농촌공간계획 수립을 위한 팜맵 기반 농경지 변화 분석 및 적용 방안 연구
Integrating Farm Map-Based Farmland Change Analysis into Rural Spatial Planning - A Framework for Practical Application -
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This study presents a quantitative framework for integrating Farm Map–based farmland change analysis into rural spatial planning under the Act on Rural Spatial Restructuring and Regeneration(2023). Using Farm Map datasets from 2017 and 2024 for Yangsan City, South Korea, the research identifies land-use transitions through change detection and spatial hotspot analysis to diagnose development pressures in peri-urban rural areas undergoing rapid land conversion. The results show that total farmland area declined by approximately 16.7%, primarily due to the conversion of paddy and dry fields into non-agricultural uses. Hotspot analysis using the Getis–Ord Gi* statistic revealed statistically significant clusters of farmland loss concentrated in southern Mulgeum-eup, southwestern Dong-myeon, and along major transport corridors in Sangbuk-myeon and Habuk-myeon. These clusters correspond to zones of intensified metropolitan expansion. In contrast, farmland restoration occurred sporadically, suggesting a lack of systematic policy intervention. The findings demonstrate the value of Farm Map data in detecting fine-scale spatial changes and identifying priority zones for managing uncontrolled development. The proposed analytical framework offers empirical evidence to support zoning and land-use management within rural spatial planning. Future research should extend this approach to multiple regions, integrate socioeconomic and environmental variables, and develop automated hotspot-based decision-support tools. Overall, Farm Map–based spatial diagnostics strengthen the scientific foundation of rural spatial planning, contributing to sustainable land use and the preservation of rurality.
Abstract
This study presents a quantitative framework for integrating Farm Map–based farmland change analysis into rural spatial planning under the Act on Rural Spatial Restructuring and Regeneration(2023). Using Farm Map datasets from 2017 and 2024 for Yangsan City, South Korea, the research identifies land-use transitions through change detection and spatial hotspot analysis to diagnose development pressures in peri-urban rural areas undergoing rapid land conversion. The results show that total farmland area declined by approximately 16.7%, primarily due to the conversion of paddy and dry fields into non-agricultural uses. Hotspot analysis using the Getis–Ord Gi* statistic revealed statistically significant clusters of farmland loss concentrated in southern Mulgeum-eup, southwestern Dong-myeon, and along major transport corridors in Sangbuk-myeon and Habuk-myeon. These clusters correspond to zones of intensified metropolitan expansion. In contrast, farmland restoration occurred sporadically, suggesting a lack of systematic policy intervention. The findings demonstrate the value of Farm Map data in detecting fine-scale spatial changes and identifying priority zones for managing uncontrolled development. The proposed analytical framework offers empirical evidence to support zoning and land-use management within rural spatial planning. Future research should extend this approach to multiple regions, integrate socioeconomic and environmental variables, and develop automated hotspot-based decision-support tools. Overall, Farm Map–based spatial diagnostics strengthen the scientific foundation of rural spatial planning, contributing to sustainable land use and the preservation of rurality.