1 CORRELATING URBAN DESIGN QUALITIES TO PERCEIVED RESIDENTIAL DENSITY USING 3D COMPUTER SIMULATION By ALMA OTHMAN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE R EQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 201 7
2 201 7 Alma Othman
3 To my f ather
4 ACKNOWLEDGMENTS First and foremost, I want to thank my advisor Professor. Ilir Bejleri. I appreciate all his contributions of t ime, ideas, and funding to make my PhD experience productive. His hardworking, vision and determination inspired me to stand against challenges and make this project a reality. In addition to my advisor, I would like to thank the rest of my dissertation c ommittee: Prof. Paul Zwick for his support during my PhD studies and for his follow up in finalizing this dissertation; Prof. Jack Stenner and Prof. Peter Prugh, for their insightful comments and encouragement. I gratefully acknowledge the support of Pales tinian Faculty Development Program coordinated by AMIDEAST (West Bank) and funded by USAID (United States Agency of International Development) and the OSF (Open Society Foundation). The unique opportunities, connections and guidance provided throughout the program, enriched my experience as a PhD student. Last but not the least, I would like to thank my father who believed in me and advised me every single day in my life. I thank my husband, my kids, my brothers and my sisters for supporting me throughout the writing process. They helped me to accomplish what I started with their encouragement and kindness. As grateful as I am now writing these words as sad as I am for not sharing this accomplishment with my mom who passed away during my program, I thank yo u Mom! Along this time, I was fortuned to be surrounded by great friends who shared with me difficult times and nice memories and whose words and advise empowered me to skip barriers and to keep going, thank you all for everything.
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ ........ 11 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ ..... 15 Problem Statement ................................ ................................ ................................ 15 Research Purpose ................................ ................................ ................................ .. 17 Research Significance ................................ ................................ ............................ 19 Research Hypothesis ................................ ................................ .............................. 21 2 L ITERATURE REVIEW ................................ ................................ ........................... 22 Theoretical Research ................................ ................................ .............................. 22 Space and Place ................................ ................................ .............................. 23 Perception in Urban Environment ................................ ................................ ..... 24 Perceived Residential Density ................................ ................................ .......... 26 Density in Planning ................................ ................................ ........................... 28 Visualization Research ................................ ................................ ........................... 30 Visualization in Urban Design ................................ ................................ ........... 30 Visu alizing Density ................................ ................................ ........................... 31 Computerized Analytical Research ................................ ................................ ......... 33 Visualization in Urban Design ................................ ................................ ........... 33 Parametric Design at Urban Level ................................ ................................ .... 35 Performance Based Design ................................ ................................ .............. 36 Applications in Perceived Density ................................ ................................ .... 37 Measuring Environmental Qualities ................................ ................................ .. 40 Geographic Extent ................................ ................................ ................................ .. 42 Research Question ................................ ................................ ................................ 43 Challenges ................................ ................................ ................................ .............. 43 3 METHODOLOGY ................................ ................................ ................................ .... 44 Unit of Study ................................ ................................ ................................ ........... 45 Conceptual Framework ................................ ................................ ........................... 46 Study Design ................................ ................................ ................................ .......... 47 Physical Variable Measure s ................................ ................................ ............. 47
6 Aesthetic variables ................................ ................................ ..................... 48 Geometric variables ................................ ................................ ................... 48 Contextual Va riables Measures ................................ ................................ ........ 54 Socio Cultural and Economic Variables Measures ................................ ........... 55 Perceived Residential Density Measures ................................ ......................... 56 The behavioral constraint model ................................ ................................ 57 Overall arousal model ................................ ................................ ................ 57 Density control mode l ................................ ................................ ................ 58 Survey Instrument ................................ ................................ ............................ 59 Participants ................................ ................................ ................................ ....... 61 Fractional Factorial D esign ................................ ................................ ............... 61 Field Visit ................................ ................................ ................................ .......... 62 Three Dimensions Modelling ................................ ................................ ............ 62 Photo Edit ing and Cleaning ................................ ................................ .............. 63 Texture Mapping Technique ................................ ................................ ............. 64 Visual Survey ................................ ................................ ................................ ... 65 Expected Results and Analysis ................................ ................................ ........ 68 4 RESULTS ................................ ................................ ................................ ................ 77 Research Hypothesis ................................ ................................ .............................. 77 Sample Characteristics ................................ ................................ ..................... 77 Test of Reliability ................................ ................................ .............................. 77 Assumption ................................ ................................ ................................ ....... 78 Data Analysis ................................ ................................ ................................ .......... 79 Within subject Independent Variables ................................ .............................. 79 Testing Association ................................ ................................ .......................... 80 Enclosure association with dependent variables ................................ ........ 80 Faade complexity association with dependent variables .......................... 81 Number of intersecting streets correlation with dependent variables ......... 82 Number of people in the street correlation with dependent variables ......... 83 Type of activity association to the dependent variable ............................... 84 Between Subjects Variables ................................ ................................ ............. 86 Gender ................................ ................................ ................................ ....... 86 Time spent in USA ................................ ................................ ..................... 87 Design background ................................ ................................ .................... 88 Age ................................ ................................ ................................ ............ 89 Type of living place ................................ ................................ .................... 89 Visual Density versus verbal Expression of Density (Validity test) ............. 90 5 D ISCUSSION ................................ ................................ ................................ .......... 99 Enclosure and Dependent Variables ................................ ................................ ...... 99 Facade Complexity and Dependent Variables ................................ ...................... 101 Number of Intersecting Streets in the Scene and Dependent Variables ............... 103 Number of People in the Street and the Dependent Variables ............................. 104 Dependent Variable (Perceived Residential Density) ................................ ........... 105
7 Dependent Variables (Space crowdedness, Level of Comfort, Space Openness) ................................ ................................ ................................ ......... 108 Type of Social Activity Association with Perceived Residential Density ................ 110 Between ................................ ................................ ................ 111 6 C ONCL U SION ................................ ................................ ................................ ...... 114 Perceived Residential Density and Human Experience ................................ ........ 114 Perceived residentia l density and Urban Design Qualities ................................ .... 115 Perceived Residential Density and Type of Activity ................................ .............. 118 Perceived Residential Density an ......................... 118 Perceived Residential Density Versus Real Residential Density .......................... 119 Comments on The Research Met hod ................................ ................................ ... 119 Recommendation and Future Research Opportunities ................................ ......... 120 APPPENDIX: VISUAL SURVEY ................................ ................................ ................. 128 LIST OF REFERENCES ................................ ................................ ............................. 147 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 157
8 LIST OF TABLES Table page 3 1 Independent variables levels used in fractional factorial experiment. .................. 69 4 1 Results of s kewness and kurtosis for dependent variables. ................................ 91 4 2 Test of normality for the two groups of gender. ................................ ................... 91 4 3 Reliability testing for a (test retest correlation). ................................ ................... 91 4 4 Dependent variables test of variance. ................................ ................................ 92 4 5 Enclosure test of correlation with the four dependent variables. ......................... 92 4 6 Enclosure correlation with perceived residential density for grey colored scenes. ................................ ................................ ................................ ............... 92 4 7 Facade complexity test of correlation with the fo ur dependent variables. ........... 93 4 8 Number of intersecting street test of correlation with the four dependent variables. ................................ ................................ ................................ ............ 93 4 9 Intersecting street test of correlation with the four dependent variables when excluding cases that has high level of facade details. ................................ ........ 93 4 10 Intrsecting street test of correlation with p erceived residential density in a black and white street view. ................................ ................................ ................ 93 4 11 Intersecting street test of correlation with perceived residential density in 3D grey color images. ................................ ................................ .............................. 94 4 12 Number of people and level of activities correlation with the four dependent variables. ................................ ................................ ................................ ............ 94 4 13 Social situation correlation with the f our dependent variables. ............................ 94 4 14 Social situation correlation with the four independent variables. ......................... 94 4 15 Models ranking in case of social situation preference. ................................ ........ 95 4 16 Work space preference (formal situation) correlation with the four dependent variables. ................................ ................................ ................................ ............ 95 4 17 Work space preference correlation with the four independent variables. ............ 95 4 18 Test of differences between two groups of the independent variable (gender) in the level of c omfort at high perceived residential density scenes. .................. 96
9 4 19 Test of differences between three groups of the independent variable (Time spent at USA) in the level of comfort at high perceiv ed residential density scenes. ................................ ................................ ................................ ............... 96 4 20 Test of differences between three groups of the independent variable (Time spent at USA) in the space crowdedness at high perceived residential densit y scenes. ................................ ................................ ................................ ... 96 4 21 Test of differences between two groups of the independent variable (design experience) in the level of comfort at high perceived residential density scenes. ................................ ................................ ................................ ............... 97 4 22 Test of differences between four of the independent variable (age) in their perceived residential density. ................................ ................................ ............. 97 4 23 Test of diffe rences between four of the independent variable (age) in their perceived comfort at high perceived residential density settings. ....................... 97 4 24 Test of differences between four of the independent variable (living place) and the perceived residential density. ................................ ................................ 98 4 25 Test of differences between four groups of the independent variable (living place) and the level of comfort at high pe rceived residential density settings. ... 98 4 26 the residential density. ................................ ................................ ........................ 98 4 27 Crosstabs for verbal and visual expression of residential density. ...................... 98 6 1 Enclosure correlation with dependent variables (comparison between Kendall Tau and Spearman). ................................ ................................ ............ 123 6 2 Faade complexity correlation with dependent variables (comparison between Kendall Tau and Spearman). ................................ ............................. 123 6 3 Intersecting street correlation with dependent variables (comparison between Kendall Tau and Spearman). ................................ ................................ ............ 123 6 4 Existence of people and activities correlation with dependent v ariables (comparison between Kendall Tau and Spearman). ................................ ......... 123 6 5 Cross tabulation to measure percentage of ties in the data between space crowdedness at different categories of enclosure. ................................ ............ 12 4 6 6 Cross tabulation to measure percentage of ties in the data between level of comfort at different categories of enclosure. ................................ ..................... 124
10 6 7 Cross tabulation to measure percentage of ties in the data between space openness at different categories of enclosure. ................................ ................. 124 6 8 Cross tabulation to measure percentage of ties in the rank of space residential density at different categories of enclosure ................................ ..... 124 6 9 Cross tabulation to measure percentage of ties in the rank of space crowdedness at different categories of number of intersecting streets. ............ 125 6 10 Cross tabulation to measure percentage of ties in the rank of level of comfort at different categories of number of intersecting stree ts. ................................ .. 125 6 11 Cross tabulation to measure percentage of ties in the rank of space openness at different categories of number of intersecting streets. .................. 125 6 12 Cross tabulation to measure percentage of ties in the rank of perceived residential density at different categories of number of intersecting streets. .... 125 6 13 Cross tabulation to measure percentage of ties in the rank of space crowdedness at different categories of number of faade complexity. ............. 126 6 14 Cross tabulation to measure percentag e of ties in the rank of level of comfort at different categories of number of faade complexity. ................................ ... 126 6 15 Cross tabulation to measure percentage of ties in the rank of space openness at di fferent categories of number of faade complexity. ................... 126 6 16 Cross tabulation to measure percentage of ties in the rank of perceived residential density at different categories of number of faade complexity. ...... 126 6 17 Cross tabulation to measure percentage of ties in the rank of space crowdedness at different categories of number of existence of people in the scene. ................................ ................................ ................................ ............... 127 6 18 Cross tabulation to measure percentage of ties in the rank of level of comfort at different categories of number of existence of people in the scene. ............. 127 6 19 Cross tabulation to measure percentage of ties in the rank of space openness at different categories of number of existence of people in the scene. ................................ ................................ ................................ ............... 127 6 20 Cross tabulation to measure percentage of ties in the rank of perceived residential density at different categories of number of existence of people in the scene. ................................ ................................ ................................ ......... 127
11 LIST OF FIGURES Figure page 3 1 Atlantic Station plan ................................ ................................ .......................... 70 3 2 Flow chart of the procedure used in creating the visual survey. ......................... 70 3 3 Image cleaning process using Photoshop (before and after). ............................ 71 3 4 Image editing for a multiple section building (before and after) .......................... 71 3 5 Image editing that requires connecting different edited photos for large building (before and after) ................................ ................................ .................. 72 3 6 Box typical unfold method and the selection of edg es as seams to use for the unfold method. ................................ ................................ ................................ .... 73 3 7 Screenshot for 3D max unwrap process for simple box model in 3D studio Max. ................................ ................................ ................................ .................... 73 3 8 Applying edited facades into unfolded models faces in Photoshop. ................... 74 3 9 Using the material created in Photoshop to building models in 3D Studio Max. ................................ ................................ ................................ ................... 74 3 10 Screenshot for one scene from the first question in the survey instrument ....... 75 3 11 Black and white top view of a street ................................ ................................ .. 76 3 12 Nine scenes in grey color with no faade details or pedestrian included ........... 76 A 1 Introduction page for the first question in the su rvey showing the type of images that will be presented in the study scenes to familiarize participants with the type of images. ................................ ................................ .................... 130 A 2 Question 1 of the survey (first scene) to rank the d ependent variables. ........... 131 A 3 Question 1 of the survey (second scene) to rank the dependent variables. ..... 132 A 4 Question 1 of the survey (third scene) to rank the dependent variables. .......... 133 A 5 Question 1 of the survey (fourth scene) to rank the dependent variables. ........ 134 A 6 Question 1 of th e survey (fifth scene) to rank the dependent variables. ......... 135 A 7 Question 1 of the survey (sixth scene) to rank the dependent variables. .......... 136 A 8 Question 1 of the survey (seventh scene) to rank the dependent variables. .... 137
12 A 9 Question 1 of the survey (eighth s cene) to rank the dependent variables. ....... 138 A 10 Question 1 of the survey (ninth scene) to rank the dependent variables. ......... 139 A 11 Question 1 of the survey (tenth scene) to rank the dependent variables. ......... 140 A 12 Question 1 of the survey (eleventh scene ) to rank the dependent var iable s. ... 141 A 13 The first type of social activity in the second question of the survey, images used to measure connection between the preference of social activity and the dependent variables. ................................ ................................ .................. 142 A 14 The second type of social activity in the second question of the survey, images used to measure connection between the preference of social activity and the dependent variables. ................................ ................................ ........... 143 A 15 The third question in the survey used to test correlation between number of intersecting streets and the perceived residential density. ............................... 144 A 16 The fo urth question of the survey; grey color images used to measure correlation between street enclosure and number of intersecting streets with the dependent variables. ................................ ................................ .................. 144 A 17 Arial Images sho wing different type of residential areas with various residential density to test correlation between participants numeric expression and visual expression about residential density. ............................ 146
13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CORRELATING URBAN DESIGN QUALITIES TO PERCEIVED RES I D E NTIAL DENSITY USING 3D COMPUTER SIMULATION By Alm a Othman December 2017 Chair: Ilir Bejleri Cochair: Paul Zwick Major: Urban and Regional Planning Perceived residential density is one of the main urban design qualities that affect people daily experience, their perception of the environment, and th eir ongoing interaction with and within their environment. D ensity in planning research has been defined it by numbers (dwelling units or building/ unit area) which is not easy to imagine by lay people or to create spatial perception about the actual space Some recent research in perceived density used experimental methods to collect people input about ind icators of perceived density My research connected earlier studie s in density perception that is based on psychological model related to reactions to hi gh density situations and the new experimental methods in measuring perceptual input about people experience of certain urban spaces. The study used a visual survey to present 3D (three dimensional models for various combination of the study independen t variables and collect feedback from participant about certain qualities of the scenes as judged by the participants. 3D models were created using 3D studio max; images used in creating texture of the building is captured from At lantic Station, Atlanta, G A 77 participants successfully
14 answered the survey, responses were used to analyze correlations between the independent and the dependent variables. It c oncluded that the two variables: enclosure ( height to width ratio) and the people existence in the sc enes are the most correlated to perceived residential density and s pace crowdedness. When controlling for the f ac ade details variable the number of intersecting street correlated positively to perceived residential density and s pace crowdedness suggesting that increasing number of streets by reducing width of buildings and increasing masses is read by people as increase of residential density. Life earned experiences and cultural meaning and expressions was found to relate largely to the concept of perceive d residential density more than personal characteristics (gender, education). The study advices that perceived residential density is better studied under situational experiment in which people get to express their feeling about a place based on their int eraction and or exposure to certain social settings. Finally, it highlights certain design techniques that can contribute to improvement in people perception in higher residential density areas.
15 CHAPTER 1 INTRODUCTION Problem Statement Planners and pol icy makers generally use numbers to describe population density of proposed developments. When presenting anticipated population density within developments, a visual illustration is often missing or misleading for the viewer. Negative perceptions of densi ty can happen when high density is sometimes (mistakenly) equated with crowding (Sussna, 1973; Chin, et. al., 1976; Carmona, et. al., 2003). Developments with high density are usually resisted, due to associations of density with crowding, crime, and other dreadful attributes. (Campoli and MacLean, 2002; Urban Land Institute, 2005). The resistance is justified by the known historical conditions associating high density projects, for example many of the developments that took place after World War II were da ngerous and crowded (Zack, n.d.; Pader 2002; Flint, 2005; Churchman, 1999). Few studies have focused on density as a perceptual element not only as a measure of how many dwelling unit exists per Acre or number of people per Acer. Density needs to be under and interaction with a proposed environment (Jacobs, 1961; Rapoport, 1973; Churchman, 1999; Yang, et. al., 2005; Campoli & MacLean, 2002). In this way, density becomes a contextual and perception based phenomenon that is attached to an environment and accommodates cultural and social experiences of the people using that environment. This contextual nature of density requires a certain level of subjectivity in defining density to accommodate people expec tations of their environment.
16 Higher density development propositions usually assume positive outputs on social and physical levels of the environment. Considering the three dimensional to higher density developments. This can be used in drawing conclusions to understand the relationship between physical settings and density perception. Consequently, the designer would manipulate the physical settings of these developments to relate to s patial interests. Few studies have addressed the relationship between perceptual dimensions of density and the design aspects of an environment (Campoli & MacLean, 2002; Urban Land Institute, 2005). These studies rely on showing existing forms of density a nd were used to inform people about the visual dimension of density and improve the way people understand the concept of residential density. Other studies have investigated the impact of change in certain physical qualities of the environment on comfort, safety, satisfaction or on the environment functions, such as increasing walkability or accessibility. However, these studies have not tested the impact of physical qualities on perceived residential density. In thi s experimental study, a survey is used t background (independent variables: socio economic and contextual data) and evaluation for different 3D views). The relationship between inde pendent variables (physical qualities and socio economic variables) and dependent variables (perceived residential density, level of comfort, space crowdedness space openness) is tested using the data collected in the survey instrument.
