Compare methods

	Spatial Design Network Analysis (sDNA)	Street Network Analysis
Field	Urban Studies	Urban Studies
Family	Process / pipeline	Process / pipeline
Year of origin	2020	2017
Originator	Crispin H. V. Cooper & Alain J. F. Chiaradia	Geoff Boeing (OSMnx); graph-theoretic street analysis tradition
Type	Link-based spatial network analysis of street and path networks	Graph-theoretic measurement of street-network structure and connectivity
Seminal source	Cooper, C. H. V., & Chiaradia, A. J. F. (2020). sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, 100525.	Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126–139.
Aliases	sDNA, Spatial Design Network Analysis, Link-Based Network Analysis, 3D Spatial Network Analysis	Street Pattern Analysis, Road Network Metrics, Urban Street Connectivity Analysis, Configurational Street Analysis
Related	4	4
Summary	Spatial Design Network Analysis (sDNA) is a toolkit for analysing street and path networks as link-based spatial graphs, measuring how individual road segments function as routes and destinations within the larger network. Developed by Crispin Cooper and Alain Chiaradia at Cardiff University, it computes closeness- and betweenness-style measures over geometrically accurate, optionally three-dimensional networks, using hybrid distance metrics that blend metric length, angular turn cost and topological steps. By weighting links and analysing them within chosen radii, sDNA predicts pedestrian and vehicle flows, land values and accessibility, bridging the configurational tradition of space syntax with mainstream geographic-information-system network 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.