Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model
Land prices, especially in an urban area, are dynamically changing. To be able to do an evaluation, the right models must have the ability to understand land price characteristics that also dynamically changing. Every land price must attach to a location (spatial based). One of the locations (spati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2020-04-01
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| Series: | Indonesian Journal of Geography |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijg/article/view/43724 |
| _version_ | 1818357980113928192 |
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| author | Alfita Puspa Handayani Albertus Deliar Irawan Sumarto Ibnu Syabri |
| author_facet | Alfita Puspa Handayani Albertus Deliar Irawan Sumarto Ibnu Syabri |
| author_sort | Alfita Puspa Handayani |
| collection | DOAJ |
| description | Land prices, especially in an urban area, are dynamically changing. To be able to do an evaluation, the right models must have the ability to understand land price characteristics that also dynamically changing. Every land price must attach to a location (spatial based). One of the locations (spatial based) models is Geographically Weighted Regression (GWR). This model can provide a local model based on the concept of attachment between observation and regression points. The main component is the determination of Optimum Bandwidth, which will determine the accuracy of the final GWR model. In the bandwidth process, it is necessary to do trial and error to get the Optimum Bandwidth value. Cross-Validation method commonly used to determine optimum bandwidth on observation point, but this study aims to minimize the process of trial and error in determining optimal bandwidth outside the observation point by using kriging interpolation. The Kriging method can substantially provide better bandwidth usage without having to do a trial process with too many errors. |
| first_indexed | 2024-12-13T20:21:43Z |
| format | Article |
| id | doaj.art-89a303ee6e1b4c00a8231e43c83402c5 |
| institution | Directory Open Access Journal |
| issn | 0024-9521 2354-9114 |
| language | English |
| last_indexed | 2024-12-13T20:21:43Z |
| publishDate | 2020-04-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | Indonesian Journal of Geography |
| spelling | doaj.art-89a303ee6e1b4c00a8231e43c83402c52022-12-21T23:32:41ZengUniversitas Gadjah MadaIndonesian Journal of Geography0024-95212354-91142020-04-01521364110.22146/ijg.4372426726Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation ModelAlfita Puspa Handayani0Albertus Deliar1Irawan Sumarto2Ibnu Syabri3Faculty of Earth Science and Technology, Institut Teknologi Bandung, Indonesia.Faculty of Earth Science and Technology, Institut Teknologi Bandung, Indonesia.Faculty of Earth Science and Technology, Institut Teknologi Bandung, Indonesia.Faculty of Earth Science and Technology, Institut Teknologi Bandung, Indonesia.Land prices, especially in an urban area, are dynamically changing. To be able to do an evaluation, the right models must have the ability to understand land price characteristics that also dynamically changing. Every land price must attach to a location (spatial based). One of the locations (spatial based) models is Geographically Weighted Regression (GWR). This model can provide a local model based on the concept of attachment between observation and regression points. The main component is the determination of Optimum Bandwidth, which will determine the accuracy of the final GWR model. In the bandwidth process, it is necessary to do trial and error to get the Optimum Bandwidth value. Cross-Validation method commonly used to determine optimum bandwidth on observation point, but this study aims to minimize the process of trial and error in determining optimal bandwidth outside the observation point by using kriging interpolation. The Kriging method can substantially provide better bandwidth usage without having to do a trial process with too many errors.https://jurnal.ugm.ac.id/ijg/article/view/43724land pricekriginggwrinterpolationbandwidth |
| spellingShingle | Alfita Puspa Handayani Albertus Deliar Irawan Sumarto Ibnu Syabri Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model Indonesian Journal of Geography land price kriging gwr interpolation bandwidth |
| title | Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model |
| title_full | Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model |
| title_fullStr | Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model |
| title_full_unstemmed | Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model |
| title_short | Bandwidth Modelling on Geographically Weighted Regression with Bisquare Adaptive Method using Kriging Interpolation for Land Price Estimation Model |
| title_sort | bandwidth modelling on geographically weighted regression with bisquare adaptive method using kriging interpolation for land price estimation model |
| topic | land price kriging gwr interpolation bandwidth |
| url | https://jurnal.ugm.ac.id/ijg/article/view/43724 |
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