Site Selection Analytics Platform for Public Complex Facilities

Overview

To address the growing demand for data-driven site analysis of public infrastructure, we designed a platform that systematically supports AI-based optimal site selection for public multi-purpose facilities on a national scale. The platform utilizes MCLP and GA (Genetic Algorithm) to ensure mathematically sound and socially equitable location planning.

Project description

The platform conducted site analysis for various public facilities—including libraries, public Wi-Fi hotspots, automated civil service machines, and heat-wave shelters—specifically targeting the cities of Cheonan and Gwangju. By analyzing the demand, usage status, and user characteristics of these facilities, the system enables the selection of sites that align with urban planning goals and regional characteristics.

A centralized platform was established to analyze the optimal locations for "Public Multi-purpose Facilities," which provide both public and private life-essential services in a single, accessible hub. The process involves mapping demand points against existing infrastructure, such as bus stops and current facilities, to identify underserved areas and ensure that the placement of new complex facilities provides the highest level of accessibility for the greatest number of residents.

Key technological features

• MCLP (Maximal Covering Location Problem) Algorithm: An optimization algorithm that determines the best placement of candidate sites to cover the maximum possible demand (population) within a limited number of facilities
• Input Data Architecture:
  • Population: Geolocation (Latitude/Longitude) and weighted population density
  • Public Facility Locations: Current geolocation coordinates
  • Bus Stops: Geolocation and passenger boarding/alighting statistics

Expected outcomes

This platform contributes to enhancing citizen convenience and maximizing cost-efficiency by ensuring objectivity and transparency in the site selection process for public facilities. By moving away from subjective decision-making and utilizing AI-driven spatial analysis, local governments can minimize budget waste and provide essential services where they are most needed. Ultimately, this approach fosters balanced regional development and improves the overall quality of life for urban residents through data-informed public administration.

Launch year: 2024

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.2, https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=29182&parentSeq=29229