An approach for simulating the street spatial patterns - Springer Link

BUILD SIMUL (2011) 4: 321 – 333 DOI 10.1007/s12273-011-0055-2

An approach for simulating the street spatial patterns

School of Architecture and Urban Planning, Nanjing University, Hankou Road 22, Nanjing 210093, China

Abstract

Keywords

The form of the urban fabric plays an important role in urban morphology which not only expresses the city’s overall morphological features, but also describes the relationships between buildings and plots, which are called patterns. The majority of morphological studies have focused on the street network pattern, the plot pattern and the building pattern or typology. In the other hand, the form of the urban fabric also reflects the city’s space character, such as street spatial outline, visual variety, and spatial typologies, which are directly related to the quality of urban space. Although these characters influence the urban design decision-taking, the current research has not reached an effective level. With a special focus on potentials for urban design, this paper proposes an account of how building position affects the street spatial pattern. Based on the viewshed analysis in GIS, the variance between the form of the urban fabric and the street viewshed pattern could be measured. The study samples were 600 meters square samples of urban fabric image selected from different cities of Europe, America, and China. This paper proposes a new kind of pattern: street spatial related line (SSRL) pattern. Through viewshed simulating it shows that there is a strong relationship between SSRL pattern and street spatial configuration, and that visual statistical diagrams could indicate the street spatial characters.

urban fabric,

1

Introduction

E-mail: [email protected]

pattern analysis, viewshed analysis, visual statistical diagram

Article History Received: 24 July 2011 Revised: 3 November 2011 Accepted: 8 November 2011 © Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2011

urban morphology description, the spatial characteristics of the city and the characteristics of the city in space (Kostof 1991). Both are designed for the description of urban morphology but from different viewpoints. The former is flying above the city, and the later standing inside the city. In the first group, the research of the urban morphology has accumulated rich materials (Gauthier and Gilliland 2005); the most important contribution is Conzenian School. The morphologic theory of geographer M. R. G. Conzen identifies plan element complexes with street system, plot pattern, and building pattern, which make use of concepts and strategies relating to transformation borrowed from geomorphology (Kropf 2001). Based on the architecture, Italian architect Saverio Muratori studied the evolution of urban morphology from the standpoint of typology, linking it with building activities. Therefore, the combination of types of building and buildings in cities forms the basic type of urban fabric, as well as basic unit of urban morphology. Both Muratori and Conzen combine the changes in the single unit and the urban fabrics from historical, social and economic perspectives (Moudon 1997). Particularly, the introduction of the concept

Architecture and Human Behavior

Urban fabric is tightly related to urban morphology, exploring urban fabric is one of the major focuses of urban morphological studies. This is an important factor in representing the form of the city since it not only reflects the characteristics of urban morphology in the meso-level, but also reflects features of spatial urban morphology in the micro-level. Research on urban fabric usually starts with a description of specific patterns of morphological characteristics, depending on the subjects of the research. Architectural historian Spiro Kostof studied urban fabric from the viewpoint of historical development and introduced the term “pattern” for the discussion of urban fabric (Kostof 1991). Kostof categorized the forms of urban fabric, which divided urban forms into 9 types and provided detailed description about the cause and specific pattern of each form. These patterns not only described the characteristics of different forms, but also analyzed the reasons why these forms of urban fabrics came into being in the development of cities. His research has shown two groups of approach in

