方法对比

	Syntactic Step Depth	Urban Form Morphometrics
领域	Urban Studies	Urban Studies
方法族	Process / pipeline	Process / pipeline
起源年份	1984	2019
提出者	Bill Hillier & Julienne Hanson	Quantitative urban-morphology tradition; momepy toolkit by Martin Fleischmann
类型	Topological measure of depth and integration between spaces	Systematic quantitative measurement of urban form across buildings, plots, blocks, and streets
开创性文献	Hillier, B., & Hanson, J. (1984). The Social Logic of Space. Cambridge University Press. ISBN: 9780521367844	Fleischmann, M. (2019). momepy: Urban Morphology Measuring Toolkit. Journal of Open Source Software, 4(43), 1807.
别名	Topological Step Depth, Mean Depth (Space Syntax), Justified Graph Depth, Syntactic Integration	Urban Morphometrics, Quantitative Urban Morphology, Morphometric Analysis of Urban Form, Built-Form Morphometrics
相关	4	4
摘要	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.	Urban form morphometrics is the systematic, quantitative measurement of the physical form of cities — the dimensions, shapes, spatial arrangement, intensity, and connectivity of buildings, plots, blocks, and streets. Rather than describing morphology in words, it computes hundreds of reproducible numerical characters on each morphological element and its local context, turning the qualitative tradition of urban morphology into a measurable science. The open-source momepy toolkit, introduced by Martin Fleischmann in 2019, standardized this workflow, building a morphological tessellation from building footprints and computing dimension, shape, distribution, intensity, and connectivity characters at scale.