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Space syntax AI simulator
(@Space syntax_simulator)
Hub AI
Space syntax AI simulator
(@Space syntax_simulator)
Space syntax
Space syntax is a set of theories and techniques for the analysis of spatial configurations. It was conceived by Bill Hillier, Julienne Hanson, and colleagues at The Bartlett, University College London in the late 1970s to early 1980s to develop insights into the mutually constructive relation between society and space. As space syntax has evolved, certain measures have been found to correlate with human spatial behaviour, and space syntax has thus come to be used to forecast likely effects of architectural and urban space on users.
The general idea is that spaces can be broken down into components, analysed as networks of choices, then represented as maps and graphs that describe the relative connectivity and integration of those spaces. It rests on three basic conceptions of space:
The three most popular ways of analysing a street network are integration, choice and depth distance.
Integration measures the amount of street-to-street transitions needed from a street segment, to reach all other street segments in the network, using shortest paths. The graph analysis could also limit measure integration at radius 'n', for segments further than this radius not to be taken into account. The first intersecting segment requires only one transition, the second two transitions and so on. The result of the analysis finds street segments that require fewest turns to reach all other streets, which are called 'most integrated' and are usually represented with hotter colours, such as red or yellow. Integration can also be analysed in local scale instead of the scale of the whole network. In the case of radius 4, for instance, only four turns are counted departing from each street segment. Measure also is highly related to network analysis Centrality.
Theoretically, the integration measure shows the cognitive complexity of reaching a street, and is often argued to 'predict' the pedestrian use of a street: the easier it is to reach a street, the more popular it should be.
While there is some evidence of this being true, the method is biased towards long, straight streets that intersect with many 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 be segmented into individual straight segments, not counted as a single line, which makes curvy streets appear less integrated in the analysis.[example needed][citation needed]
The 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 from the starting street segment to all segments that successively connect to it. Each time an intersection appears, the remaining value of flow is divided equally among 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, 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 can be restricted to limited local radii, for instance 400m, 800m, 1600m. Interpreting Choice analysis is trickier than integration. Space syntax argues that these values often predict the car traffic flow of streets, but, 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.
Space syntax
Space syntax is a set of theories and techniques for the analysis of spatial configurations. It was conceived by Bill Hillier, Julienne Hanson, and colleagues at The Bartlett, University College London in the late 1970s to early 1980s to develop insights into the mutually constructive relation between society and space. As space syntax has evolved, certain measures have been found to correlate with human spatial behaviour, and space syntax has thus come to be used to forecast likely effects of architectural and urban space on users.
The general idea is that spaces can be broken down into components, analysed as networks of choices, then represented as maps and graphs that describe the relative connectivity and integration of those spaces. It rests on three basic conceptions of space:
The three most popular ways of analysing a street network are integration, choice and depth distance.
Integration measures the amount of street-to-street transitions needed from a street segment, to reach all other street segments in the network, using shortest paths. The graph analysis could also limit measure integration at radius 'n', for segments further than this radius not to be taken into account. The first intersecting segment requires only one transition, the second two transitions and so on. The result of the analysis finds street segments that require fewest turns to reach all other streets, which are called 'most integrated' and are usually represented with hotter colours, such as red or yellow. Integration can also be analysed in local scale instead of the scale of the whole network. In the case of radius 4, for instance, only four turns are counted departing from each street segment. Measure also is highly related to network analysis Centrality.
Theoretically, the integration measure shows the cognitive complexity of reaching a street, and is often argued to 'predict' the pedestrian use of a street: the easier it is to reach a street, the more popular it should be.
While there is some evidence of this being true, the method is biased towards long, straight streets that intersect with many 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 be segmented into individual straight segments, not counted as a single line, which makes curvy streets appear less integrated in the analysis.[example needed][citation needed]
The 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 from the starting street segment to all segments that successively connect to it. Each time an intersection appears, the remaining value of flow is divided equally among 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, 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 can be restricted to limited local radii, for instance 400m, 800m, 1600m. Interpreting Choice analysis is trickier than integration. Space syntax argues that these values often predict the car traffic flow of streets, but, 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.
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