Explorative Analysisand Visualization of

Large Information Spaces

Visual Analytics of Geo-Demographic DataPeter Bak (PhD)

Motivation & Research Framework

Sociological and economic research often requires the visual analysisof large-scale high-resolution Geo-Demographic data.Challenges and constrains:•Enlarging densely and Reducing sparsely populated areas

•Guarantee continuous scaling of regions

•Adapt to local properties of the data distribution

•Preserve Neighborhoods and Privacy

Geo-Spatial Location

Additional Properties:

- Land Use- Time related changes

Multi-Dimensional

Demographic Attributes

Neighbor-

hood

Preserving

Distortion

Privacy Preservation

Visualization Analysis

Clustering

Classification & Coorelation

Pattern Recognition

Data

Figure 1: Research pipeline for creating a visual analytics environment under the constraint of neigh-borhood and privacy preservation.

Approaches of Neighborhood Preserving Distortions

•Density equalizing distortion using many center-points.

•RadialScale: Circular bins. Center-points are local maxima.

•AngularScale: Angular bins. Center-points are local minima.

RadialScale AngularScale

Table 1: Density equalizing distortion techniques applied to the USA 1999 census-data. The optimalnumber of center points depends on the properties of the data and the users preferences.

Results and Evaluation

Figure 2: US Median household income shown by the different distortions. RadialScale and Angu-larScale techniques are better than the HistoScale in use-of-space (39%, 26%, 7%) and also in thehomogeneity of distortion (92.6%, 93%, 91%).

Approaches of Privacy Preserving Distortions

•How to measure the level of privacy in a geographic map?•How to compute the optimal trade-off between the given constrains?

Figure 3: Approaches to re-defining neighborhoods through triangulation (Delaunay, K-NN, etc.).

Figure 4: Approaches to address privacy preservation by converging the map to an artificial form.

Further Research Interests

1. Visual Evaluation of Text Features for Document Summarization andAnalysis (Cooperation with Text-Mining Group (Prof. Keim)

2. Longitudinal Evaluation Methodology for Human-ComputerInteraction (cooperation with HCI Group (Prof. Reiterer)

3. Using Interaction-Logs for Temporal-Sequence Mining andVisualization (cooperation with IM Group (Prof. Berthold)

References[1] P. Bak, D. A. Keim, M. Schaefer, A. Stoffel, and I. Omer. A multi-scaling technique for density equalizing distortion of

large point sets. under review, 2008.

[2] J. Gerken, P. Bak, H. C. Jetter, D. Klinkhammer, and H. Reiterer. How to use interaction logs effectively for usabilityevaluation. Proceedings BELIV’08 Beyond Time and Errors: Novel Evaluation Methods for Information Visualization2008: A Workshop of the ACM CHI Conference, 2008.

[3] J. Gerken, P. Bak, and Harald Reiterer. Longitudinal evaluation methods in human-computer studies and visualanalytics. Proceedings Visualization 2007: IEEE Workshop on Metrics for the Evaluation of Visual Analytics, 2007.

[4] D.A. Keim, P. Bak, and Schaefer M. Dense pixel displays. In Ling zsu, M. Tamer; Liu, editor, Encyclopedia ofDatabase Systems. Springer, Not yet published. Available: May 1, 2009.

[5] D. Oelke, P. Bak, D. A. Keim, and M. Last. Visual evaluation of text features for document summarization andanalysis. IEEE Symposium on Visual Analytics Science and Technology (VAST 2008), Columbus, Ohio, USA., 2008.

DFG Colloquium Peter Bak — PostDoc — Membership since 01.09.2007Konstanz Work Group — Databases, Data Mining and Visualization

26 June, 2008 Research Training Group 1042 (GK) — Explorative Analysis and Visualization of Large Information Spaces