Moving Point Density Estimation Algorithm Based on a Generated Bayesian Prior

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.