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This paper introduces a framework that leverages spatial knowledge graphs and large language models to evaluate neighborhood livability by generating and revising household schedules based on individual resident needs. By integrating various factors such as mobility capacities and household roles, the approach reveals that traditional indicators of facility availability do not adequately capture the real experiences of residents, particularly those with limited mobility or caregiving responsibilities. The prototype demonstration in Shenzhen highlights significant discrepancies between nominal access to facilities and actual convenience, emphasizing the need for more nuanced assessments of livability.
Traditional measures of neighborhood livability fail to account for the real-world challenges faced by residents, revealing hidden burdens for those with limited mobility and caregiving roles.
Neighborhood livability is commonly assessed with static built-environment indicators, such as facility proximity, street connectivity, and access to public space. These measures describe available opportunities but do not directly represent how residents with different mobility capacities, household roles, schedules, and care responsibilities experience the neighborhood. This paper presents a prototype framework that uses a spatial knowledge graph (KG) and large language models (LLMs) to generate and revise household schedules, followed by rule-based feasibility checking and GIS-based network materialization. The spatial KG integrates residents, residences, facilities, neighborhood context, and sampled road hubs; Graph-RAG retrieves each household's nearby spatial context, including candidate POIs and approximate walking times, for the scheduling LLM. The LLM produces structured household schedules, while rules are used for lightweight repairs and auditable feasibility checks. The LLM then revises schedules in response to identified feasibility issues. A routing module derives the actual travel paths, travel times, modes, and event histories from the road network. The resulting events support synthetic resident-agent interviews about daily convenience, travel burden, activity feasibility, and household coordination. A prototype demonstration in a Shenzhen neighborhood shows that nominal facility availability does not necessarily imply convenient access: residents with limited mobility and households with care responsibilities experience greater travel and coordination burdens. The framework offers an auditable way to connect spatial opportunity, household activity constraints, and resident-specific livability interpretation, while keeping simulated experience distinct from observed perception.
