A partition-free spatial clustering that preserves topology: application to built-up density

Urban density is central to urban research and planning and can be defined in numerous ways. Most measures of urban density however are biased by arbitrary chosen spatial units at their denominator and ignore the relative location of elementary urban objects within those units. We solve these two problems by proposing a new graph-based density index which we apply to the case of buildings in Belgium. The method includes two main steps. First, a graph-based spatial descending hierarchical clustering (SDHC) delineates clusters of buildings with homogeneous inter-building distances. A Moran scatterplot and a maximum Cook’s distance are used to prune the minimum spanning tree at each iteration of the SDHC. Second, within each cluster, the ratio of the number of buildings to the sum of inter-building distances is calculated. This density of buildings is thus defined independently of the definition of any basic spatial unit and preserves the built-up topology, i.e. the relative position of buildings. The method is parsimonious in parameters and can easily be transferred to other punctual objects or extended to account for additional attributes.