Urban Network Analysis
Also known as: UNA Toolbox, Spatial Network Centrality, Building-Level Network Analysis, Street Network Centrality Analysis
Urban network analysis treats a city as a spatial graph of streets and buildings and measures the centrality of each location — how reachable, how central, and how well-connected it is along the actual travel network. Formalized in the Urban Network Analysis toolbox by Andres Sevtsuk and Michael Mekonnen in 2012, it differs from generic network science by weighting graph nodes with real urban data such as building floor area or population and by computing centralities within bounded search radii. The result is a set of metrics — reach, gravity, betweenness, closeness, straightness — that quantify the structural role of every building or street segment in the urban fabric.
Key highlights
- Computes centrality at the level of individual buildings or segments, not just abstract nodes.
- Weights destinations by real urban data (floor area, jobs, population) for behaviourally meaningful measures.
- Bounded search radii let the same network be analysed at walking, neighbourhood, and city scales.
- Provides a coherent family of metrics — reach, gravity, betweenness, closeness, straightness — in one framework.
Intuition
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How it works
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When to use it
Use urban network analysis when you need building- or segment-level measures of centrality and accessibility that respect the real travel network and the distribution of urban activity, for tasks such as predicting pedestrian footfall, siting retail, evaluating walkability, or describing morphological structure. It is well suited to fine-grained studies where buildings carry meaningful weights and where bounded search radii match the scale of the question. It is less appropriate when only a coarse street graph is available without building attributes, when the relevant flows are vehicular and capacity-constrained rather than topological, or when behavioural choice and congestion dominate, in which case a full travel-demand model is preferable.
Strengths & limitations
- Computes centrality at the level of individual buildings or segments, not just abstract nodes.
- Weights destinations by real urban data (floor area, jobs, population) for behaviourally meaningful measures.
- Bounded search radii let the same network be analysed at walking, neighbourhood, and city scales.
- Provides a coherent family of metrics — reach, gravity, betweenness, closeness, straightness — in one framework.
- Results depend heavily on the chosen search radius and the distance-decay parameter β.
- Topological shortest-path centralities ignore congestion, capacity, and time-of-day variation.
- Quality is bounded by the completeness and accuracy of the underlying street and building data.
- Betweenness on large weighted graphs is computationally expensive and sensitive to network simplification.
Common pitfalls
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Applications
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Frequently asked
How does urban network analysis differ from ordinary social network centrality?
Ordinary network centrality treats every node as an abstract, equal point in a graph with arbitrary edges. Urban network analysis grounds the graph in geographic space: edges are real street segments with metric lengths, nodes are buildings or parcels carrying weights such as floor area or population, and centralities are computed within bounded network radii. This makes the metrics spatially meaningful — reach, gravity, and betweenness describe real catchments and movement potential rather than purely topological position.
What is the difference between reach and gravity centrality?
Both measure how much destination activity lies near a building, but reach counts all weighted destinations within a fixed network radius equally, like a cumulative-opportunity measure, while gravity discounts each destination by an exponential distance-decay so that nearer activity counts more. Reach is simpler and threshold-based; gravity is smoother and more behaviourally realistic, and it ties the analysis directly to gravity-based accessibility theory.
How does urban network analysis relate to space syntax?
Both analyse the configuration of street networks to explain movement, and urban network analysis inherits the configurational logic of space syntax. The key differences are that space syntax classically works on axial lines or segments and emphasizes angular and topological depth, whereas urban network analysis works on a metric building-weighted graph and offers an explicit menu of centralities (reach, gravity, betweenness, closeness, straightness). The two are complementary descriptions of urban structure.
Sources
- 1.Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis: A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22(2), 287–305.
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Cite this page
ScholarGate. (2026, June 22). Urban Network Analysis. ScholarGate. https://scholargate.app/urban-studies/urban-network-analysis