17 Research Pur pose Smart Growth, New Urbanism and Transit Oriented Developments advocate certain physical settings required to help build communities that are more sustainable. Residential density in such settings was emphasized as part of a vibrant environment. In this framework, density is illustrated by numbers or ratios and has been loosely defined. The logic adopted by high residential density advocates usually takes the shape of environmental, economic and social arguments. However, when it comes to implementation such efforts face opposition. The source of this opposition is fear from high residential density as a concept that perceived as undesirable crowding. Urban Land Institute (2005) argued that people interviewed about their opinion of high density developme nts hold negative view, but when these developments are presented for them in images, people change their perceptions and prefer higher density over low density developments. Furthermore, planners utilize their land and policy experience in identifying app ropriate number of people per acre for a proposed plan. The impact of these numbers on existing environment in terms of urban dorms is not easy to be Density is related to social interact ions between people sharing an environment and using spaces and between people and the environment. In literature, the presence of people was linked to the success of public spaces, where everyone enjoys the urban space; in this way, individual people beco me one unit, that adds order and vitality to a space (Jacobs, 1961; Carmona, et. al., 2003). Embedded existence of people within buildings or urban spaces results from the physical arrangement of a development, and has a strong impact on the sociability of that development. The impact can also be seen
18 in the level of comfort people feel when us ing a space, and thus affects perception of how dense or crowded an environment is. obs erved beyond mere units or numbers. The concept of perceived residential density incorporates people, spaces and conscious interaction and sharing of physical and cultural aspects of these spaces. The understanding of perceived residential d ensity relation to measured density can help define best physical forms or aid in manipulating existing spaces to grasp different levels of density with less negative reaction from users. This research tests the impact of changes in physical settings on the perceived re sidential density of a given environment. Computer modeling techniques will be used to represent a 3D model of a high density urban area. The independent variables ( f aade complexity, enclosure, intersecting streets, existence of people) will be changed th roughout the experiment to provide a matrix of experimental conditions. The impact of each independent variable on the primary dependent variable (perceived residential experimental conditions in term of visual displays. Ultimately, this research will contribute to the methodological construct of perceived density and will provide the means to which the different physical qualities affect density perception. The output of this study could form a ground for creating visual tools that better describe spatial forms for different types of densities. Such tools could help planners and policy makers to comprehend the three dimensional impact of proposed developments and conseq uently be able to translate policy and numbers into
1 9 spatial forms. The study results could be used as a prototype for tangible or visual evidences of the applicability of high density developments. Empirically correlating physical form variables to the p erceived residential density measure could further help guiding perceptual input and improve criteria for contemporary trends in planning and designing form based codes and performance based design. These new trends currently depend on physical measures on ly, and still lack knowledge of possible perceptual input from users. This study will produce empirical findings based on feedback provided by people participated in the exp eriment, and will create guidelines for future research in perceived density In su m, this dissertation aims at contributing to the perceived density research as follows: First: It helps planners and policy makers to comprehend the three dimensional impact of proposed developments on existing spaces, and consequently to be able to tran slate policy and numbers into spatial forms. Secondly: The tool used will help provide people with tangible or visual evidence of the applicability of proposed development and will give alternative scenarios by which people can interactively select best fit development patterns that match their existing community. Third: Correlating physical form variables to perceived residential density will help provide criteria for setting form based codes that stem from participatory perceptual feedback from users. Research Significance High density has been a main component of new planning trends and studies, it was found to correlate positively to walkability (Jacobs, 1961; Belzer and outler, 2002; Brown, et. al., 2007; Moudon, et. al., 2003; Saelens, et. al., 20 03; Cozens, et. al., 2008),
20 sustainability (Krause and Sayani, 2006) reduced per capita vehicle miles traveled Transportation Committee revealed that doubling neighborhood density results on 20 30 percent reduced vehicle miles traveled (California Planning Roundtable, 2002), reduced gasoline consumption (Newman & Kenworthy, 1999), neighborhood accessibility (Krizik, 2009), innovation and creativity (Liu, 2003; Carlino, 2001; Urba n Land Institute, 2005; Krause and Sayani, 2006) and increasing productivity (Harris and Ionnides, 2000). Studying the perceptual dimension of density will help predict and understand the different components that contribute to successful higher density de velopments and spaces. Moreover, there is a need to further the experimental tools that relate physical forms to density measures; such tools need to emphasize perceptual components of density. Understanding perceived density can guide improvements to the physical environment and aid in making decisions concerning changes to urban structures, which will increase or decrease perceived residential density Consequently, designers and urban planners can expectations of their environment (Bau m and Davis, 1976; Churchman, 1999; Rapoport, 1977; Land Use Institute, 2005). Capturing perception of density can also help maximize the anticipated advantages of high density, such as increasing activities, bringing vitality for mix use, improving public facilities and transit systems (Jacobs, 1961; Yang, et. al., 2005). Perceived residential density portrays the pattern of interaction between people and places and helps in reading the cultural and social variables that contribute to this interaction (Ar c of innovation 495, n.d.; Yang, et. al., 2005; Campoli and MacLean,
21 2002). Further, it could reflect on concepts related to the convenience of that perceived environment, such as comfort, safety, health and refuge (Rapoport, 1977). Research Hypothesis Changes in certain physical qualities of the study area lead to psychological and perceptual reactions by study subjects. This is reflected in this research by different perceived information (overload, control level and level of freedom) captured th rough the dependent variables: perceived residential density perceived comfort, space crowdedness perceived openness. Previous literature highlights relationships between the independent and dependent variables, and suggest the testing of the following h ypotheses: Perceived residential density increases when enclosure (length to width ratio) increases. Perceived residential density increases when faade complexity increases Perceived residential density increases when number of openings between building s (or number of crossing streets) decreases. Perceived residential density increases when perceived existence of people and activities increases.
22 CHAPTER 2 LITERATURE REVIEW Existing research and literature related to perceived density belongs to t hree different categories: theoretical research, visualization research and computerized research (experimental). Theoretical Research Perception is the channel between physical environment inputs and human reaction, and encompasses mental processing for environmental cues. In this process, the mind is stimulated to attach meanings and find suitable reactions to the different situations. Studies in human perception of an urban environment have been the focus for many researchers, who believed in the need to understand the human environment interaction to design good cities (Lynch, 1960; Jacobs, 1961; Rapoport, 1977). The human mind is consciously interacting with the environment and creating decisions about where to go and what direction to take, or when to alter moods amongst happy, stressed or frustrated. This process is affected by the different experiences users have. A stranger could react differently to an environment in comparison to its inhibitors or frequent users. Bacon (1967) emphasized the imp Previous research was concerned with the analysis of individu al perceptual quality to understand how certain urban structures could affect perception for these qualities, and at the same time their overall perception of their environment. These qualities include perceived density (Rapoport, 1977; Churchman, 1999), p erceived crowding (Stokols, 1973; Baum and Davis, 1976; Chin, et. al., 1976), perceived
23 openness (Fisher Gewirtzman, 2003; Hayward & Franklin, 1974 ) and perceived enclosure (Stamps, 2005A; Stamps, 2005B; Alkhresheh, 2007). In the following sections, a th eoretical discussion regarding environmental perception will serve as the contextual framework for perception and how it is created. Space and Place The American Heritage Dictionary defines space (from Latin spatium elements or points satisf ying specific geometric conditions in a three dimensional field of everyday experience; the distance between two points or the area of volume between meaning to it, they are c hanging a space into a place. In addition, they construct al., 2003, p.97). The way an environment and people interact has been described in three different concepts: envi ronmental determinism, environmental possibilism, and environmental probabilism. The environmental determinism assumes that environment has a determining influence on behavior. The environmental possibilism suggests that an environment gives options for pe ople from which they can find what best fits their lives. While the environmental probabilism implies that in a certain environmental setting some decisions become more likely to be taken than others (Carmona, et. al., 2003). Research has shown that there are levels of interpretability of human behavior, based on previously tested settings, such as building forms or greenery impact on human behavior (Nasar, 1994; Bacon, 1967). My study leans more toward environmental probabilism, because it assumes that spe cific environmental settings have a set of decisions.
24 Trancik (1986) suggests that designers should focus not only on creating spaces, but on creating places through synthesi zing all components of an environment, including the social and cultural components. This is the base of the Place Theory in physical and linkage components. Per the Place The ory a space becomes place only when adding its contextual aspects to its physical structure; these contextual elements the urban environment (Trancik, 1986, p.98). P erception in Urban Environment Both Lynch (1960) and Rapoport (1977) argued that perception is an active process and not a passive reception; during this process the perceiver extracts information, analyzes it and redirects his actions. As interaction rec urs a user could have a different course of action. Perceived environment, per Rapoport is the result of made. Researchers emphasized the importance of perception in ur ban design because what defines a convenient space for their daily lives. Because people perceive things differently, they define problems and solutions in different ways; they define standards, such as safety or comfort differently, and they define ideas such as density or compactness differently and they define terms such as neighborhood or slum in different ways (Bacon, 1967; Rapoport, 1979; Zube, et. al., 1982; Ervin and Steinitz, 2003). E nvironmental perception involves current and stored stimulus information,
25 ambition, and hope), as well as real and imagined elements (Rapapport, 1977, p. 26). Perception is used in evaluating environmental qualities in three different ways: Environmental Evaluation of preference: in this method perception is used to evaluate the environment, for example: evaluating environmental qualities to find the m ost likely preferred direction for migration. Environmental cognition: perception here is used to describe the way people learn, change and construct mental maps of the environment. Environmental perception: perception in this domain describes the direc t interaction between people and the environment in which they exist at a given time (Rappaport, 1977). the holistic image of that environment. According to Bacon (1960, p. 46) e ven though each individual image or perception is unique and has something that is hard to communicate, there is a level of similarities between perception on one general image or a series of public images. Bacon saw that form should be used to reinforce c ommon meaning, and not to contradict it. Though, an opposing opinion regarding the value of environmental perception does in fact exist. Ravetz (1971) suspected the validity of operational values for human perception. He argued that if designers and survey respondents in an environmental study have different perception for the environment the result will be of no value for the design ideas (Rappaport, 1977).
26 environmental perception. It is not dealing with the complex cognitive processes behind environmental perception. Perceived Residential Density Density as one characteristic of a vital urban environment, has been a research focus as early as the sixties and seventies (Jacob s, 1961; Dean and Gunderson 1975; Rappaport, 1975; Sussna, 1973). Its importance is a part of a comprehensive ideal of how a city should function. However, one needs to distinguish between density as physical diameter and perceived residential density (de scribed, if negative, as crowding or isolation) as subjective and psychological experience (Dean and Gunderson, 1975; Jacobs, 1961). Density should also be distinguished from crowding; d ensity is a physical limitation of space, while crowding involves the perception of the restrictive aspects of limited space (Stokols, 1976; Rapoport, 1975). The negative consequences (1961, p.221) suggested that performance of space defines proper residential density, and residential density should not be based on abstraction of how much land is needed for (X) number of people. She argued that high density means large number of dwellings per acre of land, while overcrowding means very large number of people in dwellings where space is in fact limited (1961). Interestingly, many researchers between the 1940s to the 1970s have studied the correlation between high density and increased social problems, but their research results and analy sis were inconsistent (Schmidt, et. al., 1979). Generally, a dense environment with ill defined spatial settings can negatively affect human behavior and physical wellbeing, leading to the experience of crowding (Chin, et. al., 1976; Stokols, 1972; Baum an d Davis, 1976).
27 Recently, as high density is in different ways encouraged as an ing redient of new planning trends, acceptance for higher density design proposals becomes a major concern. Researchers, thereafter, start investigating the idea of de nsity perception and how density could impact interaction and judgment of the environment (Churchman, 1999; Yang, et. al., 2005; Fisher_Gewirtzman, et. al., 2003). Subjective measures of a place as dense or not depends on different physical characteristics such as degree of enclosure (Rapaport, 1977, p.96; Trancik, 1986, p.66), natural settings, space uses, and the provision of facilities (Marcus and Sarkissian, 1989; Rapoport, 1977, p.96), temporal rhythm, lighting, people traces, landscape, in addition t o people preferences for these characteristics (Rapapport, 1977, p. 96). M arcus and Sarkissian (1989) proposed few guidelines to improve perceived residential density These guidelines include: relatively small development size, greater spacing between bu ildings, visual and functional accessibility from a dwelling unit to open spaces, produce privacy opportunities by design solutions, division into small clusters, a variety of faade designs, fewer number of households using the same entrance, minimal nois e, well located community facilities that do not interfere other activities, availability of parking, access to adequate open space. Existence of these aspects in an environment does not necessarily guarantee lower perceived residential density because of the multifaceted nature of the idea of perceived residential density Other factors related to social and cultural dominant values can affect perceived residential density as well. Meanings attached to physical settings indicate level of stimulation and co l ace as dense or not. Consequently, highly perceived residential density in some u rban environments could
28 lead to the feeling of threat or stress, especially when the presence of large numbers of people, along wit h other components of environment, generate information overload (Rapoport, 1977, p. 201). Rapport (1977) further suggested that design can help reduce the actual flow of information and consequently reduce the perceived residential density or the negative perception of density. Studying the relationship between density and visibility, Martin and March (1973) ty design based on artistic images and visual experiences; such images represents high density by using h igh rise buildings (Yang, et. al., 2005). Martin and March (1973) explored arrangements and strategies in designs with high density, by manipulating b uilding typologies. They concluded that some site configurations could achieve more land use efficient design solutions than other configurations. As city problems resulted from overcrowding in certain areas, that lack healthy designs, solutions start to a ppear. Scholars such as Raymond Unwin (1912) targeted the concept of overcrowding, and he wrote his essay took place without regard to the need for open spaces and fres argued that lower perceived residential density is one of the characteristics of high quality environment (Rapoport, 1977). Density in Planning Density in planning is a reference of proportional numbers of units in a certain pre defined set of finite space and time (Yang, et. al., 2005). Sometimes a development density is measured as gross density which is number of dwelling units per acre or the total dwelling units divided by lot area exclusive of right of ways In other locatio ns
29 density is defined as net density which is the total dwelling units divided by overall area, ( Forsyth, 2003 ). In other locations, density is given as Floor Area Ratios (FARs) and in others as number of people per acre (City of Boulder, n.d.). The use of the different measures for density depends on the purpose of the planner or the planning activity in which the concept of density is used. Density as an urban form variable was used intensively in planning and in transportation research. It was used alo ng with other variables for studying urban environmental impacts on travel choices, physical activities, walking in general, automobile trips, etc. Density was found to be correlated positively to walkability (Belzer and outler, 2002; Brown, et. al., 2007; Moudon, et. al., 2003; Saelens, et. al., 2003; Cozens, et. al., 2008; Forsyth, et. al., 2009 ), reduced per capita vehicle miles traveled (Lopez and Hynes, 2003; Frank, et. al., 2001; California Planning Roundtable, 2002) and neighborhood accessibility (Kr izik, 2009). In physical settings, having more buildings along the street creates vertical lines that could function as references for users (Jacobs, 1993). Also, having more buildings gives an opportunity for more diversity in shape and activities. Jac obs further emphasized that density and land use matters are very important for street design because streets are activated by people. One problem that Jacobs thought could affect density application is that zoning regulations require the provision of larg e open spaces, which in return will only allow high density dwellings to be packed in high rise buildings, that are not too diverse in design and at the same time does not support the urban street definition (Jacobs, 1993).
30 Generally, planning research ha s used density as a planning tool to support planning decisions, or to forecast future growth trends, and not as an indicator of Visualization Research Visualization in Urban Design Visual simulation or visualizati on is a way of presenting design ideas that are still to be realized for users who, by viewing these simulations, can imagine the future spaces (Kwartler and Longo, 2008; Simpson, 2001). Visual information is very important for people to comprehend complex natural phenomena, ideas and abstract data Al Kodmany, 2002). Visual representations can be categorized into stationary or static simulation and dynamic simulation (Lang, 1994). Static simulation takes the form of photographs or models as seen by a static observer, while dynamic simulation shows the proposed design in terms of moving images, as seen by a moving subject (pedestrians) such as (v ideo) or computer animation. Moving images in terms of videos or other media helps mimic pedestrian experiences (Ewing, et. al., 2006) and provides multiple perspectives for certain viewpoints rather than restricting users to one angle, compared to using s tatic images (Lang, 1994). Nasar and Heft (2000) compared people responses for dynamic and static types of display t hey found that willingness to explore and learn more about the scene is higher in cases of dynamic display, while curiosity and preference is in fact higher in cases of static display. A study by Stamps (2000) reviewed empirical literature in simulation indicated that preferences for static color simulation is highly correlated to their preferences within
31 actual site. He further asse rted that strong correlation has been reported for the enclosure ( r = .83), area ( r = .90), and depth ( r r = .89 with its evaluation onsite. In discussion a bout the pedestrian experience of an area and the designer experience or the one reflected in classic representation models Bacon (1967, p 29) highlighted the need for establishing better techniques to present design concepts, which reflect as much real ex perience as possible. He also called designers attention to the need for establishing in depth ideas about the effect of design on users before making final decisions. Hence, visualization is used to help experts in evaluating the visual impact of a propos ed project, showing ideas for the public and incorporating public opinion into expert decisions (Lang, 1994). However, visualization is more useful if it is used during the early stages of planning and before implementation (Lang, 1994). Further, visualiz ation can empower a local community by shifting their attention to common concerns; it can lead to a more equitable land use policy and could reduce possible misun derstanding that might result s from the use of 2D plans and elevations in presenting design t o the community (Levy, 2009). Visualizing Density Recent researches related to perceived residential density try to move toward application of the idea in terms of visual presentation. Density visualization studies come as a response to the need for vi sual and easy to comprehend description of what numbers means. Campoli and MacLean (2007), in effort to bridge the gap between measured density and perceived residential density have created a catalog that illustrates different neighborhoods configuration s with various densities to show how
32 design could affect perception of high density developments. They argue that the provision of a comprehensive collection of images for different densities and designs would be informative for planners and policy makers when discussing density levels. Densities covered in the catalogue range from less than one unit per acre to 134 units per acre. The catalogue includes 300 images of more than 80 neighborhoods from across the United States. For each case the catalog includ es four views that show: block level details, context level, vertical view (landscape image taken from street level, almost two dimensional), and neighborhood level, that adds three dimensional information to the vertical view. The inclusion of different s cale views for each example in this catalogue stems from the need to grasp the context characteristics, as well as the block level characteristics for the sake of providing comprehensive understanding of density in the specific area. Yet the catalog lacks analytical components, nor does it give preferences for one approach over others. Authors assert that this catalog can be used as a visual tool from which communities can simply decide on what design approach better matches their preferences. Another stud y was done by the Local Government Commission and the U.S EPA (2003), whose report discusses successful discussed to show why high residential density has been a successfu l approach and what other design and planning characteristics are implemented. Visual tools were limited to few photographs taken for each case study to show few design alternatives used in these sites. The same limitations are seen in the Urban Land Insti tutes report (2005) on myths and facts of high density developments. Case studies are also used to
33 illustrate how positive impact could be achieved through high densities when well planned. Earlier research in relating physical form to density measures were done by Martin and March (1973), who studied the different typological arrangement and their relationships to land use. They argued that different forms of buildings can generate the same population density and that some buildings form on a certain bl ock size and shape can be seemingly dense and vice versa. These variations or possibilities, in their term, are important because it helps increasing the land use efficiency. They used three various factors to determine population capacity of a site: plot ratio (figure ground ratio of a site), general plan efficiency and floor space allocation per person. Furthermore, systematic analysis of building form relationship to site efficiency they divided building forms into three different types: pavilion (tower) which is limited in plan form, court (a building form that extends infinitely in two directions), and street (a building form that extends infinitely in one direction). Previous and recent efforts for quantifying perceived residential density is a normal result of the common consensus between scholars, that perceptual quality of an al interaction. Computerized Analytical Research Visualization in Urban Design There seems to be an agreement among researchers in visual simulation that computer based simul ation is the most reliable and approximate to reality method to represent design ideas. Lange, 1994; Simpson, 2001; Levy, 2006; Budthimedhee, et. al., 2002; Stamps, 2005A ). Three dimensional representations include 3D models, virtual reality and urban simu lation can work as a common medium for interaction
34 between users, designers and decision makers. The 3D modeling visualization is not interactive and is considered the simplest of the three, while the virtual reality and the urban simulation are interactiv e because they allow users to interact with the environment. The urban simulation has, in addition to interactivity, dynamic virtual processes with which users interact (Al Kodmany, 2002) Inspired by technical possibilities there evolved different represe ntations and modeling techniques in architecture and design that are based on internal logic and objectives that designers set forth ahead of design processes itself (Stavric and Marina, 2011). These techniques are sometimes described as generative, relyin g on computer processing for predefined objectives to generate different possible design alternatives, from which designers can choose appropriate configurations. This design process is referred to as parametric defined design strategy (Kolarevic, n.d.). The task of designers in this way becomes to design not the shape of buildings, cities or landscape but to design standards that will be encoded as a parametric equatio n; the product is several design options which could branches of parametric design: conceptual parametric design and constructive parametric design. In the conceptual pa rametric design, different values are given to the parameters so different configurations are generated. The parameters of a certain design are declared and not its shape. Software such as Maya or Rhinoceros offer script editors for this type of parametric design. Constructive parametric design involves embedding data to define relationships between the different elements of the design and the resulted 3D objects are predetermined (Woodbury, et. al., n.d; Starvic and
35 Marina, 2011). This type of parametric d esign can be realized in CAD software packages, such as Autodesk Revit, Soft Plan, Nemetschek, ArchCAD or Chief Architect (Starvic and Marina, 2011). Another software that can be useful to generate alternative faade treatments on building level is Rhino w ith its a new plugin Riknowbot Parametric Design at Urban Level The generation of architectural space from data input is called parametric design. The parametric design is based on certain criteria about site constrains, potentials and projec t goals; this process mirrors the emergence theory or the biological mode of genetic DNA combinations (morphogenesis) (Ottchen, 2009). Morphogenesis is a term used in biology, and it refers to the process of form generation. In design, this term was found relevant to the idea of designing based on bottom up logic or what researchers called form finding or formation. The emphasis in this process is on optimizing performance of material and function before defining appearance in a bottom up process (Leach, 2 009). In urban design parametricism is a contemporary term refers to urban design ideas generated using parametric design systems or standards ( Schumacher, 2008 ). Nevertheless, on a large scale design schemes, parametric design systems are less developed. One widely used software for fast modeling on large scales Google Sketchup has a parametric plugin called Modelur, it was created to build objects that embed detailed information about (built up area, number of floors, gross floor area). These p arameters are internally related; if one parameter changes the related parameters change respectively. For example, if floor number changes the built up area changes consequently. However, sketch up models generated in this way do not have generative capac ity on micro level details such as streetscapes and facades treatments.
36 building design alternatives contemporary design utilizes parametr ic design techniques to produce performance based designs. These designs are generated based on target optimal performance standards ( economical, ecological, structural, social, cultural and behavioral ) rather than just the building form (Kolarevic, n.d.). The performance based design d epends on embedding quantitative performance characteristics in to the process of generating form s (Grobman, et. al.n.d.). In this process designer set forth performance criteria such as: degree of sun shading, maximizing certain views, they write scripts a nd set parameters accordingly (Ottchen, 2009). Performance Based Design The idea of performance here could expand beyond form and functions; Grobman (2008) suggests a multifold description of the idea to include: empirical, cognitive and percep tual dimensions. The empirical dimension is concerned with physical measurable characteristics such as temperature, light, strength and can be easily translated into computer language. While, the cognitive dimension is concerned with the way human cognitio n translate the space t he perceptual dimension focuses on the transition of human perception into space and vice versa. C ognitive and perceptual dimensions are in fact not possible to be directly translated to computer language r ather they need to be tes ted statistically. For the form to sustain its intended function, it should satisfy all three dimensions (Grobman, 2008) Along the li n e of digital culture discourse, in which culture is seen as a series of events, performalism is seen as the cap acity of architecture to become part of the ongoing events in a world that is more defined by occurrences rather than bare relations between objects (Grobman, 2008). The notion of performance in design is also attached
37 to the idea of motion in the space; t he movement of people around structures, the experience of the existence of these spaces by engaging eyes and body (Kolarevic, 2005). Applications in Perceived Density Human visual perception as measured from subjective input of people alone ca nnot be a reliable quantitative measure (Yang, et. al., 2005). Practically density perception needs to be represented by quantitative indicators using scalar built form dimensions and environmental visual qualities (Jie, et. al., 2005; Yang, et. al., 2005; Fisher Gewirtzman and Wagner, 2003). As early as in the seventies researchers have empirically examined the impact of manipulating social and physical settings of an environment on the experience of crowding (Stokols, 1972; Baum and Davis, 1976; Baron, e changes in social and physical settings can alter individual perception of crowding in a space Quality of an environment has been linked to the visibility of high quality featur es from the study viewpoints (Jie, et. al., 2005; Tuner, et. al., 2000; Ervin and Steinitz, 2002; Fisher_Gewitzman and Wagner, 2003). Different techniques were used to measure visibility such as Isovist field (Tuner, et. al., 2000; Batty, 2001) GIS spatial analysis (Jie, et. al., 2005; Yang, et. al., 2005; Ervin and Steinitz, 2002). Isovist is a term used to describe the area in a spatial environment visible in two dimensions from a viewer location within the space, it provides geometrical measures of the v isual space (Batty, 2001; Ervin and Steinitz, 2002; Llobra, 2003; Bilsen, 2009). Even though most methods used to calculat e Isovists have been two dimensional the original definition of Isovist by Benedikt (1979) has been three or four dimensional. Bendik t explored
38 different properties of the Isovists such as area, perimeter, occlusivity, variance, to provide a spatial objective description of the visual space (Llobra, 2003). Jie et. al. (2005) have introduc ed a method for assessment of visual resources of high density development in Hong Kong. To calculate viewshed, spatial diversity and land use specification for each unit in the study area, they used 3D and spatial analysis tools in GIS They also used GIS to generate maps for resource quality inventory, perception quality and visual quality indices distribution for each individual viewpoint or viewpoints group. The perceived density in their research was part of a general visual quality analysis. However, c orrelation between positive and negative perception of a dense environment with certain physical settings of that environment was not examined. Along the same line, Yang, et. al. (2005) have tested the visual qualities of different 3D models of high den sity developments to create a perceived density index. Their study was based on visibility analysis for the different 3D models, by using the Viewsphere 3D Analyst to measure the volume of space or (volume of sight VoS) they were able to define and analyze segments of ambient photonic arrays spatially. The Viewsphere 3D Analyst is an analysis tool that was generated by customized GIS by the researchers. Yet, their analysis was limited to changes in visibility that is due to changes in the geometry of an urb an form F or example, they concluded that more horizontally when comparing the perceived density values with the planned ones on the same patterns they were found to be different Their research has also shown limitation in terms of the effective range or area in which the measures can be taken and considered
39 as valid. Yang, et. al. (2007) assert that previous visibility tools such as Viewshed have just used 2D or 2.5D analysis in GIS such as Viewshed and Isovist; hence they suggested that such tools are not reliable and that a 3D measure for visibility analysis can better describe spatial visibility. Maloy and Dean (2001) have also compared GIS Viewshed delineation tec hniques with field surveyed viewsheds and found that existing viewshed techniques produce less than 50% accuracy in predicting visibility. In predicting perceived density for different space configurations Fisher Gewirtzman and Wagner (2003) have used a quantitative index (Spatial Openness Index SOI) that is based on 3 metric expressed in term of 3D visual spatial information: it measures the volume of free Fisher Gewirtzman and Wagner, 2003). Their findings show that the interaction between a volume to its envelope is dependent on the ratio of that volume to its envelop. They based their analysis on the idea that higher level of spatial openness in a certain configuration would make it perceived as openness. The existing research on measuring perceived d ensity has focused on calculating level of openness and or visibility based on the massing or (how built up is the area) while rarely integrating contextual aspects of the environment and social input. The visibility analysis, as criticized by Ervin and St eintiz (2003), can be described as visibility that occurs between
40 different viewpoints and the unpredictable and changing atmospheric effects of the environment from one viewpoint to the other. Computerized research in perceived density has either used 3D models in combination with computer algorithm to run spatial analysis for different qualities and predict perceived density based on the output of the analysis ( Fisher Gewirtzman and Wagner, 20 03 ) or has used GIS spatial analysis to measure level of visibility of good quality features from specific viewpoints a s indicator of high or low perceived density (Jie, at. Al., 2005; Yang, et. al., 2005). Measuring Environmental Qualities Environment al qualities here refer to perceptual and physical characteristics of the environment; such characteristics could affect judgment of the environment as good or bad or as friendly or unfriendly. Different environmental qualities have been linked to human ac tivities such as walking, socializing and children playing. Tools to measure or to operationalize these qualities is an area of concern for researchers in the ir try to predict the behavioral and social consequences of a design (Ewing, et. al., 2005; Necker man, 2010; Tuner, et. al., 2000). Ewing, et. al. (2005) suggest that physical features of an environment are the ones who define urban design qualities (imageability, legibility, visual enclosure, human scale, transparency, linkage, complexity, coherence); and that these design qualities, together with perceptual qualities constitute people reactions to that environment. The quality of the environment is then measured based on the three components (people reaction, physical features and perceptual qualities ). They used video clip for pedestrian level street views that show samples from different places with various design qualities By measuring the physical features of each scene, the researchers correlate physical features of the
41 scenes to urban design qua lities in these scenes. One drawback of their research is that they rely on expert judgment for evaluat ing the scenes rather than lay people. design qualities on a large ar ea. They sampled 588 block facades in New York City for which they have enough data about physical characteristics specified by Ewing to describe each urban design quality (Purciel, 2009). Such kind of research aims in producing planning decision support tools because it tries to find operational method to describe urban design qualities using existing resources and to integrate data about these with spatial information (land use, transportation networks etc). Finding an operational method for measuri ng environmental qualities has extended to inter pret more specific relationships between physical features and environment perceptual qualities to define an objective model for understanding human behavior in different urban settings (Stamp, 2009; Nasar an d Stamps, 2008; Ervin and Steinitz, 2002). In his research, Stamps (2009), has studied the relationship s between perceived spaciousness and perceived with boundary permeability, amount of light, area, and boundary depth. His study was based on using interi or space s for the experiment inside a lab environment of an octagonal room for the varied stimuli. This kind of research aims at theorizing relationships between different components of an environment to understand the construct of good environmental quali ties. The idea of environmental quality is related to people judgment of that environment which originates from complex interaction and experiences; perception is part of this interaction. Density perception along with other perceptual measures such as per ceived openness,
42 perceived imageability can work all together to contribute to primary judgment of an environment as good or bad. Geographic Extent The geographic extent in which study should takes place is dependent on the type of research questions. In planning literature two types of geographic areas were used for the study locations: Macro (metropolitan level) and Micro (neighborhood or street level). An auto mobile level research would use a large scale area to allow measuring the effect of large urb an form elements that extends over long distances and could be even on regional level. For example, Lopez and Hynes (2003) have created sprawl index for all metropolitan areas using density values in urbanized census tracts, Carlino (2001) has used employm ent densities in urbanized metropolitan areas to investigate relationship between density and innovation. A pedestrian oriented research would consider smaller scale geographic areas to test the effect of certain characteristics on micro level elements ( Rapport, 1977; Krizik, 2009; Forsyth, et. al., 2009; Brown, et. al., 2007). Generally, level of consistency of the urban characteristics in a neighborhood is higher than in large scale areas (Rapoport, 1977). Krizik (2009) suggests a geographic limit of ( 1 Mile) for research that tests urban qualities at pedestrian level. However, the definition of neighborhood differs between users of the space, the limit of once neighborhood as perceived is also different than the formal boundaries defining it (Churchman 1999). Hereon, a relevant scale for study environment and its perceived boundaries.
43 Research Question Generally, studies that focus on creating density index have not incorporate subjective values to their measures (Yang, et. al., 2005). Rather, studies have used existing tools to measure aspects related to density such as visibility or day light (Yang, et. al., 2005) or landscape qualities to calculate perceived de nsity. A mere calculation for perceived density cannot work everywhere, and can lead to misrepresentation of how community perceives density because human perception involves more than seeing, it involves temporal, social and cultural dimensions. T his rese arch aims at answering the following questions: How does change of urban physical qualities affect perceived residential density ? How does perceived residential density relate to traditionally measured density? Challenges For a higher residential densit y development to function and succeed it will need proper land use that can generate enough mix of use and spatial settings that allows social interaction. This coexistence of various components leads to a major challenge in quantifying urban form variable s; increasing the difficulty in isolating the effect of each variable alone from other urban form variables (Saelins, et. al., 2003; Moudon, et. al., 2003; Khattak and Rodriguez, 2004; Shay, et. al., 2003; Rodriguez, et. al., 2004). Socio economic fact ors are also important indicators of how an environment w orks. Researchers who study the relationship between different densities and other urban form variables on a certain activity, such as walkability or accessibility, have in most cases controlled for socio economic factors to avoid skewing the results and affecting the analysis (Khattak and Rodriguez, 2004; Rodriguez, et. al., 2004; Brown, et. al., 2007; Forsyth, et. al., 2009).
44 CHAPTER 3 METHODOLOGY Physical environment, its spatial configurations, its qualities and its characteristics have been argued to have a major impact on the human per ception and behavior (Alexander et. al., 1987; Lang, 1994; Trancik, 1986; Gehl, 1987). Negatively judging an environment of being dense is in fact what Rapoport and for him is read by its users through what it provides of indications or signs, or in his (crowding) or deprivation (isolation) i s what shapes density perception framed in social the human experience of his environment and has been related to various aspects of an environment as discussed ear lier. Variables that affect perceived density can be listed under three different groups: Contextual variables: variables that depict area characteristics such as land use, design and adjacent neighborhood characteristics. As previously discussed mix o f use can add more activity to the area and encourage sociability of the space. Recent research on regional or metropolitan size studies have shown that specific trends of population distribution could occur based on existing resources or area characterist ics. Density tends to increase in areas that ha ve already existing high density patterns or in areas that ha ve potential for infill development s, but at the same time are in proximity to activity centers such as shopping centers, play grounds, public tra nsit, recreational centers, etc Socio cultural and economical: The physical limitation of an environment alone is not the only constituent for a stressful spatial experience and the feeling of
45 crowding (Chine, et. al., 1976; Schmidt, et. al., 1979) T here are personal and interpersonal factors that can stir feelings of restrict ion or limit ation, especially in a competitive type of environment (Stokols, 1972). Further more previous familiarity plays an important role in user experience and therefore their judgment s of crowd ing (Baum and Davis, 1976; Rapoport, 1975; Schmidt, 1979). A study regarding density perception needs to integrate social and economic factors to further enhance the development of human experience s in an environment. Such factors would i nclude: gender, residential location type, age, employment, etc. Physical environment variables: Which are the measured characteristics of the environment that depict spatial relationships. These includes: block size, street width, length of buildings, wid th to length ratio (enclosure), open space ratios to masses, existence of street furniture and natural elements. Unit of Study Literature shows that pedestrian level studies should use appropriate area sizes to be able to measure micro level qualities, whi ch in larger size areas could be hard to measure (Rapport, 1977; Krizik, 2009; Forsyth, et. al., 2009; Brown, et. al., 2007; Moudon and Lee, 2003). In studying accessibility Krizik suggested a 1/4 mile grid as the unit of analysis to capture pedestrian le vel data, while Churchman (1999) suggested that the perceived boundaries need to be uncovered by incorporating perception of what constitute their own neighborhoods. In a study to correlate objective and perceptual variables to walkability Moudon, et. al. (2006) concluded that objective environmental measures significantly relate d to the perception of their existence but only at 1 KM airline buffer and is not significant at higher limits. Human s can be influenced by distant elements in an environment ; ped estrians move slower than automobile which makes
46 their perception of the space and surrounding details more profound than a car rider perception In defining the study limits it was important to consider that a n adult, o n average, walk in 250 350 Feet per Minute or in average 3 Miles per Hour h is viewshed is wide and more precise about the surrounding visual sensors (Nellsen, 1993). Conceptual Framework The study examines correlations between physical components of the environment and the perceived residential density V ariations of the study scenes that are different in the value of independent variables will be used to analyze each effect of independent variables on perceived residential density Positive density perception is the one contrast t he feeling s of environmental limitations or crowding. From this view point r esearches have assumed positive density perception if the spaces are seemingly open to each other and the sky is seen from different angles resulting in a higher level of ease and comfort for users of the environment (Yang, et. al., 2005; Fisher Gewirtzman and Wagner, 2003). However as previously discussed these studies have been limited to two dimensional measures (Yang, 2005) or to visibility measured in interior spaces (Fisher Gewirtzman and Wagner, 2003). Further more the analysis was not based on authentic human interaction with actual or simulated environments The study was primarily based on computer algorithms that measure one aspect of an environment such as visibilit y or sky views. The use of computer modeling in visualizing urban spaces for experimental studies dominates the work of Stamps and Smith (2002), Nasar and Stamps ( 2002 ) Alkhresheh, (2007) and Stamps ( 2009). Computer simulation facilitates controlling th e different components of a study area ; ithis is more effective than the use of real life
47 settings or video tape for existing spaces as reported in the works of Stamps and Smith ( 2002 ), Ewing, et. al., ( 005 ) and Nasar ( 2008). With the wide use of parametr ic design software experimental design could be more flexible and the generation of alternative design components for experimental purposes is possible. Study Design The s a mix use urban dev elopment in Midtown Atlanta Georgia that extends over 138 Acre (F igure 3 1) Atlantic station is described as a national model for Smart Growth where mix of affordable and upper scale housing exists along with different amenities (shopping, entertainmen t, restaurants) and office building s ( http://www.atlanticstation.com/ ). The perception created in the human mind at a n observation point could be influenced by certain features at distance point in the stud y area, taken that pedestrians move slowly compar ed to other modes of transportation. An adult, on average, walk s in 250 350 f eet per m inutes or a n average of 3 m iles per h our H is viewshed is wide and more precise concerning the surrounding visual sensors (Nellsen, 1993). In this study, the unit of analysis is defined by a 500 Meter (1640 Feet) buffer around the subject viewpoint. Physical Variable Measures The physical variables in the research can be categorized into two different groups: Aestheti c: (amenities and design features, soft scape such as trees and water elements)
48 Geometric: includes block sizes, street dimensions and enclosures, the frequency of intersecting streets, distance between buildings, faade complexity and solid to void ratio s Aesthetic v ariables The provision al amenities for pedestrian s, such as sidewalks and tree lines ha ve always been associated with well perceived environment s and used to reduce stress, noise, and feeling s of crowding (Jacobs, 1961; Churchman, 1999; Kapla n, 1987; Gehl, 1987). Other amenities such as curbs and sidewalks encourage walking as they separate the pedestrian realm from traffic, and it adds definition and scalar references to the space. In this research, aesthetic elements will be added to all th e scenes equally and will not be considered as an independent variable. Geometric variables Number of intersecting streets: A s streets interlace with land they reduce long blocks around urban street scene Intersecting streets in an urban scene are also seen as a way for adding more public spaces to the existing fabric since streets are considered a public space and a space for social interaction (Jacobs, 1961; Churchman, 1999). A dding more intersecting streets at the pedestrian level give s people differe nt choices in navigating the urban space (physical permeability), and it increases visual permeability to different visual qualities (Carmona, et. al., 2003). Jacobs (1993, p 243) compared streets surrounded by long block s that have few intersecting stree ts with streets surrounded by shorter blocks that ha ve frequent intersecting streets she concluded that frequent streets in an urban scene help in generating diversity by attracting mixture of users. As a pedestrian moves in the main street intersecting s treets will introduce different spatial experience and affect his
49 general perception of the space. The frequency of streets will be measured by 1 3 scale where one is the lowest number of intersecting streets in the scene and three is the highest number of streets in the scene. Enclosure (height to width ratio) : Jacobs (1961) stressed that the height of buildings along the is less than 100 feet. Buildings acting as street walls allows streets to be well defined. Trees can also be used for t he same purpose (Churchman, 1999; Jacobs, 1961). Within interior settings, Nasar (2008) argued that people perceive variation s of enclosure more than variation s in the area enclosed when this change result ed from decreasing or increasing the height or per meability of the surrounding edges. Enclosure is considered one of the most important factor s that affect human perception of an environment It affects the volume of what human s can see and predict about their environment T herefore it affects the sense of safety level of comfort and control over the space when navigating in the environment (Nasar, 2008; Stamps, 2005B). Empirical research ha s shown that a significant relationship exists between perceived comfort and safety with changes in enclosure s (Sta mps, 2005A; Alkhreshesh, 2007; Nasar, 2008). Enclosure a mathematical term is defined as the ratio between height to width of the space I n an urban setting enclosure is the height of a building to the width of the street (Thiel, et. al., 1986). However empirical studies proved that ratio is not the only determin ing factor of perceived enclosure ; researchers have studied different factors in spatial settings that are hypothesized to relate to perceived enclosure such as scale of the area (a room or a st reet) (Hayward and Franklin, 1974; Stamps, 2005B; Alkhresheh, 2007), the level of lighting (Stamps and
50 Smith, 2001), level of complexity of the surrounding context such as the closest point of amps, 2005B). Recent studies relate to this nonlinear relationship between measured enclosure and level s of comfort and sense s of safety in urban settings ; Alkhresheh (2007, p.18) fort and safety, while both high and low degrees of enclosure correspond to lower level of comfort and safety He tested different street compositions to find that the ideal ratio for width to height in urban street s is 3: 4, in which safety and comfort a re on the highest range. Further he founds that a ratio of 1: 2.5 which is suggested by Carmona et al (2003) has a probability of comfort and safety of .81 and .75. Accordingly previous studies on comfortable width to height ratio bounced between 1:1 r atio and 1:2.5 as a balanced and acceptable value for observer s to still feel enclosed and comfortable in urban street s Wh en the ratio exceeds 1:2.5 interactions and relations between buildings and spaces become hard to conceive (Carmona, et. al., 2003). In my research b uildings will be given a rate of 1 3 based on their measured enclosure where 1 is the lowest 1:1 and 3 is the highest 1:2.5. Building facades complexity: A complex facade affect s the cha racter of the street; light passes through voids and reflected on the varying textures and design elements leading to a different pedestrian experience in comparison with facades that have simple designs and textures (Jacobs, 1961; Churchman, 1999). These propositions are consistent with the findings by Rapo port and Kantor (1967) where they saw that oversimplification of design s in interior spaces can lead to undesirable outcomes on people s perception of their environment (Baum and Davis, 1976).
51 Mumford (1953) suggested that lack of visual complexity especi ally like the ones seen (Baum and Davis, 1976) and will therefore eliminate possible alternative reactions that could result from omplex environments. This kind of reaction demonstrated by different activity that takes place in urban spaces T he intensity and distribution of these activities are linked to the design and complexity of the faades framing that space (Gehl, 1987). Em pirically, Stamps (1999) has examined the expression of complexity as an objective measure that depends on other differing variables. He argued that usually in urban design s, discussions building facade complexity is vaguely and subjectively described. He tested the correlation between three different aspects relate d to faade complexity judgment: silhouette, surface complexity, faade articulation A s ilhouette is the number of turns or edges that constitute the frame of the faade A surface complexity is the intensity of details such as the windows, doors, frames and ornaments Lastly, articulation is the existence of recessed surfaces in which certain parts of the faade are more emphasized to give an identity to the faade itself. His study proved that surface complexity has the highest significance in people visual preference of the faade. Stamps (1999) method for measuring faade complexity was used in this research. Three elements of the faade were measured: ornament, door and window trim and t exture. Based on the existence of these elements faade s were g iven three levels from one to three : t hree was the rank for the facades with highest c omplexity ( which has shingles, an ornament, a cornice and door and windows trim) ; one was the
52 rank for fa ades with lowest complexity and is empty from any details except windows and doors openings, and two was the rank given for facades that have shingle and texture In this research each building was given a rank for faade complexity separately. Each scene used in the visual survey included groups of buildings that share same level of complexity. People existence: the number of people using a space and the type of activities they lead affect perceived density in that space (Jacobs, 1961; Churchman, 1999) Jacobs (1993) synthesis for great streets discussed the value of closeness and recognition of other people that are by default attributes of small spaces. On an urban spatial level, such spaces with high number s of neighbors using the same walk ing area e ncourage social interaction s and help creating community (Jacob, 1993). Regardless of the physical quality of an environment, if no human interaction exists the space becomes lifeless The u rban spaces inhabited by people interacting with each other are a lways more simulating and full of experiences compar ed to th o se spaces with no people. The idea of floor area/site ratio or building density by itself does not convey how activities are distributed or concentrated (Gehl, 1987). Urban design can increase vi tality of a space through proper ly e mphasiz ing people s' existence and a ctivities To measure the independent variable people existence in the street researcher used three levels that are coded from one to three respectively : one: few people walking alone two: few people, sometimes two people walking and three: group of people interacting and talking or using street furniture such as benches.
53 Control physical variables Some physical qualities of an environment could affect the perception or the me asured effect of one or more of the study independent variables over the dependent variables. These qualities need to be treated carefully and not to be overlooked throughout the study experiment. To get the most accurate measure of the correlation between the independent variable s over the dependent variables it was important to control aspects of the environment as follows: Spacing between buildings along the street : previous research shows that spacing between buildings on the street is more effective when the space is tighter (Jacobs, 1961; Alexandar, et. al., 1977) T he street wall seems more continuous and the space at street level seems more enclosed when buildings are closer to each other I n this experiment, the size of the study area does not al low large variations in building distances to be implemented. The number of intersecting streets is in away related to the distances between building variable s, so spacing between buildings c an be inferred from the number of intersecting street variable s Percentage of masses: adding more masses helps in increasing the variety of design which enriches the environment and makes it more enjoyable (Jacobs, 1961) helps in defining the urban space s (streets, plazas) and helps in creating a coherent urban spa ce apart from buildings and surrounding spaces (Trancik, 1986). Positive perception was argued to reflect level s of openness (Churchman, 1999) W hen people judge an environment as an open environment they eliminate the expression of crowding. The s olid an d void configuration of a space forms the urban fabric connects the various functions and affect perception about space openness The study of
54 openness of an environment needs to consider three dimensional structures (vertical dimension) not only horizon tal one s The percentage of mass to voids is not a quality that can be grasped easily on the eye level ; therefore a percentage of mass to void in the study scenes will be recorded for reference purposes only. Mix use: by adding nonresidential buildings to the fabric of dwelling areas additional active spaces are created (Jacobs, 1961; Churchman, 1999). In this manner, a mix of uses produce potential for interaction between people and reflect more activities in an environment, which leads to higher or lo wer perceived density. However, this variable is not geometrically measurable compa red to the other independent variables in this study. Taking into consideration the important mix use role the scenes will be equally similar in observed uses and will conv ey the same sense of activity. Contextual Variables Measures The contextual measure of the study area, as previously discussed, is a prerequisite for the success of high density development. Though, due to the size of the study area, the contextual mea sures could not be comprehensively included as independent variables. However, mixed use as a control variable will be controlled equally between the scenes. Based on findings of p revious research in the field a mix use needs to be reflected in the study s cenes to suggest a vibrant urban life. Other contextual aspects such as proximity to shopping, schools, activity centers, will not directly affect pedestrian level perception on the scale considered in this study. Future research might expand this study to include larger scale area where contextual dimension effect on perceived residential density can be further investigated.
55 Socio Cultural and Economic Variables Measures The n umber of people and social interaction s alter the perception of density. Howe ver, the accepted level of interaction is culturally and contextually defined. The perception of physical limitation could be considered as a precursor to stressful crowding but not necessarily a sufficient condition alone. Other aspects such as the perso n past experiences and personal attributes, duration of exposure to the dense environment and the type of activity that individual is planning could affect how individuals perceive physical density and limitations (Stokols, 1976; Proshansky, 1972; Zlutni ck and Altman, 1972). Further, cultural differences characterize people with different levels of tolerance to higher density. For example, in Chinese culture the established behavioral and spatial standards reduce the need for large personal space and th e stress that could result from dense environment. Higher level s of social heterogeneity increase unpredictability of the environment and will increase the time and effort required by users for information processing which results on a perceived residential density Aspects such as shared common cues of control over the environment play important role s in people density judgment. These cues could be realized by environment al characteristics such as existence of thoroughfare, permeabi lity, perceived openness, etc. This is represented in physical terms as defense means which enables control over interaction, when such control enabling configuration exist s density is perceived as lower because people face less stress in that environmen t (Rapoport, 1975). To understand the effect s of the socio economic and context measures subjects will be required to answer questions about their age, gender, current housing, number of years spent in their current residence, race and employment.
56 Perce ived Residential Density Measures Perceived density as discussed in literature and previous empirical studies is framed by hypothetical constructs and is resulted from environment sensorial impact on users of the surrounding spaces. Users cannot pass judgment on the density of a place because density is often learned in a mathematical framework, which would lead judgment to be inaccurate Therefore, perceived residential density in the study scenes will be inferred from other behavioral and verbal resp onses by the subject s Distress result ing from sense of crowding (badly designed higher density areas) can be reflected in different ways. As suggested by Chin, et, al. (1972), measuring cognitive and perceptual indications of crowding (negatively perceive d density) direct In trying to understand negative subjective experience of perceived density (crowding) researchers have used three different models. First: t he behavioral constraint model ; in this model, moveme nt is restricted, or goal accomplishment is obstructed and in general freedom is reduced. The second model is the control density model ; i n this model, unpredictable environments which allow less control over situations and over privacy are evaluated as crowded (Rodin, 1976). Fin ally, the overall arousal model, which suggests that crowding occurs in situation where density generates excessive simulation that causes overload of sensory systems (Evan & Lepore, 1992; Churchman, 1999). Different studies reve aled that a combination of these three models could simultaneously exist and affect the overall density perception or the negatively each of these models contributes to d ensity perception, rather these models where
57 tested empirically under controlled conditions of different levels of density and social situations. These models deal with psychological and behavioral reactions that are not measurable by number s T echniques u sed in the se models focused on correlating human reaction to actual high or low density environment. The behavioral constraint model The model of behavioral constraint or social interference speculates that when density or other conditions restricts or lim its once activities i n an environment are evaluated as crowded. This approach is based on the theory of psychological reactance, defined by Brehm (1966) as the need to maintain freedom of choice as an important motivating factor in human behavior and perce ption. According to him people tend to maintain or restore freedom when it is threatened and their behavior and reactions to a setting is a result of this maintenance and restoration process (Stokols, 1976). If this process is restricted stress occurs lead ing t o unpredictable behavioral output o r as described by Proshansky (1972) the experience of crowding occurs when a number of people with whom individual is in contact is sufficient to prevent him from behaving in a certain way (restrict his freedom of c hoice). The different studies in this area have linked aspects of spatial settings the type of activity done in a space, or the amount time spent in a space to the level of stress encountered in a dense environment. Altman (1975) defined crowding as a condition in which interpersonal boundary control mechanism break down, such that achieved privacy is less than the level of privacy desired. Impinging Overall arousal model Overload is defined by Milgram (1970) as a situation in which the amount an d rate of environmental inputs imposed on an organism exceed its capacity to cope with them
58 (Stokols, 1976). According to him, the user should react by coping to the situation by certain behavioral input or adjusting his personal expectations from an envir onment when exposed to overloaded conditions for him to survive such environment. From social interaction perspective, unfamiliar or inappropriate social contact leads as well to this form of stimulus overload (Dessor, 1972). Density control model Density c ontrol is the means by which individual augments space to reduce limitation s of the environment (Stokols, 1976), or to reduce or increase social interaction (Zlutnick and Altman, 1972). High density could lead to reduced control over on ch oices, or his freedom of choice T his reaction is described as crowding (Rodin, 1976). The control model related to physical settings of the space is more applicable in interior conditions where the space can be easily manipulated by users by moving furniture or changi ng locations or improving environmental conditions such as temperature or even by leaving the room seeking other conditions. Physical control becomes harder on urban level as street furniture, weather conditions and built environment is unchangeable direct ly by users. The control on urban level is more adjustable by altering the type of social activities that the place holds or that users involve in. The physical environment on urban level could offer possible solutions for people to overcome unwanted socia l interaction through utilizing the available thorough fares and shielded spaces or visual barriers. The three models profoundly overlap in the control level over once social interaction, surrounding environment and behavioral choices In the overall arous al model, individual reacts to the increase of environmental simulations by withdrawal or by shifting the type of activity he is involved in or changing direction of movement In
59 behavioral constraint model s, er when the environ ment sti l l allows alternative behavior when the environmen t limits his primary expected behavior I n contrast a high density with negative impact would lead to stress and limit behavioral adjustment and will not allow control over behavioral choices. Based on the several studies in psychological effect s, my research hypothesizes correlat es between perceived residential density and the models discussed above. Accordingly, three variables are to be used in trying to empirically obtain refer ence s for these models to perceived residential density. The dependent variable space crowdedness will contribute to understanding behavioral constrain experienced by the participants in certain urban settings. The space openness could possibly contribu te to how restricting or limiting on activity is. The level of comfort will help understand how the environment is affecting participants coping ability. The four dependent variables (perceived residential density, level of comfort, space crowdedness, openness) are measured in the study on a Likert scale from 1 to 6 while 1 is very low and 6 is very high. Survey Instrument The survey instrument is employed in this study to collect different data about tual input about the 3D displays presented. There are two sets of variables that will be measured by this survey: independent variables that reflect socio economic and contextual data and dependent variables or perceptual input pertain ing to perceived dens ity and level of control. The socioeconomic and contextual data: in the first section of the survey respondent s will be asked to answer questions about his social and economic background about his living place and the context of his daily environment.
60 The second section will be a visual survey section. Participants will be asked to view a different set of each scene in terms of static displays. The participant will be given enough time to view each scene and to answer questions about its perceptual mea sures (dependent variables) on a scale of 1 6. The use of an even number ( 6 ) as the limit for the Likert scale is commonly used to prevent bias by many participants selecting a middle point on the scale as neutral choice which could happen if the scale is an odd number 1 5 or 1 7 (Rea and Parker, 2005; Fellows and Liu, 2008). To create the experimental tool this study followed three procedures: Collecting needed images through field visits Use the captured images from field visits and prepare images libr ary to be use d in creating 3D models. The process includes editing photos of building facades using Photo editing software (Photoshop Cs 5) Creating 3 D models: In this experiment 3 D Studio Max is used to build 9 models that vary in 4 characteristics (inde pendent variables). Each variable has 3 different levels, to create all the possible combination of the 4 variables while considering all 3 levels of each of them a sum of (3 4 = 81 models) will be generated. The time required to generate this number of mode ls is very long and by including all these models and variation in visual survey participants will be required to spend long time to answer the survey. To go about this problem a fraction of these experimental runs is selected following the Fractional Fact orial design method, accordingly nine models were created. T his step includes creation of nine 3D models, creating texture maps librar y using 3d modeling software (3D S tudio max) and photo editing software
61 (Photoshoph), prepare textures library, applying t extures to 3D models and finally creating environment for final render and the production of 3D model images. Participants The frame for the study sample is the University of Florida students and staff. A stratified random sample of the study population will be created; the stratification is done on the gender of participants to ensure equal presentation of males and females, because some research studies have shown differences between males and female s in evaluating dense conditions (Schmidt and Keating 1979). The type of experiment (within subject) requires exposing the same group of participants to all the experimental conditions (all levels of the independent variables) (Fellows and Liu, 2008). Therefore, all participants will be asked to answer que stions about all the scenes with various experimental conditions Fractional Factorial Design To satisfy all possible combinations of the four variables, and each variable has three different levels it will be required to create 81 models. Fractional f actorial design was a solution for this issue ; i t allows measuring the main effect of the independent variables on the dependent variables. That renders the study as a screening experiment from which important findings will be revealed. It can lead to poin ting out the most effective factor among the four study factors on the value of perceived residential density The fractional factorial design suggested the following combination s of variable levels ( 0000, 0111, 0222, 1021, 1102, 1210, 2012, 2112, 2201 ) 1021 refers to the model that has the dependent variables: number of intersection level equal to 1, enclosure level equal to 0, faade complexity level equal to 2 and existence of peo ple level equal to 1
62 where 0 is the lowest and 2 is the highest level. Following this design 9 three dimensional models were created and used for the visual survey (T able 3 1) Field Visit Researcher used images for building faades from Atlantic Stati on Atlanta G eorgia Atlantic Station was selected because it is an American example of a vibrant urban mixed use environment U sing images from Atlantic station allows the creation of simulated environment that has reference to a real urban environment. A sum of 270 images were captured for the buildings surrounding the main street in the area (Atlantic Drive), and the building facades overlooking all intersecting streets. All faades for each building were captured with effort to reduce the number of obj ects that might block the view of the building such as cars and pedestrians. Three Dimensions Modelling 3D Studio Max software was used to create nine 3D models. 3D studio Max was used to create 9 models that have no details and no texture. Each mode l encompasses one main street that is 60 FT wide and in average 1500 FT long and intersecting streets that are 40 FT wide as well as buildings surrounding the main street. The s treet was set at 60 Ft width to accommodate 2 ways with one lane at each direct ion and 15 Ft side walk that includes space for streetscape and landscape as well as sidewalks. To apply texture and material to the 3D models texture mapping technique was used. Texture mapping as explained earlier is useful when many models are needed a nd when detailed focus model is not required such as the case of urban design experimental models that cover large urban areas. The process of applying textures on 3D models includes six steps. First: each building in the models was exported as a .3DS sepa rate file. Second: a 3D Studio Max modifier (Unwrap UVW) was used inside each
63 separate file to flatten each building alone. Third: the Unwrap UVW modifier allows rendering a .TGA file extension of the resulted flattened models, TGA are images that embed co ordinate data and scale data of the models that allows it to be placed on the 3D models as material on their designated surfaces after editing in Photoshop. Fourth: .TGA file is opened in Photoshop to allow creating texture on top of the model polygons. Fi fth: the textured file is saved again in Photoshop as .JPG file. Sixth: the resulted file is used again in 3D Studio max as map in material editor and applied to the model. This process was repeated over each building in the nine combinations. After all bu ildings are prepared for each case they were imported back to the large model. Figure ( 3 6 ) shows one example of how the unwrapping/ texturing process was done it summarizes the process of creating one of the nine models. A 3d Studio max modifier (unwrap UVW) was used to flatten building facades, the resulted images were then exported to a Photoshop readable file format (TGA). The Unwrap modifier creates image file s that embed coordinate data and scale of the texture which allows it to be placed on the 3 D models as material on their designated surfaces after editing in Photoshop. Facades have been later edited in Photoshop software using the existing image library as a reference and saved as JPG file format to be used as material in 3d Max for each specif ic building. Using 3D Studio Max, the created material was applied to buildings using material editor. Photo Editing and Cleaning Photoshop Cs5 software was used to correct and clean images taken from the field and to prepare images to be used in cre ating 3D model textures. In urban street settings like the Atlantic Station it was not possible to avoid barriers between camera and buildings while capturing faade views. Objects such as cars, plants, and people
64 were erased, further image editing was d one to prepare them to be used as material in 3D models (Fi gure 3 3 ). To use images in 3D modeling software for the process of applying textures it is necessary that facades are orthogonal, and they should not have distortion or a perspective effect. For that reason, additional editing was required to minimize distortion and perspective effect in the images using Adobe Ph o toshop editing tools (F igure 3 4 ) Some building facades need to be clipped to isolate other attached and or surrounding buildings, othe r facades needed to be created out of multiple images by joining them t ogether and then editing them (F igure 3 5 ). As a result, a library of 80 different building facades was created from the Atlantic Station context to be used in creating the experiment al models. Texture Mapping Technique Texture mapping is a process used to apply two dimensional images referred to as bitmaps to add textures for three dimensional models. It allows a more natural looking virtual space (Wang and Chen, 2009). For e xample, a 2D faade images are applied to polygons of a 3D box model to create a building in 3D modeling software. This method is commonly used in creating gam ing environment s 3D avatars for games or training purposes or in large scale urban development p rojects. Texture mapping process es help increas e the details of the 3D model without the need to model these details which results in less number of polygons and reduces the render time required. Each 3D model is unfolded ( unwrapped ) to 2D polygons on a flat surface. The process is identified in 3D studio max as UVW Unwrap modifier. The UVW is a coordinate system like XYZ space coordinate but it refers to surface coordinates which works as a grid on the model surface ( Ingrassia, 2009). For this research, all model are
65 primitive boxes therefor a basic unwrap process was needed. To unwrap a simple box, one selects edges to create seams for the model, based on these seams the box is unfolded, (Figure 3 6 ) S ome of the polygons are deleted f rom the unwrap wind ow o ther polygons are going to have similar textures such as side facades of the buildings and the front and back of the buildings because the main faade of the buildings are the ones seen most clearly by the viewers. The unwrap modifier allows to copy a nd paste polygons on top of each other, accordingly similar texture applies for the similar polygons at the same resolutions and ratios. Figure ( 3 7 ) shows example of one building unwrap process; the unwrap modifier window shows the selected parallel polyg on highlighted in red. The unwrap window has a Render UVW option in which the unfolded building layout can be saved as image in TGA format The .TGA file then can be edited in a Photoshop CS (Figure 3 8). The resulted images are then used to apply on th e building polygons at high resolution in 3D Studio Max in which final rendering is done and final 3D models are created to be used in the visual survey (Figure 3 9) Visual Survey ics online survey services for university related use. Qualt rics features creation, delivering, collecting and analysis of online surveys ( https://services.it.ufl.edu/task/all/online survey ). Survey is accessed through accessing the webpage ( https://ufl.qualtrics.com/jfe/form/SV_blvExZ6AGHjdpZj ). The University of Florida requires that all surveys that involve human subject participation need s to be approved by the IRB (Institutional Rev iew Board ); forms needed to obtain approval from T he University of Florida IRB were completed before launching the survey.
