An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia
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 proble...
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| Format: | Article |
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Mathematics Department UIN Maulana Malik Ibrahim Malang
2022-03-01
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| Series: | Cauchy: Jurnal Matematika Murni dan Aplikasi |
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| Online Access: | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13266 |
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| author | Naomi Nessyana Debataraja Dadan Kusnandar Rossie Wiedya Nusantara |
| author_facet | Naomi Nessyana Debataraja Dadan Kusnandar Rossie Wiedya Nusantara |
| author_sort | Naomi Nessyana Debataraja |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d51446b9b0f0451aa379228ef6d6484e |
| institution | OA Journals |
| issn | 2086-0382 2477-3344 |
| language | English |
| publishDate | 2022-03-01 |
| publisher | Mathematics Department UIN Maulana Malik Ibrahim Malang |
| record_format | Article |
| series | Cauchy: Jurnal Matematika Murni dan Aplikasi |
| spelling | doaj-art-d51446b9b0f0451aa379228ef6d6484e2025-08-20T02:12:49ZengMathematics Department UIN Maulana Malik Ibrahim MalangCauchy: Jurnal Matematika Murni dan Aplikasi2086-03822477-33442022-03-017218619410.18860/ca.v7i2.132666156An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, IndonesiaNaomi Nessyana Debataraja0Dadan Kusnandar1Rossie Wiedya Nusantara2Tanjungpura UniversityTanjungpura UniversityTanjungpura UniversityGeographically 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.https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13266chemical parametersgeographically weighted regressionmodellingt-value mapping |
| spellingShingle | Naomi Nessyana Debataraja Dadan Kusnandar Rossie Wiedya Nusantara An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia Cauchy: Jurnal Matematika Murni dan Aplikasi chemical parameters geographically weighted regression modelling t-value mapping |
| title | An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia |
| title_full | An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia |
| title_fullStr | An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia |
| title_full_unstemmed | An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia |
| title_short | An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia |
| title_sort | application of geographically weighted regression for assessing water polution in pontianak indonesia |
| topic | chemical parameters geographically weighted regression modelling t-value mapping |
| url | https://ejournal.uin-malang.ac.id/index.php/Math/article/view/13266 |
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