Porovnat metody

	Syntactic Step Depth	Spatial Design Network Analysis (sDNA)
Obor	Urban Studies	Urban Studies
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1984	2020
Tvůrce	Bill Hillier & Julienne Hanson	Crispin H. V. Cooper & Alain J. F. Chiaradia
Typ	Topological measure of depth and integration between spaces	Link-based spatial network analysis of street and path networks
Původní zdroj	Hillier, B., & Hanson, J. (1984). The Social Logic of Space. Cambridge University Press. ISBN: 9780521367844	Cooper, C. H. V., & Chiaradia, A. J. F. (2020). sDNA: 3-d spatial network analysis for GIS, CAD, Command Line & Python. SoftwareX, 12, 100525.
Další názvy	Topological Step Depth, Mean Depth (Space Syntax), Justified Graph Depth, Syntactic Integration	sDNA, Spatial Design Network Analysis, Link-Based Network Analysis, 3D Spatial Network Analysis
Příbuzné	4	4
Shrnutí	Syntactic step depth is the space-syntax measure of how topologically far apart spaces are — how many turns, transitions or moves separate one space from another, regardless of metric distance. Formalised by Bill Hillier and Julienne Hanson in The Social Logic of Space (1984), it is computed from a justified graph in which every space is a node and every direct adjacency an edge, and a single step is one move between connected spaces. Aggregated into mean depth and normalised into an integration value, step depth becomes the workhorse of configurational analysis, predicting which spaces will be most used, most accessible and most central in a building or city.	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.