Abstract

Analysis of spatio-temporal event data is of central importance in many domains of science and policy making. Current visualization methods rely on animation, small multiples, and space-time cubes to enable spatio-temporal data exploration. These methods require the user to remember state spaces or deal with layout occlusions when exploring their data. To overcome such issues, we propose a novel visualization technique for such data that applies the topological notion of Reeb graphs to identify hotspots as areas of relatively high event density within kernel density estimates. We illustrate that the topological identification of hotspots proposed in this paper is able to elucidate lifetime, properties, and relationships of hotspots by visualizing their temporal evolution based on the spatio-temporal Reeb graph. To validate our approach, we demonstrate our method on an epidemiological and a crime dataset. The resulting visualizations assist users in quickly identifying and comprehending important dates, events, hotspot properties, and relationships between hotspots.

Original languageEnglish (US)
Title of host publicationGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
PublisherAssociation for Computing Machinery
Volume03-06-November-2015
ISBN (Print)9781450339674
DOIs
StatePublished - Nov 3 2015
Event23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 - Seattle, United States
Duration: Nov 3 2015Nov 6 2015

Other

Other23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015
CountryUnited States
CitySeattle
Period11/3/1511/6/15

Fingerprint

Visual Analytics
Hot Spot
Visualization
visualization
Reeb Graph
Crime
Animation
Kernel Density Estimate
crime
temporal evolution
Cube
Spatio-temporal Data
policy making
Date
Occlusion
Layout
Lifetime
State Space
Space-time
method

Keywords

  • Density estimation
  • Geovisualization
  • Hotspots
  • Reeb graph
  • Spatio-temporal event data
  • Topology

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modeling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Lukasczyk, J., Maciejewski, R., Garth, C., & Hagen, H. (2015). Understanding hotspots: A topological visual analytics approach. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (Vol. 03-06-November-2015). [a36] Association for Computing Machinery. https://doi.org/10.1145/2820783.2820817

Understanding hotspots : A topological visual analytics approach. / Lukasczyk, Jonas; Maciejewski, Ross; Garth, Christoph; Hagen, Hans.

GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. Vol. 03-06-November-2015 Association for Computing Machinery, 2015. a36.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lukasczyk, J, Maciejewski, R, Garth, C & Hagen, H 2015, Understanding hotspots: A topological visual analytics approach. in GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. vol. 03-06-November-2015, a36, Association for Computing Machinery, 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015, Seattle, United States, 11/3/15. https://doi.org/10.1145/2820783.2820817
Lukasczyk J, Maciejewski R, Garth C, Hagen H. Understanding hotspots: A topological visual analytics approach. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. Vol. 03-06-November-2015. Association for Computing Machinery. 2015. a36 https://doi.org/10.1145/2820783.2820817
Lukasczyk, Jonas ; Maciejewski, Ross ; Garth, Christoph ; Hagen, Hans. / Understanding hotspots : A topological visual analytics approach. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. Vol. 03-06-November-2015 Association for Computing Machinery, 2015.
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