用自然语言查询地铁周边房价，实时分析25年数据
RailEstate: An Interactive System for Metro Linked Property Trends
- 结合25年房产与地铁数据，支持交互式空间查询
- 通过自然语言转SQL，可直接问房价历史最高值
- 适合城市规划者、投资者快速获取地铁房价值趋势
地铁通达性对城市住房市场具有关键影响，显著提升区域可达性并驱动房产需求。本文提出 RailEstate，一个基于Web的交互式系统，整合空间分析、自然语言接口与动态预测功能，分析华盛顿都会区地铁站对住宅价格的影响。相比静态地图工具或通用房源平台，RailEstate融合25年历史房产数据与轨道交通基础设施，支持低延迟地理空间查询、时间序列可视化与预测建模。用户可交互探索邮编级价格模式，研究长期趋势，并预测任意地铁站周边未来房价。其核心创新在于自然语言聊天机器人，能将自然语言问题（如“2000年弗尔斯教堂最高房价是多少？”）转化为可执行的SQL语句，实现空间数据库查询。该统一交互平台使城市规划者、投资者及居民无需技术背景即可从地铁关联房产数据中获取可行动洞察。
原文摘要 · Abstract (English)
Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low latency geospatial queries, time series visualizations, and predictive modeling. Users can interactively explore ZIP code level price patterns, investigate long term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions e.g., What is the highest price in Falls Church in the year 2000? into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro linked housing data without requiring technical expertise.
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