BibTex Citation Data :
@article{J.Gauss2744, author = {Maulana Permana and Hasbi Yasin and Agus Rusgiyono}, title = {ANALISIS FAKTOR-FAKTOR TINGKAT KEMISKINAN DI KABUPATEN WONOSOBO DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION}, journal = {Jurnal Gaussian}, volume = {2}, number = {1}, year = {2013}, keywords = {Poverty, Geographically Weighted Regression, Weighted Least Square, Wonosobo}, abstract = { Poverty reduction is the main priority in development strategies in Indonesia, but during this computation is modeled as a function of the poor global regression. That is, the value of the regression coefficient applies to all geographic regions. In reality each region has different characteristics according to the geographical location, therefore Geographically Weighted Regression models are developed (GWR). GWR model is used to consider the element of geography or location as the weighting in estimating the model parameters. In the model GWR model parameter estimation is obtained by using Weighted Least Square (WLS) is to give a different weighting at each location. This study discusses the factors that affect the level of poverty in the District Wonosobo. The results of testing the suitability of the model shows that there is no spatial factors influence the level of poverty in the District Wonosobo. Based on research, there are 3 variables thought to affect the level of household poverty in Wonosobo district, percentage of the number of families that have slums, percentage number of families severely malnourished, percentage of the number of families who have agricultural land. These variables have a similar effect in each district. Keywords : Poverty, Geographically Weighted Regression, Weighted Least Square, Wonosobo}, issn = {2339-2541}, pages = {59--68} doi = {10.14710/j.gauss.2.1.59-68}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/2744} }
Refworks Citation Data :
Poverty reduction is the main priority in development strategies in Indonesia, but during this computation is modeled as a function of the poor global regression. That is, the value of the regression coefficient applies to all geographic regions. In reality each region has different characteristics according to the geographical location, therefore Geographically Weighted Regression models are developed (GWR). GWR model is used to consider the element of geography or location as the weighting in estimating the model parameters. In the model GWR model parameter estimation is obtained by using Weighted Least Square (WLS) is to give a different weighting at each location. This study discusses the factors that affect the level of poverty in the District Wonosobo. The results of testing the suitability of the model shows that there is no spatial factors influence the level of poverty in the District Wonosobo. Based on research, there are 3 variables thought to affect the level of household poverty in Wonosobo district, percentage of the number of families that have slums, percentage number of families severely malnourished, percentage of the number of families who have agricultural land. These variables have a similar effect in each district.
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