Linganisha mbinu

	Syntactic Step Depth	Urban Form Morphometrics
Nyanja	Urban Studies	Urban Studies
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1984	2019
Mwanzilishi	Bill Hillier & Julienne Hanson	Quantitative urban-morphology tradition; momepy toolkit by Martin Fleischmann
Aina	Topological measure of depth and integration between spaces	Systematic quantitative measurement of urban form across buildings, plots, blocks, and streets
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	4	4
Muhtasari	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.