Modelling of Poverty Cases in West Java Using Mixed Geographically and Temporally Weighted Regression with Cluster
AbstractGeographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model is not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model is more suitable to use. This study examines the effects of clustering on MGTWR modeling. The cluster approach is used to reduce the modeling area and make the objects in the same clusters are more similar to each other than objects in other clusters. This study aimed to determine the model to be used in West Java’s poverty cases in 2012 to 2018. The result showed that the GRDP, the percentage of literacy, the percentage of expenditure per capita on food, and health index are global variables. Whereas the variable expected years of schooling and households buying rice for poor (raskin) are local variables. Furthermore, based on Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and was showed that MGTWR with cluster (MGTWRC) better than MGTWR when it used in West Java’s poverty cases.
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