The use of k-nearest neighbour (k-nn) approaches for the creation of bespoke neighbourhoods has become more common in segregation research in recent years. The reasons are manifold but include increased availability of high-resolution data, increasing computational power and the development of software designed to process huge numbers of k-nn commands. In this chapter, we present and test a new geo-computational add-on that has been introduced in the latest version of EquiPop (EquiPop Flow). An important novelty is that bespoke neighbourhoods do not necessarily need to grow radially until they reach a designated k-value but can make use of user-defined networks to grow at different speeds at different locations, such as following street and transportation infrastructure. We compare the geographical compositions of two different k-nn based bespoke neighbourhood techniques and discuss the pros and cons of expanding traditional k-nn computations to include data on infrastructure. Results indicate that infrastructure-integrating bespoke neighbourhoods are considerably better in depicting neighbourhoods, especially in areas with complex geographies that restrict mobility in some directions. However, the increase in computational time and complexity in setting up a network k-nn model makes a traditional radial growth approach attractive in areas where variation in connectivity between locations is limited.