67 measured in the previous question with the psychological demand for a highly social situation such as friend gathering as well as association between perceived residential density and the demand of formal activity for a less active conditions or a more formal situa tion such as a work meeting. Question 4 presents three top views of street plans in black and white (figure ground); the three street plans differ in the number of streets intersecting the main street and accordingly the size of masses included. Participa nts were required to rate these images from 1 to 3 from the least dense to the densest. The question pertains to further assess the effect of number of intersecting streets on perceived residential density when only using a simple black and white top view. This, to some extent could provide of a proposed development (F igure 3 11 ) People and faade details generally add richness to streetscapes and are always advocat ed as important factors in creating vibrant spaces. Further, people existence in street could be misleading when residential density needs to be evaluated and might affect results accordingly. These two facts suggest adding a question that measures how oth er aspects of space when isolated from people and faade detail influence the concept of residential density. Question 5 shows 9 different spaces; camera location was identical for all the 9 spaces. The spaces presented in this question are in grey scale c olor s and do not include people nor does it include faade details (F igure 3 1 2 ) The second part of the survey collects basic information about participants informatio n about where participants have been living (housing type and residential
68 density of the places where they lived). This part of the survey is pertained to measure the effect of these demographic factors and people past experiences on residential density pe Expected Results and Analysis The variation between actual density of the study area and the perceived residential density will be also tested. Per literature, perceived residential density is not e xpected to parallel actual density because physical, personal and contextual aspects affect how people see their environment; this study will help understand these variations and to highlight the most relevant visual qualities of an environment that affect people judgment of residential density.
69 Table 3 1. Independent variables l evels used in fractional factorial experiment Independent variables Intersecting Streets Enclosure Faade Complexity People Existence 0: scene has just one street in 250 Ft 1 : two streets intersecting the main urban street every 2 50 Ft 2 : scene has three streets intersecting the main urban street every 250 250 FT is measured from location of the viewer along sight line 0 : when the ratio of the width of buildings to thei r height is in average 1:1 in the urban scene 1: when width to height ratio is 3:4 2: when width to height ratio is 1:2.5 0: facades are lowest in complexity and has plain material and no details (include just the main faade components ) 1: some detai ls included such as shingle texture 2: t he highest in complexity (has shingles, ornament s a cornice and door and windows trim), 0 : very few people in the scene walking alone) 1 : few people, but has some more social interaction such as group of two people walking and talking 2 : moderate number of people, one can see people in cafs interacting and talking, or using street furniture such as benches or bus station 0 0 0 0 0 1 1 1 0 2 2 2 1 0 2 1 1 1 0 2 1 2 1 0 2 0 1 2 2 1 2 0 2 2 0 1
70 Figur e 3 1 Atlantic Station p lan ( http://atlanticstation.com/ ) Figure 3 2. Flow chart of the procedure used in creating the visual survey Texture mapping Faade photography (in site) Photo cleaning and editing (Photoshop) Creating alternative faades (deferent levels of details) Creating 3d environment and rendering
71 Figure 3 3 Image cleanin g process using Photoshop (before and after ), (Atlantic Station, Atlan ta, 2009). Figure 3 4 Image editing for a multiple section building (before and after) (Atlantic Station, Atlanta, 2009).
72 Figure 3 5 Image editing that require s connecting different edited photos for large building (before and after) (Atlantic Stat ion, Atlanta, 2009).
73 Figure 3 6 Box typical unfold method and the selection of edges as seams to use for the unfold method Figure 3 7 S creenshot for 3D max unwrap process for simple box model in 3D studio Max
74 Figure 3 8. Applying edited fa cades into unfolded models faces in Photoshop Figure 3 9. Using the material created in Photoshop to building models in 3D Studio Max
75 Figure 3 10 Screenshot for one scene from the first question in the survey instrument ( Online Survey, Q uestio n 1 Scene 1 )
76 Figure 3 11 Black and white top view of a street ( Online Survey, Question 3) Figure 3 12 Nine scenes in grey color with no faade details or pedestrian included ( Online Survey, Question 4)
77 CHAPTER 4 RESULTS Research Hypothesis Changes in certain physical qualities of the study area lead to psychological and perceptual reactions by study subjects. This is reflected in this research by different perceived information overload, control level and level of freedom captured thr ough the primary dependent variables: perceived residential density, perce ived openness, level of comfort and space crowdedness Physical qualities of the environment are represented by the dependent variables (enclosure, faade complexity, number of inter secting streets, existence of people and activities) Sample Characteristics Data was tested for skewness and kurtosis and results showed that data was not normally distributed. Dividing skewness and kurtosis on their standard error returned values above 1.96 or below 1.96 which reflect s non normal distribution. Further more, the Shaprino Walik test for normality showed significant variances in the data with p= .000 which is below 0.05 (Shapiro and Walik, 1 965). This leads to rejecting the null hyp othesis that the data is normally distributed. Test of Reliability To test reliability of the survey instrument researcher has used the Test R etest R eliability when t For the research to be valid it should satisfy the requirement, Test_Retest Reliability ; t wo questions were repeated and w ere answered by the same group of people who participated in the survey. R epeated ranks were then correlated ; this correlation is
78 known as which is a measure of internal reliability of a scale. Nunnally (1978) stated that acceptable reliability was an alpha above .70 and unacceptable reliabili ty was an alpha below .70. When running the analysis for the two repeated models the results w ere reliable for the variables ( space crowdedness with alpha= 0.755 and slightly for level of comfort with alpha = 0.61) but w ere not reliable for space openness and perceived residential density with alpha around 0.5. When running the same analysis for each model separately the results returned for the model 0000 was higher in reliability (around 0.7) for all the four dependent variables than the model 1102. For the dependent variable space crowdedness the reliability test results reported were Alpha= 0.755, df= 153 and P=0000 ; For the dependent variable level of comfort the test of reliability returned significantly good correlation with alpha= 0.605, df= 153 and p=.0000 For the dependent variable openness, the test of reliability p=.0000. For the dependent variable P erceived r esidential density, the test of reliab ility p=.0000 (T able 4 3) Assumption The Spearman Rank Order Correlation was chosen to measure the correlational relationships between dependent and independent v ariables. A correlation coefficient ranges from 1.0 to +1.0; a positive correlation means that the two variables are increasing together while a negative correlation will mean that the one variable is decreasing when the other increase (Freedman, Pisani, & Purves, 2011). The Spearman correlation is used when at least one variable is ordinal, and the other is
79 ordinal or ratio in level of measurement and this is the first assumption and it was met. The second assumption states that there must be a monotonic relationship between each set of hypothesized bivariate correlations. Monotonic is when both variables move in the same direction together or when one increases the other one decreases. Scatterplots are used to check for monotonici ty and all met the assump tion. Data Analysis Within subject I ndependent V ariables The nine models used in the study ha ve varied levels of enclosure, number of intersecting streets, no of people, level of faade complexity. Each of these independent variables ha ve three levels fro m low to high as their value coded as 0, 1, 2 The four dependent variables ( perceived residential density space crowdedness perceived level of comfort, perceived openness) are expected to vary between the nine models as a result of changes in independe nt variables. Dependent variables are ranked by participant s using a Likert scale from 1 to 6 where 1 is very low and 6 is very high. To test variances between the 9 model s, the Friedman test was used for each of the dependent variables. For perceived res idential density, variable analysis returned significant results at P= 0.0000, n=77, df=40. For space crowdedness results was also significant at p= 0.000, n=77, def=40, for the dependent variable sense of comfort the result was significant at p= 0.000, n= 77 and df=40 and finally results were also significant for the variable space openness with p=0.000, n=77 and df=40. Model 2222 ranked the highest for perceived residential density and perceived space crowdedness ; the model 1201 ranked the highest in level of comfort and perceived openness. In the other hand model 1021 ranked lowest in perceived residential density; model 0000
80 ranked lowest in perceived space crowdedness ; model 1102 ranked lowest in perceived level of comfort and the model 2201 ranked lo wes t in perceived openness ( T able 4 4 ). Testing Association Enclosure association with dependent variables Alternative Hypothesis: dependent variables ( perceived residential density, space crowdedness level of c omfort, o penness ) are correlated with enc losure Enclosure is expected to be positively correlated with space crowdedness and r esidentia l density, but negatively with level of comfort and o penness across the nine y by length to width ratio and as the value increases, so does the level of enclosure. Since perceived residential density was measured on ordinal scales, the Spearman Rank Order Correlation was chosen to measure the correlat ing relationship between the five variables. A correlation coefficient ranges from 1.0 to +1.0 and a positive correlation means the two variables are increasing together. W hen e nclosure levels increased so did the ratings of space c rowdedness and r esidential density and when e nclosure levels increased then the rat e of level of comfort s and o penness decreased, so the hypothesis is fully supported (Table 4 5) Enclosure was positively correlated with s pace c rowdedness ( r = .116, p = 0 .001) and r esidential d ensity ( r =.155, p < 0.0001); however, e nclosure was negatively correlated with l evel of c omfort ( r = .160, p < 0.0001) and o penness ( r = .221, p < 0.0001). In a different question, the researcher tested the impact of en closure and number of intersecting street on the dependent variable ( perceived residential density ) to isolate the effect of these two variables from the effect of (existing people in the street and the
81 level of faade complexity or details). Enclosure was significantly correlated with perceived residential density at ( r = .450, p = 0.0001). This means as the models enclosure level increased, perceived residential density ratings also increased. The correlation was a little higher and t hat might be a result of reducing distraction caused by other details in the scene that maybe have disturb ed (T able 4 6) Faade complexity association with dependent variables Alternative hypothesis : dependent variables ( space crowdedness level of comfort, o penness and p erceived residential d ensity) are cor related with the independent faade c omplexity across the nine models. Faade c c omplexity increased as its value in the model increased. Perceived residential density was measured on a Likert low density high density perception Because p erceived residential d ensity and f aade c omplexity were measured on ordinal scales, the Spearman Rank Order Correlation was chosen to measure the correlational relationship between the five variables. Faade c omplexity was positively correlated with the l evel of c omfort ( r = .163, p = 0.001), so the hypot hesis was partially supported. This means as f aade c omplexity increased, so did the ratings for l evel s of c omfort. f aade c omplexity was not correlated with s pace c rowdedness ( r = .014, p = 0.721), o pen ness ( r = .036, p = 0.349) and p erceived r esidential d ensity ( r = .035, p = 0.362), ( Table 4 7 )
82 Number of intersecting streets correlation with dependent variables Alternative Hypothesis: Dependent variables ( space crowdedness l evel of c omfort, o pennes s and p erceived r esidential d ensity) are correlated with the independent variable ( number of intersecting streets) The number of intersecting streets in a scene w as ranked with 0 b ecause dependent variables and the number of openings were measured on ordinal scales, the Spearman Rank Order Correlation was chosen to measure the correlational relationship between the five variables. T he number of openings is not correlated with space crowdedness ( r = .005 p = 0.151), l evel of c omfort ( r = .016, p = 0.683), o p enness ( r = .014, p = .715) and p erceived r esidential d ensity ( r = .002, p = .960) (Table 4 8) Thus, the hypothesis is not supported Since the level of details in a faade is expected to affect people perception the scenes that have high level of f aade details (level 2) was excluded and the test was run again The test re turned significant correlation between the independent variable intersecting street and the dependent variables (perceived residential density and space crowdedness) with r=.136, p = .003 and r=.271, p=.000 respectively. W hile no correlation was found between the independent variable intersecting street and the dependent variables openness and sense of comfo r t (Table 4 9 ). Further investigation about correlation between intersecting streets and perceived residential density was done through ranking three black and white street views. The three street views of intersection A (highest number of intersections), B ( l owest number of intersections), and C (medium number of intersections) w ill be looked at in terms of
83 total perceived residential density Perceived residential density was measured on a Perceived r esidential d ensity is n umber of i r =0.273, P =000 n = 231) (Table 9 10) The positive correlation means that as intersecting streets increased space w as perceived to be higher in residential density. In a different question, the researcher tested the impact of enclosure and number of intersecting street using 3D scenes in grey color to isolate the effect of these two variables from the effect of (existing people in the street and the level of faade complexity or details) (Table 9 11) N um ber o f intersecting streets was not correlated to perceived residential density (r= .031, p= .418) when using grey scale 3D scenes not including faade details or people in the scene. Number of people in the street correlation with dependent varia bles Alternative hypothesis: ( perceived r esidential density, space crowdedness level of c omfort, o penness ) are correlated with the number of people and activities across the umber variables were measured on a Likert s Since dependent variables and the variable number of people and activit ies were measured on ordinal scales, the Spearman Rank Order Correlation was chosen to measure the correlational relationship between the five variables. The number of people and level of activities was positively correlated with ratings of the dependent variable space crowdedness ( r = .399, p = .001) and residential d ensity ( r = .188, p = .001). The hypothesis is partially supported and as the number of people and activities
84 increased, in the models, so do the ratings of space crowdedness and perceived r esidential density. The number of people and activities was not correlated with Level of Comfort ( r = .005, p = 0.886) and Openness ( r = .027, p = .483) ( Table 4 12) Type of activity association to the dependent variable The type of activity people has i n a space is believed to affect their perception of the space qualities. It is expected that a social situation in which higher level of interaction is needed such as friends gathering will abide for higher density settings more than a situation where form al setting or a more organized and quiet setting is needed. For that purpose two imaginary scenarios of social activities was explained to participants and they were asked to rank the scenes in terms of their preference for the space in such social scenari os. Two alternative hypotheses were tested : Alternative Hypothesis 1 : Dependent variables ( perceived r esidential density, space crowdedness level of comfort, o penness ) are correlated with prefere nce for s ocial s ettings across the nine models. The percei ved preference for social s ettings was residential d ensity, space crowdedness level of comfort, and o penness were measured on a Likert type scale low density per Sin ce dependent variables and the social s ettings and activities were measured on ordinal s cales, the spearman rank order c orrelation was chosen to measure the correlational relationship between the five variables. The independent variable social setting is not correlated with space crowdedness ( r = .048, p = 0.204 ) and perceived r esidential d ensity ( r = .061, p = .110) (Table 4 13) But a very small negative correlation was found between social preference and level of c omfort and openness with ( r = .098, p = 0.010) and ( r = .078, p = .04) respectively this negative correlation
85 means that as ranking of openness and comfort increase the preference for social settings increase because preference fo r social space is from 1 to 10 as 1 is the most preferred and 10 is the least preferred space. When testing correlation between preference for social space and the four independent variables the results showed that enclosure is significantly correlated to preference for a social space with r= 0.214, p=.000 as enclosure increase s preference for a social space increase s (because 1 is the highest preference and 10 is lowest preference). N umber of people and number of intersecting streets in scene has corre lated significantly with preference for social space with r= .25, p=.000 and r= 0.23, p=.000 respectively which reflects positive correlation as well. Faade complexity has also small negative correlation r=.079, p=.000 (T able 4 14 ). The results are also reflected in ranking means for the nine models (T able 4 15). I t was seen that scene that has the highest mean rank has the lower enclosure and highest number of interesting street (model 2012) While t he scene that received the least rank has the highest l evel of enclosure and lower number of people (1210) Alternative Hypothesis: Dependent variables are correlated with preference for Work Space across the nine models. The perceived preference for Work Space was measured on ordinal scales, the Spearman Rank Order Correlati on was chosen to measure the correlational relationship between the fiv e variables. Preference for Work Space is not correlated with space crowdedness ( r = .033, p = 0.388), Level of Comfort
86 ( r = .043 p = 0.260), Openness ( r = .013, p = .742) and Perceiv ed Residential Density ( r = .024, p = 0.502) (Table 4 16) Thus, the hypothesis is not supported. When testing correlation between work space preference and the four independent variables it returns a very low significant correlation with the independent variable enclosure with r= 0.079 and p=.037, n=693. While no correlation was found between the other three independent variables an d work space preference (T able 4 17 ) Between Subjects Variables Based on literature it is expected that between variables such as age, gender, design experience and type of residence where participant live could affect how people perceive an environment. In the following section results of variance in these variables and correlation with dependent variables will be reported. Gender Alternative h ypothesis : Men and women are different in their perception to density (residential density) and how comfortable they would feel in a dense space (woman prefer high density settings so the higher the density perceived in a space the higher the perceived level of comfort ). For this end models that scored high est in perceived residential density were included in the analysis (337 cases out of 693) and the two groups (men and woman) was compared in their perceived residential density and level of comfort. When comparing (group1 male) with (group 2: Female) in their level of comfort at the cases when scenes where perceived high in density it was clear that women ranked the scenes that they perceived as high in density by higher level of comfort than men. Men results for Perceived Residential Density ( M = 4.34, SD = .5335) and in Level of Comfort (M= 3.688, SD= .995) which is lower than (group2 Female) ( M = 4.54, SD = 1.0657) and for level of comfort (M= 3.78, SD= 1.05).
87 Howeve r, Mann Whitney U test of differences for the two groups (male, female ) returned no significant difference between males and females in level of comfort at spaces that are ranked high in perceived residential density (T able 4 18 ). The null hypothesis that no different between male and femal e in level of comfort at a high residential density setting could not be rejected. Time s pent in USA Alternative Hypothesis : p eople who lived long in the USA because of common believes and culture as well as the geog raphy of USA are expected to associate higher perceived residential density to lower comfort and higher space crowdedness 377 cases were used out of 693 cases, these cases are the scenes that ranked 4, 5, 6 in perceived residential density. H1: time spen t in USA is correlated to level of comfort: the longer people lived in USA the lower the level of comfort they have in higher density scenes. H2: time spent in USA is correlated to space crowdedness : the longer people lived in USA the higher they rank sen se of space crowdedness in higher density s ettings The time spent in USA was recoded into three groups instead of four where the last category (people live in USA more than 20 years w as merged to the previous category) In comparing means between the thr ee groups for the dependent variable (level of comfort) the difference between the t hree groups was not significant. The Krusal Wallis test returns no correlation with (Chi square= 1.912 and p=.384) (T able 4 19).
88 For the space crowdedness there was a dif ference in the mean value for the three groups and the Krusal Wallis test results show a significant difference between the three groups with Chi squa re=9.884 and p=.007 (T able 4 20) Analysis results could not reject the null hypothesis that people lived i n USA longer would perceive high density spaces as less comfortable than people who live shorter in USA However, the analysis results rejected the null hypothesis: there is no difference between people lived in USA for sho r t time and people lived in USA f or long time in their perceived space crowdedness at spaces with high perceived residential density. Design b ackground P eople who are from background related to design are different in their percepti on to density than people from other professional backgr ound. Alternative hy p othesis : people with design background would be more comfortable in high density urban settings (people with design experience rank high density spaces higher in level of comfort than people with no design experience). By comparing m eans for the two groups ( a : people with design experience and group b: people with no design experience ) the first group has smaller mean for perceived residential density which is in line with the study expected findings. However, Mann Whitney U test of d ifference results returns no significant statistical difference with p= The results could not reject the null hypotheses that there is no difference between the two groups in level of comfort at high perceived residential density scenes (Table 4 21)
89 Age Age is expected to correlate positively to perceived residential density and negatively to sense of comfort. Alternative hypothesis 1 : Older people will rank spaces higher in perceiv ed residential density than young people Comparing means for age gro ups illustrated that mean rank for perceived residential density generally increase as the age increase. Krusal Wallis test also confirm this finding with significance variance and p=.002. This result rejects the null hypothesis that there is no difference between the age groups in their perceived residential d ensity. Alternative h ypothesis 2: Older people will feel less comfortable in spaces that has ranked of higher perceived residential density than young people The same analysis was run to test assoc iation between age and sense of comfort in selected cases (scenes that has 4 6 perceived residential density rank). By comparing means for age groups, we found no significant variation s in means for level of comfort Krusal Wallis test also returned no s ignifican ce variance with p=.354 (Table 4 23 ) Type of living p lace Alternative h ypothesis : People who live in higher density locations would rank scenes higher in perceived residential density comparing to people who live in low density areas. Comparin g means between the four groups (where area type 1 has lower actual density and area 4 has the highest actual density) analysis shows difference in means which in general increase as density decrease. With the people who live in urban area
90 and high actual density have mean for perceived residential density = 3.4 significantly different than that of people live in low density areas who have Mean= 4.8. The Krusal Wallis test shows significance variance with p =.010, ( T able 4 24 ) Further analysis was conducte d to test people ranking for comfort in scenes that have higher rank in perceived residential density (337 cases) in which perceived residential density was ranked 4 or larger. Comparing means show significant variations between groups ranking for level of comfort The Krusal Wallis test has also showed significant variance at p=.006. The null hypothesis is rejected (T able 4 25 ). Visual Density versus verbal Expression of Density (Validity test) In this section people input about the residential density i n the area they reside and their selection of visual illustration that they think represent or relate in some way to their living area is compared. These two questions aim at explaining the different between verbal expression of density (very high resident ial density, high, moderate, moderately low, very low) and visual one (Figure 4 8). Both variables are coded 1 to 5 conducted revealed very low reliability between t he two variables (residential density area) and (visual expression of density) (Table 4 26). Further descriptive statistics also emphasized the mismatch between the two variables which indicate that visual representation of residential density is not nece ssarily a reflection of actual density (T able 4 27) Another reason for this variation is the use of ranking measure for residential density instead of actual numbers
91 Table 4 1. Results of skewness and kurtosis for dependent variables. Perceived resi dential density Perceived c omfort Space o penness Space crowdedness Skewness .038 .308 .017 .043 Std error of skewness .093 .185 .093 .093 Kurtosis .372 .308 .339 .554 Std error of kurtosis .185 .0446 .185 .185 Table 4 2. Test of normality f or the two groups of gender. Dependent Variables Gende r Kolmogorov Smirnova Shapiro Wilk Statisti c df Sig. Statisti c df Sig. Space c rowdedness 1.00 .198 324 .000 .914 324 .000 2.00 .152 369 .000 .931 369 .000 Level of c omfort 1.00 .190 324 .000 .92 4 324 .000 2.00 .194 369 .000 .921 369 .000 Space o penness 1.00 .171 324 .000 .932 324 .000 2.00 .171 369 .000 .929 369 .000 Perceived r esidential d ensity 1.00 .194 324 .000 .922 324 .000 2.00 .172 369 .000 .936 369 .00 0 a. Lilliefors Significance Correction Table 4 3. Reliability testing for a (test retest correlation) Space crowdedness Level of c omfort Space o penness Perceived r esidential d ensity Alpha .755 .602 .498 .41 No items 2 2 2 2 Alpha .755 .602 .498 .41
92 Table 4 4. Dependent variables test of variance Model No Perceived r esidential d ensity Space crowdedness Level of c omfort Space o penness Mean Mean Mean Mean 0000 3.2208 2.4935 3.7922 3.6234 1111 3.2857 2.9740 3.7662 3.5714 2 222 4.0649 3.7013 3.8182 3.1818 1021 3.0000 2.6883 4.0779 4.0000 1102 3.7403 3.2857 3.1688 3.6753 1210 3.1818 2.5455 3.2208 3.2078 2012 3.5195 3.5844 3.9351 3.7143 2120 3.3377 2.6753 3.8701 3.4805 2201 3.7403 3.402 6 3.4805 3.0649 F Test N 77 N 77 N 77 N 77 Chi Square 114.2 Chi Square 158.7 Chi Square 104.9 Chi Square 133.21 D f 40 D f 40 df 40 df 40 Assym p Signific ant 0.0000 Assymp Signific ant 0.000 Assym p Signific ant 0.000 Assym p Signific ant 0.000 Tab le 4 5 Enclosure test of correlation with the four dependent variables Sense crowdedness Level of c omfort Perceived residential density Space o penness Enclosure R ho 116** .160** .155** .221** P .001 .000 .000 .000 N 693 693 693 693 **. Correla tion is significant at the 0.01 level (1 tailed). Table 4 6. Enclosure correlation with perceived residential density for grey colored scenes Perceived residential density Enclosure rho .450** p .000 N 693 **. Correlation is significant at the 0 .01 level (2 tailed).
93 Table 4 7. Facade complexity test of correlation with the four dependent variables Sense crowdedness Level of Comfort Perceived residential density Space openness Faade Complexity R ho .014 .163** .035 .036 P .721 .001 .362 .349 N 693 693 693 693 **. Correlation is significant at the 0.01 level (1 tailed). Table 4 8. Number of intersecting street test of correlation with the four dependent variables Sense crowdedness Level of c omfort Perceived residential density Spc ae o penness No of intersecting streets rho 005 .016 .002 .014 p .151 .683 .960 .715 N 693 693 693 693 Note. *. Correlation is significant at the 0.01 level (1 tailed). Table 4 9 Intersecting street test of correlation with the four depende nt variables when excluding cases that has high level of facade details Sense crowdedness Level of Comfort Perceived residential density Space openness No of intersecting streets R ho .271** .026 .136** .077 P .000 .573 .003 .100 N 462 462 462 462 Note. **. Correlation is significant at the 0.01 level (1 tailed). Table 4 10. Intersecting street test of correlation with perceived residential density in a black and white street view Perceived residential density No of intersecting streets rho .273** P .000 N 231 Note. **. Correlation is significant at the 0.01 level (1 tailed).
94 Table 4 11 Intersecting street test of correlation with perceived residential density in 3D grey color images. Perceived residential density No of intersecti ng streets rho .031 P .418 N 693 Table 4 12 Number of people and level of activities correlation with the four dependent variables Sense crowdedness Level of c omfort Perceived residential density space openness No of people R ho .339** .005 .1 88** .027 P .000 .886 .000 .483 N 693 693 693 693 Note. **. Correlation is significant at the 0.01 level (1 tailed). Table 4 13 Social situation correlation with the four dependent variables Sense crowdedness Level of Comfort Perceived residenti al density Space openness Social situation R ho .048 .098** .061 .078* P .204 .010 .110 .040 N 693 693 693 693 Note. **. Correlation is significant at the 0.01 level (1 tailed). *. Correlation is significant at the 0.05 level (2 tailed). Table 4 14 Social situation correlation with the four independent variables Intersecting streets Enclosure Faade complexity No of people Social situation rho .231** .214** .079* .246* P .000 .000 .000 .000 N 693 693 693 693 Note. **. Correl ation is significant at the 0.01 level (1 tailed). *. Correlation is significant at the 0.05 level (2 tailed).
95 Table 4 15 Models ranking in case of social situation preference Model # Mean N Std.Deviation Variance 0000 4.87013 77 2.576976 6.641 0111 6.40260 77 1.995124 3.981 0222 5.16883 77 2.435497 5.932 1021 5.16883 77 2.022262 4.090 1102 4.50649 77 2.239733 5.016 1210 6.81818 77 2.366027 5.598 2012 2.42857 77 2.451791 6.011 2120 5.09091 77 2.059521 4.242 2201 4.54545 77 2.588159 6.699 Total 5.00000 693 2.583854 6.676 Table 4 16 Work space preference (formal situation) correlation with the four dependent variables Space crowdedness Level of comfort Space o penness Perceived Residential Density Work space preference R ho .0 33 .043 .013 .024 P .399 .260 .742 .527 N 693 693 693 693 Table 4 17 Work space preference correlation with the four independent variables Intersecting streets Enclosure Faade complexity No of people Social situation rho .070 .079* .069 .0 03 P .065 .037 .068 .940 N 693 693 693 693 Note. *Correlation is significant at the 0.05 level (2 tailed).
96 Table 4 18 Test of differences between two groups of the independent variable (gender) in the level of comfort at high perceive d residential density scenes Gender Mean St Dev 1 3.688 .995 2 3.78 1.05 Gender Mann Whitney U 13083 Asymp. Sig. (2 tailed) .437 N 337 Table 4 19 Test of differences between three groups of the independent variable (Time spent at USA) in the level of comfort at high perceived residential density scenes Time spent in USA Level of comfort Mean Std.Dev 1 3.79 1.0188 2 3.45 1.01076 3 3.74 1.03519 F Test N 77 Chi square 1.912 D f 2 Assymp significance .384 Table 4 20 Test of diff erences between three groups of the independent variable (Time spent at USA) in the space crowdedness at high perceived residential density scenes Time spent in USA Space crowdedness Mean Std. Dev 1 3.62 1.13347 2 3.57 .95799 3 3.18 1.18319 F Test N 77 Chi square 9.884 D f 2 Assymp significance .007
97 Table 4 21 Test of differences between two groups of the independent variable (design experience) in the level of comfort at high perceived residential density scenes Design experience Mean ( level of comfort ) St Dev 1 3.649 .929 2 3.840 1.1145 Gender Mann Whitney U 13162.5 Asymp. Sig. (2 tailed) .223 N 337 Table 4 22 Test of differences between four of the independent variable (age) in their perceived residential density Time sp ent in USA Perceived residential density Mean Std. Dev 1 3.516 1.21044 2 3.498 1.19569 3 3.160 1.24408 4 3.60 .83195 5 4.11 .60093 F Test N 77 Chi square 17.424 D f 4 Assymp significance .002 Table 4 23 Test of differences between four of the independent variable (age) in their perceived comfort at high perceived residential density settings Time spent in USA Level of comfort Mean Std. Dev 1 3.7207 1.04595 2 3.8718 1.02165 3 3.5400 1.01439 4 3.6667 1.07083 5 4.0000 .53452 F Tes t N 77 Chi square 4.408 D f 4 Assymp significance .354
98 Table 4 24 Test of differences between four of the independent variable (living place) and the perceived residential density Place of living Perceived residential density Mean Std. Dev 1 3.3846 1.22454 2 3.4775 1.10314 3 3.2222 1.42325 4 4.8889 1.45297 F Test N 77 Chi square 11.390 D f 3 Assymp significance .010 Table 4 25 Test of differences between four groups of the independent variable (living place) and the level of comf ort at high perceived residential density settings Place of living Level of comfort Mean Std. Dev 1 3.8696 .98695 2 3.6927 1.02813 3 4.1000 1.37032 4 2.7143 .48795 F Test N 77 Chi square 12.546 D f 3 Assymp significance .006 Table 4 26 A C the residential density Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items .177 .178 2 Table 4 27 Crosstabs for verbal and visual expression of residential density Visual density Total 1.00 2.00 3.00 4.00 5.00 Verbal Density 1.00 18 9 63 9 0 99 2.00 27 9 45 9 0 90 3.00 117 81 18 99 0 315 4.00 9 81 36 36 0 162 5.00 0 9 0 0 18 27 Total 171 189 162 153 18 693
99 CHAPTER 5 DISCUSSION In this chapte r, the study analysis and findings will be discussed and referenced to literature and research done in the field of perception in general and perceived residential density in specific. The first part of the chapter will cover discussion related to within s ubject ind ependent variable s and their i mpact on dependent variables (perceived residential density, level of comfort space crowdedness and space openness) The second part will disc uss findings related to between variables. It is important to n ote that most previous studies of perceived residential density have been done within psychological stud y domain s M ost research in this area targ ets psychological reaction and social preference result ing from high level s of crowding. The dependent variabl es in this research are selected based on the review of literature around perceived residential density including the models used in examining psychological reactions to crowding As discussed earlier perce iv ed residential density is a quality that is co nnected to a wide range of psychological and behavioral reaction s The multifaceted nature of perceived residential density and the variety of psychological and behavioral description of the concept of density suggest s testing the association between the i ndependent variables and f our dependent variables (perceived residential density, space crowdedness openness, and level of comfort). The four dependent variables of the study were enclosure, level of comfort, space crowdedness and perceived residential d ensity. Enclosure and Dependent V ariables The statistical analysis shows that increasing level of en closure lead to increase in the rank for space crowdedness and perceived residential d ensity and decrease of rank
100 for l evel o f c omfort and o penness E ncl osure was positively correlated with space crowdedness ( r = .116, p = 0.001) and perceived r esidential d ensity ( r =.155, p < 0.0001) and negatively correlated with level of c omfo rt (r = .160, p < 0.0001) and o penness (r = .221, p < 0.0001). The least e nclosure used in the experiment was 1:1 width to height ratio moderate level of enclosure was 3:4 and highest level of enclosure was 1:2.5. All level used are within the comfortable range as suggested by previous research done by (Alkhresheh, 2007, p.18; Carmona et. al., 2003). Previous studies suggested that very high and very low enclosure levels rank low in perceived comfort and sense of safety which suggest s nonlinear relationship between measured enclosure and level of comfort and sense of safety in urban settings (Alkhresheh, 2007, p.18). In this study, the increase of enclosure was created by increasing height of buildings in relation to street width. The incre ase of height of building in the scenes has in fact add ed actual residential space s which increase actual residential density. Enclosure correlation to perceived residential density was positive; when enclosure increase s perceived res idential density also increase s and perceived space crowdedness increase s Enclosure correlation to comfort and openness was negative; as enclosure increases openness and level of comfort decreases. The lower enclosure used was ( 1:1 ) and it ranked the highest in level of comfort (Mean= 3.9) and openness (Mean= 3.8) The two independent variables used in this stud y that are most related to physical configuration of a street (enclosure and number of intersecting streets in the scene) were isolated from the other two variables to further investigate their impact on
101 perceived residential density. T his was done by runn ing the analysis using grey color models that has no f aade details, no landscaping, street furniture and people. Enclosure in this case was significantly correlated to perceived residential density at ( r = .450, p = 0.0001). This means as the models enc losure level increased, perceived residential density ratings also increased. The correlation was a little higher than correlation found in the previous test. It is expected that level of faade complexity and details, traces of people activities as well a s material, texture and color of buildings have affected correlation results between the independent variable (enclosure) and people judg ement about residential density ( T able 4 6) This suggested that the visual richness of an urban environment contribut e highly to the perceptual experience and judgment of the environment. Enclosure was positively correlated to people preference of a place for social activity a s enclosure increased preference for a social type of activity increase d (r=0.214, p= .000) As enclosure in this study increases so does actual residential density and t his suggests positive correlation between higher residential density and preference for an active social activity such as (meeting friends) F acade Complexity and Depend ent V ariables Faade complexity variable was used to check how the design elements; ornaments articulations, material selection and colors of a facades could affect perception of residential density. In urban design theories, visual complexity of buildin g faade contributes to space definition and judgment of the space as boring or rich it also affects the type of activity that is stimulated to take place in these spaces. The use of faade details to manipulate the effect of building height or to improve pedestrian experience at street level is a common design practice (Jacob, 1961; Churchman,
102 1999). In connection to the dependent variables of this study the curiosity and or boredom that a space could infuse in users could be described in terms of how com fortable users ar e in a space and how dense a space is perceived. Results shows positive significant correlation between level of comfort and faade complexity ( r = .163 p = 0.001) No correlation was found between faade complexity and the other three d ependent variables ( space crowdedness, openness and perceived residential density). This could suggest that richness in faade design is not necessary le ading to the feeling of discomfort The increase of faade complexity is related to adding more details and elements to a faade which increase s simulations aroun d users. In relation to perceived residential density models; the overall arousal model suggests that sense of crowding occurs when environment generates excess of simulation and leads to overload of sensory system and that may lead to discomfort. The study findings did not relate to findings proposed in the perceived residential density models discussed earlier The nature of social situation and the social interaction expected to occur might af fect people preference for space in these situations. The study tested the impact of independent variable (faade complexity) on people preference for spaces in different social situations (meeting a friend / work related meeting) Analysis r esults returne d a small negative c orrelation between faade complexity and preference for social space ; spaces that has higher faade complexity received lower user preference a s a space more suitable for a social activity. While preference for the imaginary wor k meeting situation analysis show no correlation between faade complexity and the preference for a place to attend a work meeting In the two social situations, the
103 correlation between faade complexity and the preference of users was not considerably hig h enough to be reliable Number of Intersecting Streets in the Scene and D epende nt V ariables At urban level, the experience of pedestrian is affected by what street elements they see around them and how these elements define their movement and limit or c ontrol their use of the space. Intersection within the sight of pedestrian affect their feeling of control over their space and time and their freedom of movement and possibly the leisure of seeing through these intersection that could enrich the pedestria n experience of the urban space. When using all experimental cases there was no correlation between number of intersecting streets and the dependent variables. When excluding scenes which have high level of faade complexity the results show that number of intersecting streets has positive correlation with perceived residential density and space crowdedness with r=.136, p= .003 and r=.271, p=.000 respectively and has no correlation w ith openness and level of comfort The increase of intersecting street obse rved in a scene caused increased in perceived residential density; this could be justified in that the increase of opening between buildings increase the observed number of masses (buildings) which may be read by pedestrian by extra residential or living s pace s. This finding seems to agree with Martin and Nash (1973) proposition that different building forms and typological arrangement could affect how dense a space perceived by users It was expected that the increase of openings around the street will inc rease the space openness by increasing access to the surrounding context and will contribute to reducing the space crowdedness as suggested by SOI Spatial Openness Index_ by Fisher Gewirtzman and Wagner (2003) However, it is possible that research
104 resul ts are affected by other elements in the scene such as number of people seen by participants Number of interesting streets correlated positively to people preference for social space with r= 0.23 ; as number of intersecting streets increases people seem t o prefer the space for a social situation that demands more interaction such as friend meeting. While it did not correla te to preference for work space. This suggests that number of intersecting street in the scene could affect people preference for activi ties that involved more social interaction. Number of P eople in the S treet and the D ependent V ariables The number of people seen in a scene and the type of activity they do is expected to affect the sense of space crowdedness and perceived residential de nsity The pattern of interaction between people in a space defines the culture of the place and creates; along with physical aspects of the space; meanings and character of the place (Rappoport, 1977). Taken the nature of the study experiment and the type of survey used the presentation of activity and number of people in the place was limited due to the static nature of the visual survey The concept of people activities is difficult to express through static images because human expression, sounds, and t he spontaneous nature of human movement is not easily experienced or imagined through static images. However, it was important to include people and activities in the scenes for three reasons; first: to provide a scale reference to the scene, second: to f ind out if increasing number of people would affect perceived residential density and or perceived characteristics of the space ( level of comfort, space crowdedness and openness) and
105 finally to relate to the urban nature of the study area in which social a ctivities and pedestrian movement is frequent The analysis results show positive correlation between the independent variable (number of people and activities in the street) with ratings of the dependent variable space crowdedness ( r = .399, p = .001) an d residential density ( r = .188, p = .001 ). The number of people and activities was not correlated with Level of Comfort ( r = .005, p = 0.886) and o penness ( r = .027, p = .483). The results could relate to the model of behavioral constraint or social inte rference in that higher sense of crowding is a result of feeling of restriction caused by increased number of people and activities (Brehm (1966, Stokols, 1976, Proshansky, 1972). This suggests that way people and activities are distributed in a space is v ery important for human perception about space and their positive or negative view of the space. In a situation such as friend meeting the number of people in the street was found to correlate positively with the ranking of space as suitable for such situ ation (r= .246 P=0.000) In a work meeting situation, the number of people in the space has no correlation to people preference for a work meeting place. This advocates that space that reflects on going social interaction is more attractive for people wi lling to spend time in socially engaged activities than a more formal activity Dependent V a riable ( P erceived R esidential D ensity ) According to the analysis enclosure and the number of people in the street were significantly correlated to perceived resid ential density (r=.155) while faade complexity and number of intersecting streets in the view were not correlated.
