城市空间热力反映了人口聚集与街道活力。为探究城市轨道交通与空间热力分布的互动关系，从微观层面的轨道站点切入，采用百度热力图和轨道站点客流数据，以上海为例，对轨道站点的网络中心性、客流与站域空间热力进行耦合分析。首先采用Pearson双变量相关性研究两类轨道站点属性与空间热力的总体耦合关系，然后引入双变量空间自相关和地理加权回归分析方法分别挖掘网络中心性与站域热力、站域热力与站点客流的空间关联模式，并对比两类耦合性的空间差异。结果表明：轨道站点的网络中心性与空间热力的耦合性明显优于轨道客流与空间热力的耦合性，交通区位优势通常能够形成较高的空间热力，客流水平的影响因素则更为复杂；空间热力更适合量化核心区以外区域的轨道交通与城市空间互动关系，轨道交通网络化对空间热力提升具有乘数效应，而在开发密度低的区域提升空间热力更有助于刺激轨道客流；利用空间热力数据评估城市核心区以外区域的新建站点客流潜力具有可行性，但仅用热力预测客流具有局限性；轨道站点周边城市更新可参考不同空间区位站点的两类耦合性差异进行优化。该研究探索了结合城市空间热力分布完善轨道交通线网布局、针对不耦合因素优化轨道站点公共交通导向型开发（TOD）的分析框架，为微观层面衡量城市轨道交通“人-地”关系提供了新视角。

Urban spatial heat map reflects population aggregation and street vitality. In order to explore the interactive relationship between urban rail transit and spatial heat map, this study used Baidu heat map and rail transit station ridership data to analyze the coupling characteristics between network centrality, ridership and nearby spatial heat index of rail transit stations on a micro level, taking Shanghai as a case study. Firstly, it investigated the overall coupling relationship between two categories of station attributes and spatial heat through Pearson bivariate correlation analysis. Then, bivariate spatial autocorrelation and geographically weighted regression analysis methods were introduced to explore the spatial association patterns between network centrality and spatial heat, as well as between spatial heat and ridership, followed by a spatial differentiation comparison between the two coupling types. The results show that the coupling relationship between rail network centrality and spatial heat is obviously better than that between ridership and spatial heat at station level, since traffic location advantage can usually develop higher spatial heat, while ridership may be affected by more complex factors. Spatial heat map is more suitable for quantifying the interaction between rail transit and urban space in areas outside the urban core, where increasing rail network centrality has a multiplier effect on spatial heat improvement, but improving spatial heat in areas with low-density development is more conducive to stimulating ridership. It is feasible to evaluate the ridership potential of new stations outside the urban core area by using spatial heat map, but this data alone is not enough to predict ridership. The urban renewal around rail transit stations can be optimized by referring to the differences between the two types of coupling at different spatial locations. This study explored the analytical framework for improving the layout of rail transit network based on urban spatial heat map, and optimizing TOD (Transit-Oriented Development) stations for factors negatively affecting their coupling. It provides a new perspective for measuring the man-land relationship of urban rail transit on the micro level.