scholarly journals A Visual Analytics Approach to Exploration of Large Amounts of Movement Data

2008 ◽  
pp. 1-4 ◽  
Author(s):  
Gennady Andrienko ◽  
Natalia Andrienko
Keyword(s):  
Visual Analytics ◽  
Movement Data

scholarly journals AntVis: A web-based visual analytics tool for exploring ant movement data

2020 ◽  
Vol 4 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Tianxiao Hu ◽  
Hao Zheng ◽  
Chen Liang ◽  
Sirou Zhu ◽  
Natalie Imirzian ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Web Based ◽  
Movement Data

Challenges and Perspectives in Big Eye-Movement Data Visual Analytics

2015 ◽  
Author(s):  
Tanja Blascheck ◽  
Michael Burch ◽  
Michael Raschke ◽  
Daniel Weiskopf
Keyword(s):  
Eye Movement ◽  
Visual Analytics ◽  
Movement Data

scholarly journals Visual Analytics for Understanding Spatial Situations from Episodic Movement Data

2012 ◽  
Vol 26 (3) ◽  
pp. 241-251 ◽  
Author(s):  
Natalia Andrienko ◽  
Gennady Andrienko ◽  
Hendrik Stange ◽  
Thomas Liebig ◽  
Dirk Hecker
Keyword(s):  
Visual Analytics ◽  
Movement Data

scholarly journals Visual analytics of sensor movement data for cheetah behaviour analysis

2021 ◽  
Author(s):  
Karsten Klein ◽  
Sabrina Jaeger ◽  
Jörg Melzheimer ◽  
Bettina Wachter ◽  
Heribert Hofer ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Automated Analysis ◽  
Environmental Data ◽  
Impact Factors ◽  
Data Sets ◽  
Movement Data ◽  
Data Loggers ◽  
Intuitive Interfaces ◽  
Gps Data Loggers

Abstract Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments. Graphic abstract

scholarly journals Visual Analytics for Characterizing Mobility Aspects of Urban Context

2021 ◽  
pp. 727-755
Author(s):  
Gennady Andrienko ◽  
Natalia Andrienko ◽  
Fabian Patterson ◽  
Siming Chen ◽  
Robert Weibel ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Research Topic ◽  
Data Exploration ◽  
Spatiotemporal Data ◽  
Multiple Perspectives ◽  
Numerous Application ◽  
Urban Context ◽  
Spatial Objects ◽  
Movement Data ◽  
Spatial Locations

AbstractVisual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.

scholarly journals A VISUAL ANALYTICS OF MOVEMENT DATA OF A WASTE COLLECTION SERVICE: A TOOL FOR SMART CITIES

2020 ◽  
Vol VI-4/W2-2020 ◽  
pp. 111-118
Author(s):  
A. Moreno ◽  
D. I. Hernandez ◽  
D. Moreno ◽  
M. Caglioni ◽  
J. T. Hernandez
Keyword(s):  
Waste Management ◽  
Visual Analytics ◽  
Visual Analysis ◽  
Solid Waste Management ◽  
Smart Cities ◽  
Test Case ◽  
Analysis Tool ◽  
Waste Collection ◽  
Movement Data ◽  
Management Context

Abstract. Solid waste management is an important urban issue to be addressed in every city. In the smart city context, waste collection allows massive collection of data representing movements, provided by satellite tracking technologies and sensors on waste collection equipment. For decision makers to take advantage of this opportunity, an analytical tool suitable for the waste management context, able to visualize the complexity of the data and to deal with different types of formats in which the data is stored is required.The aim of this paper is to evaluate the potential of an interactive data analysis tool, based on R and R-Shiny, to better understand the particularities of a waste collection service and how it relates to the local city context. The User-centered Analysis-Task driven model (AVIMEU) is presented. The model is organized into seven components: database load, classification panel, multivariate analysis, concurrency, origin-destination, points of interest and itinerary. The model was implemented as a test case for the waste collection service of the city of Pasto in the southwest of Colombia. It is shown that the model based on visual analysis is a promising approach that should be further enhanced. The analyses are oriented in such a way that they provide practical information to the agents or experts of the service. The model is available on the site https://github.com/MerariFonseca/AVIMEU-visual-analytics-for-movement-data-in-R .

Visual Analytics Methods for Movement Data

2008 ◽  
pp. 375-410 ◽  
Author(s):  
G. Andrienko ◽  
N. Andrienko ◽  
I. Kopanakis ◽  
A. Ligtenberg ◽  
S. Wrobel
Keyword(s):  
Visual Analytics ◽  
Movement Data

scholarly journals A visual analytics design for studying rhythm patterns from human daily movement data

2017 ◽  
Vol 1 (2) ◽  
pp. 81-91 ◽  
Author(s):  
Wei Zeng ◽  
Chi-Wing Fu ◽  
Stefan Müller Arisona ◽  
Simon Schubiger ◽  
Remo Burkhard ◽  
...  
Keyword(s):  
Visual Analytics ◽  
Daily Movement ◽  
Movement Data
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