Author(s)

Winfred Mbinya Manetu^*, Felix Mutua, Benson Kipkemboi Kenduiywo

Department of Geomatic Engineering and Geospatial Information System, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.

Water scarcity is currently still a global challenge despite the fact that water sustains life on earth. An understanding of domestic water demand is therefore vital for effective water management. In order to understand and predict future water demand, appropriate mathematical models are needed. The present work used Geographic Information Systems (GIS) based regression models; Geographically weighted regression (GWR) and Ordinary Least Square (OLS) to model domestic water demand in Athi river town. We identified a total of 7 water determinant factors in our study area. From these factors, 4 most significant ones (household size, household income, meter connections and household rooms) were identified using OLS. Further, GWR technique was used to investigate any intrinsic relationship between the factors and water demand occurrence. GWR coefficients values computed were mapped to exhibit the relationship and strength of each explanatory variable to water demand. By comparing OLS and GWR models with both AIC value and R2 value, the results demonstrated GWR model as capable of projecting water demand compared to OLS model. The GWR model was therefore adopted to predict water demand in the year 2022. It revealed domestic water demand in 2017 was estimated at 721,899 m3 compared to 880,769 m3 in 2022, explaining an increase of about 22%. Generally, the results of this study can be used by water resource planners and managers to effectively manage existing water resources and as baseline information for planning a cost-effective and reliable water supply sources to the residents of a town.

Geographically Weighted Regression, Ordinary Least Square, Water Demand

Manetu, W. , Mutua, F. and Kenduiywo, B. (2019) Spatial Modelling of Current and Future Piped Domestic Water Demand in Athi River Town, Kenya. Journal of Geographic Information System, 11, 196-211. doi: 10.4236/jgis.2019.112014.