Road density is a useful index widely applied in the analysis of ecological effects, road network planning, delimitation of urban areas and road network generalization. Extraction of dense and sparse areas of the road network is a key issue in the filed of automated map generalization. This paper proposes a method for road density partition. The method creates the Voronoi diagram of road intersections and endpoints, and then uses Gi*
to identify statistically significant spatial clusters of high values and low values for the area of a Voronoi cell. Finally, the method aggregates neighboring Voronoi cells from the statistically significant spatial clusters of high values and low values at a 95% confidence level. The road network of Hong Kong at the 14, 13 and 12 levels of Google Map are used as experimental data for evaluation of road network selection. Experimental results showed that the road density partitions using the proposed method generally reflected the density of the road network, while the road density contrasted well before and after road selection. Our method is superior to the grid density approach.