Visual analysis design to support research into movement and use of space in Tallinn: A case study

Zhang, Qiuju C.; Slingsby, Aidan; Dykes, Jason; Wood, Jo; Kraak, M.J.; Blok, C.A.; Ahas, Rein

doi:10.1177/1473871613480062

Cited by 14 publications

(9 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In MOVE, Zhang et al [ 166 ] present a timeline display developed for a set of identification, localisation and movement comparison tasks to study urban movement trajectories. Outside MOVE, Wang and Yuan [ 167 ] use a similar set of temporal visualisations, including a timeline, a straightness plot and others to investigate spatio-temporal patterns in urban movement.…”

Section: Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and visualisation of movement: an interdisciplinary review

et al. 2015

View full text Add to dashboard Cite

The processes that cause and influence movement are one of the main points of enquiry in movement ecology. However, ecology is not the only discipline interested in movement: a number of information sciences are specialising in analysis and visualisation of movement data. The recent explosion in availability and complexity of movement data has resulted in a call in ecology for new appropriate methods that would be able to take full advantage of the increasingly complex and growing data volume. One way in which this could be done is to form interdisciplinary collaborations between ecologists and experts from information sciences that analyse movement. In this paper we present an overview of new movement analysis and visualisation methodologies resulting from such an interdisciplinary research network: the European COST Action “MOVE - Knowledge Discovery from Moving Objects” (http://www.move-cost.info). This international network evolved over four years and brought together some 140 researchers from different disciplines: those that collect movement data (out of which the movement ecology was the largest represented group) and those that specialise in developing methods for analysis and visualisation of such data (represented in MOVE by computational geometry, geographic information science, visualisation and visual analytics). We present MOVE achievements and at the same time put them in ecological context by exploring relevant ecological themes to which MOVE studies do or potentially could contribute.

show abstract

Section: Reviewmentioning

confidence: 99%

“…Many MOVE studies mentioned above employ the linked views paradigm (e.g. [ 165 , 166 , 183 ]). Others include not only spatio-temporal displays, but also attribute visualisations.…”

Section: Reviewmentioning

confidence: 99%

Analysis and visualisation of movement: an interdisciplinary review

et al. 2015

View full text Add to dashboard Cite

show abstract

“…For example, it is possible to transform and merge multiple geospatial datasets together into a common viewing framework. 12 Alternately, multiple coordinated views of the different datasets may be provided, [13][14][15] such that manipulations in one representation (e.g. panning and zooming) automatically update the configurations of the other views.…”

Section: Related Workmentioning

confidence: 99%

A geovisual analytics approach for analyzing event-based geospatial anomalies within movement data

Hoeber

Hasan

2017

Information Visualization

View full text Add to dashboard Cite

Comparing data collected on the movement of an entity to data on the location where the entity was reported to have been can be useful in monitoring and enforcement situations. Anomalies between these datasets may be indicative of illegal activity, systematic reporting errors, data entry errors, or equipment failure. While finding obvious anomalies may be a simple task, the discovery of more subtle inconsistencies can be challenging when there is a mismatch in the temporal granularity between the datasets, or when they cover large temporal and geographic ranges. We have developed a geovisual analytics approach called Visual Exploration of Movement-Event Anomalies (VEMEA) that automatically extracts potential anomalies from the data, visually encodes these on a map, and provides interactive filtering and exploration tools to allow expert analysts to investigate and evaluate the anomalies. Using two case studies from the fisheries enforcement domain, the value of VEMEA is illustrated for both confirmatory and exploratory data analysis tasks. Field trial evaluations conducted with expert fisheries data analysts further support the benefits of the approach.

show abstract

“…This framework, shown in an adapted version in Figure 1, posits that the study of movement consists of a continuum of research ranging from understanding movement to construct knowledge of the behavior of dynamic objects, to using this knowledge for modeling and prediction of movement. Visualization facilitates this process through data exploration, hypothesis generation, and communication of the outcomes (Wood et al 2011, Zhang et al 2013, Xavier and Dodge 2014. The framework relies on an iterative validation process, where analytics and models are parameterized, calibrated, and improved using real movement observations.…”

Section: Introductionmentioning

confidence: 99%