Research Article

Wowo Ding(), Ziyu Tong

322

“plot” allows direct connection with urban development guidelines. The second group deals with the characteristics of the city in space. Research on spatial form of the city can be traced back to Camillo Sitte, who studied different types of city spaces through specific buildings, and proposed traffic organization methods and street landscapes for modern cities (Sitte 1889). In his book, Sitte uses visual experience in the urban space as an important reference for city planning, which is not only the arrangement of physical structures and functions, but also the presentation of the spatial morphology of the physical structures in the city. Similarly, other urban planning theoreticians, including Kevin Lynch (1960), Rob Krier (1979) and Alexander Christopher (1987) regard buildings as the basic units of urban morphology in their discussion about the shaping of spatial morphology in the city. In follow-up studies, Lynch integrated elements of morphology that reflect the quality of the city into discussions about the forms of urban fabric, which he described as the morphological layers of the city. In general, they are called urban form. The secondary layer is called urban texture. In one book, Kevin Lynch holds that, for the quality of a city, the inner texture is more important than the outer profile (Lynch 1981). However, unlike first group, there aren’t sufficient patterns to describe urban spatial form, so that research in this field lacks a strong methodology for support. Therefore, despite the interdependent relationship between the material form of a city and the urban fabric, the researches on the micromorphology from the angle of the urban space are not satisfying (Whitehand 2001). On the other hand, considering the urban design, we urgently need to establish a relationship between the material form of a city and its urban space on a quantitative basis. As no mature methodology for urban spatial morphological research is available at the present time, the only way to solve this problem is to gradually move our knowledge of the urban morphology toward the operational level. The biggest challenge is the methods employed, i.e., the methods for site survey, sampling and description of the spatial morphology of the city. Recent years have witnessed some new advances in urban morphology studies using the fractal method to research the urban profile of the street (Cooper 2005), and the raster to analyze the urban space of the urban blocks (Ratti 2004). Particularly, progress in geological information system technology directly aids research on the city growth mechanism and its morphology. Our prior research indicates that a key factor for both urban fabric and urban spatial form is how buildings stand next to each other along the urban streets (Ding and Liu 2007). Based on urban fabric patterns, our idea is to try to simulate the street spatial form, and translate it to two dimensional maps with form data,

Ding and Tong / Building Simulation / Vol. 4, No. 4

which might prove to be an alternative approach. This paper has conducted a series of studies: first, the research will discuss why we need a new pattern and how the new pattern could work for analyzing urban fabric characters and statistical data. Second, the new pattern will be simulated by viewshed analysis method in the GIS system to see if street spatial characters can be recognized systematically. In the end, the paper will examine the relationship between street spatial morphological characteristics and visual character statistical data, trying to establish the street spatial types on a quantitative basis. 2

The street spatial configuration

2.1 Patterns study Urban fabric could be translated into different kind of patterns. From the viewpoint of urban morphology, there are the street network pattern, the plot division pattern, and the building size and position pattern (Conzen 1960) (Fig. 1). The method of pattern description is connected directly with the subjects of the study, for instance, the street pattern is seen from the structure of urban form. Another tool for analyzing urban form has been the figure-ground diagram (Rowe and Koetter 1984) for describing the “spatial predicament” of the Modernist city. The advantage of the figure-ground pattern (Fig. 2) is that it represents urban spatial configuration much better than others. The figure-ground pattern represents street pattern and plot pattern in 2D drawings, but building pattern is expressed with axonometric drawings (Kostof 1991). Obviously, this method is more concerned with the impact of 3D spatial information of physical buildings on urban morphology as a whole. Research on the spatial morphology of streets depend not only on existing street patterns, but also on new patterns generated by the building pattern with 3D information. In order to study available drawings for urban spatial pattern our research decided to go beyond the existing patterns and return to urban fabric and urban spatial phenomena. We tried to study urban space through two parallel viewpoints: aerial view and human eye view. The airscape (aerial photograph) and the pictures of streets within the

Fig. 1 Morphological pattern

Ding and Tong / Building Simulation / Vol. 4, No. 4

Fig. 2 Figure-ground pattern

physical form of the same city reflect two different dimensions and viewpoints between urban morphology and urban space. With the help of Google Map, we selected cities with typical urban fabric forms with the size of 600 m ´ 600 m each. The urban fabric samples are selected according to the following principles: a) the sample cities shall have different morphological characteristics; b) each sample city should

Fig. 3 Six samples

323

have a number of sample plots with different fabric characteristics; and c) pictures of specific streets in the area are available. First, the aerial photographs are converted into site plan drawings based on the projection lines of the buildings and streets (Fig. 3(a)). Separating site plans, the street patterns and building patterns are easily produced (Fig. 3(b), (c)). Comparing six samples of street patterns and building patterns, in the first five samples, the character of street pattern and building pattern kept similar form logic with their site plan and with the morphological characteristics of their aerial photograph as well, but the Nanjing sample does not. In the Nanjing sample, the logic of the street pattern is completely missing in its building pattern, where is no distinct morphological connection between the building pattern and the street pattern. Obviously, building patterns are most important in reflecting the morphological characteristics of the urban fabric. Lynch (1960) analyzed methods and approaches for urban cognition from the viewpoint of human cognition, and summarized five key elements for the cognition of famous cities. “Paths” shape the basic framework of the urban fabric. “Edges” are the key to identification of the urban fabric. In Lynch’s research, “edges” are buildings and objects with vertical facets along the streets (Lynch 1960). Urban spatial cognition experiments prove that, among the elements that

