Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia
Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Environmental Advances |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666765724001224 |
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| author | Riantini Virtriana Kalingga Titon Nur Ihsan Tania Septi Anggraini Albertus Deliar Agung Budi Harto Akhmad Riqqi Anjar Dimara Sakti |
| author_facet | Riantini Virtriana Kalingga Titon Nur Ihsan Tania Septi Anggraini Albertus Deliar Agung Budi Harto Akhmad Riqqi Anjar Dimara Sakti |
| author_sort | Riantini Virtriana |
| collection | DOAJ |
| description | Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. |
| format | Article |
| id | doaj-art-21e1cd2d130a4330812ae1e340548f57 |
| institution | Kabale University |
| issn | 2666-7657 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental Advances |
| spelling | doaj-art-21e1cd2d130a4330812ae1e340548f572024-12-05T05:21:36ZengElsevierEnvironmental Advances2666-76572025-04-0119100604Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, IndonesiaRiantini Virtriana0Kalingga Titon Nur Ihsan1Tania Septi Anggraini2Albertus Deliar3Agung Budi Harto4Akhmad Riqqi5Anjar Dimara Sakti6Remote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia; Center for Remote Sensing, Bandung Institute of Technology, Bandung 40132, Indonesia; Corresponding author.Center for Remote Sensing, Bandung Institute of Technology, Bandung 40132, Indonesia; Doctor Program in Program Study of Geodesy and Geomatic Engineering, Faculty of Earth Sciences and Technology, Bandung 40132, IndonesiaCenter for Remote Sensing, Bandung Institute of Technology, Bandung 40132, Indonesia; Doctor Program in Program Study of Geodesy and Geomatic Engineering, Faculty of Earth Sciences and Technology, Bandung 40132, Indonesia; Geographic Information Science, Universitas Pendidikan Indonesia, Bandung 40111, IndonesiaRemote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia; Center for Spatial Data Infrastructure, Bandung Institute of Technology, Bandung 40132, IndonesiaRemote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia; Center for Remote Sensing, Bandung Institute of Technology, Bandung 40132, IndonesiaRemote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia; Center for Spatial Data Infrastructure, Bandung Institute of Technology, Bandung 40132, IndonesiaRemote Sensing and GIS Research Group, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia; Center for Remote Sensing, Bandung Institute of Technology, Bandung 40132, IndonesiaAccess to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites.http://www.sciencedirect.com/science/article/pii/S2666765724001224Health FacilityGISRemote SensingMachine Learning |
| spellingShingle | Riantini Virtriana Kalingga Titon Nur Ihsan Tania Septi Anggraini Albertus Deliar Agung Budi Harto Akhmad Riqqi Anjar Dimara Sakti Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia Environmental Advances Health Facility GIS Remote Sensing Machine Learning |
| title | Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia |
| title_full | Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia |
| title_fullStr | Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia |
| title_full_unstemmed | Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia |
| title_short | Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia |
| title_sort | development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and gis in west java indonesia |
| topic | Health Facility GIS Remote Sensing Machine Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666765724001224 |
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