Spatial-Knowledge-Graph-Grounded LLM Agents for Neighborhood Livability Evaluation

This research introduces a prototype framework leveraging spatial knowledge graphs (KGs) and large language models (LLMs) to enhance neighborhood livability evaluation. Unlike traditional static indicators, this approach aims to capture the dynamic, individual experiences of residents by generating and revising household schedules, followed by feasibility checks and GIS-based network materialization. This methodology integrates diverse data points, including residents, facilities, and neighborhood context, to provide a more nuanced understanding of how different individuals interact with their environment.

This development is strategically important as it offers a novel, dynamic method for assessing urban environments, moving beyond static metrics to understand how diverse populations experience their surroundings. This shift enables more data-driven decision-making in urban planning, resource allocation, and policy development, potentially leading to more equitable and efficient service provision and infrastructure investments.

Traditional neighborhood livability assessments often rely on static built-environment indicators that do not fully capture resident experiences.

Forward consideration, not a verified fact.