Human activities and more generally the phenomena related to human behaviour take place in space and in the majority of cases in a network-constrained subset of the geographical space. These phenomena can usually be expressed as locations with their positions being configured by a road network as address points with street numbers. Although these events are considered as points on a network, point pattern analysis and the techniques implemented in a GIS environment generally consider events as taking place in a uniform space, with distance expressed as Euclidean and over a homogeneous and isotropic space. Network-spatial analysis has developed as a research agenda where the attention is drawn towards point pattern analytical techniques applied to a space constrained by a road network. .Little attention has been put on first order properties of a point pattern (i.e., density) in a network space, while mainly second order analysis as nearest neighbour and k-functions have been adapted to network configurations. In this paper a method for examining clustering of human-related events on a network, called Network Density Estimation (NDE), is examined and implemented using spatial statistical tools and GIS packages. The method is presented and compared to conventional first order spatial analytical techniques as Kernel Density Estimation (KDE). Network Density Estimation is tested using the locations of bank and insurance company branches in the central areas of two medium-size European cities, Trieste (Italy) and Swindon (UK).
