Topological Street-Network Characterization Through Feature-Vector and Cluster Analysis

Abstract

Complex networks provide a means to describe cities through their street mesh, expressing characteristics that refer to the structure and organization of an urban zone. Although other studies have used complex networks to model street meshes, we observed a lack of methods to characterize the relationship between cities by using their topological features. Accordingly, this paper aims to describe interactions between cities by using vectors of topological features extracted from their street meshes represented as complex networks. The methodology of this study is based on the use of digital maps. Over the computational representation of such maps, we extract global complex-network features that embody the characteristics of the cities. These vectors allow for the use of multidimensional projection and clustering techniques, enabling a similarity-based comparison of the street meshes. We experiment with 645 cities from the Brazilian state of Sao Paulo. Our results show how the joint of global features describes urban indicators that are deeprooted in the network's topology and how they reveal characteristics and similarities among sets of cities that are separated from each other.

The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia

“…SNs also provide useful information that shapes commutes, travel behaviors, and location decisions of households and firms (Kang, 2017; Shatu and Yigitcanlar, 2018). Theoretically, an analysis of SNs requires measuring both topological and geometric characteristics (Spadon et al., 2018). The topological character explains the SNs’ connectivity, centrality, and clustering, whereas the geometric character describes SNs’ distances, areas, and densities (Boeing, 2018a).…”

Can Urban Street Network Characteristics Indicate Economic Development Level? Evidence from Chinese Cities

“…At the urban agglomeration level, researchers have pointed out the lack of methods for characterizing the relationship between cities that use road network topological features [37]. As such, recent studies have explored whether cities that have similar street network topology properties share similar characteristics in their populations and economic development levels.…”

“…As such, recent studies have explored whether cities that have similar street network topology properties share similar characteristics in their populations and economic development levels. For instance, Spadon, Gimenes, and Rodrigues classified 645 cities in the state of Sao Paulo, Brazil based on their street network features and found that the topological characteristics of urban streets could, at a large scale, indicate or even predict their demography [37]. Along similar lines, Lee and Jung analyzed the topological features of street networks using multi-centralities methods and classified a series of cities into several groups that share common meta-information based on centrality.…”

“…We are proposing an analytical framework adapted from Spadon et al [37], as shown in Figure 1, which includes three steps: data acquisition, characterizing urban street networks, and investigating the relationship between urban street networks and economic development. First, we acquired cities' street network data from OpenStreetMap using OSMnx [31], a software for downloading spatial entities and for modeling, projecting, visualizing, and analyzing complex street networks from OpenStreetMap's Application Programming Interfaces (APIs).…”

“…Centrality analysis is commonly used to characterize street network structure by quantifying the nodes and/or links within the network [22,37]. Among various centrality measurements, we employed two widely used indicators: betweenness centrality and closeness centrality [39][40][41].…”