Review on Geographically Weighted Regression (GWR) approach in spatial analysis
DOI:
https://doi.org/10.11113/mjfas.v16n2.1387Keywords:
Geographically Weighted Regression (GWR), Spatial Modelling, Spatial Non-stationarity.Abstract
In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship between these variables. The main goal of this study is to review the potential of the GWR in modelling the spatial relationship between variables either dependent or independent and its used as the spatial prediction models. Based on the application of GWR such as house property indicates that GWR is the best model in estimating the parameters. Hence, from the GWR model, the significance of the variation can also be tested
References
Bivand, R., 2017. Geographically Weighted Regression. Applied Spatial Data Analysis with R, (2008), pp.305–308.
Brunsdon, C., Fotheringham, A. S., Charlton, M., 1999. Some notes on parametric significance tests for Geographically Weighted Regression. Journal of Regional Science, 39(3), pp.497–524.
Brunsdon, C., Fotheringham, A. S., Charlton, M. E., 1996. Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), pp.281–298.
Brunsdon, C., Fotheringham, S., Charlton, M., 1998. Geographically Weighted Regression. Journal of the Royal Statistical Society: Series D (The Statistican), 47(3), pp.431–443.
Casetti, E., 1972. Generating models by the expansion method: Applications to geographical research. Geographical analysis, 4(1), pp.81–91.
Dobson, A. J., 1990. An Introduction to Generalized Linear Models. London: Chapman & Hall/CRC.
Eboy, O. V., Samat, N., 2015. Modeling property rating valuation using Geographical Weighted Regression (GWR) and Spatial Regression Model (SRM): The case of Kota Kinabalu, Sabah. Geografia-Malaysian Journal of Society and Space, 11(11).
Ehlkes, L., Krefis, A. C., Kreuls, B., Krumpkamp, R., Adjei, O., Ayim-Akonor, M., Kobbe, R., et al., 2014. Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana. International Journal of Health Geographiics, 13, pp.35.
Fotheringham, A. S. S., Brunsdon, C., Charlton, M., 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. England: Wiley.
Jamhuri, J., Azhar, B. M. S., Puan, C. L., Norizah, K., 2016. GWR-PM - Spatial variation relationship analysis with Geographically Weighted Regression (GWR) - An application at Peninsular Malaysia. IOP Conference Series: Earth and Environmental Science, 37, p.12032. Available at: http://stacks.iop.org/1755-1315/37/i=/a=012032?key=crossref.a228250cfa07597971c8b7bec4acd380.
Leung, Y., Mei, C. L., Zhang, W. X., 2000. Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A, 32(1), pp.9–32.
Lu, B., Charlton, M., Fotheringham, A. S., 2011. Geographically Weighted Regression using a non-euclidean distance metric with a study on London House Price Data. , 7, pp.92–97.
McKenzie, N. J., Ryan, P. J., 1999. Spatial prediction of soil properties using environmental correlation. Geoderma, 89(1–2), pp.67–94.
Robinson, T. P., Metternicht, G., 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture, 50(2), pp.97–108.
Yu, D., Peterson, N. A., Reid, R. J., 2009. Exploring the Impact of non-normality on spatial non-stationarity in Geographically Weighted Regression Analyses: Tobacco Outlet Density in New Jersey. GIScience & Remote Sensing, 46(3), pp.329–346.
