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ISPRS Int. J. Geo-Inf. 2015, 4(2), 515-534; doi:10.3390/ijgi4020515

Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

1
Research & Development Group, Center for Technology Innovation-Systems Engineering, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, 185-8601, Tokyo, Japan
2
Social Innovation Business Office, Hitachi Ltd., Hitachi Omori 2nd Building, 27-18, Minami-Oi6-chome, Shinagawa-ku, 140-8572, Tokyo, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Steve H.L. Liang, Mohamed Bakillah and Wolfgang Kainz
Received: 11 December 2014 / Revised: 28 February 2015 / Accepted: 27 March 2015 / Published: 7 April 2015
(This article belongs to the Special Issue 20 Years of OGC: Open Geo-Data, Software, and Standards)
View Full-Text   |   Download PDF [4792 KB, uploaded 7 April 2015]   |  

Abstract

To improve decision making, real-time population density must be known. However, calculating the point density of a huge dataset in real time is impractical in terms of processing time. Accordingly, a fast algorithm for estimating the distribution of the density of moving points is proposed. The algorithm, which is based on variational Bayesian estimation, takes a parametric approach to speed up the estimation process. Although the parametric approach has a drawback, that is the processes to be carried out on the server are very slow, the proposed algorithm overcomes the drawback by using the result of an estimation of an adjacent past density distribution. View Full-Text
Keywords: variational Bayesian estimation; density estimation; moving features; Big Data variational Bayesian estimation; density estimation; moving features; Big Data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Asahara, A.; Hayashi, H.; Kai, T. Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior. ISPRS Int. J. Geo-Inf. 2015, 4, 515-534.

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