Visualizing the results of interactive queries for geographic data on mobile devices
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Generating summaries and visualization for large collections of geo-referenced photographs
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
A Taxonomy of Clutter Reduction for Information Visualisation
IEEE Transactions on Visualization and Computer Graphics
Content visualization and management of geo-located image databases
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Digital Footprinting: Uncovering Tourists with User-Generated Content
IEEE Pervasive Computing
A Neural-Network-Based Geographic Tendency Visualization
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Online Geovisualization with Fast Kernel Density Estimator
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Hierarchical cluster visualization in web mapping systems
Proceedings of the 19th international conference on World wide web
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
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Plotting lots of geographical data points usually clutters up a map. In this paper, we propose an approach to provide a summary view of geographical data by efficiently clustering. We present a novel data structure, called Geo-tree, which is extended from quad tree, and then develop two algorithms, which use Geo-tree to cluster geographic data and visualize the clusters with a heat map-like representation. The experimental results show that our approach is very efficient in a large scale, compared to K-means and HAC, and the clustering results are comparable to theirs.