# Spatio-Temporal Linkage over Location Enhanced Services

arXiv: https://arxiv.org/abs/1801.04101 · HTML: https://mlsanity.com/paper/1801.04101/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Spatial Entity Resolution between Restaurant Locations and Transportation Destinations in Southeast Asia](https://mlsanity.com/paper/2401.08537/) (2024)
- [Correlation-Attention Masked Temporal Transformer for User Identity Linkage Using Heterogeneous Mobility Data](https://mlsanity.com/paper/2504.01979/) (2025)

## Abstract (arXiv)

We are witnessing an enormous growth in the volume of data generated by various online services. An important portion of this data contains geographic references, since many of these services are location-enhanced and thus produce spatio-temporal records of their usage. We postulate that the spatio-temporal usage records belonging to the same real-world entity can be matched across records from different location-enhanced services. Linking spatio-temporal records enables data analysts and service providers to obtain information that they cannot derive by analyzing only one set of usage records. In this paper, we develop a new linkage model that can be used to match entities from two sets of spatio-temporal usage records belonging to two different location-enhanced services. This linkage model is based on the concept of k-l diversity --- that we developed to capture both spatial and temporal aspects of the linkage. To realize this linkage model in practice, we develop a scalable linking algorithm called ST-Link, which makes use of effective spatial and temporal filtering mechanisms that significantly reduce the search space for matching users. Furthermore, ST-Link utilizes sequential scan procedures to avoid random disk access and thus scales to large datasets. We evaluated our work with respect to accuracy and performance using several datasets. Experiments show that ST-Link is effective in practice for performing spatio-temporal linkage and can scale to large datasets.