Space Syntax

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Space Syntax is a term developed by Bill Hillier to define "spaces can be broken down into components, analyzed as networks of choices, then represented as maps and graphs that describe the relative connectivity and integration of those spaces".[1]

"Space syntax is a set of techniques for the analysis of spatial configurations of all kinds, especially where spatial configuration seems to be a significant aspect of human affairs, as it is in buildings and cities. Originally conceived by Professor Bill Hillier and his colleagues at The Bartlett, UCL in the 1980s as a tool to help architects simulate the likely effects of their designs, it has since grown to become a tool used around the world in a variety of research and areas and design applications. It has been extensively applied in the fields of architecture, urban design, planning, transportation and interior design. Over the past decade, space syntax techniques have also been used for research in fields as diverse as archaeology, information technology, urban and human geography, and anthropology".[2]

The Space Syntax Theory rests on three basic conceptions of space:

Isovist, Axial and Convex Space

  • an isovist (popularised by Michael Benedikt at University of Texas), or viewshed or visibility polygon, the field of view from any particular point
  • axial space (idea popularized by Bill Hillier at UCL), a straight sight-line and possible path, and
  • convex space (popularized by John Peponis and his collaborators at Georgia Tech), an occupiable void where, if imagined as a wireframe diagram, no line between two of its points goes outside its perimeter, in other words, all points within the polygon are visible to all other points within the polygon.[3]

Integration, Choice and Depth Distance

The three most popular Space Syntax analysis methods of a street network are Integration, Choice and Depth Distance.[4]

  • Integration measures how many turns one has to make from a street segment to reach all other street segments in the network, using shortest paths. If the amount of turns required for reaching all segments in the graph is analyzed, then the analysis is said to measure integration at radius 'n'. The first intersecting segment requires only one turn, the second two turns and so on. The street segments that require the least amount of turns to reach all other streets are called 'most integrate' and are usually represented with hotter colors, such as red or yellow. Integration can also be analyzed in local scale, instead of the scale of the whole network. In case of radius 4, for instance, only four turns are counted departing from each street segment. Theoretically, the integration measure shows the cognitive complexity of reaching a street, and is often argued to 'predict' the pedestrian use of a street. It is argued that the easier it is to reach a street, the more popularly it should be used. While there is some evidence of this being true, the method is also biased towards long, straight streets that intersect with lots of other streets. Such streets, as Oxford street in London, come out as especially strongly integrated. However, a slightly curvy street of the same length would typically not be counted as a single line, but instead be segmented into individual straight segments, which makes curvy streets appear less integrated in the analysis.
  • Choice measure is easiest to understand as a 'water-flow' in the street network. Imagine that each street segment is given an initial load of one unit of water, which then starts pouring out of the starting street segment onto all the other segments that successively connect to it. Each time an intersection appears, the remaining value of flow is divided equally amongst the splitting streets, until all the other street segments in the graph are reached. For instance, at the first intersection with a single other street, the initial value of one is split into two remaining values of one half, and allocated to the two intersecting street segments.Moving further down, the remaining one half value is again split among the intersecting streets and so on. When the same procedure has been conducted using each segment as a starting point for the initial value of one, then a graph of final values appears. The streets with the highest total values of accumulated flow are said to have the highest choice values. Like Integration, Choice analysis too can be restricted to limited local radii, for instance 400m, 800m, 1600m etc. Interpreting Choice analysis is trickier than Integration. Space Syntax argues that these values often predict the car traffic flow of streets. However, strictly speaking, Choice analysis can also be thought to represent the number of intersections that need to be crossed to reach a street. However, since flow values are divided, not subtracted at each intersection, the output shows an exponential distribution. It is considered best to take a log of base two of the final values in order to get a more accurate picture.
  • Depth Distance is the most intuitive of the three analysis methods, it explains the linear distance from the center point of each street segment to the center points of all the other segments. If every segment is successively chosen as a starting point, then a graph of accumulative final values is achieved. The streets with lowest Depth Distance values are said to be nearest to all the other streets. Again, the search radius can be limited to any distance.[5]

Related Reading


  1. Wikipedia - Space Syntax.
  3. Ibid.
  4. Ibid.
  5. Ibid.