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sDNA: 3-d spatial network analysis for GIS, CAD, command line & Python

Cooper, Crispin H.V. 2020. sDNA: 3-d spatial network analysis for GIS, CAD, command line & Python. SoftwareX 12 , 100525. 10.1016/j.softx.2020.100525

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Abstract

Spatial Design Network Analysis (sDNA) is a toolbox for 3-d spatial network analysis, especially street/path/urban network analysis, motivated by a need to use network links as the principal unit of analysis in order to analyse existing network data. sDNA is usable from QGIS & ArcGIS geographic information systems, AutoCAD, the command line, and via its own Python API. It computes measures of accessibility (reach, mean distance/closeness centrality, gravity), flows (bidirectional betweenness centrality) and efficiency (circuity) as well as convex hull properties, localized within lower- and upperbounded radial bands. Weighting is flexible and can make use of geometric properties, data attached to links, zones, matrices or combinations of the above. Motivated by a desire to base network analysis on route choice and spatial cognition, the definition of distance can be network-Euclidean, angular, a mixture of both, custom, or specific to cyclists (avoiding slope and motorized traffic). In addition to statistics on network links, the following outputs can be computed: geodesics, network buffers, accessibility maps, convex hulls, flow bundles and skim matrices. Further tools assist with network preparation and calibration of network models to observed data. To date, sDNA has been used mainly for urban network analysis both by academics and city planners/engineers, for tasks including prediction of pedestrian, cyclist, vehicle and metro flows and mode choice; also quantification of the built environment for epidemiology and urban planning & design.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Geography and Planning (GEOPL)
Sustainable Places Research Institute (PLACES)
ISSN: 2352-7110
Date of First Compliant Deposit: 24 June 2020
Date of Acceptance: 20 May 2020
Last Modified: 25 Jun 2020 12:15
URI: http://orca.cf.ac.uk/id/eprint/132764

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