기계학습과 최대 커버링 입지 모형을 이용한 광주시 스마트 공공복합편의시설 최적 입지선정

Local cities face challenges in securing adequate usage rates for public service facilities due to greater variability in population densities compared to cities in the Seoul metropolitan area. This study proposes optimal locations for smart public multi-use facilities based on a smart library infrastructure within Gwangju Metropolitan City, taking into account the specific conditions of local cities. Specifically, demand for smart public multi-use facilities is estimated using machine learning, and a MCLP (Maximal Covering Location Problem) is applied to identify optimal locations. The proposed locations are approached from two perspectives: first, establishing new facilities while excluding demand already covered by existing library service areas; and second, relocating existing libraries without regard to their current locations. Finally, this study proposes the optimal locations within Gwangsan-gu by combining the new and relocation sites. This research provides a foundation for developing effective policies to support sustainable development and enhance welfare in local cities.