| Sumario: | 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.
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