Geographical Weighted Regression Model for Poverty Analysis in Jambi Province - Inti Pertiwi Nashwari (Faculty of Geography, Universitas Gadjah Mada, 2017)
Agriculture sector has an important contribution to food security in Indonesia, but it also huge contribution to the number of poverty, especially in rural area. Studies using a global model might not be sufficient to pinpoint the factors having most impact on poverty due to spatial differences. Therefore, a Geographically Weighted Regression (GWR) was used to analyze the factors influencing the poverty among food crops famers. Jambi Province is selected because have high number of poverty in rural area and the lowest farmer exchange term in Indonesia. The GWR was better than the global model, based on high value of R2, lowers AIC and MSE and Leung test. Location in upland area and road system had more influence to the poverty in the western-southern. Rainfall was significantly influence in eastern. The effect of each factor, however, was not generic, since the parameter estimate might have a positive or negative value.