STO2Vec
A Multiscale Spatio-Temporal Object Representation Method for Association Analysis
Bibliographic Data
| ID | 22031308 |
|---|---|
| Authors | Nanyu Chen (0000-0001-5150-1997, National University of Defense Technology), Anran Yang (0000-0001-6106-759X, National University of Defense Technology), Luo Chen (0000-0003-2665-6086, National University of Defense Technology, corresponding author), Wei Xiong (0000-0002-6938-5223, National University of Defense Technology), Ning Jing (0000-0003-3445-6658, National University of Defense Technology) |
| Year | 2023 |
| Volume | 12 |
| Issue | 5 |
| Pages | 207 |
| Publication date | 2023-05-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12050207 |
| OpenAlex | W4377288864 |
| Language | EN |
| Citations received | 1 |
| References cited | 42 |
Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation
Data mining · Embedding · Graph · Information retrieval · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Artificial Intelligence · Theoretical Computer Science
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |