Módszerek összehasonlítása

	Urban Network Analysis	Spatial Design Network Analysis (sDNA)
Tudományterület	Urban Studies	Urban Studies
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2012	2020
Megalkotó	Andres Sevtsuk & Michael Mekonnen	Crispin H. V. Cooper & Alain J. F. Chiaradia
Típus	Graph-based centrality analysis of spatial urban networks	Link-based spatial network analysis of street and path networks
Alapmű	Sevtsuk, A., & Mekonnen, M. (2012). Urban network analysis: A new toolbox for ArcGIS. Revue Internationale de Géomatique, 22(2), 287–305.	Cooper, C. H. V., & Chiaradia, A. J. F. (2020). sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, 100525.
Alternatív nevek	UNA Toolbox, Spatial Network Centrality, Building-Level Network Analysis, Street Network Centrality Analysis	sDNA, Spatial Design Network Analysis, Link-Based Network Analysis, 3D Spatial Network Analysis
Kapcsolódó	4	4
Összefoglaló	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.	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.