043 What can typology explain that configuration can not? - SSS10

SSS10 Proceedings of the 10th International Space Syntax Symposium

043 What can typology explain that configuration can not? Meta Berghauser Pont

Chalmers University of Technology [email protected]

Lars Marcus Chalmers University of Technology [email protected]

Abstract This paper aims to contribute to a better understanding of the relation between space syntax and the adjacent field of urban morphology. We believe that this can benefit both fields in their further development and more specifically, this paper will show how typical approaches in urban morphology can be helpful in explaining variations in correlations between space syntax measures and pedestrian movement. That these correlations vary is shown by various scholars and the reoccurring argument is missing data input such as, amongst others, density, land use and public transport. We also see a problem in space syntax analysis in that there seems to be little consistency in exactly how pedestrian movement is best captured, that is, with what measure and at which radius. Hillier and Iida (2005) show for instance in their study of four London areas that the ‘best radius’ can be found with a radius of analysis varying from 12 to 102 segments. This is troublesome, especially if we are not able to explain why this is the case. In this paper we propose to use two typomorphological approaches to explain such variations: the classification system for street morphologies developed by Marshall (2005) and the integrated density approach ‘Spacemate’ developed by Berghauser Pont and Haupt (2009; 2010). The results presented in this paper show that different neighbourhood types, in terms of density and street morphology, indeed have different patterns driving pedestrian behaviour and following that, ask for tailored spatial analysis. It is shown that in denser and more ‘griddy’ street patterns, the betweenness centrality measure is able to capture pedestrian behaviour, but in other neighbourhood types pedestrian behaviour is better captured when also closeness centrality and the distribution of attractions is included. Further, it is shown that what may be called the ’scale of operation’ of each neighbourhood plays a crucial role which needs to be considered when choosing the radius of analysis. This paper shows further that a first indication of pedestrian intensity and pedestrian distribution can be arrived at by using two relative simple spatial measures: ‘accessible density’ and ‘attraction betweenness’ respectively. Although this study is just a first tentative exploration in combining urban morphology with space syntax, we suggest that we based on these preliminary results can see many advantages in pursuing research in this direction. Keywords Urban morphology, spatial configuration, typology.

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1. Introduction Results from a study in two neighbourhoods in Stockholm (Ståhle, 2008) show that the predictive power of the variables central to space syntax varies, depending on the type of neighbourhood. While the closeness measure correlates strongly with pedestrian movement intensity in Södermalm (R2=0,68), an old neighbourhood in central Stockholm, it proves not so effective in Högdalen (R2=0,25), a suburban neighbourhood in south Stockholm, built in the 1950s around a metro station following the principles of the neighbourhood unit. Netto et al. (2012) showed in their study in Rio de Janeiro that in rapidly growing cities, as in the highly planned neighbourhood in Stockholm, variations in movement cannot be explained by accessibility alone. The explanation, according to Ståhle (2008) and Netto et al. (2012), is that dissonances between patterns of accessibility, density, and activity are at work impacting the patterns of movement (see Figure 1). The process of alignment, that is, the tendency of certain states in one pattern to match specific states in other patterns, is by Hillier et al. (1993) reduced to multiplier effects of the street network. Netto et al. (ibid.) describe a more dynamic and complex dependence between these patterns where convergence and dissonance interplay in time. What we are interested in is whether the convergence between patterns differs depending on the type of neighbourhood. Our hypothesis is that the way a neighbourhood is planned impacts the process of alignment between patterns because, for instance, shopping centres or public transport nodes are planned in dissonance with the street network and changes over time are difficult or even restricted due to the zoning laws.

Figure 1: Relations of urban patterns of different materialities, roles and temporalities (left) and a hypothesis of convergence of these patterns (right): interrelations and mutual dependences would lead to progressive convergence in time following the theory of natural movement of Hillier et al. (1993). (Netto et al. 2012, p. 8167:4-5).

In this paper we aim to address the question whether different neighbourhood types have different patterns driving pedestrian behaviour, and if so, whether these different neighbourhoods therefore ask for different spatial analysis in order to better forecast pedestrian movement. The first question shows similarities to the question central in the work of Netto et al. (2012). They, however, looked into differences of walking behaviour in streets of the same centrality, while we are more interested whether the morphological setup of a neighbourhood impacts walking behaviour to such an extent that we need to think of adjusting the analysis. We thus start with a definition of morphological types combining two typo-morphological approaches, that is, the classification system for street morphologies as developed by Marshall (2005) and the integrated density approach developed by Berghauser Pont and Haupt (2009; 2010). In a second step we add typical space syntax analyses. If indeed different neighbourhood types show variance in movement patterns it is of practical value to know which analysis best fits what type of neighbourhood, since this opens for the possibility to not in each case validate the model by observation. However recommended this may be, it is not always an option why we here may see the possibility of substantiated short-cuts.

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SSS10 Proceedings of the 10th International Space Syntax Symposium 2. Urban morphology and space syntax Urban morphology (Moudon, 1997) is a tradition that, generally speaking, has evolved in close concert with history and the humanities. Normally one identifies three schools here: first the English, with its origin in the historic micro-scale geography of settlements and the subsequent development of ‘town-plan analysis’ by M. Conzen (Whitehand et al., 2001); second the French, where the analysis of land division and cadastres has been central in studies on the relation between space and society, not least by Philippe Panerai (Samuels, 2004); and third the Italian, with its origin in historic description of building types and the idea of an ‘operational history’, formulated by Saverio Muratori and Gianfranco Caniggia, aiming to support new development (Carniggia et al., 2001). A fourth can be added that is referred to by Moudon (1992) as space-morphology studies focusing on the fundamental characteristics of urban geometries where she actually even includes space syntax, but we get back to this later. The general approach that all schools have in common, that is, the (historic) study of typologies of urban form at different scales, also come close to how urban form often has been dealt with in architecture, both academically, for instance, in architectural history (Kostof, 1992), and in normative theory for practice, such as the notion of the urban transects in New Urbanism (Duany et al., 2000; 2003). In general terms, we can therefore speak of a close affinity to architecture and urban design when it comes to urban morphology. The difference between urban morphology, including space-morphology, and space syntax is, in simple terms, that urban morphology rather examines the individual components of urban form, such as streets, squares and blocks, often including the historic process of its development, while space syntax stresses the relative or systemic dimension of such components and how they aggregate into neighbourhoods and cities, that is, urban morphology expands urban form in time, while space syntax expands it in space, if you like. Urban morphology also deals with larger aggregates but generally sticking to the methodology of classifying specimens into types due to their form, hence creating typologies of neighbourhoods and even cities as a whole, such as organic, gridor circular cities. Or as Peponis and his colleagues described it, urban morphology is good at classify and even quantifying the differences between areas but is not able to quantify the differences in the same area (Peponis et al., 2007). Syntactic measures, on the other hand, typically also capture these differences. Another difference between urban morphology and space syntax is that one in urban morphology defines urban elements from a conceived rather than perceived point of view. For instance, one characteristically concerns oneself with such elements as the urban block, which typically is easy to identify on a map but actually is very difficult to perceive in urban space. The morphological descriptions developed within space syntax on the other hand typically have their rationale from the point of view of human perception and cognition. We here find a vital characteristic to space syntax, namely its strong link to cognition science, but especially the ecological approach to human perception developed by James Gibson (1979), where space syntax modes of geometric representation, such as the axial map, prove highly interesting extensions of this theory (Marcus, 2015). The reason for the success of the axial map in capturing pedestrian movement is likely to be its ability to geometrically capture both the energy effort and the informational effort for a moving subject in an urban area, or as Hillier (2003) argued: If we make a straight line crooked “we do not add significantly to the energy effort required to move along it, but we do add greatly to the informational effort required” (ibid., p. 3). This introduction of what can be called a cognitive geometry (Marcus, 2015) must be the reason why the rather plain appearance of the axial map has proved to have such predictive power. However, the predictive power varies, as mentioned earlier, depending on the type of neighbourhood. The fact that space syntax analysis is better at predicting movement in premodernist more ‘griddy’ morphologies with a more or less natural pattern of growth than in modernist (planned) morphologies is a critique that several scholars have pointed out (e.g. Netto et al., 2012; Ståhle, 2008; Ratti, 2004). Ratti (ibid.) questions whether what Hillier calls “the hidden role of geometry in cities” (Hillier, 1999, p.182) applies to all cities and if not, which are the conditions under which space syntax analysis can be used? Ståhle (2008) argues that the difference in M Berghauser Pont & L Marcus What can typology explain that configuration can not?

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SSS10 Proceedings of the 10th International Space Syntax Symposium distribution of density, attractions and land use should be included for a better explanation of pedestrian movement in modernistic neighbourhoods. Others add public transport and the width and capacity of streets, retail on the ground floor and even window density to the list of lacking data input (Pereira et al. 2012; Netto et al. 2012; Ratti 2004). Besides the argument of missing data input, we also see a reoccurring problem in space syntax analysis in that there seems to be little consistency in exactly how pedestrian movement is best captured, that is, with what measures and at which radii,. While performing space syntax analysis we can vary between axial and segment analysis, choose between topological, geometric and metric distance, and finally set the radius for all of these analyses at any topological, geometric or metric distance. Hillier and Iida (2005) show in their study of four London areas that the ‘best radius’, that is, the radius which gives the highest correlation with pedestrian flows, can be found at analysis-radii varying from 12 segments to 102 segments (ibid., p. 561, Table 27). This is troublesome, especially if we are not able to explain why this is the case. We therefore plea for an integrated approach where typical space syntax analysis are combined with different morphological analyses, something other scholars have argued for before (Gil et al., 2012). We especially see the potential in typomorphological studies, which could help identify neighbourhood types related to variances in movement behaviour. 3. Two typo-morphological studies: Spacemate and the ABCD typology Typo-morphological studies describe and explain how the built environment is produced by classifying systematically the elements which structure the physical form of cities over time (Moudon, 1997). We will use two analytical approaches of such classification studies, the first based on quantification of built density as proposed by Berghauser Pont and Haupt (2009; 2010) and the second based on graph theoretical analysis of street morphologies as suggested by Marshall (2005). Berghauser Pont and Haupt (ibid.) developed a classification system to distinguish different types of neighbourhoods and buildings based on the distribution of density. They have shown that only by expressing urban density through a composite of variables, Floor Space Index (FSI), Ground Space Index (GSI), Open Space Ratio (OSR) and building height (L), can various morphological types be distinguished numericallyi. Each spatial solution, high and spacious or low and compact, results in a unique combination of the density variables and thus has a unique position in the Spacemate diagram they developed (see Figure 2). FSI on the y-axis gives an indication of the built intensity in an area and GSI on the x-axis reflects the ground coverage, or compactness, of the development. The OSR and L are gradients that fan out over the diagram. Earlier research by Berghauser Pont and Haupt (ibid.) shows that morphological types cluster in different positions in the Spacemate diagram. The examples within the cluster marked with G in Figure 2 have, for instance, both a high FSI and GSI and mostly contain mid-rise buildings (three to seven storeys) dominated by perimeter blocks (the ‘court type’). Examples with both low FSI and GSI (cluster marked A) consist of low-rise detached houses with large gardens (the ‘pavilion type’). Examples in between these two can be described as more linear developments (the ‘street type’) such as row houses up to three storeys (cluster B), slabs of three to seven storeys (cluster E) or slabs higher than seven storeys (cluster H). Steadman (2013) recently showed, based on the work of Martin and March (1972), that the clustering Berghauser Pont and Haupt found empirically with samples from the Netherlands, Germany and Spain, also holds on a theoretical level.

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Figure 2: Clustering of the different morphological types in the Spacemate measured at the scale of the island (excluding streets). A=low-rise pavilion type; B= low-rise street type; C=low-rise hybrid type; D= low-rise court type; E=mid-rise street type; F= mid-rise hybrid type; G=mid-rise court type; H= high-rise street type (Berghauser Pont and Haupt, 2010, p. 182) and projection of the ‘pavilion’, ‘street’ and ‘court’ type as defined by Martin and March (1972, p. 36) based on Steadman (2013, p. 12).

Marshall (2005) developed a classification system to differentiate between various kinds of street morphologies. This so called ABCD typology distinguishes an A-type or Altstadt type for core areas of old cities with irregular, short and crooked streets going in all directions; a B-type or Bilateral type for gridiron patterns with regular, orthogonal streets going mainly in two directions; a C-type or Conjoint type for street patterns that astride one or more arterial routes; and a D-type or Distributory type for modern hierarchical layouts with a strongly differentiated traffic system separating car from slow traffic. Type B has the highest connectivity, followed by type A and C and the lowest connectivity is found in type D measured both in amount and type of junctions (X-ratio and T-ratio) and number of cells and cul-de-sacs (cell ratio and cul ratio)ii. This classification results in a diagram where, as with the Spacemate, each neighbourhood type has a unique position and helps to describe how ‘griddy’ or ‘tree-like’ a street pattern is (see Figure 3).

Figure 3: Compositional and configurational properties of ABCD types (Marshall, 2005, p.89) plotted in the diagram developed by Marshall (2005, p.100). We can note that the Altstadt type (A) lies midway between being a pure T-tree and a pure T-cell type; the Conjoint type (C) is mostly a T-cell type; the Bilateral type (B) is mostly a grid-cell type and the Distributory type (D) is mostly a T-tree type.

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SSS10 Proceedings of the 10th International Space Syntax Symposium What we want to show is how these typologies can contribute to a more coherent understanding of why certain space syntax measures, distances and radii are more effective in capturing pedestrian behaviour in certain neighbourhood types than others, hence identifying more specifically what drives variation in walking behaviour in these different neighbourhood types. 4. Space syntax measures and tools We apply the two centrality measures most common in space syntax research: ‘integration’ and ‘choice’ which are in effect very close to typical measures frequently used in many forms of network modelling, referred to as ‘closeness centrality’ and ‘betweenness centrality’ respectively (Hillier and Iida, 2005). The closeness measure calculates the least mean distance cost from each line segment (or axial line) to all others in a system (Hillier and Hanson, 1984). This measure thus shows, for each and every line, how many steps (measured topologically) you are away from all other lines. The betweenness measure shows how often a line segment is part of the shortest path between all pairs of segments in a system. In other words, line segments that are needed more often when moving through the city have a higher betweenness value than those that are not so often used. Taking such a segment out of the system will affect a lot of routes that cannot be chosen anymore. The lines with a high betweenness can thus be said to be more important for the functioning of the system. A striking difference between these two measures can be observed on the map where closeness ranges tend to cluster while betweenness ranges are scattered over the urban system (see Figure 4 and 5). For both measures (closeness and betweenness), three different weight definitions can be used to represent different distance cost relations between adjacent lines or segments: least length (metric), fewest turns (topological) and least angle change (geometric). Furthermore, all analyses can be tested for different radii, again defined metrically, topologically or geometrically giving an extended series of possible analysis to be tested. In this paper closeness is measured at radius 2, 6, 8, up to 60 axial steps and betweenness from 500 meter up to 4 km. Besides the typical space syntax measures, we use two additional measures that can capture the distribution of gross floor area through the network, ‘attraction accessibility’ and ‘attraction betweenness’, which Ståhle (2008) has proven plays an important role for pedestrian movement in modernistic planned neighbourhoodsiii. We even add distance to public transport nodes as this is a factor that is highly planned and hard to change. The three study areas chosen for this paper are City/Norrmalm, Södermalm and Högdalen, all in Stockholm (Sweden), representing both a variety of street morphologies and density types following the methods of Marshall (2005) and Berghauser Pont and Haupt (2009; 2010) respectively. The observations used for the empirical analysis concern the average amount of people passing a selection of street segments, measured per hour on a normal weekday (observed between 8 am and 5 pm). We use earlier observations by Ståhle (2008) and Spacescape (2011) in the areas with 36 observation points in City/Norrmalm, 31 in Södermalm and 40 in Högdalen. The software used in the analysis is the Place Syntax Tool (PST), a plug-in software for the GIS software package Mapinfo (Ståhle et al., 2005). Statistical analyses, including spatial autocorrelation tests, are done using GeoDa (Anselin, 2005).

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Figure 4: Spatial analysis of Stockholm: closeness centrality (NN).

Figure 5: Spatial analysis of Stockholm: betweenness centrality (NN).

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SSS10 Proceedings of the 10th International Space Syntax Symposium 5. Morphological description of City/Norrmalm, Södermalm and Högdalen Högdalen is a suburban district in south Stockholm, built in the 1950s around a metro station with the prevailing planning model for residential areas dominant at that time, the neighbourhood unit (Figure 5). Central to this concept was the functionally self-contained neighbourhood contrasting the industrial grid-iron city where crowding, traffic and pollutions was seen as a threat to community life (Perry, 1929). Major arterials are placed along the perimeter of the neighbourhood rather than running through its central parts. Moreover, local streets are given a curvilinear form in the aim to discourage through-traffic to enhance the safety of pedestrians. Högdalen has a cell ratio of 0,18 and a T-ratio of 0,88; the area has a neighbourhood density of FSIf=0,55 and GSIf=0,16; the average observed movement rate of pedestrians at the gates in this area is 90 persons per hour (see Table 1).

Figure 5: Högdalen with a presentation of the location of the neighbourhood in Stockholm (left), closeness centrality R-60 (middle) and betweenness centrality 4 km analysis (right).

City/Norrmalm is the central area of Stockholm just north of the oldest part of Stockholm, Gamla Stan (Figure 6). Despite some modernistic interventions that separate car traffic and pedestrian M Berghauser Pont & L Marcus What can typology explain that configuration can not?

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SSS10 Proceedings of the 10th International Space Syntax Symposium flows at different levels, a regular, orthogonal street system dominates the area. The area has a cell ratio of 0,88 and a T-ratio of 0,30 ; the area has a high neighbourhood density with FSIf=3,00 and GSIf=0,47; the average movement rate of pedestrians at the gates in the area is more than 700 persons per hour, which is eight times higher than the average rate in Högdalen (see Table 1).

Figure 6: City/Norrmalm with a presentation of the location of the neighbourhood in Stockholm (left), closeness centrality R-60 (middle) and betweenness centrality 4 km analysis (right).

Södermalm is a district in central Stockholm covering the large island of the same name with sheer cliffs and rocky hills (Figure 7). The area has been occupied for a long time, but it was not until the beginning of the 20th century that urbanisation was to extend over the entire island. Södermalm is an area with a mixture of regular grid-patterns and more irregular infill projects of later date in the modernistic style. An important fact is also that the area is an island with only a few bridges connecting it to the rest of the city. This creates a strong hierarchy between the streets connecting to the main land and passing through the island as main arteries, and local streets. Södermalm has a cell ratio of 0,82 and a T-ratio of 0,52; the density in the area is relatively high with FSIf=1,86 and

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SSS10 Proceedings of the 10th International Space Syntax Symposium GSIf=0,34; the average observed pedestrian rate is 340 persons per hour, which is half the rate found in City/Norrmalm, but almost four times the rate in Högdalen (see Table 1).

Figure 7: Södermalm with a presentation of the location of the neighbourhood in Stockholm (left), closeness centrality R-60 (middle) and betweenness centrality 4 km analysis (right).

Table 1: Results of the morphological analysis based on the measures proposed by Marshall (2005) and Berghauser Pont and Haupt (2009, 2010) and pedestrian movement observations by Ståhle (2008) and Spacescape (2011).

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SSS10 Proceedings of the 10th International Space Syntax Symposium By positioning the three areas in the Spacemate diagram we can clearly see the differences between the three areas in all density variables: the highest FSI and GSI with a dominance of closed perimeter blocks (i.e. ‘court type’) in City/Norrmalm followed by Södermalm and the lowest levels with a dominance of more open building configurations (i.e. ‘street type’) in Högdalen (Figure 8). Furher, plotting the cell ratio, cul ratio, X-ratio and T-ratio of the three areas in the combined diagram of Marshall shows clearly that Högdalen is a T-tree type, City/Norrmalm an X-cell type and Södemalm lies midway between being a pure T-cell and a pure X-cell type (see Figure 9).

Figure 8: Spacemate diagram with Högdalen, Södermalm and City/Norrmalm positioned in the diagram including the pedestrian flow intensity in these neighbourhoods (average amount of people at the observations points).

Figure 9: Marshall diagram with Högdalen, Södermalm and City/Norrmalm positioned in the diagram including the pedestrian flow intensity in these neighbourhoods (average amount of people at the observations points).

The observed average amount of people walking the streets in the three areas seems to be related to the morphology of the areas: denser, more compact and ‘griddy’ types have a higher intensity of people walking the streets. The linear regression analysis confirms this with a correlation between accessible densityiv and pedestrian movement of 0,37. However, when looking at the correlations within each area separately, the results are different. It is only in Högdalen we find a moderate correlation between accessible density and pedestrian flows (R2=0,25, see Figure 10). In other words, the differences of the average pedestrian intensity between the areas seems possible to explain with accessible density, but not the distribution of pedestrian flows within each area. This confirms what Peponis et al. (2007) said about the inability of urban morphology to quantify differences within the area as it does not take into account syntactic characteristics, but we know from Netto et al. (2012) that even when including syntactic measures, this is not sufficient for convincing results in all areas. This is confirmed by our analysis of the three areas in Stockholm (see Figure 11). M Berghauser Pont & L Marcus What can typology explain that configuration can not?

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Figure 10: Correlation between accessible density and pedestrian movement for all three neighbourhoods is 0,37; however, for the separate neighbourhoods the correlations vary and only in Högdalen a significant correlation is found (R2=0,25).

Figure 11: Correlation between global integration (i.e. closeness) and pedestrian movement for all three neighbourhoods is 0,40; however, for the separate neighbourhoods the correlations vary and only in Södermalm a significant correlation is found (R2=0,59).

In the analysis that follows the results of a straightforward space syntax analysis are presented, not so much to find the ‘best correlations’, but rather to start an investigation to find out 1) whether different spatial analysis should be used for different neighbourhood types and 2) whether other measures than space syntax measures are needed to explain pedestrian behaviour in these different neighbourhood types. In other words, do dense, X-cell street morphologies need another spatial analysis than spacious, T-tree street morphologies to capture the distribution of pedestrian flows? 6. Spatial analysis of City/Norrmalm, Södermalm and Högdalen The correlation between pedestrian movement and the closeness centrality measures is high in Södermalm (R2 = 0,48) in contrast to the other two areas, Högdalen and City/Norrmalm (see Table 2). Looking at the other centrality measure, angular betweenness, we find relatively high correlations for all three areas, but for each area the highest value is found at another radius (see Table 3). In other words, the three areas operate at different scales. In Högdalen the highest correlation is found at radius 1 km (R2 = 0,37), in City/Norrmalm at radius 1,5 km (R2 = 0,57) and in Södermalm at radius 2,5 km (R2 = 0,44). That Högdalen operates at the smallest scale conforms to the fact that the street pattern is designed as a T-tree type, spatially separating the area from its surroundings, and the road enclosing Högdalen on all sides delimits an area that has a radius of approximately 1 km. At higher M Berghauser Pont & L Marcus What can typology explain that configuration can not?

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SSS10 Proceedings of the 10th International Space Syntax Symposium radii through traffic passes the neighbourhood and this measure is not able to capture the pattern of pedestrian movement within the area. City/Norrmalm has the highest correlations at a radius of 1,5 km, but also demonstrate high correlations at many other radii. This can be explained by the ‘griddy’ character of the street pattern (i.e. X-cell type) giving the same streets a high value at various scales, or in other words, a match between local and global integration (i.e. closeness centrality), something that is found to correspond to a higher inflow of non-locals (Legeby, 2013) which in turn can explain the high intensity of pedestrians. In Södermalm the betweenness measure predicts movement better at the higher radii which can be explained by the fact that the main arteries only get high betweenness values when they reach the main land which occurs at radii above 2,5 km.

Table 2: Results of spatial analysis: closeness centrality.

Table 3: Results of spatial analysis: betweenness centrality.

These results indicate that, first of all, betweenness centrality is a more robust measure than closeness centrality when it comes to predicting the distribution of pedestrian movement in areas of varying morphological character, but also that the best radius to capture pedestrian behaviour depends on the neighbourhood type. The correlation between attraction accessibility and pedestrian movement is in general higher than the correlations between closeness centrality and pedestrian movement confirming earlier findings by Ståhle (2008). Högdalen shows higher correlations, but still only moderate R2=0,29 at a radius R-2 (see Table 4). City/Norrmalm also shows higher correlations, but still only moderate R2=0,27 at radius R-10. Södermalm has high correlations at all radii (highest R2=0,64). The correlations between attraction betweenness and pedestrian behaviour are slightly better in Högdalen when compared to betweenness centrality, but in the other two areas the results are at a similar level (see Table 5). What is noteworthy is that Södermalm correlates much higher on the lowest radius when the accessible gross floor area is included in the betweenness measure. This indicates that pedestrian behaviour is driven by the network at the higher scales, but at the most local scale it is driven by the distribution of density. The same phenomenon can be seen in Högdalen, but to a lesser degree.

Table 4: Results of spatial analysis: attraction accessibility.

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Table 5: Results of spatial analysis: attraction betweenness.

To also test the effect of combinations of variables on pedestrian flows, we applied a multiple linear regression analysis including all four variables at all radii plus the distance to metro stations to include the impact of public transport nodes on walking behaviour. The results show that pedestrian movement in the three areas studied cannot be explained using one and the same set of variables, on the contrary, they ask for a totally different approaches: - In Högdalen, attraction betweenness (radius 1 km) in combination with the distance to the metro station explain pedestrian movement patterns best. This combination gives a 50% explanation of pedestrian behaviour. - In Södermalm the set of relevant explanatory variables is betweenness centrality (radius 2,5 km), closeness centrality (R-6) and attraction accessibility (R-2) giving a 75% explanation of pedestrian behaviour. - In City/Norrmalm betweenness centrality (radius 1,5 km) alone explains 57% of pedestrian behaviour and this is not improved by adding other variables to the analysis. 7. Discussion on when to use what kind of spatial analysis We have seen that the correlation results differ in the studied areas and that this, at least partly, can be explained by their distinct morphological characteristics, both in terms of street layout and density. This indicates that indeed different factors drive pedestrian behaviour in different neighbourhood types and that, as a result of this, each neighbourhood type asks for a tailored spatial analysis. Betweenness centrality is found to be a more robust measure than closeness centrality. With betweenness we can explain pedestrian behaviour for 37% in Högdalen, 44% in Södermalm, and 57% in City/Norrmalm where closeness gave a more diverse result with a high correlation for Södermalm, but very low correlations for the other two areas. Returning to the measure betweenness, we have also seen that the most effective radius varies between the areas: the distribution of pedestrian flows in Högdalen is best captured at a radius of 1 km; in Södermalm at a radius of 2,5 km and in City/Norrmalm at most radii. Each neighbourhood type seems thus to ‘operate’ at a different scale. To define this so called ‘scale of operation’ and through that the radius of an effective spatial analysis, community detection techniques could be used where “the network is divided in communities of densely connected nodes with the nodes belonging to different communities being only sparsely connected” (Blondel et al., 2008, p.2). We should, however, bear in mind that only the size of the community (i.e. neighbourhood) can be detected in this manner and be used to define the most effective radius of analysis, but that it is the type of neighbourhood that determines whether this size means a minimum radius for the analysis or a maximum radius. Neighbourhoods of the Distributory type, such as Högdalen, are designed to keep movement from neighbouring communities out and the size of the community indicates therefore a maximum radius for an effective spatial analysis; for the Conjoint type such as Södermalm, on the contrary, the size of the community indicates the scale when movement from neighbouring communities into the area can be captured, thus setting the minimum radius of the spatial analysis. Further, it is shown how adding the distribution of attractions (in our case gross floor area and public transport) to the analysis increases the correlation values in the highly planned neighbourhood Högdalen with its relative low density and dominant T-tree street layout. This result corresponds with the results by Netto et al. (2012) who found that density plays an important role for pedestrian behaviour in globally less integrated streets that dominate Högdalen (recall Figure 4). The differences between Södermalm and City/Norrmalm, however, are not explained by Netto et al. M Berghauser Pont & L Marcus What can typology explain that configuration can not?

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SSS10 Proceedings of the 10th International Space Syntax Symposium (ibid.) as these neighbourhoods are of the same global integration range. Here, it seems to be the street morphology that plays an important role where street patterns with a higher X-ratio, and thus with a more neutral grid, can be analysed effectively with betweenness centrality alone as neighbourhoods with a lower X-ratio and thus a more hierarchical setup with a few arterial routes crossing the neighbourhood, are better captured if the closeness centrality measure is also included in the model. Although more research is needed to verify our findings in other neighbourhoods and other cities, we have found strong indications that the type of spatial analysis can and should be related to the neighbourhood type that is analysed. All this said, however, we might want to keep things simpler and follow the principle of Occam’s Razor stating that whenever something can be described in more fundamental and simple terms, it should be done sov. Following this rule, we might argue for the use of the measure attraction betweenness at a radius of 1 km as a good indicator for the distribution of pedestrian flows. This measure explains 40% of the pedestrian flows in the three areas. In other words, with two relative simple spatial measures, density and attraction betweenness, we can predict pedestrian intensity and pedestrian distribution respectively. This could be used as a starting point to in a deeper analysis use the combination of analysis that fits the area best, including the effective radius that corresponds to the type of neighbourhood and community size. On a theoretical level we can see how we may start to better understand what aspects work as drivers for movement behaviour in different types of neighbourhoods when we combine the tradition of urban morphology with space syntax. Although this study is just a first tentative exploration in this direction, we suggest that we based on these preliminary results can see many advantages in pursuing research in this direction. References Anselin, L. (2005), Exploring spatial data with GeoDa: a workbook, Urbana-Champaign: University of Illinois. Berghauser Pont, M. and Marcus, L. (2014), ‘Innovations in measuring density: from area density and location density to accessible and perceived density’. In: Nordic Journal of Architectural Research, Vol. 2, pp. 1131. Berghauser Pont, M. and Haupt, P. (2010), Spacematrix. Space, density and urban form, Rotterdam: NAi Publishers. Berghauser Pont, M. and Haupt, P. (2009), Space, density and urban form, Delft: University of Technology Delft (PhD thesis). Blondel, V., Guillaume, J.L., Lambiotte, R. and Lefebvre, E. (2008), ‘Fast unfolding of communities in large networks’. In Journal of Statistical Mechanics: Theory and Experiment, JSTAT 2008: P10008. Carniggia, G. and Maffei, G. (2001), Architectural composition and building typology: interpreting basic building, Firenze: Alinea Editrice. Duany, A. (2003), ‘Neighbourhood design in practice’. In: Neil, P. (eds.), Urban villages in the maing of communities, London: Spon. Gibson, J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin. Reproduced in 1986 by Lawrence Erlbaum Associates. Gil, J., Beirao, J., Montenegro, N. and Duarte, P. (2012), ‘On the discovery of urban typologies: data mining the many dimensions of urban form’. In: Urban morphology, Vol. 16(1), p. 27-40. Hillier, B. and Hanson, J. (1984), The social logic of space, Cambridge: Cambridge University Press. Hillier B. (1999), ‘The hidden geometry of deformed grids: or, why space syntax works, when it looks as though it shouldn't’. In: Environment and Planning B: Planning and Design, Vol. 26, p. 169 – 191. Hillier, B. (2003), ‘The architectures of seeing and going: Or, are cities shaped by bodies or minds? And is there a syntax of spatial cognition?’ In: Hanson, J. (eds.), Proceedings of the Fourth International Space Syntax Symposium, London: University College London Vol. 06, p. 1−34. Hillier, B. and Iida, S. (2005), ‘Network and psychological effects in urban movement’. In: van Nes, A. (eds.), Proceedings of the Fifth International Space Syntax Symposium, Delft: University of Technology. Kostof, S. (1992), The city assembled: the elements of urban form through history, London: Thames & Hudson Publishers. Legeby, A. (2013). Patterns of co-presence. Spatial configuration and social segregation. Stockholm: KTH University.

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SSS10 Proceedings of the 10th International Space Syntax Symposium Marcus, L. (2015), ‘Ecological space and cognitive geometry – linking humans to the environment in space syntax theory’. In: Proceedings of the tenth International Space Syntax Symposium, London: University College London. Marshall, S. (2005), Streets & Patterns, Oxon: Spon Press. Martin, L. and March, L. (1972), Urban space and structures, Cambridge: Cambridge University Press. Moudon, A. (1992) ‘A catholic approach to organizing what urban designers should know’. In: Journal of Planning Literature, Vol. 6, no. 4, p. 331-349. Moudon, A. (1997), ‘Urban morphology as an emerging interdisciplinary field’. In: Urban morphology, Vol. 1, p. 3-10. Netto, V., Sabayo, R., Vargas, J., Figueiredo, L., Freitas, C. and Pinheiro. M. (2012), ‘The convergence of patterns in the city: (Isolating) the effects of architectural morphology on movement and activity’. In: Greene, M., Reyes, J. and Castro, A. (eds.), Proceedings of the eighth International Space Syntax Symposium, Santiago de Chile: PUC. Peponis, J., Allen, D., French, S., Scoppa, M. and Brown, J. (2007), ‘Street connectivity and urban density: spatial measures and their correlation’. In: Kubat, A.S., Ertekin, Ö., Guney, Y.I. and Eyubolou, E. (eds.), Proceedings of the Sixth International Space Syntax Symposium, Istanbul: ITU Faculty of Architecture. Pereira, R., Holanda, F., Medeiros, V. and Barros, A. (2012), ‘The use of space syntax in urban transport analysis: limits and potentials’. In: Greene, M., Reyes, J. and Castro, A. (eds.), Proceedings of the 8th International Symposium in Space Syntax. Santiago de Chile: PUC, paper ref 8214. Perry, C. (1929). The Neighbourhood Unit, London: Routledge/Thoemmes (reprinted 1998). Ratti, C. (2004), ‘Urban texture and space syntax: some inconsistencies’. In: Environment and planning B: planning and design, Volume 31, pp 487-499. Samuels, I., Panerai, P., Castec, J. and Depaule, J. (2004), Urban forms. The death and life of the urban block. London: Architectural Press. Steadman, P. (2013), ‘Density and built form: integrating «Spacemate» with the work of Martin and March’. In: Environment and Planning B: Planning and Design, Vol. 40, pp 341−358. Ståhle, A., Marcus, L. and Karlström, A. (2005), ‘Place Syntax Tool – GIS Software for analysing geographic accessibility with axial lines’. In: Ståhle, A. (2008), Compact sprawl: Exploring public open space and contradictions in urban density, Stockholm: KTH University, pp. 81-98. Ståhle, A. (2008). Compact sprawl: Exploring public open space and contradictions in urban density, Stockholm: KTH University. Whitehand, J. and Hart, C. (2001), Twentieth century suburbs: a morphological approach. London: Routledge.

For definitions of these density variables, see Berghauser Pont and Haupt (2010), p. 107-114. For definitions of these street morphology variables, see Marshall (2005), p. 88-101. iii For definitions of ‘attraction accessibility’ and ‘attraction betweenness’, see Ståhle et al. (2005). iv Accessible density is measured as FSI within a radius of R-2; for a more detailed explanation, see Berghauser Pont and Marcus (2014). v Stanford Encyclopedia of Philosophy, author’s interpretation. i

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