Street Network Analysis
Also known as: Street Pattern Analysis, Road Network Metrics, Urban Street Connectivity Analysis, Configurational Street Analysis
Street network analysis treats a city's streets as a mathematical graph — intersections as nodes, street segments as edges — and measures its structure with graph-theoretic indicators of connectivity, density, centrality, and efficiency. From this representation come the metrics that distinguish a permeable grid from a tree-like cul-de-sac suburb: intersection density, average node degree, the share of dead-ends, betweenness centrality, and circuity (how much longer network routes are than straight lines). Tools such as Geoff Boeing's OSMnx made it routine to download, model, and analyse the street network of any place on Earth from OpenStreetMap, turning street-pattern analysis into a reproducible, comparative science of urban form.
Key highlights
- Brings the full power of graph theory to bear on urban form with well-defined, comparable metrics.
- Reproducible and global: OSMnx lets anyone model and analyse any city's network from OpenStreetMap.
- Connectivity, centrality, and circuity metrics directly inform sprawl, walkability, and transport analyses.
- Scales from a single neighbourhood to thousands of cities for large comparative studies.
Intuition
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How it works
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When to use it
Use street network analysis when the structure of streets themselves matters — comparing the connectivity and walkability of neighbourhoods or whole cities, feeding network metrics into sprawl and walkability indices, identifying high-traffic corridors, or studying how street patterns relate to travel behaviour, safety, and emissions. It is the right tool when you have or can obtain a clean street graph and want reproducible, comparable metrics across places. It is less appropriate when the question is really about routing individual trips with realistic costs (use a routing engine), when OpenStreetMap coverage is poor or inconsistent in the study area, or when configurational human-movement prediction is the goal, where space syntax's axial and angular analysis may be more suitable.
Strengths & limitations
- Brings the full power of graph theory to bear on urban form with well-defined, comparable metrics.
- Reproducible and global: OSMnx lets anyone model and analyse any city's network from OpenStreetMap.
- Connectivity, centrality, and circuity metrics directly inform sprawl, walkability, and transport analyses.
- Scales from a single neighbourhood to thousands of cities for large comparative studies.
- Results depend on OpenStreetMap completeness and consistency, which varies widely between regions.
- Graph simplification choices (how intersections and multi-edges are handled) affect the computed metrics.
- Topological metrics ignore street width, sidewalk quality, traffic, and the lived pedestrian experience.
- Betweenness and other global metrics are sensitive to the chosen study-area boundary and edge effects.
Common pitfalls
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Applications
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Frequently asked
How is street network analysis different from space syntax?
Both analyse street structure as a network, but they model it differently and ask different questions. Street network analysis here uses the primal graph — intersections as nodes, segments as edges — and computes metrics like intersection density, node degree, circuity, and betweenness, emphasizing sprawl and connectivity. Space syntax uses a dual, configurational representation (axial lines or segment angular analysis) and measures integration and choice to predict human movement. They overlap in spirit but differ in graph construction and theoretical focus.
What does circuity tell me that intersection density does not?
Intersection density measures how fine-grained the grid is — how many junctions per unit area — but a network can be dense yet still force detours. Circuity measures the actual travel penalty: the ratio of network distance to straight-line distance between locations. A grid has circuity near one (you can almost go as the crow flies), while a dense but loop-and-cul-de-sac layout can still have high circuity. Together they describe both how fine and how efficient the network is.
Why is OpenStreetMap data quality such a concern?
Street network metrics are only as good as the underlying graph, and OpenStreetMap is crowd-sourced, so its completeness and accuracy vary by region. In well-mapped cities the network is highly reliable; in under-mapped areas missing streets or misclassified paths can distort node counts, density, and connectivity. Analysts should check coverage, use consistent graph-simplification settings, and be cautious when comparing places that may be mapped to different standards.
Sources
- 1.Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.
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Cite this page
ScholarGate. (2026, June 22). Street Network Analysis. ScholarGate. https://scholargate.app/urban-studies/street-network-analysis