D. Wei, and C.Wu, "Modeling spatial dimensions of housing prices in Milwaukee, WI," Environment and Planning B: Planning and design, vol. 34, pp. 1085-1102, 2007.

A.S. Matthews, and T-C. Yang, "Mapping the results of local statistics: using geographically weighted regression," Demographic Research, vol. 26, pp.151-166, 2015.

T. Benson, J. Chamberlin, and I. Rhinehart, I, Why the Poor Rural in Malawi Are Where They Are: An investigation of the spatial determinants of the local prevalence of poverty, International Food Policy Research Institute, Washington DC, 2005.

M. Lindu, "Studi penyisihan COD-organik pada tahap nitrifikasi dan denitrifikasi dalam SBR menggunakan air limbah coklat (Study of COD-organic removal in the nitrification and denitrification stages in SBR)," J. Teknologi Lingkungan, vol. 2, pp. 78-86, 2001.

G. M. Foody, "Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship," Remote Sensing of the Environment, vol. 3, no. 88, pp. 283-293, 2003.

S. Brown, L. V. Versace, L. Laurenson, D. Ierodiaconou, J. Faweett, and S. Salzman, "Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using Geographically Weighted Regression," Environmental Modeling and Assessment, vol. 17, pp. 241-254, 2012.

D. Kusnandar. N.N. Debataraja, S.W. Rizki, and E. Saputri, "Water quality mapping in Pontianak City using multiple discriminant analysis," The 4th IndoMS International Conference on Mathematics and Its Applications, AIP Conference Proceedings, Vol 2268, pp. 020006-1- 020006-2, 2020

A. S. Fotheringham, C. Brunsdon, and M. Charlton, Geographically weighted regression: The analysis of spatially varying relationships', John Wiley & Sons, Chichester, 2002.



  • There are currently no refbacks.

Copyright (c) 2022 Naomi Nessyana Debataraja, Dadan Kusnandar, Rossie Wiedya Nusantara

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Editorial Office
Mathematics Department,
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang, East Java, Indonesia 65144
Faximile (+62) 341 558933

Creative Commons License
CAUCHY: Jurnal Matematika Murni dan Aplikasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.