基于空间知识图谱的大语言模型智能体用于社区宜居性评估
Spatial-Knowledge-Graph-Grounded LLM Agents for Neighborhood Livability Evaluation
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中文总结 AI 辅助
该研究提出结合空间知识图谱与大语言模型智能体的框架，通过生成修订家庭日程等评估社区宜居性，发现设施可用性不代表便利可达，为关联空间机会与居民体验提供可审计方法。
中文摘要 AI 辅助
社区宜居性通常通过静态建成环境指标评估，如设施可达性、街道连通性及公共空间获取情况。这些指标描述了可用机会，但未直接反映具有不同移动能力、家庭角色、日程安排和照护责任的居民对社区的体验。本文提出一个原型框架，使用空间知识图谱（KG）和大语言模型（LLM）生成并修订家庭日程，随后进行基于规则的可行性检查和基于GIS的网络具象化。空间KG整合了居民、住宅、设施、社区环境及采样道路枢纽；Graph-RAG为日程安排LLM检索每个家庭附近的空间环境，包括候选兴趣点（POI）和大致步行时间。LLM生成结构化家庭日程，规则用于轻量修复和可审计的可行性检查，LLM随后根据识别出的可行性问题修订日程。路由模块从道路网络推导实际出行路径、出行时间、方式及事件历史，生成的事件支持关于日常便利性、出行负担、活动可行性及家庭协调的合成居民智能体访谈。在深圳某社区的原型演示表明，名义上的设施可用性不一定意味着便利可达：移动能力受限的居民和有照护责任的家庭面临更大的出行和协调负担。该框架提供了一种可审计的方式，将空间机会、家庭活动约束和特定居民的宜居性解释联系起来，同时保持模拟体验与观察到的感知相区分。
英文摘要
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.
