VISUAL ANALYTICS

Natalia Andrienko and Gennady Andrienko, Lead scientists on visual analytics research, IAIS, Sankt Augustin, Germany

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Visual analytics aims to combine the strengths of human and computer data processing. Visualization, whereby humans and computers cooperate through graphics, is the means through which this is achieved. Sophisticated synergies are required for analyzing spatio-temporal data and solving spatio-temporal problems. It is necessary to take into account the specifics of the geographic space, time, and spatio-temporal phenomena and processes.

While a wide variety of methods and tools are available, it is still hard to find guidelines for considering a data set systematically from multiple perspectives. To fill this gap, we systematically consider the structure of spatio-temporal data, possible transformations, and demonstrate several workflows of comprehensive analysis of different data sets, paying special attention to the investigation of data properties. Using publicly available software prototypes, we shall demonstrate several workflows of analysis of real data sets on human mobility, city traffic, aviation, animal movement, and football.

Lecture 1 (09:00 – 10:30)
Introduction to visual analytics
– Definition of visual analyticss
– Principles of visualization
— Examples of visualizations and interactions
– Data types and structures
— Types of data
— General analysis tasks: describe, locate, compare, relate
– Analysis of multi-dimensional data
— Dimensionality reduction
— Partition-based clustering
– Demo

Lecture 2 (11:00 – 12:30)
Visual analytics of spatio-temporal Data
– Types and examples of spatio-temporal data
— events, spatial time series, trajectories, origin-destination
moves, spatial links and flows
— properties of the data
– Transformation between types
— aggregation
— event extraction
– Transformation of time and space
— transformation of times
— transformation of space
— artificial (semantic) spaces for non-spatial data
– Demo

Lecture 3 (13:30 – 15:00)
Data mining techniques in visual analytics
– Density-based clustering
— events, origin-destination moves, trajectories
— clustering of streaming data
– Analysis and modelling of spatial time series
— two-way partition-based clustering
— extracting dependencies between time-variant attributes
— modelling
– Demo

Session 4: hands-on (15:30 – 17:00)
*** content is to be defined depending on the number of students and
their background

Reference Textbooks:
1. General introduction:
“Mastering the Information Age – Solving Problems with Visual
Analytics”, Eurographics 2010
http://www.vismaster.eu/book/

2. Deeper consideration:
“Visual Analytics of Movement”, Springer 2013
http://geoanalytics.net/vam/index.html
http://www.springer.com/de/book/9783642375828

Software;
http://geoanalytics.net/V-Analytics/
*** a new version will be uploaded on Tuesday, 11.7.2017

Meet the speakers

Courses on Data Science and Big Data