近年来，手机数据被广泛应用于人类移动性分析。识别个体活动地点是手机数据处理的基础步骤。现有方法通常通过聚合多日空间相邻的位置记录来识别活动地点。然而，仅考虑空间关系而忽略时间关系可能导致活动地点识别不准确，并影响活动模式分析。本研究提出一种基于时空知识图谱（STKG）的方法，用于从手机数据中识别活动地点。构建了用于描述个体移动特征的STKG，将个体停留的空间与时间关系推断并转化为时空图。采用模块度优化的社区检测算法识别具有密集时空关系的停留点，将其视为活动地点。以上海市为例进行了案例研究，验证所提方法的性能。结果表明，相较于两种基线方法，基于STKG的方法能够在合理空间范围内，将最长白天停留的活动地点数量减少45%；此外，该方法在不同日期活动起止时间上的方差更低，表现优于两种基线方法约10%至20%。同时，基于STKG的方法能有效区分地理上接近但时间模式不同的地点。这些发现证明了基于STKG的方法在提升空间精度和时间一致性方面的有效性。

In recent years, mobile phone data has been widely used for human mobility analytics. Identifying individual activity locations is the fundamental step for mobile phone data processing. Current methods typically aggregate spatially adjacent location records over multiple days to identify activity locations. However, only considering spatial relationships while overlooking temporal ones may lead to inaccurate activity location identification, and also affect activity pattern analysis. In this study, we propose a spatiotemporal knowledge graph-based (STKG) method for identifying activity locations from mobile phone data. An STKG is designed and constructed to describe individual mobility characteristics. The spatial and temporal relationships of individual stays are inferred and transformed into a spatiotemporal graph. The modularity-optimization community detection algorithm is applied to identify stays with dense spatiotemporal relationships, which are considering as activity locations. A case study in Shanghai was conducted to verify the performance of the proposed method. The results show that compared with two baseline methods, the STKG-based method can limit an additional 45% of activity locations with the longest daytime stay within a reasonable spatial range; In addition, the STKG-based method exhibit lower variance in the start and end times of activities across different days, performing approximately 10% to 20% better than the two baseline methods. Moreover, the STKG-based method effectively distinguishes between locations that are geographically close but exhibit different temporal patterns. These findings demonstrate the effectiveness of STKG-based method in enhancing both spatial precision and temporal consistency.