106 It was only when controlling for the faade complexity through excluding scenes which have high level of fa cades complexity that the numbe r of intersecting streets correlated positively to perceived residential density and space crowdedness The increase in the enclosure value w as done by increasing the height of masses to the street width and accordingly increasing residential spaces and a ctual residential density. The positive correlation between enclosure and perceived residential density suggested that, for pedestrian, enclosure is a good predictor of residential density. Empirical studies related to perceived residential density defined perceived residential density in terms of the volume of free space seen from a given point quantitatively and described as (Spatial openness index) which is based on three dimensional visual analysis (Fisher Gewirtzman and Wagner, 200 6 ). Their study is based on the idea that higher level of spatial openness in a certain configuration would make it perceived as more spacious and less compressed The increase of enclosure in my study increases the amount of built up area to the free space and resulted in i ncrease of perceived residential density and space crowdedness It also found to correlate negatively to space openness and level of comfort which is in line with Fisher and W a gner (2006) findings. In this experiment buildings did not take irregular shapes buildings followed straight line surrounding streets forming a wall like configuration The study findings revealed the importance of enclosure in defining how open space is perceived and accordingly how comfortable it is. The corresponding increase of s pace crowdedness and decrease of level of comfort suggests negative physiological reaction to such configuration. It is expected however, that working toward increasing openness in urban configurations through alternative building configurations could redu ce the negative
107 impact. This is possible through introducing certain details such as recessed parts of building facades to break the continuity of masses (street wall s) When controlling for faade complexity by excluding scenes with very high level of faade complexity the number of intersecting streets in the scene correlates to the perceived residential density and space crowdedness As number of intersecting streets increase the perceived residential density increase. When number of intersecting stre ets increase in the scene the number of masses (buildings seen by the participant) increases, and this is not necessarily a reflection of how much extra residential spaces added. In a different question, a top view of an urban street in black and white was displayed to the participants and again the street that has more intersecting streets and more masses was perceived as higher in residential density. The results show that number of intersecting streets in urban space maybe read by pedestrian as more res idential spaces reflected in larger number of smaller buildings even if the building width does not really reflect larger spaces The number of people in the street and the level of interaction and level of activities in the scene was found to correlate po sitively to the perceived residential density. As number of people increase in the urban space perceived residential density increased. This increase in perceived residential density was parallel to increase in the space crowdedness In relation to previou s studies crowdedness was defined as the negative outcome of high density areas through limited freedom of choice and high level of social interaction that in certain situations not needed (Stokols, 1976; Zlutnick and Altman, 1972; Rodin, 1976). If the hig h level of space crowdedness is to be considered as negative psychological reaction to high density as proposed in previous studies, then the study results suggests
108 that high number of people in the street could trigger such feeling and a reduced level of comfort. Dependent Variables ( Space crowdedness L evel of Comfort, Space O penness) Based on literature these variables were considered as related and sometimes a s a definition of how dense an environment perceived. The study results show that when enclos ure correlated positively to perceived residential density and space crowdedness it correlated negatively to level of comfort and space openness. Since increasing enclosure in this experiment was done by adding height to the building and increasing the act ual space for residential use the positive correlation between enclosure and space crowdedness and p erceived residential density suggests that actual increase in density reflected by increasing building height to street width ratio has in fact led to incre ase of perceived residential density and caused higher sense of space crowdedness A higher sense of space crowdedness is a negative aspect of higher residential density expressing people lack freedom and control in space (Stokols, 1976; Proshansky, 1972) ; again, this is declared by the results as a lower perceived sense of comfort and space openness Dependent variables space crowdedness and space openness did not correlate to the independent variable fa ade complexity. While a small positive correlation was found between faade complexity and the level of comfort ( r = .163, p = 0.001) With faade, as one important design element of the urban environment it is possible to argue that increase of faade complexity might have contributed to increasing level of comfort even in high density settings which is in away suggesting less negative experience of high residential density settings This is in line with Rappor (1977)
109 suggestion that design techniques and element can reduce the negative perception of dens ity. Again, number of intersecting streets in the scene was correlated to perceived residential density and space crowdedness with r =.136, p = .003 and r =.271, p =.000 respectively. No correlation was found between number of intersecting streets and sen se of comfort and space openness. The independent variable number of intersecting streets was used to test the rule of visibility and boundary permeability on people per ception of residential density In logical connection to Stamps (2009) studies of inter ior space perceived spaciousness and enclosure to boundary permeability i t was expected that the number of intersecting streets will contribute to increasing level of comfort and space openness through adding visibility to surrounding streets Different qu est ions were used to test the correlation between number of intersecting streets and the perceived residential density; findings illustrated that increasing the number of streets might increase perceived residential density and sense of crowding ; this coul d be explaine d in that the increase of number of intersecting streets lead to increas ing masses in the view c reating a perception of more available residential spaces. Space crowdedness and perceived residential density have be en both affected by increa sing the number of people in the scene; both were found to be positively correlate d with ( r = .399, p = .001) and ( r = .188, p = .001 ) respectively. No correlation was found between number of people and level of comfort. But as crowding is one negative conseq uence of high residential density it is possible to suggest occurrence of stress as people in the scene increases which lead to judging the place as crowded
110 This complies with (Rapoport, 1977) idea that increase of number of people in space can generate i nformation overload leading to feeling of stress Type of Social Activity A ssociation with Perceived Residential D ensity To check for association between the type of activity people aim to have in a space and their perception of residential density two dif ferent social situati ons were suggested: one that requires higher level of social interaction (meeting friends) and another situation that require more formal interaction (work meeting). The study results show that in the case of friend meeting peo ple pref erence of a space was not correlated to perceived residential density or to space crowdedness but it was positively correlated to level of comfort and space openness. Research findings did not match with previous research related to social settings and cr owding perception which; in contrast; suggests that changes of social settings could alter people perception to Dean, et. al.1975). However, a s people perceived space as more comfortable they gave it higher preference for meeting a friend, the s ame applies for openness confirming positive association between the level of comfort and preference for friend meeting and the same for openness Testing the correlation between the p reference for social space and the four independent variables in case of friend meeting situation returns significant positive correlation s with number of intersecting streets, number of people and enclosure, and a significant negative correlation with fa ade complexity ; the positive correlation means that as the in dependent variable increases the space receive s higher preference for the friend meeting. The increase in actual density by adding height to the buildings reflected in increase of enclosure led t o increase of preference for a social situation such as
111 friend meeting. This along with positive correlation between sense of comfort and preference for social situation suggests that a social situation that requires higher interaction and more active medi um could fit better in high density settings. In the case of work meeting people preference was not found to correlate to any of the four dependent variables (perceived residential density, space crowdedness level of comfort and openness). When testing c orrelation between work space preference and the four i ndependent variables it returns a weak negative significant correlation with only one independent variable : enclosure. The negative correlation means t hat as enclosure increase the pref erence for work space decrease; which might be read as : increase of actual density ( increase of space available for residential use) might reduce preference for a formal social situation such as a work meeting. Between ariables Human perception is affected in ma n y ways by personal characteristics and previous experiences. The impact of high residential density on people behavior, their ongoing interaction with the environment as well as on the psychological process es occur during this interaction is also relat ive. It is expected that between variables such as age, gender, type of residence, design background and the previous exposure to various type of environments will affect the way human experience a high residential density environment. Women and men were expected to have different level of comfort at high residential density places. T he study analysis did not confirm any difference between women and men in level of comfort at scenes with highly perceived residential densities. These findings do n ot relate to previous research in the field of crowding perception where women perception to crowding was found to be smaller than male perception to
112 crowding under similar environmental and social settings and that women were more comfortable than men in completing tasks under high density settings (Stokols et al., 1973). Further, the study found no significant different between people with design background and those with no design background in their level of comfort in highly perceived residential densi ty scenes. With the growth of advocacy in favor of urban living and against sprawl the negative reacti ons against high residential densities is shifting B ut there are still culturally defined and deep rooted believe s about undesirable expectations from h igher residential densities that rise to the s urface when people make actual decision about buying home s or when judging new high density developments. Accordingly, p eople who lived in USA for long time were expected to judge spaces with highly perceived r esidential density as less comfortable and crowded. Statistical analysis has shown that people lived in USA longer have judged spaces with higher perceived residential density as more crowded than people who lived in USA for short time with significant dif ference in mean values ( Chi square=9.884 and p=.007 ) Further, s ignificant difference between people perceived residential density was found between people from different age groups. Older people have more often ranked spaces as higher perceived reside ntial density than young people. This could suggest that older people are affected by the negative aspects historically attached to higher density areas, another possible reading for this result is that older people are less adapting to fast based vigorous urban life style which slowly becoming a preference for younger generation.
113 People who live in places that are higher in actual densities (urban) have ranked scenes as lower in perceived densities than people who live in lower densities areas. They also h ave ranked highly perceived residential densities scenes with higher level of comfort. Significant difference between the two groups in their ranking for perceived residential densities and level of comfort can suggest that people living conditions and typ e of their residence affect their reaction to high residential densities; people who are familiar with higher residential densities are expected to cope better in high residential densities area s. The differences found between people based on age, type o f residence they have suggest that life earned experiences have large influence on how people perceive and environment and how residential density affect their interaction with an environment. The design experience and gender wer e not found to affect peopl e judgment of high density spaces.
114 CHAPTER 6 CONCLUSION This research aims at exploring perceived residential density which is an important element in urban design and planning because it affects decision making and people acceptance of proposed planning projects. T he concept of residential density tied itself to multiple meanings in planning and design due to the many changes in urban form theories, planning techniques, and ongoing social impacts on the environment. Through using ex perimental approach this research tested the effect of change o f four urban design qualities on people perception for residential density and three other perceptual reactions which are expected to further explain people reaction to high residential den sity. It concluded that the two variable enclosure s (height to width ratio) and people existence in the scenes correlated to perceived residential density and the space crowdedness It also proposed few design guidelines that can contribute to improveme nt in people perception of higher residential density areas This chapter will discuss research contribution s to existing knowledge about perceived residential density and will recommend future development in the field as well as methodological drawbac k s and lesson s learned. Perceived Residential Density and Human Experience Human interaction with an environment, its continuity and its consequences define s the quality of spaces and contributes to the success of a community at different levels. The h uma n brain process es images and an ongoing interaction with an environment in a complicated way called percept ion; this process will shape and define people satisfaction, belonging and appreciation of the space. With advancement of
115 research method s and expe rimental techniques contemporary research concern with improving the ability to interpret the impact of a design on the city function before even implementing it It aims at mitigating any possible negative consequences on the space social experience. Em pirical research in perceived residential density can be traced back to the late sixties with research on impact of space crowdedness on human behavior and social interaction. It was seen that high densities described as space crowdedness leads to unwante d social pressure and consequently increase s stress, reduces productivity and weaken s people connection s to their spaces. This body of research suggested a strong connection between high density and people sense of comfort and control over their enviro nment. My research illustrates that earlier research in crowding perception is very useful reference for contemporary experimental research. The study app roach in troduced important tools that could be further tested with more advanced techniques such as p arametric design software. Perceived residential density and Urban Design Q ualities My research shows that manipulating certain urban design qualities c an improve people perception of residential density and this was reflected in the results by low perceived residential density, low space crowdedness and high sense of comfort. Enclosure was found to be an essential element in the bui lt environment and could largely affect the perceived environment. Enclosure was manipulated by changing the height o f buildings W hen enclosure increased the perceived residential density and the space crowdedness increased while sense of comfort and space openness decreased. Increasing enclosure in the scenes leads to increase in the actual residential density
116 becaus e it was based on increasing height of the buildings and consequently increas ed potential spaces for residential use. Analysis showed that increase of enclosure through increasing height of the buildings surrounding the street cause increase of p erceived residential density and space crowdedness This suggests that intended increase of density by increasing height of buildings should take into consideration the resulted enclosure and its impact on residential density perception It is important to use design techniques that can reduce negative impact of height to width ratio on residential density perception S ome faade treatments such as recessing parts of the faade and creating voids allow to redu ce rigidity of the masses surrounding streets A nother technique to help reduc e enclosure is through increasing street width to maintain a sense of safety and comfort at proper levels. The n umber of people in the scenes has also correlated positively to perceived residential density and to the space c rowdedness but did not correlate to the sense of comfort and space openness. This correlation suggests that people existence and the ir activities and the concentration of social interaction in a space is an important factor in defining perception to res idential density. Accordingly, people distribution in a space, type of activity and level of interaction should be a subject of attention for designer s and should not be a byproduct of functional and financial demands of the design process Even though the faade complexity did not correlate directly to perceived residential density it was found to positively correlate to sense of comfort w hich
117 suggest s that manipulating faade design s might improve people experience to high residential density space s. The n umber of intersecting streets in the scene was not found to correlate to perceived residential density and the other three dependent variables. But when excluding the scenes that have highest level of faade complexity the analysis showe d positive correlation between the number of intersecting street in the scene and the perceived residential density and space crowdedness This is inconsistent with the Fisher Gewirtzman and Wagner (2003) proposition that increasing the openings around th e street will increase the space openness and will contribute to reducing the space crowdedness This r esearch included visual survey that displayed 3D models created using images captured from an urban area. The images used where colored and det ailed to a high degree and mimicked actual streetscape. On the other hand, the researcher added few questions in which detailed buildings were replaced with grey color masses to compare between correlation results in the two cases. T he questions that used grey color image s returned higher correlation between the dependent and independent variables comparing to the correlation results found in the realistic scenes The difference in correlation suggests that the use of abstract models in visual survey could allow misleading results about correlation between urban design qualities and perceived qualities This suggests that visual survey used in testing impact of urban design qualities on perceived qualities may produce more realistic results when using scene s that has faade details material landscape elements and stre et activities
118 Perceived Residential Density and Type of Activity The study reveals no correlation between perceived residential density and people preference for a formal or informal mee ting place s In the case of informal situation (meeting a friend) the study reveals positive correlation between people preference for informal meeting place and the two independent variables: sense of comfort and level pf openness. It also found positive correlation s between people preference s for space and the independent variables: level of enclosure, number of people in the scene, and number of intersecting streets and a negative correlation with faade complexity. In a formal situation (such as work meeting) no correlation was found except for small negative correlation s between preference for work space and the independent variable: enclosure. These findings suggest that social situation s that demand more interaction are more spatially defined an d are influenced more by the set up and design quality than a formal situation that requires less social interaction. Further, activities which require higher level s of interaction are important for the creation of street s and for the daily experience of the spaces. In a design process ; from the schematic design stage to the actual implementation of the design ; a designer need s to take into consideration the different type of activities expected to occur in a space and the impact of these activities on the perception. When u rban designer s understand the expected type of activity in a street and the potential social interaction linked to these activities they become more involved in realistic design process. Perceived R esidential Density and Between Subject ariables The study results show that no differences between p articipants perceived residential density based on gender and design experience While it shows significant
119 difference based on participants age, time spent at USA and the type of residence This suggests that perceived residential density is based on daily living experiences and is highly influenced by cultural values Perceived Residential Density Versus Real Residential Density One drive behind this research is to try to find a connection b etween real density and perceived residential density. However, the method used did not allow to accommodate that direction, and the type of scenes used in the experiment w ere all from urban area s and did not have reference to actual density This suggests possible extension to this research by using actual scenes from different areas that have different actual residential densities and investigating various perceptual reactions and judgments of these scenes. This will require careful selection of scenes wh ile taking into consideration other urban design qualities of the scenes. Comments on The Research Method The dependent variables used in the study were an ordinal type of data ; 77 participants contributed to the visual survey and they ranked 9 scenes fo r four dependent variables (level of crowdedness, sense of comfort, perceived residential density and space openness) on a Likert scale from 1 6 The nine scenes were varied in characteristics based on four independent variables, which in turn have three l evels each. Therefore, the experiment generated a total of 693 responses for each level of the independent variables there was 231 responses out of the total The research aimed at measuring correlation between the dependent and the independent variables. Based on the study assumptions the researcher used Spearman Rho correlation test to measure correlation between the dependent and the independent variables Although Spearman rho test is the most popular non parametric correlation test it is suggested
