An analytical framework for regional integration development in the Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration using fuzzy clustering

The Greater Bay Area (GBA), which comprises Guangdong, Hong Kong, and Macao, has issues in collaboration, resource distribution, and work division. Lack of an adequate municipal cooperation plan may lead to inefficiencies, overlapping duties, and disputes that hinder regional integration and economic growth. Methodical development is necessary to review and streamline municipal operations, thereby increasing collaboration and resource sharing. This research study solves this challenge using the Fuzzy Clustering Algorithm-based Collaborative Urban Development (CUD-FCA). Collaboration and resource allocation will be maximized through the CUD-FCA, creating a more integrated and cost-effective urban agglomeration. GBA's economic, social, and industrial similarities help the FCA cluster cities. Fuzzy clustering improves city-to-city knowledge. This knowledge helps identify each city's primary functions, eliminating overlap and conflict. The research emphasizes collaborative urban planning and fair resource sharing to maximize the city's potential. The fuzzy clustering technique enables data-driven urban management and decision-making. This paradigm promotes efficient, sustainable development.