Stepping stone graph for public movement analysis

Kannangara, Sameera; Tanin, Egemen; Harwood, Aaron; Karunasekera, Shanika

doi:10.1145/3274895.3274913

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…We assume that any centroid found by this algorithm is a location that has been posted by one or more users to LBSNs. The generation of the graph is based on a data structure called the Gabriel graph [9]. To predict the next centroid of a group, the algorithm finds all the potential paths that start from the current centroid of the group.…”

Section: Baseline Algorithms For Centroid Predictionmentioning

confidence: 99%

Tracking Group Movement in Location Based Social Networks

Kannangara

Xie

Tanin

et al. 2020

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

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We study the problem of tracking the movement of groups using sparse trajectory data extracted from Location Based Social Networks (LBSNs). Tracking group movement using LBSN data is challenging because the data may contain a large amount of noise due to the lack of stability in group entity, spatial extent and posting time. We propose a first-of-its-kind solution, Group Kalman Filter (GKF), which aims to improve the effectiveness of group tracking by predicting the spatial properties of groups with a group movement model. Our experiments with real LBSN data and synthetic LBSN data show that GKF can detect groups and predict group movement with a high level of accuracy and efficiency.

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Section: Baseline Algorithms For Centroid Predictionmentioning

confidence: 99%

Tracking Group Movement in Location Based Social Networks

Kannangara

Xie

Tanin

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

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Stepping Stone Graph

Kannangara

Tanin

Harwood

et al. 2019

ACM Trans. Spatial Algorithms Syst.

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There are many real world applications that require identifying public movements such as identifying movement corridors in cities and most popular paths. If one is not given user trajectories but rather sporadic location data, such as location-based social network data, finding movement related information becomes difficult. Rather than processing all points in a dataset given a query, a clever approach is to construct a graph, based on user locations, and query this graph to answer questions such as shortest paths, most popular paths, and movement corridors. Shortest path graph is one of the popular graphs. However, the shortest path graph can be inefficient and ineffective for analysing movement data, as it calculates the graph edges considering the shortest paths over all the points in a dataset. Therefore, edge sets resulting from shortest path graphs are usually very restrictive and not suitable for movement analysis because of its global view of the dataset. We propose the stepping stone graph, which calculates the graph considering point pairs rather than all points; the stepping stone graph focuses on possible local movements, making it both efficient and effective for location-based social network related data. We demonstrate its capabilities by applying it in the Location-Based Social Network domain and comparing with the shortest path graph. We also compare its properties to a range of other graphs and demonstrate how stepping stone graph relates to Gabriel graph, relative neighbourhood graph, and Delaunay triangulation.

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Stepping stone graph for public movement analysis

Cited by 2 publications

References 12 publications

Tracking Group Movement in Location Based Social Networks

Tracking Group Movement in Location Based Social Networks

Stepping Stone Graph

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