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Spatial Allocation Based on Physiological Needs and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia)

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Remote Sensing and Geographic Information Science Research Group, Department of Geodesy and Geomatics, Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia
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Lokahita, Research Center for Sustainable Ecology and Geospatial, Bandung 40265, Indonesia
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(4), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040259
Received: 13 January 2021 / Revised: 10 March 2021 / Accepted: 4 April 2021 / Published: 12 April 2021
Indonesia currently has 269 million people or 3.49% of the world’s total population and is ranked as the fourth most populous country in the world. Analysis by the Ministry of Public Works and Public Housing of Indonesia in 2010 shows that Java’s biocapacity is already experiencing a deficit. Therefore, optimization needs to be done to reduce deficits. This study aims to optimize and assess spatial allocation accuracy based on land-use/land cover suitability. In this study, the ecological footprint (EF) is utilized as a spatial allocation assessment based on physiological needs. The concept of land suitability aims for optimal and sustainable land use. Moreover, the land suitability model was conducted using the support vector machine (SVM). SVM is used to find the best hyperplane by maximizing the distance between classes. A hyperplane is a function that can be used to separate land-use/land cover types. The land suitability model’s overall-accuracy model was 86.46%, with a kappa coefficient value of 0.812. The final results show that agricultural land, plantations, and pastureland are still experiencing deficits, but there is some reduction. The deficit reduction for agricultural land reached 510,588.49 ha, 18,986.14 ha for plantations, and 1015.94 ha for pastures. The results indicate that the SVM algorithm is efficient in mapping the land-use suitability and optimizing spatial allocation. View Full-Text
Keywords: spatial allocation; land suitability; ecological footprint; SVM (support vector machine) spatial allocation; land suitability; ecological footprint; SVM (support vector machine)
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MDPI and ACS Style

Safitri, S.; Wikantika, K.; Riqqi, A.; Deliar, A.; Sumarto, I. Spatial Allocation Based on Physiological Needs and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia). ISPRS Int. J. Geo-Inf. 2021, 10, 259. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040259

AMA Style

Safitri S, Wikantika K, Riqqi A, Deliar A, Sumarto I. Spatial Allocation Based on Physiological Needs and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia). ISPRS International Journal of Geo-Information. 2021; 10(4):259. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040259

Chicago/Turabian Style

Safitri, Sitarani, Ketut Wikantika, Akhmad Riqqi, Albertus Deliar, and Irawan Sumarto. 2021. "Spatial Allocation Based on Physiological Needs and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia)" ISPRS International Journal of Geo-Information 10, no. 4: 259. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040259

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