Visual analytics tools for analysis of movement data

By Gennady L. Andrienko, Natalia V. Andrienko, Stefan Wrobel. SKDD, 2007

365 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Transportation

View Paper: https://www.semanticscholar.org/paper/24170ddd15f6d0ad1b5d15fe9fcea2d4309c4d8b

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Geography, Planning and Development

In Transportation

Citations in the corpus

Visual analytics of movement: An overview of methods, tools and procedures (2013, positive): “Several distance functions suitable for clustering of trajectories are suggested by Andrienko et al.(22) The procedure of progressive clustering is illustrated in Figure 2.”
Social big data: Recent achievements and new challenges (2015, background): “…and store new data, are rapidly increasing in number, including the ability to analyse these data volumes[150], to record data about the movement of people and objects at a large scale [151], and to analyse spatio-temporal data and solve spatio-temporal problems in social media [152], among others.”
Big Data computing and clouds: Trends and future directions (2013, background): “[7] proposed interactive visual display for analysis of movement behaviour of people, vehicle, and animals.”
Visual Analytics: Definition, Process, and Challenges (2008, positive): “In [5] we argue that by using the right visual analytics tools for the analysis of massive collections of movement data, it is possible to effectively support human analysts in understanding movement behaviors and mobility patterns.”
Unveiling the complexity of human mobility by querying and mining massive trajectory data (2011, background): “− Common destination: dd(T1, T2) is given by the Euclidean distance δ(p1, p2) between the last point p1 of T1 and the last point p2 of T2 − Common origin: do(T1, T2) is given by the Euclidean distance between the first point of T1 and the first point of T2 − Common origin and destination: dod(T1, T2”
Visual Analytics of Movement (2013, methodology): “2010; Andrienko et al. 2010). Data with spatial and temporal components (including movement data) are inherently complex as a result of the complexities of space and time, in particular, their heterogeneity, the abundance and diversity of objects populating them, events and processes occurring in th”