324

Ding and Tong / Building Simulation / Vol. 4, No. 4

impact urban environment, vertical elements tend to have a much stronger impact than horizontal elements (Yang et al. 2007). Building configuration is best characterized by area coverage showing the regularity of the spatial arrangement, which is an important spatial metric for study (Herold et al. 2003). To further explore the impact of building patterns for street spatial configuration, we have to purify the building patterns. 2.2

The street views and spaces

The movement space constituted by streets forms the essential connective tissue of urban public space—from the micro scale of circulation within buildings to the macro scale of whole cities (Marshall 2006). Drawing up urban spatial pattern on the basis of human eyes will be a bottleneck in the study of urban spatial morphology. So far, there have been some studies on spatial profiles of streets based on the human viewpoint and visibility, analyzing photos from a given street to extract key elements as a way to measure the spatial characteristics of the street (Porta and Renne 2005). Another approach tried to calculate street openness through the visual sphere model and mark the results directly on street maps, so that readers could directly perceive the visual experience of the street space. It successfully addresses the challenge concerning the calculation of openness of street spaces with different heights along the same street (Gewirtzman et al. 2005). The advantage of these methods is that they link spatial form with human eyes, giving meaning to street spatial form (Fig. 4).

Returning to the patterns, on the street level, the line of buildings pattern becomes the street wall; the more continuous the line is, the more defined the wall, which gives a strong spatial form to the street (Fig. 4(a)). If the line is sparse, disconnected, or even does not exist at all, it will have a road but no street spatial form, or only a weak, unclear form (Fig. 4(b)). Practically, with regard to design and decision making for urban space, the visual perception of space along the street is more important, which depends on the distribution of buildings along the street. Street spatial configuration could be described as a series of disjoint convex spaces composed of two sides (Fig. 5). 2.3

The street spatial related line pattern

Based on existing research findings, we designed to go further by using visual analysis methods to record the street spatial characteristics. The results will lead us to search for a new type of pattern. The first important thing is to find the proper pattern for street visual study. In the first place, we overlapped the existing street pattern and building pattern with the street spatial related line pattern (SSRL pattern). To make it easier to describe, we simplified the contour lines of the buildings1 (Fig. 6). From the figure, we can see that the SSRL pattern consists of a series of continuous or disconnected concave/convex lines. In line with the concave/convex characteristic of the SSRL pattern and the visual analysis method, we extracted one section to study the relationship between the visual results and the concave/convex geometric characteristics for categorization. 3

Street visual types and spatial figure

The study of the sample slices should take into consideration variation in the street width, the sizes of concave/convex lines, and the mutual impact of the concave/convex groups. Convex spaces can be directly associated with isovists, which is a field of vision from which various geometrical properties, such as area and perimeter, can be calculated (Batty 2000). 3.1

Concave/convex characteristics

First, we used a concave slice as the basic unit of our research (Fig. 7). The description of the characteristics of a concave slice involves four parameters: x, x1, x2 and y, or the so-called “shape parameters”. Specifically, x1 and x2 are the 1

Fig. 4 Street view and building pattern

As our study focused on the measurement of street space, we kept the lines that directly impact the spatial profile of the street, including those perpendicular walls. To simplify the research, the length of all perpendicular walls was set at 10 m.

325

Ding and Tong / Building Simulation / Vol. 4, No. 4

Fig. 5 Street spatial configuration

Fig. 6 Street spatial related line pattern

lengths of two sides of the concave, while x is the distance between inside of the concave and the building redline. And y is the width of the concave openness. In addition to the shape characteristics of the concave itself, parameters affecting the result of street visibility rely on y1, y2 and s, or the so-called “relevant parameters”. Particularly, s is the distance from the building redline to the center line of the street; y1 and y2 depend on the position of the slice. Based on the parameters, the diverse concave space can be described quantitatively even for some special shapes: If x1≠x2; x1 ≤ x and x2 ≤ x, the lengths of both sides of the concave are not identical, and the boundaries of buildings on both sides are not on the same line. It is a common building profile, or what we call a “standard concave” (Fig. 7). If x1 = 0 and x2 = 0, y is meaningless, and the boundary of the buildings along the street is smooth without concave or convex (Fig. 8(a)). If x2 (or x1) = 0, the concave becomes a broken line (Fig. 8(b)), and y remains meaningless. If x1 = x2 ≤ x, the lengths of both sides of the concave are identical (Fig. 8(c)). In actual situations, there’s another possibility: the concave leads to an alley. In this case, x exceeds the value range of the slice (Fig. 8(d)).

As a standard concave space it has five surfaces and four turning points (Fig. 9). With regard to the visual perception of the street space, as the sightline is a straight line starting from the point of observation, each turning point would block a part of the view. The times that each surface is observed vary depending on the relations between the four shape parameters and the three relevant parameters of the concave. Through geometric calculations, we could analyze the functional relations of the parameters under the critical perception conditions of the surfaces. For a concave slice, the farthest end along the path of observation is the most disadvantaged position to observe the concave. When three points (the P1 point, the intersection point of surfaces W1 and W2, and the intersection point of surfaces W3 and W4) are linked by one sightline: (s + x - x1)/ y1 = x1/ y

(1)

So: y = y1´ x1/(s + x - x1)

(2)

With similar method of calculation, when the other three points (the P1 point, the intersection point of surfaces W1 and W2, and the intersection point of surfaces W4 and W5) are linked by one sightline: y = y1´ ( x1 - x 2)/(s + x - x1)

(3)

Also, when the three points (the P2 point, the intersection point of surfaces W4 and W5, and the intersection point of surfaces W2 and W3) are linked by one sightline: y = y 2 ´ x 2/(s + x - x 2)

(4)

If Eq. (2) and Eq. (4) are satisfied simultaneously, the function between y1 and y2 is: Fig. 7 A basic concave slice

y1 = y 2 ´ x 2 ´ (s + x - x1)/( x1´ (s + x - x 2))

(5)

326

Ding and Tong / Building Simulation / Vol. 4, No. 4

Fig. 9 Faces and turning points of the concave space and sightlines

3.2

Viewshed and simulating

We selected ArcGIS as the platform for the further research, since its built-in tool viewshed analysis could be used as a calculation tool to measure the geometric features of the street space. The idea is that if we categorized the visual characteristics of the street with viewshed, then we could match the geometric characteristic of the street space with the visual characteristics. In an effort to describe street spatial patterns for space decision making, we described the street spatial configuration with quantitative metrics of the geometric characteristics of the street space. Viewshed analysis creates a raster, recording the number of times each area can be seen from the observation points. With such operation, we can get the number of times that each point along the building boundary of the street space could be observed from the center line of the street, which reflects the varying visual perception degree of the building boundary. Extracting the profile along the building boundary from the viewshed result, the visual statistical diagram (VSD) could describe the visual perception of the street space (Fig. 10). The x-axis of the VSD represents all the points

Fig. 8 Some special concave space with diverse parameters (a) x1 = x2 = 0; (b) x2 = 0; (c) x1 = x2≤x; (d) x = ∞

Thus, we have shown visibility from one piece of concave space under extreme conditions. Based on these functions we could easily get to know the whole streets’ spatial configuration quantitatively through a variety of calculations.

Fig. 10 Viewshed and visual statistical diagram (VSD)

Ding and Tong / Building Simulation / Vol. 4, No. 4

on the contour line of the building boundary, and y-axis indicates the number of times that each point of the x-axis that could be seen from the center line of the street. Obviously, as the x-axis of the VSD is the contour line of the entire building boundary, its total length is always larger or equal to the street length. As the VSD is generated from the view-

327

shed analysis, the cell size of the viewshed will determine the smoothness of the VSD. With the viewshed analysis method and VSD, we conducted experiments on concaves with different parameters and looked for possible correlations between their spatial characteristics and visual perception characteristics (Fig. 11).

