Online geovisualization with fast kernel density estimator

Hajime Hotta, Masafumi Hagiwara

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

5 Citations (Scopus)

Abstract

Visualization of geographic log-data is one of the key issues on geovisualization, which is defined as a research field of visualizing geographic information. This paper aims to visualize them interactively using graphics like thermograph, mashuped with interactive mapping system (IMS), such as Google Map. While conventional researches employ probability density function estimation algorithms, the problems are twofold. One is that the focused data should be analyzed rapidly online during the interaction between systems and users, for the map size and location can be changed flexibly with IMS. The other is that focused data may be sparse when the map is zoomed in. In general, EM algorithm, a commonly-used probabilistic density approximator, is not robust to sparseness and it takes long time for model construction. Parzen window is also a simple, well-known technique but it requires many kernels that make calculation costs high. The proposed method is a novel, simple kernel density estimator which is fast for model construction with high robustness to sparse data. The proposed method is based on Parzen window and employs a clustering algorithm inspired by fuzzy ART (Adaptive Resonance Theory) to reduce kernels. From the experimental results, estimation accuracy excels the conventional methods with various benchmarking models.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
Pages622-625
Number of pages4
Volume1
DOIs
Publication statusPublished - 2009
Event2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009 - Milano, Italy
Duration: 2009 Sep 152009 Sep 18

Other

Other2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009
CountryItaly
CityMilano
Period09/9/1509/9/18

Fingerprint

Benchmarking
Clustering algorithms
Probability density function
Visualization
Costs

Keywords

  • Fuzzy ART
  • Geovisualization
  • Soft-computing approach
  • Web interaction

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

Cite this

Hotta, H., & Hagiwara, M. (2009). Online geovisualization with fast kernel density estimator. In Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009 (Vol. 1, pp. 622-625). [5284907] https://doi.org/10.1109/WI-IAT.2009.105

Online geovisualization with fast kernel density estimator. / Hotta, Hajime; Hagiwara, Masafumi.

Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009. Vol. 1 2009. p. 622-625 5284907.

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

Hotta, H & Hagiwara, M 2009, Online geovisualization with fast kernel density estimator. in Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009. vol. 1, 5284907, pp. 622-625, 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009, Milano, Italy, 09/9/15. https://doi.org/10.1109/WI-IAT.2009.105
Hotta H, Hagiwara M. Online geovisualization with fast kernel density estimator. In Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009. Vol. 1. 2009. p. 622-625. 5284907 https://doi.org/10.1109/WI-IAT.2009.105
Hotta, Hajime ; Hagiwara, Masafumi. / Online geovisualization with fast kernel density estimator. Proceedings - 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009. Vol. 1 2009. pp. 622-625
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