Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square
This study proposes the development of a spatio-temporal model with geographic weights containing elements of location, time and the correlation between the two. The spatio-temporal model is a spatial regression model that combines geographic information and time series simultaneously. The model can...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-06-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124000591 |
| _version_ | 1797319407697920000 |
|---|---|
| author | Sifriyani I Nyoman Budiantara M. Fariz Fadillah Mardianto Asnita |
| author_facet | Sifriyani I Nyoman Budiantara M. Fariz Fadillah Mardianto Asnita |
| author_sort | Sifriyani |
| collection | DOAJ |
| description | This study proposes the development of a spatio-temporal model with geographic weights containing elements of location, time and the correlation between the two. The spatio-temporal model is a spatial regression model that combines geographic information and time series simultaneously. The model can overcome the problem of spatial heterogeneity and spatial effects. The spatial temporal model used is the Geographically Weighted Panel Regression (GWPR) model with a within estimator. Therefore, it is necessary to determine the best geographic weighting with the optimal bandwidth value and the lowest Cross Validation (CV). The geographic weights used were the Gaussian kernel function, the Bisquare kernel function and the exponential kernel function. Estimation of spatio-temporal model parameters using Weighted Least Square (WLS). The GWPR model was applied to food security index data in 34 Indonesian provinces. The problem of food security is an important problem to be solved in Indonesia, one way is to find the factors that influence the food security index through spatio-temporal modeling. This study consists of data exploration, descriptive statistics, spatial mapping distribution, selection of geographic weights and GWPR modeling. The results showed that the spatio temporal statistical model of GWPR was more accurate with a good model of 92.78 % and a Root mean Square Error value of 3.41. Some highlights of the proposed approach are: |
| first_indexed | 2024-03-08T04:07:29Z |
| format | Article |
| id | doaj.art-495ccecb000348a3a6cbf38ea5adf919 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-08T04:07:29Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-495ccecb000348a3a6cbf38ea5adf9192024-02-09T04:48:20ZengElsevierMethodsX2215-01612024-06-0112102605Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square Sifriyani0I Nyoman Budiantara1M. Fariz Fadillah Mardianto2 Asnita3Study Program of Statistics, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, Indonesia; Corresponding author.Department of Statistics, Faculty of Science and Data Analytics, Sepuluh Nopember Institute of Technology, Jl. Arif Rahman Hakim, Surabaya, Samarinda 60111, IndonesiaStudy Program of Statistics, Department of Mathematics, Faculty of Sciences and Technology, Airlangga University, Surabaya, IndonesiaLaboratory of Applied Statistics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, IndonesiaThis study proposes the development of a spatio-temporal model with geographic weights containing elements of location, time and the correlation between the two. The spatio-temporal model is a spatial regression model that combines geographic information and time series simultaneously. The model can overcome the problem of spatial heterogeneity and spatial effects. The spatial temporal model used is the Geographically Weighted Panel Regression (GWPR) model with a within estimator. Therefore, it is necessary to determine the best geographic weighting with the optimal bandwidth value and the lowest Cross Validation (CV). The geographic weights used were the Gaussian kernel function, the Bisquare kernel function and the exponential kernel function. Estimation of spatio-temporal model parameters using Weighted Least Square (WLS). The GWPR model was applied to food security index data in 34 Indonesian provinces. The problem of food security is an important problem to be solved in Indonesia, one way is to find the factors that influence the food security index through spatio-temporal modeling. This study consists of data exploration, descriptive statistics, spatial mapping distribution, selection of geographic weights and GWPR modeling. The results showed that the spatio temporal statistical model of GWPR was more accurate with a good model of 92.78 % and a Root mean Square Error value of 3.41. Some highlights of the proposed approach are:http://www.sciencedirect.com/science/article/pii/S2215016124000591Geographically Weighted Panel Regression |
| spellingShingle | Sifriyani I Nyoman Budiantara M. Fariz Fadillah Mardianto Asnita Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square MethodsX Geographically Weighted Panel Regression |
| title | Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square |
| title_full | Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square |
| title_fullStr | Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square |
| title_full_unstemmed | Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square |
| title_short | Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square |
| title_sort | determination of the best geographic weighted function and estimation of spatio temporal model geographically weighted panel regression using weighted least square |
| topic | Geographically Weighted Panel Regression |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124000591 |
| work_keys_str_mv | AT sifriyani determinationofthebestgeographicweightedfunctionandestimationofspatiotemporalmodelgeographicallyweightedpanelregressionusingweightedleastsquare AT inyomanbudiantara determinationofthebestgeographicweightedfunctionandestimationofspatiotemporalmodelgeographicallyweightedpanelregressionusingweightedleastsquare AT mfarizfadillahmardianto determinationofthebestgeographicweightedfunctionandestimationofspatiotemporalmodelgeographicallyweightedpanelregressionusingweightedleastsquare AT asnita determinationofthebestgeographicweightedfunctionandestimationofspatiotemporalmodelgeographicallyweightedpanelregressionusingweightedleastsquare |