Fig. 11 Viewshed, VSD and perspective simulation of the concaves with different parameters. The vertical direction indicates the range of values of street width s, while the horizontal direction shows changes to the relations between x1 and x2. In addition, the concaves along both sides of each street indicates the range of values of y. Obviously, the value ranges of s and y affects mainly the extent of the VSD, while changes to the relations between x1 and x2 would affect the basic morphological characteristics of the VSD

328

Ding and Tong / Building Simulation / Vol. 4, No. 4

We conducted more experiments to analyze the impact of concave environmental parameters y1 and y2 (Fig. 12). The value ranges of y1 and y2 affect the basic morphological characteristics of the VSD. From the results of the above experiments, we could see that there are distinct correlations between the value ranges of the concave parameters and their visual perception. A given parameter value would inevitably lead to its corresponding VSD, while changes to a given VSD would indicate building boundaries of specific forms. Further analysis indicates that the cause of morphological changes to the VSD is the relation between observation points and the turning points of the concave space. However, it also shows that starting–ending points for calculation also affect the result of viewshed and VSD as well. 3.3

Street configuration and geometric graph

As indicated in the VSD, a street concave may have three forms (Fig. 13). A line parallel with the x-axis is drawn at the top of the y-axis (the peak form) to indicate the most frequently perceived street boundary. In the event that y-axis values change drastically, a straight line in an almost vertical direction from the x-axis (the cliff form) is drawn to indicate street boundary with drastic change to perception times. Beside these two forms, a zigzag line is drawn with moderate y-axis value changes (the wave form). All of the above three forms are directly correlated with the spatial characteristics of the street. First, the peak form indicates that those points on the building boundary along the street are visible from any point along the observation path. The observable times are

identical with the number of observation points. We call this kind of boundary a fully effective perception boundary. In terms of street spatial type, the most important factor is the position of the street wall, which is indicated by a fully effective perception boundary in a very precise way. It’s interesting that those fully effective perception boundaries are not always on the same line as we thought before, which means that continuous street walls are not necessarily limited in the same edge position. This result could yield new thinking for classifying the type of the street and designing a street. On the other hand, how long a fully effective perception boundary for one street is very much depends on the parameters x1, x2, y (shape parameters) and s (relevant parameters) of its SSRL pattern. Second, with regard to the cliff form, all points exist on the perpendicular surfaces (W2 and W4) of the concave without exception in the experiments. The characteristics are almost free from the impact of the shape parameters and relevant parameters of the concave. So if the cliff form appears in VSD, we can directly conclude that there’s a concave in that position. The number of the cliff mode is positively correlated with the number of concaves. As each concave has two perpendicular surfaces, two cliff forms should appear in the VSD. If a view statistical pattern has only one cliff mode, it means that the side along the street is not in concave lines, but in broken lines. In addition, the specific length of the cliff form is affected by the shape parameters and relevant parameters of the concave. Third, for the wave form, most of the points exist on the inner concave surface (W3), and are closely related to the shape parameters and relevant parameters of the concave. Particularly, x1 and x2 play a decisive role on the shape of

Fig. 12 Viewshed and VSD of the concaves with different parameters y1 and y2

Fig. 13 Three forms of the VSD

329

Ding and Tong / Building Simulation / Vol. 4, No. 4

the wave form. Our experiments show that, when the relevant parameters are unchanged, x1 = 10 m, and x2 varies from 10 m to 5 m to 0 m, the wave form in the VSD shifts from “U” shape to “W” shape to “V” shape. The experiments also show that y, y1, y2 and s affect the shape of wave form mainly through the extent of change, and have little impact on the basic morphological characteristics. The VSD obtained through viewshed analysis could accurately reflect the visual perception characteristics of the street space. The result indicates that the VSD is an alternative method for the measurement of street space. 4

Application

Viewshed experiment and VSD measurement has to be tested in the real street, where the situation is more complicated. What kind of relation between VSD form (the peak form, the cliff form and the wave form) and street spatial configurations could be verified in the real street? 4.1

Viewshed application

First, we selected one street from each of the six SSRL pattern samples as the object for testing. Then we set up observation points with an interval of 10 m, using ArcGIS for viewshed calculation to obtain the visual pattern of the street. In reality, buildings along the street were far more complicated than they were assumed to be in our pilot model. Pretreatment of the streets should be conducted before entry into ArcGIS to ensure the effectiveness of the test result. Since complexity had shaped the characteristics of the street space, we could not simplify those conditions without analysis. With regard to the streets selected, the following situations were often seen: (1) The size of an individual block is very small, which means the testing street might pass through a number of blocks, so that sizes and the number of the blocks directly impact the spatial characteristics of the street. (2) In an individual block the number of alleys would directly impact the spatial characteristics of the street, since the alleys are very narrow, while the length of individual alleys has little impact on the main street space. (3) With regard to buildings along the street, the extent of the

Fig. 14 Sample streets selected run across the entire sample areas

concave/convex change of the buildings’ outer lines is a very important element that affects the spatial characteristics of the street. (4) The block is open space or the buildings in an individual block are located apart from each other, which directly affects the spatial characteristics of the street. Characteristics of those four situations should be considered before viewshed calculation, and has to be read in visual patterns. To make it easier for follow-up analysis and comparison, the sample streets selected run across the entire sample areas (Fig. 14). Due to the fact that some street characteristics are missing at the SSRL pattern, some revision is necessary. Moreover, if the lines in the SSRL pattern are not continuous for viewshed calculation is impossible, so that it is necessary to consider how to convert disconnected line segments into a continuous line during the revision process. Therefore, we added a number of additional restrictions in our experiments. (1) In order to properly describe the characteristics of the crosses along the street, each cross has to have at least one observation point. The length of the crossing street is set as 60 m, and the end is closed by a line segment. (2) The length of each alley is set as 10 m, and closed by a line segment. (3) Since concave/convex facets of buildings along the street have an important impact on the spatial characteristics of the street, it is necessary to complete the outer lines of all the building facets along the street. (4) If the buildings along the street are separated by large openness or there is no building at all, the street is merged spatially with the openness. In other words, they do not compose independent, identifiable street space, and therefore are not in the scope of this paper. Importing the revised streets into ArcGIS for calculation, the viewshed patterns of the six streets are calculated (Fig. 15). Based on these viewshed patterns, the VSD of each street is obtained (Fig.16). 4.2 Visual statistical diagram analysis VSD shows the result of street facet perception at the observation points. Analyzing VSD we obtained eight groups data (Table 1): a. Number of perception points along whole boundary (Nw); b. Number of fully effective perception points along building boundary (Nf ); c. Number of minimum

330

Ding and Tong / Building Simulation / Vol. 4, No. 4

Fig. 15 Viewshed patterns of the six streets

Fig. 16 VSD of the six streets Table 1 Statistical values of VSDs of six samples

City

Number of points along the whole buildings boundary (Nw)

Number of points with fully effective perception Ratio 1 (Nf) (Nf/Nw)

Number of points with minimum perception Ratio 2 (Nm) (Nm/Nw)

Number of Points with other perception (Nn)

Number of times been percept along the whole buildings Ratio 3 boundary (Nn/Nw) (Tw)

Length of Number building of times boundary Length with fully with fully of effective effective perception Ratio 4 street perception Ratio 5 (Tf) (Tf/Tw) (Ls) (Lf) (Lf/Ls)

Manhattan left

5339

1847

34.59%

3145

58.91%

347

6.50%

127366

112667

88.46%

600

461.75

76.96%

Manhattan right

5338

1755

32.88%

3186

59.69%

397

7.44%

123073

107055

86.98%

600

438.75

73.13%

Paris left

4266

1883

44.14%

2138

50.12%

245

5.74%

121934

114863

94.20%

600

470.75

78.46%

Paris right

4446

1560

35.09%

2273

51.12%

613

13.79%

107888

95160

88.20%

600

390

65.00% 70.63%

Barcelona left

3603

1695

47.04%

1460

40.52%

448

12.43%

114674

103395

90.16%

600

423.75

Barcelona right

3567

1660

46.54%

1424

39.92%

483

13.54%

112979

101260

89.63%

600

415

69.17%

Berlin left

4775

2053

42.99%

2541

53.21%

181

3.79%

134961

125233

92.79%

600

513.25

85.54%

Berlin right

5238

1968

37.57%

3048

58.19%

222

4.24%

131323

120048

91.41%

600

492

82.00%

London left

5066

2082

41.10%

2905

57.34%

79

1.56%

133365

127002

95.23%

600

520.5

86.75%

London right

4487

1758

39.18%

1775

39.56%

954

21.26%

125295

107238

85.59%

600

439.5

73.25%

Nanjing left

3127

905

28.94%

120

3.84%

2102

67.22%

102535

55205

53.84%

600

226.25

37.71%

Nanjin right

2910

844

29.00%

147

5.05%

1919

65.95%

99313

51484

51.84%

600

211

35.17%

perception points along whole boundary (Nm)2; d. Number of points between fully effective perception and minimum perception along whole boundary (Nb); e. Number of times perceived along the whole boundary (Tw); f. Number of times with fully effective perception (Tf ); g. Length of street (Ls); h. Length of building boundary with fully effective perception (Lf ). Furthermore, five ratios are calculated to mine the potential of these data, which include Ratio 1 (Nf/ Nw), Ratio 2 (Nm/ Nw), Ratio 3 (Nb/Nw), Ratio 4 (Tf/ Tw), and Ratio 5 (Lf/ Lw).

