A spatiotemporal knowledge graph-based method for identifying individual activity locations from mobile phone data

TL;DR

This paper introduces a spatiotemporal knowledge graph approach to accurately identify individual activity locations from mobile phone data, improving spatial and temporal precision over existing methods.

Contribution

The study presents a novel spatiotemporal knowledge graph method that integrates spatial and temporal relationships for better activity location detection.

Findings

Limits 45% of activity locations with long daytime stays within a reasonable range

Reduces variance in activity start and end times by 10-20%

Distinguishes between geographically close locations with different temporal patterns

Abstract

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.