An Algorithmic Definition of the Axial Map

Alasdair Turner, Alan Penn, Bill Hillier

Environment and Planning B Planning and Design · 2005 · 206 citations · 21 references

Concepts

TL;DR

The axial map is a primary tool in space syntax, defined in natural language to produce concise maps whose graph measures aid analysis of pedestrian movement and related activities, yet its formal definition has been difficult to translate, leading to criticism of its lack of rigour. The study clarifies the original definition of the fewest‑line axial map and demonstrates that it can be implemented algorithmically, arguing that its analytical power derives from efficiently capturing key spatial configuration properties. The authors clarify the original definition and present an algorithmic implementation of the fewest‑line axial map. They show that the original definition yields maps similar to hand‑drawn ones, that differences arise from algorithmic detail, and that the axial map’s analytical power comes from efficiently representing key spatial properties.

Abstract

The fewest-line axial map, often simply referred to as the ‘axial map’, is one of the primary tools of space syntax. Its natural language definition has allowed researchers to draw consistent maps that present a concise description of architectural space; it has been established that graph measures obtained from the map are useful for the analysis of pedestrian movement patterns and activities related to such movement: for example, the location of services or of crime. However, the definition has proved difficult to translate into formal language by mathematicians and algorithmic implementers alike. This has meant that space syntax has been criticised for a lack of rigour in the definition of one of its fundamental representations. Here we clarify the original definition of the fewest-line axial map and show that it can be implemented algorithmically. We show that the original definition leads to maps similar to those currently drawn by hand, and we demonstrate that the differences between the two may be accounted for in terms of the detail of the algorithm used. We propose that the analytical power of the axial map in empirical studies derives from the efficient representation of key properties of the spatial configuration that it captures.

References

21