From Table 1, we can see that for the same length of street, the number of perception points along a building’s boundary are complete different, which means that various building boundary situations are recorded by VSD. Based on VSD data we could determine the relationship between street and buildings along it. (1) The standard number of perception points along the whole boundary (Nrs) should be 24013. The cliff form and wave form can make the number of perception 3

2

In the experiments, the minimum perception value range is specified from 0 to 10% of the fully effective perception value.

In the experiments, the cell size of viewshed analysis is specified as 0.25 m. As the length of the street is 600 m, the standard number of perception points along the whole boundary is 600/0.25+1=2401.

331

Ding and Tong / Building Simulation / Vol. 4, No. 4

points along the whole boundary more than 2401. In the other words, if the number of perception points is much more than the standard points, there are some cliff form and wave form existence. (2) From Ratio 2 (Nm/Nw) we can see that five samples are 39.56% - 59.69%, but the Nanjing sample is only 3.84% 5.05%, while Ratio 3 (Nb/Nw) is reversed. In Ratio 3 Nanjing sample is 67.22% - 67.95%, but the others are 1.56% - 13.79%. This means that there are more cliff forms in the other five samples, and more wave forms in Nanjing sample. (3) From Ratio 4 (Tf/Tw) we could judge the situation of building boundary, since Tf records how many times there is fully effective perception along the street, which very much depends on the position of the buildings and shapes of buildings along the street. In Nanjing sample the value is only 51.84%, while other five samples are all over 85%. (4) Ratio 5 (Lf/Lw) most directly shows how long building boundaries fit the street lines, which could judge the extent to which building position fits street boundary. The most important value is that VSD records the perceptibility of the street wall: the times that each building boundary is perceived, the total length of the peak form, and continuous lengths of the peak form. The characteristics of street spatial facets could be defined by counting the fully effective perception times, and the rules of the cliff form and the wave form. 4.3

Street spatial character and visual statistical diagram

The next step is converting VSD analysis and statistics into street spatial characters and spatial types. (1) If the space of the street is completely closed, which means that there is neither cross nor alley along the street, and the facade of the buildings are also kept in

about the same line, then a. Nw = Nrs, b. Nf/Nw = 100%, Tf/Tw = 100%, and Lf/Lw = 100%, c. Nm/Nw and Nn/Nw have no meaning because there is neither cliff form nor wave form. (2) If the facade of the buildings along the street is kept in about the same line but with the crosses along the street, then a. Nw ＞Nrs, b. Tf/Tw＞80%, and c. the number of crosses is equal to two times the cliff form. The value of Lf/Lw depends on the width and number of crosses. If the value of Tf/Tw is much higher than Lf/Lw in the same street, then the size of the street block is small, the street network is dense, and the facade of the buildings along the street are kept to about the same line, e.g., in Barcelona (Fig. 17). (3) In the same block, the alley and concave/convex facets of buildings are also important for the street spatial characters; alleys produce cliff forms and concave/ convex facets of buildings produce both cliff forms and wave forms. (4) If the facade of the buildings along the street tends to be disordered, then the value of Nb/Nw will be very high, and Nm/Nw and Lf/Lw will be very low, e.g., in Nanjing (Fig. 18). 5 Conclusions Pattern is an important tool that needs to be studied in urban morphologic research, whether for formal analysis or for decision making in urban design. Our experiments have shown that urban spatial experiences can be simulated by group data based on special patterns. Knowing this will help us to understand urban space better in the following ways. First, based on the street pattern and building pattern, this paper developed a new pattern: SSRL pattern, which effectively reflects the quality of urban fabric and urban spatial characters. The study shows that researching urban fabric

Fig. 17 Comparing the fully effective perception points and length of six samples

