Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

ISPRS International Journal of Geo-Information

Akinori Asahara³  Hideki Hayashi³  Takashi Kai²  Steve H.L. Liang¹  Mohamed Bakillah¹ 

[1] Research & Development Group, Center for Technology Innovation-Systems Engineering, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, 185-8601, Tokyo, Japan; E-Mail;Social Innovation Business Office, Hitachi Ltd., Hitachi Omori 2nd Building, 27-18, Minami-Oi 6-chome, Shinagawa-ku, 140-8572, Tokyo, Japan; E-Mail:;Research & Development Group, Center for Technology Innovation-Systems Engineering, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, 185-8601, Tokyo, Japan; E-Mail:

关键词: variational Bayesian estimation;    density estimation;    moving features;    Big Data;   

DOI  :  10.3390/ijgi4020515

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland