THE EFFECT OF BUILT SPACE ON WAYFINDING IN URBAN ENVIRONMENTS: a study of the historical peninsula in İstanbul 1 Ayşe Sema KUBAT, Ayşe ÖZBİL, Özlem ÖZER,

THE EFFECT OF BUILT SPACE ON WAYFINDING IN URBAN ENVIRONMENTS:

a study of the historical peninsula in İstanbul1

Ayşe Sema KUBAT, Ayşe ÖZBİL, Özlem ÖZER, Harun EKİNOĞLU

8th International Space Syntax Symposium,

3-6 January, 2012

Santiago, Chile

1 This study is part of a research project supported by Istanbul Metropolitan Municipality, Directorate of Strategical Planning.

• Introduction

• Research Background

• Analytical Framework

• Study area

• Spatial Layout and Wayfinding Behavior

• Conclusion

The Effect of Built Space on Wayfinding in Urban Environments: A Study of the Historical Peninsula in İstanbul

KUBAT, A. S.; OZBIL, A.; OZER, O.; EKINOGLU, H. 2

3

• Introduction



• Study area


• Conclusion


KUBAT, A. S.; OZBIL, A.; OZER, O.; EKINOGLU, H.

• This paper reconsiders the question of how the built

environment, in terms of the spatial configuration of urban layout

and land-use patterns, affects wayfinding performance.

• The study investigates mainly the accessibility and visibility

properties of urban layouts but also aims to unveil the

significance of local urban design parameters in shaping the

patterns of wayfinding behavior.

4

Introduction

• The question asked here is whether the configuration of urban

space influences the patterns of exploration in an urban

environment, over and above the influence that may be exerted

by development densities.



• This study is part of an

ongoing research supported

by Istanbul Metropolitan

Municipality, whose primary

aim is to enhance the

spatial and visual

organization of Istanbul’s

Historic Core by evolving a

spatial regeneration strategy

from the perspective of the

pedestrian.

5

Introduction

Golden Horn

Bosphorous

Marmara Sea



6

• Introduction



• Study area


• Conclusion



• A wide range of disciplines has investigated the ways in which

people understand, represent and move around the built

environment. The common difficulty within these disciplines

is finding ways to describe and quantify the configurational

variables of the physical environment in a systematic way.

7

Research Background



• In an attempt to quantify the built environment, most of the

related literature has addressed two dimensions of urban form:

accessibility and visibility patterns of spatial layouts.

8

Research Background



• Theories regarding how the accessibility patterns of urban

networks affect wayfinding behavior have mainly focused on

two aspects of urban structure:

metric distance (total path length);directional distance (number of turns along the path).

• Direction changes have been confirmed to have significant

impact on cognitive effort during navigation. Studies on spatial

cognition demonstrate that increased number of direction

changes reduce the accuracy of orientation and spatial cognition

as well as affecting the estimation of metric route length.

9

Research Background



• Since understanding the layout of a city involves

operationalizing the sequence of views created with one's

movement in the environment, researchers have developed a set

of analytical measures –isovists, viewsheds, ‘all lines map’, etc.–

to quantify the visual dimensions of spatial environments.

• Using these measures, studies on wayfinding research have

found that the visual dimension of built space has a strong

influence on the way people move around in such complex

built environments.

• However; since visual perception is relatively a local spatial

property compared to the overall pattern of street connections

within the entire urban network, navigation through any

complex environment requires a multilayered description of the

spatial structure.

10

Research Background



11

• Introduction



• Study area


• Conclusion



• Building on the existing literature, the theoretical question

that this paper deals with is:

How do connectivity patterns, visual fields, and land-use

compositions interact to anticipate patterns of wayfinding

behavior?

• In exploring this question, we concentrate on a syntactic

analysis of directed search performance. Thus, we chose to

describe the configurational properties of the urban space with

regard to both street connectivity and visibility patterns to

determine the extent to which spatial patterns affect

navigational performance.

12

Analytical Framework



• Segment-based Measures of Connectivity

Street connectivity measures applied in this research were

developed by Peponis et al.1 at GaTech. They offer a

systematic framework through which to evaluate the urban

fabric in terms of its potentiality (density of streets) and

structure (directional bias based on configuration).

13


1 Peponis, J., Bafna, S., Zhang, Z. (2008). The connectivity of streets: reach and directional distance. Environment and Planning (B): Planning and Design, 35: 881-901.

The analysis is based on standard segment-based

representations of street networks according to street center-

lines.




The unit of analysis is the road segment.

Road segments may contain one or more line

segments.

14


1 Peponis, J., Bafna, S., Zhang, Z. (2008). The connectivity of streets: reach and directional distance. Environment and Planning (B): Planning and Design, 35: 881-901.




Metric reach captures the density of streets and street connections accessible from each individual road segment.

15

This is measured by the total

street length accessible from

each road segment moving in

all possible directions up to a

parametrically specified metric

distance threshold.

Peponis et al. (2008).





Directional reach measures the extent to which the entire street

network is accessible with few direction changes.

16

This is measured by the street

length which is accessible from

each road segment without

changing more than a

parametrically specified

number of directions.




Peponis et al. (2008).

• Visibility Graph Analysis

VGA identifies the extent to which locations are mutually visible

in a spatial network. Two nodes are connected if the

corresponding locations are mutually (both) visible (for each

other).

17


Isovists are of interest to spatial analysis, because they offer a

systematic framework of addressing the relationship between

the navigator and her immediate spatial environment.



Depthmap2 was used for the visibility graph analysis. To study a large urban complex such as the Sultanahmet district, one global and two local measures were used.

visual integration (HH)

describes the relative visibility of a point to all others within the system; reflects the centrality of a node with respect to the whole graph.

• Visibility Graph Analysis

18




visual clustering coefficient (Ci)

reveals the intervisible space within the visibility neighborhood of a point; detects how visual information varies as one navigates the system.

visual connectivity

is a local measure which captures the amount of space directly visible from each node, and thus approximates isovist area.

Parcel-based data

were categorized into

residential and

non-residential

(office, retail, institution,

recreation, industrial) to

distinguish between

the effects of each on

the distribution of

movement.

19




• Land-use Compositions

Gross densities of land use were calculated as a linear measure at the street segment scale.

20





Residential and non-

residential building

square meter

associated with each

individual street

segment was

computed and

relativized by

segment length.

21





22

• Introduction



• Study area


• Conclusion



23

The Study Area



• Sultanahmet

Sultanahmet district, the heart of historic old Istanbul and a popular sightseeing area, was chosen as the study area.

24

The Study Area



• Sultanahmet

The old city of istanbul, once the capital of the Roman, Byzantine and Ottoman Empires, reflects the unique values of these cultures through urban, historical, archeological and natural sites.

• Sultanahmet

Embedded within a rich cultural and historical heritage, the peninsula includes monuments, examples of civil architecture, and archeological remains.

25

The Study Area



• Sultanahmet

The spatial layout of the area is comprised of two separate layers:

the retail-led live centre including the prominent historic landmarks;

the seaside Promenades on the Northern and Southern sides serving as entry points opening onto the centre.

26

The Study Area



Golden Horn

BosphorousMarmara Sea

27

The Study Area



• Sultanahmet

"High Street” which survived through the Byzantine and Ottoman times to the present is the main spine and the dominant axis of the old city.

The “High Street” linking the Golden Gate, the largest of the city Wall Gates, to the city center passed through several urban spaces, before leading to the Hagia Sofia Square, which is the historical centre of the city.

28

The Study Area



• Sultanahmet

Golden Horn

Marmara Sea

• Sultanahmet

The “High Street” crosses the historic core on the east-west axis, linking the centre with the surrounding neighborhoods.

29

The Study Area



• Sultanahmet

The area was recently exposed to an intense vehicle and pedestrian demand that led to its degeneration.

30

The Study Area

In spite of the recent attempts by the Municipality to encourage walking by banning vehicle circulation along certain streets and remodeling sidewalks, the touristic area seems to generate a number of wayfinding problems.



• Wayfinding problems

In spite of their proximity to the historic core, the Northern and Southern Promenades are isolated from each other due to their reduced visual permeability.

Hence; they fail to form a unified city center.

31

The Study Area

Centre



32

The Study Area



views from the promenades into the historic core


Uninviting views from the Promenades into the center hinder urban legibility and lack in appeal for visitors to enter into the neighborhood behind.

Consequently, seaside Promenades fail to operate at levels required for a thriving gate to the historic centre.


The adjacent intersecting streets are disconnected from the High Street both spatially and visually. Hence, pedestrian activity is distributed along this linear axis, failing to penetrate into the adjacent spaces.

33

The Study Area



34

• Spatial Analysis of the Area

In order to reveal the visibility patterns of the Sultanahmet district

as a function of its spatial configuration, a visibility graph of the

area was constructed.

The Study Area



visual clustering coefficient.

The historic core of the district and the part of the High Street

that is closest to the core are favored by visual connectivity.

The pattern of clustering coefficient shows that Sultanahmet square, the High Street and the spaces linking the pier with the core offer multidirectional fields of view, suggesting a space system with potential for possible copresence and interaction.

As we move through the transitional spaces surrounding the historic core towards the neighboring areas, spaces become gradually more clustered.

visual connectivity

35

In the directional accessibility model the High Street, secondary

streets intersecting the High Street perpendicularly, and the

diagonal street connecting the Sultanahmet Square with the

Cistern Basilica are picked up.

The Study Area



• Spatial Analysis of the Area

streets are color-coded at the same scale according to 0-directional reach (10o).

36

• Introduction



• Study area


• Conclusion



To understand how urban spatial configuration affects

wayfinding performance, first-time visitors’ directed search

patterns within a 2x2km area in the historical peninsula were

recorded.

The recorded data were analyzed to determine the extent to

which spatial measures are related to spatial learning –path

selection for exploratory movement.

19 undergraduate architecture students without any prior

familiarity with the urban context were asked to conduct directed

searches for specific locations.

37

Spatial Layout and Wayfinding Behavior

• Wayfinding Data



38

Students were asked to navigate

between 6 predetermined

landmarks in the area.

These specific locations are the

most frequently visited locations

by tourists and the main sites

included in guided walking tours.

Subjects were divided into two

groups with different starting but

same ending points.




39

Northern Promenade

Southern Promenade




40

Before performing

the search tasks,

subjects were shown

an abstract map to

get familiarized with

the area.

The tracks they

followed were recorded

with a GPS device.


Sea of Marmara

starting point: pier

railway terminus

Old Postoffice

Grand Bazaar

Beyazıt Mosque

Nuriosmaniye Mosque Topkapı

Palace

Basilica Cistern

Turkish-Islamic

Museum

N

Palace of Justice

Hagia Sophia

High Street and tramline

Little Hagia Sophia

Blue Mosque

tram

line

rampart



starting point

railway terminus

Old Postoffice

Grand Bazaar

Beyazıt Mosque

Nuriosmaniye Mosque Topkapı

Palace

Basilica Cistern

Turkish-Islamic

Museum

N

Palace of Justice

Hagia Sophia

High Street and tramline

Little Hagia Sophia

Blue Mosque

tram

line

Sea of Marmara

rampart

41

Subjects were

instructed to read

signage available but

not to ask any questions

during their search.

At the end of their

search subjects were

interviewed with regard

to their wayfinding

behaviors.




42

Wayfinding routes starting

from and ending at the

Northern Promenade

Wayfinding routes starting

from the Southern

promenade and ending at

the Northern Promenade




43

• The overall search patterns demonstrate that visitors’

distribution is strongly biased towards the central spaces at the

core of the district. They then tend to move along the central

axis of the area along the High Street and along a series of

shorter cross axes.


• This indicates that analyzing the movement patterns as a

function of configurational variables may prove fruitful in

understanding wayfinding behavior in the Sultanahmet area.



• To carry out quantitative analysis, we developed regression

models. When bivariate regression equations were estimated

for metric reach for 1, 0.5, and 0.25 mile ranges and directional

reach for 0 and 2 direction changes separately, the highest

coefficient of determination (r2) was obtained for 1 mile range

and 0 direction changes. As a result, we report the coefficients

of linear models computed with metric reach (1 mile) and 0-

directional change (10o).

45




• In the multivariate model, we first included the visibility properties in the model, and then added the connectivity measures, and land-use measures to the model respectively.

46

Visibility measures

B t std β

constant L̶ 6.04 L̶

avg. HH -0.06 -0.21 -0.02 avg. Ci 0.08 0.05 0.00 avg. connectivity 0.00 9.18 0.64 Metric Reach (1mile) L̶ L̶ L̶

0-Directional Reach (10o) L̶ L̶ L̶

residential m2 /100m L̶ L̶ L̶

non-residential m2 /100m L̶ L̶ L̶

N=273

R20.39

R2 adjusted 0.38

+ Accessibility measures

B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41

Note: Numbers in bold=significant difference (p<0.01); * significant difference (p < 0.05)




• Results of multivariate regressions estimating the frequency of

use of road segments reveal a considerable effect of urban

layout on directed search performance.

47

Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41




Note: Numbers in bold=significant difference (p<0.01); * significant difference (p < 0.05)

• 42% of space occupancy can be explained by the spatial structure of the urban environment.

48

Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41

Note: Numbers in bold= significant difference (p<0.01); * significant difference (p < 0.05)




• That is; the highest levels of visitors’ road choice were observed in visually most connected areas and in street networks with more direct connections.

49

Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41





50




Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41


• The average visual connectivity is found to be the strongest determinant of search patterns.

51




Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41


• Adding accessibility measures, metric reach (1 mile) and 0-directional reach (10o), increases the explanatory power of the model, though marginally.

52




Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41


• Among the accessibility measures street network density; that is, metric reach has a higher significance level than 0-directional reach (10o).

53




Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41


• Land-use variables, residential and non-residential square meters per 100m, do not enter the model as statistically significant variables.

54




Visibility measures

B t std β






N=273

R20.39

R2 adjusted 0.38


B t std β

L̶ -1.29 L̶

-0.11 -0.38 -0.04

0.01 0.00 0.00

0.00 9.78 0.67

0.07 3.32 0.16

3.48* 2.33* 0.11*

L̶ L̶ L̶

L̶ L̶ L̶

0.42

0.41

+ Land-use measures

B t std β

L̶ -0.97 L̶

-0.09 -0.30 -0.03

0.04 0.02 0.00

0.00 0.00 9.41

0.07 2.73 0.15

3.36* 2.23* 0.11*

0.00 -0.57 -0.03

0.00 0.28 0.01

0.42

0.41


• It can be speculated that since the study area includes a rather homogenous land-use composition, wherein non-residential use dominates, it fails to capture the variations in route selections.

55

• The variables not significant at 5% level were eliminated one

at a time to produce a “reduced” model.

• The primary factors in explaining path selection are average

visual connectivity, and metric and directional accessibility.

Reduced Model

B t std β constant

L̶ -1.41 L̶ avg. Connectivity

0.00 13.65 0.65 Metric Reach (1mile)

0.07 3.30 0.16 0-Directional Reach (10o)

3.42* 2.35* 0.11*

N=273

R2 0.42

R2 adjusted 0.41





• This finding supports the argument that direction changes

are critical to the way in which environments are

understood, and that people orient themselves with respect

to frames of reference that are as linear as possible.

56




57

• Introduction



• Study area


• Conclusion



58

• This study, albeit a work in progress, is a contribution to better

understanding the interplay between

visibility patterns (VGA measures),

Conclusion



accessibility patterns (segment-based street connectivity measures)

land-use compositions (parcel-based)

in the description of visitors’ movement patterns in complex

built environments.

59

• Study Outcomes:

The case study reported here indicates that the spatial configuration of urban layout is significantly associated with visitors’ search patterns as they move through urban spaces.

Conclusion



The observed wayfinding paths were shown to be structured in relation to the patterns of visibility and accessibility of spaital configuration.

Thus, we expect inexperienced users to follow the pattern of visual connectivity and spatial accessibility, preferring to travel along road segments with higher values on these measures.

Furthermore, the preliminary results underscore the significance of direction changes in wayfinding behavior. Consistent with theory, route selection is sensitive to the directional accessibility offered by street networks.

• Study Outcomes:

60

Conclusion



Hence, it can be concluded that increasing density of available streets and reducing direction changes within urban areas results in higher levels of pedestrians using the space.

61

In light of these study outcomes, strategic planning and urban design guidelines for efficient layouts of complex pedestrian spaces can be developed.

Conclusion



• Implications:

Veysi Altıntaş, proposed design Pelin Albayrak, proposed design


KUBAT, A. S.; OZBIL, A.; OZER, O.; EKINOGLU, H. 62

In light of these study outcomes, strategic planning and urban design guidelines for efficient layouts of complex pedestrian spaces can be developed.

Conclusion

• Implications:

Notwithstanding the significant and independent effect of configurational parameters on space-use, urban design features (building height profiles and signage) and other urban form dimensions (such as topographical variations and choice values) might have significant effects on patterns of movement.

63

Conclusion



• Further Research:

64

Conclusion



• Further Research:

Hence, further research is needed to unveil the influence of these urban form and urban design attributes on the extent to which spatial configuration of urban space affects wayfinding behavior.

65

Thank you.



8th International Space Syntax Symposium,

3-6 January, 2012

Santiago, Chile

Documents

THE EFFECT OF BUILT SPACE ON WAYFINDING IN URBAN ENVIRONMENTS: a study of the historical peninsula in İstanbul 1 Ayşe Sema KUBAT, Ayşe ÖZBİL, Özlem ÖZER,