DeepMove: Learning Place Representations through Large Scale Movement Data

Zhou, Yang; Huang, Yan

doi:10.1109/bigdata.2018.8622444

Cited by 28 publications

(13 citation statements)

References 21 publications

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“…Recent studies have applied the embedding methods to mobility data with more spatio-temporal details. Models have applied ideas similar to word embeddings in NLP using social media check-in data, where the analogy of locations = words, trajectories = sentences was made [5,6,12,13]. The drawback of these models is that the local context window approach and negative sample sampling ignore the overall relationship, which is more likely to cause high-exposure words to get too much weight.…”

Section: Related Workmentioning

confidence: 99%

Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs

Tian,

Zhang,

Weng

et al. 2021

Preprint

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GPS coordinates and other location indicators are fine-grained location indicators that are difficult to be effectively utilized by machine learning models in Geo-aware applications. Previous location embedding methods are mostly tailored for specific problems that are taken place within areas of interest. When it comes to the scale of the entire cities, existing approaches always suffer from extensive computational cost and significant information loss. Increasing amount of location based service (LBS) data are being accumulated and released to public and enables us to study the urban dynamics and human mobility. This study learns vector representations for locations using the large-scale LBS data. Different from existing studies, we propose to consider both spatial connection and human mobility, and jointly learn the representations from a flow graph and a spatial graph through a GCN aided skip-gram model named GCN-L2V. This model embeds context information in human mobility and spatial information. By doing so, GCN-L2V is able to capture relationships among locations, and provide a better notion of semantic similarity in a spatial environment. Across quantitative experiments and case study, we empirically demonstrate that the representations learned by GCN-L2V are effective. GCN-L2V can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.

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Section: Related Workmentioning

confidence: 99%

Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs

Tian,

Zhang,

Weng

et al. 2021

Preprint

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“…With the recent increase in availability of large scale mobility data such as mobile phone location data [3,8] and taxi GPS data, recent studies have applied the embedding methods to mobility data with more spatio-temporal detail [9,16]. A recent study created embeddings of GPS coordinates from large scale location datasets [13].…”

Section: Related Workmentioning

confidence: 99%

Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories

Shimizu¹,

Yabe

Tsubouchi³

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places, and could be applied as essential resources to various downstream tasks including land use classification and human mobility prediction. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution could degrade the quality of embeddings due to data sparsity, especially in less populated areas. Our proposed method addresses this issue by leveraging the hierarchical nature of spatial information, according to the local density of observed data points. We evaluated the effectiveness of our fine grained place embeddings via next place prediction tasks using real world trajectory data from 3 cities in Japan, and compared it with non-hierarchical baseline methods. Our technique of incorporating spatial hierarchical structure can complement and reinforce various other geospatial models using place embedding generation methods. CCS CONCEPTS • Information systems → Spatial-temporal systems; • Computing methodologies → Learning latent representations.

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“…Recently, trust was introduced in the concept of social cloud [62,63,64,65,66,67,68,69,70]. In [63], Mohaisen et al employ trust as a metric to identify good workers for an outsourcer through her social network.…”

Section: Trust In Cloud Computingmentioning

confidence: 99%

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

Liu¹,

Li²,

Yang

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. We validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP.

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DeepMove: Learning Place Representations through Large Scale Movement Data

Cited by 28 publications

References 21 publications

Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs

Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs

Enabling Finer Grained Place Embeddings using Spatial Hierarchy from Human Mobility Trajectories

NeuralWalk: Trust Assessment in Online Social Networks with Neural Networks

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