Modern technology allows us to track essentially anything that moves, be it animals, people, vehicles, or hurricanes. As a result, many efficient computational methods have been developed to analyze movement data, including methods for similarity analysis, clustering, segmentation, classification, and pattern detection. However, movement rarely occurs in isolation and to truly understand move-ment data it is of paramount importance to understand the intrinsic and extrinsic factors that influ-ence movement, such as or health conditions or motivation (intrinsic) or the (natural) environment, weather, and other surrounding entities (extrinsic). Often the data that describes these factors is available together with the tracked object data for analysis, but comparatively few computational techniques fully utilize the potential of such multifaceted data. The 1st Workshop on Computing with Multifaceted Movement Data (MOVE++ 2019) brings together researchers who are interested in developing computational techniques to analyze movement data in conjunction with other data sources that capture (some of) the factors which influence movement.
Proceeding Downloads
Understanding Movement in Context with Heterogeneous Data
Movement data, as captured by myriad sensors, has been growing exponentially. Hence, multidisciplinary approaches for analyzing movement has become feasible. Though, movement pertains to a large variety of domains and applications, the focus of this ...
Shared micro-mobility patterns as measures of city similarity: Position Paper
Micro-mobility services, such as dockless e-scooters and e-bikes, are inundating urban centers around the world. The mass adoption of these services, and ubiquity of the companies operating them, offer a unique opportunity through which to compare ...
Inferring Semantically Enriched Representative Trajectories
In the analysis and visualisation of clustered spatial trajectories, the computation of a representative trajectory for a given cluster of data trajectories plays an important role. Usually, such a representative trajectory is computed based upon the ...
Latent Terrain Representations for Trajectory Prediction
In natural outdoor environments, the shape of the surface terrain is an important factor in selecting a traversal path, both when operating off-road vehicles and maneuvering on foot. With the increased availability of digital elevation models for ...
A Repository of Network-Constrained Trajectory Data (Position Paper)
We propose the creation of a repository which collects and makes available network-constrained trajectory data. The repository should become a central instance for researchers who want to work with network-constrained trajectory data on a large scale, ...
Location Graphs for Movement Data Modeling, Analytics and Visualization
Modeling movement through an environment can be a complicated task given the variations of data scale, quality, temporal sampling, and fidelity of location information. We present recent work in modeling location information both spatially, temporally ...
Index Terms
- Proceedings of the 1st ACM SIGSPATIAL International Workshop on Computing with Multifaceted Movement Data
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Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| MOVE'19 | 8 | 5 | 63% |
| Overall | 8 | 5 | 63% |