An Algorithm for Density Estimation in a Network Space

In this paper an extension of the Kernel Density Estimation (KDE),called Point Pattern Network Density Estimation (PPNDE) is proposed. Circular clusters of points distributed in the geographical space may be found by using Kernel Density Estimation; other configurations of cluster of points, depending on the net-work space, are also possible. In order to take into account this possibility the ideais to consider the kernel function as a density function based on network distancesrather than on the Euclidean one. Some simulation experiments end the paper.