An Application of Geographically Weighted Regression for Assessing Water Polution in Pontianak, Indonesia - Debataraja, Naomi Nessyana; Kusnandar, Dadan; Nusantara, Rossie Wiedya (Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang, 2022)
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.