Urban Spatial Order: Street Network Orientation, Configuration, and Entropy
20 Pages Posted:
Date Written: August 1, 2018
Spatial networks such as streets organize and constrain urban transportation. These networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city’s spatial logic and order. Measures of entropy reveal a city’s streets’ order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in weighted and unweighted network models, along with each city’s street length entropy, median street segment length (a linear proxy for grain), average circuity, average node degree (how many streets emanate from each intersection/dead-end), and the network’s proportions of four-way intersections and dead-ends. It also develops a new indicator of grid-order that quantifies how a city’s street network follows the ordering logic of a single orthogonal grid. It finds significant statistical relationships between a city’s griddedness/entropy and other indicators of spatial order, including street circuity and multiple measures of connectivity. These indicators, taken in concert, reveal the extent and nuance of the grid. On average, American cities are far more grid-like than cities in the rest of the world and exhibit far less orientation entropy and street circuity. These methods demonstrate automatic, scalable, reproducible tools to empirically measure and visualize city spatial order, illustrating urban transportation system patterns and configurations around the world.
Keywords: city planning, urban form, urban design, urban morphology, OpenStreetMap, Python, data science, GIS, geospatial, spatial analysis, entropy, orientation, configuration, network analysis, street networks, graph theory, transportation, transportation planning, data visualization
JEL Classification: R00, R40
Suggested Citation: Suggested Citation