Urban spatial structure is an important feature for assessing the effects of urban planning. Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments. Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures. However, these methods cannot efficiently reflect the influence of human activities. With the wide application of big data, analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning. In this study, we constructed a human-activity space network using the taxi trip big data. Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning. This method was applied to a case study based on one-month taxi trip data of Dongguan City. Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020, which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan. We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure. The analysis demonstrated that the taxi trip data are important big data on social spatial perception, and taxi data should be used for evaluating spatial structures in future urban planning.

城市空间结构是评估城市规划效果的重要特征。量化城市空间结构不仅有助于识别当前规划中的问题，而且还可以为将来的调整提供基本参考。对于规划人员和研究人员而言，空间结构的评估是一项艰巨的任务，通常是通过比较不同的土地利用结构来进行的。但是，这些方法不能有效地反映人类活动的影响。随着大数据的广泛应用，越来越多地进行了关于人类出行行为的数据分析，以揭示城市空间结构与城市规划之间的关系。在这项研究中，我们使用出租车行程大数据构建了一个人类活动的太空网络。不同规模的聚类揭示了用于评估城市规划的适当性和不足之处的空间结构的层次性和冗余性。将该方法应用于基于东莞市一个月的出租车出行数据的案例研究。检索不同规模的现有城市空间结构，并将其用于评估针对2000年至2015年和2008年至2020年设计的总体规划的有效性，这可以帮助确定在这两个版本的总体规划中设计的空间结构的局限性和改进之处。通过为重建和优化未来的城市空间结构提供参考，我们还评估了2016年至2035年总体规划的潜在影响。分析表明，出租车旅行数据是关于社会空间感知的重要大数据，