Optimizing service accessibility using GIS-driven and genetic algorithms for sustainable neighborhoods

Urbanization has accelerated globally, with over half of the world’s population now residing in cities. This rapid growth underscores urbanization’s role as a catalyst for economic development and underscores the need for sustainable, accessible neighborhoods. This research aims to optimize neighborhood accessibility within sustainable urban frameworks to improve residents’ quality of life and bolster environmental sustainability. Focusing on nine prominent sustainable cities, the study identifies fundamental pillars and urban indicators, establishing a framework promoting sustainable development at the neighborhood and city levels. The framework operates in three stages: (1) identifying neighborhood services, (2) evaluating service importance, and (3) optimizing accessibility to these services. Initially, 27 essential urban services were identified through literature reviews and case studies. In the second stage, urban planning experts categorized these services, and the Analytical Hierarchy Process (AHP) was used to determine their relative importance. The third stage involves enhancing service accessibility and walkability. This is achieved using Genetic Algorithms (GAs) and the Location-Allocation tool in ArcGIS Pro to facilitate proximity analysis and optimal location identification. A case study of a residential district in Egypt’s new administrative capital demonstrates the proposed framework, optimizing service accessibility and identifying optimal service locations. Tools like the closest facility feature provide a comparative analysis of optimized and existing service placements. The study concludes with an evaluation phase that achieves up to a 76% improvement in accessibility over initial designs, demonstrating the framework’s effectiveness. This research provides valuable insights for future urban planning, showcasing a structured approach to optimizing service locations and accessibility in sustainable neighborhoods.