332

Ding and Tong / Building Simulation / Vol. 4, No. 4

Fig. 18 Comparing three ratios of VSDs for six samples

(patterns) based on urban space is not only meaningful, but also of great potential. Second, SSRL pattern focuses on the spatial characteristics of entire urban streets, which is more of a microscopic approach depending on observation inside of streets. It could be further studied quantitatively in VSD analysis with 5 ratios. The advantage of VSD is that it not only describes the spatial type of the street but also indicates the physical position of the spatial variations, which promises great potential for urban design research. Among these data the most important one is the length of building boundary with fully effective perception (Lf), which directly describes the spatial character of the street. There are four types indicated by VSD statistical data: a. The street space is completely closed, b. The street space is well-defined but with the crosses along the street, c. The street space is well defined but with the alley and concave/convex facets of buildings along it, d. The street’s spatial form is very week, the facade of the buildings long the street tends to be disordered. Third, our experiment shows that it is worthwhile to introduce GIS, which mostly was used in the larger scale, to a microscopic approach. Based on the SSRL pattern we have focused on the space of the street, which is one of the most important elements for urban morphology at the micro-level. The viewshed technique in GIS is used as the measuring tool, which can draw a viewshed pattern and generate VSD. The former reflects the perception about the street space, while the later reveals the spatial relations about the observation. As this research is still in its initial stage, there is much work to be done. First, the SSRL pattern has to be further modified. Second, the analysis on non-fully effective perception times and spatial positions has yet to be completed. Third, as the models used are relatively simple, this research does not cover height changes, which are also important in

street spatial characters. More research should be conducted to determine whether our application of Viewshed is still valid after the incorporation of the height factors. We will think over all of those questions as we perform more detailed investigations. Nevertheless, searching for a way to work in two different scales simultaneously, the meso-level and the micro-level, seems promising. Acknowledgements We would like to thank the graduated students of the School of Architecture and Urban Planning, Nanjing University who provided the statistics of city samples. References Batty M (2001). Exploring isovist fields: space and shape in architectural and urban morphology. Environment and Planning B: Planning and Design, 28: 123  150. Christopher A (1987). A New Theory of Urban Design. New York: Oxford University Press. Conzen MRG (1960). Alnwick, Northumberland: A Study in Townplan Analysis. London: Orge Philip & Son . Cooper J (2005). Assessing urban character: the use of fractal analysis of street edges. Urban Morphology, 9: 95  107. Ding W, Liu Q (2007). The resolution at cognitive scale to urban physical spatial form. Modern Urban Research, 22(8): 32  41. (in Chinese) Gauthier P, Gilliland J (2005). Mapping urban morphology: a classification scheme for interpreting contributions to the study of urban form. Urban Morphology, 10: 41  20. Gewirtzman DF, Pinsly DS, Wagner IA, Burt M (2005). View-oriented three-dimensional visual analysis models for the urban environment. Urban Design International, 10: 23  27. Herold M, Liu XH, Clarke KC (2003). Spatial metrica and image texture for mapping urban land use. Photogrammetric Engineering & Remote Sensing, 69: 991  1001.

Ding and Tong / Building Simulation / Vol. 4, No. 4 Krier R (1979). Urban Space. London: Academy Edition. Kostof S (1991). The City Shaped—Urban Patterns and Meanings Through History. London: Thames and Hudson. Kropf KS (2001). Conceptions of change in the build environment. Urban Morphology, 5: 29  42. Lynch K (1960). The Image of the City. Cambridge, Massachusetts: The MIT Press. Lynch K (1981). A Theory of Good City Form. Cambridge, Massachusetts: The MIT Press. Marshall S (2006). Streets & Patterns. New York: Spon Press. Whitehand JWR (2001). British urban morphology: the Conzenian tradition. Urban Morphology, 5: 103  109. Moudon AV (1997). Urban morphology as an emerging interdisciplinary

333 field. Urban Morphology, 1: 3  10. Porta S, Renne JL (2005). Linking urban design to sustainability: formal indicators of social urban sustainability field research in Perth, Western Australia. Urban Design International, 10: 51  64. Ratti C (2004). Raster analysis of urban form. Environment and planning B: Planning and Design, 31: 297  309. Rowe C, Koetter F (1984). Collage City. Cambridge, Massachusetts: The MIT Press. Sitte C (1889). City Planning According to Artistic Principles. Vienna: Verlag von Carl Graeser. Yang PP, Putra SY, Chaerani M (2007). Computing the sense of time in urban physical environment. Urban Design International, 12: 115  129.