120 tha t Kendall tau correlation test should be used when large number of tied rank exists (Field, 2009, p.181) For this reason, researcher co nducted measure for tie d rank in the data which show s that some variable s have around 20% tied data (Tables 6 1 6 2, 6 3, 6 4) To check for d ifference between the Spearman r ho correlation results and K e nd a ll t au rank correlation results researcher ran the analysis on same dataset using Kendall The comparison sho ws very small difference between the two tests which s uggests tha t Spearman r ho does, indeed increases the correlation because it does not correspond as good as Kendall tau t o the number of ties in the data (Tables 6 5 to 6 2 0 ) However, the difference wa s very sm all to affect the study findings that reveal small to medium correlation between the dependent and independent variables With improvement of the statistical packages t he output between the two tests are almost the same For example: in this study S PSS was used to run the two tests, SPSS advanced algorithms take into consideration tied ranks and correctly and automatically calculate them ( Statistics Solutions, 2017 ). This finding suggests that researcher s hould consider the issue of tied ranks in the data w hen working with correlation between ordinal and ranked data as early as in the study design stage s Recommendation and Future Research Opportunities The correlation between independent variables and the dependent variables were between moderate and weak due to small sample size and the small number of combinations used. The experiment relies on using 3D models created from scratch to simulate various combinations of the independent variables Capturing photos from relevant study area, editing ima ges and creating 3D models was time demanding for the researcher. It was also time demanding for people participating in the survey study to
121 run through each question of 9 different combinations. F urther analysis with larger sample and a larger number of c ombinations could contribute to improving understanding of the effect of the independent variables on perceived residential density However, such research will require other presentation technique than the one used in my research. It can use walk through videos of the simulated environment to limit time needed; research can run the experiment over various sessions to allow enough time for larger number of combinations and to limit the effect of boredom on quality of answers. The type of resea rch I used ( fractional factorial design) relies on using partial cases among many possible combinations of the factors studied. This reduces the accuracy of measure of impact of each independent variables on the dependent variables. For this reason, this study can be considered under the category of screening experiment in which different independent variables are tested to identify the ones that have larger impact on the dependent variables. Based on the results of this research a future research can test fewer number of variables to deliver some more specific findings such as testing enclosure and number of people in the scene effect on perceived residential density Life earned experiences and cultural meaning and expressions was found to relate largely to the concept of perceived residential density more than personal characteristics (gender, education). Age, type of residence where people lived, and time spent in the USA are factors that reflects people length of exposure to shared values and American cul ture that for long time supp orted lower residential density. This emphasizes the dominance of the social aspect on the concept o f perceived residential
122 density a nd advice that perceived residential density might be better studied under situational experime nt in which people get to express their feeling about a place based on their interaction and or exposure to certain social settings. The research done in early seventies related to psychological reaction of people to different physical configurations and u nder different social settings can provide good guidance for future research in this area ( 1975; Dean, et. al.1975; Chin et. Al. 1976)
123 Table 6 1 Enclosure correlation w ith dependent variables (comparison between Kendall Tau and Spearman). Dependent Variables Kendall Tau b Sig. (2 tailed) Spearman rho Sig. (2 tailed) Space crowdedness .100** .002 .116** .002 Level of comfort .138** .000 .160** .000 Space o penness .190** .000 .221** .000 Perceived residential density .132** .000 .155** .000 Table 6 2. Faade complexity correlation with dependent variables (comparison between Kendall Tau and Spearman) Dependent Variables Kendall Tau b Sig. (2 tailed) Spearman rho Sig. (2 tailed) Space crowdedness .014 .669 .017 .660 Level of comfort .142** .000 .166** .000 Space o penness .030 .357 .035 .356 Perceived residential density .036 .264 .042 .270 Table 6 3. Intersecting street correlation with dependent variables ( comparison between Kendall Tau and Spearman) Dependent Variables Kendall Tau b Sig. (2 tailed) Spearman rho Sig. (2 tailed) Space crowdedness .046 .152 .055 .151 Level of comfort .012 .705 .016 .000 Openness .012 .702 .014 .715 Perceived residenti al density .001 .971 .002 .960 Table 6 4. Existence of people and activities correlation with dependent variables (comparison between Kendall Tau and Spearman) Dependent v ariables Kendall Tau b Sig. (2 tailed) Spearman rho Sig. (2 tailed) Space crow dedness .295** .000 .339** .000 Level of comfort .003 .917 .005 .886 Space o penness .022 .493 .027 .483 Perceived residential density .164** .000 .188** .000
124 Table 6 5. Cross tabulation to measure percentage of ties in the data between space crowd edness at di fferent categories of enclosure. Space crowdedness Total 1 2 3 4 5 6 Enclosure 0 27 *58 *78 45 20 3 231 1 21 *50 *96 43 19 2 231 2 28 36 *60 74 31 2 231 Total 76 144 234 162 70 7 693 T hese values are considered high in number of ti es as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 6. Cross tabulation to measure percentage of ties in the data between level of comfort at different categories of enclosure Level of Comfort Total 1 2 3 4 5 6 Enclosure 0 4 19 *50 *95 41 22 231 1 1 36 *68 *82 37 7 231 2 5 38 *71 *79 28 10 231 Total 10 93 189 256 106 39 693 T hese values are considered high in number of ties as it represents more than 20% of the to tal data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 7. Cross tabulation to measure percentage of ties in the data between space openness at different categories of enclosure Space o penness Total 1 2 3 4 5 6 Enclo sure 0 2 26 *63 *83 44 13 231 1 5 29 *73 *82 36 6 231 2 20 46 *83 49 27 6 231 Total 27 101 219 214 107 27 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percent age of ties= (count/ 231*100%) Table 6 8. Cross tabulation to measure percentage of ties in the rank of space residential density at different categories of enclosure Perceived residential density Total 1 2 3 4 5 6 Enclosure 0 14 35 *93 *62 23 4 23 1 1 9 37 *76 *67 33 9 231 2 13 32 47 *79 48 12 231 Total 36 104 216 208 104 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*10 0%)
125 Table 6 9. Cross tabulation to measure percentage of ties in the rank of space crowdedness at different categories of number of intersecting streets Space c rowdedness Total 1 2 3 4 5 6 No of intersecting streets 0 24 46 *80 *56 24 1 231 1 3 6 *55 *75 43 19 3 231 2 16 43 *79 *63 27 3 231 Total 76 144 234 162 70 7 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) T able 6 10. Cross tabulation to measure percentage of ties in the rank of level of comfort at different categories of number of intersecting streets Level of comfort Total 1 2 3 4 5 6 No of intersecting streets 0 5 22 *57 *92 42 13 231 1 4 46 *72 66 28 15 231 2 1 25 *60 *98 36 11 231 Total 10 93 189 256 106 39 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 1 1. Cross tabulation to measure percentage of ties in the rank of space openness at different categories of number of intersecting streets Space openness Total 1 2 3 4 5 6 No of intersecting streets 0 4 43 *74 *71 31 8 231 1 18 23 *56 *76 46 12 231 2 5 35 *89 *67 30 5 231 Total 27 101 219 214 107 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 12. Cross tabulation to measure percentage of ties in the rank of perceived residential density at different categories of number of intersecting streets Perceived residential density Total 1 2 3 4 5 6 No of intersecting streets 0 14 29 *69 *65 49 5 231 1 11 46 *75 *71 16 12 231 2 11 29 *72 *72 39 8 231 Total 36 104 216 208 104 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%)
126 Table 6 13. Cross tabulation to measure percentage of ties in the rank of space crowdedness at different categories of number of faade complexity Space crowdedness Total 1 2 3 4 5 6 Faade complexity 0 20 48 *87 *51 24 1 231 1 34 40 *71 *59 24 3 231 2 22 *56 *76 *52 22 3 231 Total 76 144 234 162 70 7 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 14. Cro ss tabulation to measure percentage of ties in the rank of level of comfort at different categories of number of faade complexity Level of comfort Total 1 2 3 4 5 6 Faade complexity 0 5 42 *73 *70 30 11 231 1 5 32 *61 *88 33 12 231 2 0 19 *55 *98 43 16 231 Total 10 93 189 256 106 39 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 15. Cross tabulation to m easure percentage of ties in the rank of space openness at different categories of number of faade complexity Space openness Total 1 2 3 4 5 6 Faade complexity 0 6 37 *80 *70 30 8 231 1 16 31 *60 *78 38 8 231 2 5 33 *79 *66 39 9 231 Total 27 101 219 214 107 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 16. Cross tabulation to measure percentage of ties in the rank of perceived residential density at different categories of number of faade complexity Perceived residential density Total 1 2 3 4 5 6 Faade complexity 0 11 30 *65 *74 44 7 231 1 12 40 *78 *69 25 7 231 2 13 34 *73 *65 35 11 231 T otal 36 104 216 208 104 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%)
127 Table 6 17. Cross tabulation to measure percenta ge of ties in the rank of space crowdedness at different categories of number of existence of people in the scene Space crowdedness Total 1 2 3 4 5 6 Existence of people in the scene 0 *50 *70 *61 31 17 2 231 1 18 47 *98 49 18 1 231 2 8 27 *75 82 35 4 231 Total 76 144 234 162 70 7 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 18. Cross tabulation to measu re percentage of ties in the rank of level of comfort at different categories of number of existence of people in the scene Level of comfort Total 1 2 3 4 5 6 Existence of people in the scene 0 8 37 *63 *68 35 20 231 1 2 24 *60 *93 42 10 231 2 0 32 *66 *95 29 9 231 Total 10 93 189 256 106 39 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 19. Cross tabulatio n to measure percentage of ties in the rank of space openness at different categories of number of existence of people in the scene Space openness Total 1 2 3 4 5 6 Existence of people in the scene 0 18 43 *59 *56 41 14 231 1 5 31 *75 *79 35 6 23 1 2 4 27 *85 *79 31 5 231 Total 27 101 219 214 107 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (count/ 231*100%) Table 6 20. Cross t abulation to measure percentage of ties in the rank of perceived residential density at different categories of number of existence of people in the scene Perceived residential density Total 1 2 3 4 5 6 Existence of people in the scene 0 21 49 *64 58 27 12 231 1 10 37 *86 *64 30 4 231 2 5 18 *66 *86 47 9 231 Total 36 104 216 208 104 25 693 T hese values are considered high in number of ties as it represents more than 20% of the total data (within the group) counted as: percentage of ties= (co unt/ 231*100%)
128 APPENDIX A VISUAL SURVEY Florida December 15th 2013 to February 28th 2014 This survey is part of a research study conducted by Alma Othman, a PhD student at the University of Florida Department of Urban and Regional Planning ( https://dcp.ufl.edu/ ). The study aims to measure and understand the effe ct of urban space qualities on perception of residential density. You are invited to participate in the study because you live in suburban, low density type of housing within my study target area. 80 participants will be enrolled in the study. Sur vey requires about 12 15 minutes for completion. Each participant will receive a check for 15 dollars for participating in this study to their mailing address provided by them. There is no anticipated benefit for participants; there are no known risk to pa rticipate in this study. Participation in the study is voluntary; you may decline to answer any questions you do not wish to answer. You may decide to withdraw from this study at any time without consequences simply by advising the researcher. This is a two parts survey. In the first part, you will be shown some computer images of city streets and you will be asked to rank the images based on certain criteria. In the second part, you will be asked a few basic questions about you, such as age, gender, wor k experience and academic background. To answer the survey, you will have to open the survey link at your convenient time and place. If you decide to leave before finishing all the questions the system will save your answers; as you reopen the link it will start where you ended your answer the first time. Please answer the survey no later than February 28th 2014; after that date survey link will no longer be active. All study data will be collected through Qualtrics; an online survey program. Your identity will be kept confidential to the extent provided by law. All information collected in the survey will be stored in a password protected account. Data collected in this survey does not reveal participant identities or their computer IP addresses. Researche r will use the data only for scientific research purposes and will not share it with entities other than the research committee members and committee chair (Prof. Ilir Bejleri) from Urban and Regional Planning/ the policy can be obtained at https://www.qualtrics.com/privacy statement/ For questions about the study or survey procedure please contact me, Alma Othman by phone at 847 701 5031 or via email a t email@example.com. You may also contact my research advisor, Dr. Ilir Bejleri at firstname.lastname@example.org. For questions or concerns about your rights as a research participant you may contact the IRB02 office, University of Florida, Box 112250, Gainesville, FL 32 611 2250; phone (352) 392 0433
129 Q52 I have received the invitation letter to participate in this study. Based on the information I received in the invitation letter and in this survey introduction I voluntarily agree to participate in the study by answerin g an online survey and I have received a copy of this description by mail. I have had an opportunity to ask any questions related to this study, to receive satisfactory answers to my questions, and any additional details I wanted. I understand that I may w ithdraw this consent at any time by informing the researcher without penalty or consequences and that I will receive a compensation of 15 dollars for participating in this study. I agree (1) I disagree (2) Part 1: In the following screens, you will be s hown 11 different groups of computer images of the same urban space. Each group contains four images of the same space from different viewpoints. Based on your perception, please rate the space on a scale of 1 to 5 (where 1 is very low and 5 is very high) based on the following criteria: a Space crowdedness (How crowded does the space feel?) b Level of comfort (How comfortable would you be when visiting or walking in the space?) c Openness (How open or spacious does the space feel?) d Residen tial density (What is the residential density of people living at this street) Please look carefully at the images before answering the questions In the following page you will see an illustration of 9 different spaces, the purpose of this illustration i s to make sure you are familiar with the variations and the contexts of the spaces presented in the next 11 questions click >>> to continue
130 Intro duction: These are the spaces that you are going to see. We will ask various questions about these spaces. Figure A 1. Introduction page for the first question in the survey showing the type of images that will be presented in the study scenes to familiarize participants with the type of images.
13 1 Space 1 shown from 4 different viewpoints (Note : Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 2. Question 1 of the survey (first scene) to rank the dependent variables.
132 Space 2 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 3 Question 1 of the survey (second scene) to rank the dependent variabl es.
133 Space 3 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowd edness level of comfort openness residential density Figure A 4 Question 1 of the survey (third scene) to rank the dependent variables.
134 Space 4 shown from 4 different viewpoints. (Note: Hover the mouse over the urb an space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 5 Question 1 of the survey (fourth scene) to rank the dependent variables.
135 Space 5 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) ( 3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 6 Q uestion 1 of the survey (fifth scene) to rank the dependent variables.
136 Space 6 shown fro m 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of com fort openness residential density Figure A 7 Question 1 of the survey (sixth scene) to rank the dependent variables.
137 Space 7 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 8 Question 1 of the surv ey (seventh scene) to rank the dependent variables.
138 Space 8 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) ( 4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 9 Question 1 of the survey ( eighth scene) to rank the dependent variables.
139 Space 9 shown from 4 different viewpoin ts. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 10 Question 1 of the survey (ninth scene) to rank the dependent variables.
140 Space 10 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (ver y low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) (6) space crowdedness level of comfort openness residential density Figure A 11 Question 1 of the survey (tenth scene) to rank the dependent variables.
141 Space 11 shown from 4 different viewpoints. (Note: Hover the mouse over the urban space qualities to see their definition) (very low) (1) (low) (2) (moderately low) (3) (moderately high) (4) (high) (5) (very high) ( 6) space crowdedness level of comfort openness residential density Figure A 12 Question 1 of the survey (eleventh scene) to rank the dependent variables. Next, you will be shown 9 different spaces and you will be asked to put these spaces in order from your most preferred to the least preferred based on some questions. Click >> to continue. I f you are going to meet a friend, which spaces would you prefer best? Please list spaces in the order of preference from the most preferred to the least preferred using a
142 scale from 1 9 where 1 is your most preferred space and 9 is the least preferred space. (Please use label shown in the corner of each space). ______ A ______ B ______ C ______ D ______ E ______ F ______ G ______ H ______ I Figure A 13 The f irst type of social activity in the second q uestion of the survey images used to measure connection between the preference of social activity and the dependent variables If you were to work in this area, which spaces would you prefer best? Please list spaces in the order of preference from the most preferred to the least preferred using a
143 scale from 1 9 where 1 is your most preferred space and 9 is the least preferred space. (Please use label shown in the corner of each space). ______ A ______ B ______ C ______ D ______ E ______ F ______ G ______ H ______ I Figure A 14 The second type of social activity in the second question of the survey images used to measure connection between the preference of social activity and the dependent variables.
144 The image below shows three different views of a city street looking from above with black (buildings) and white (street and yard areas). Please rank each one of them from 1 to 3 where 1 is the least dense street and 3 is the densest street Figure A 15 The third question in the survey used to test correlation between number of intersecting streets and the perceived residential density. The images below sh ow 9 different configurations of the same street. Please rank the following images in terms of how dense they feel using a scale of 1 to 9 where 1 is the least dense and 9 is the densest. (Please use label shown in the corner of each space). ______ A ______ B ______ C ______ D ______ E ______ F ______ G ______ H ______ I Figure A 16. The fourth question of the survey; grey color images used to measure correlation between street enclosure and number of intersectin g streets with the dependent variables.
145 Part 2 In this section, you will be asked few questions about yourself and your experience. What is your age 18 25 years 26 35 years 36 45 years 45 60 years more than 60 (5) What is your gender Male Female How long have you been living in the USA 1 5 years 6 12 years 13 20 years More than that Do you have any academic or professional design knowledge or experience? Yes No Please choose one field or more from the following Architecture Urban Desig n Urban Planning Landscape Architecture Interior Design Other, please specify ____________________ Think about where you have lived during the last 10 years. Please answer the following questions based on your living experience of the last ten years: 1 I have lived mostly in urban area suburban area rural areas other please specify ____________________
146 I have lived mostly in very high population density area high population density area Moderate population density area low population density area very low population density area Residential density is measured by the number of dwelling units per acre; 1 acre is roughly 200 feet by 200 feet; for the metric system 1 acre is about 4,047 square meters or roughly 60 meter x 60 meter) I have mos tly lived in an area that has almost similar residential density to the following area: Figure A 17. Arial Images showing different type of residential areas with various residential density to test correlation between participants numeric expression and visual expression about residential density. Note that once you click the next button to finalize this survey you will not be able to go back and revisit any of your answers.
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157 BIOGRAPHICAL SKETCH Alma Othman, is a n Architect and Urban Planner with experience in design, urban planning and teaching In 2002, s he received her b s degree in architecture from Annajah National University, Palestine. She worked in historic preservation projects and as a supervising engineer for one year. Later, s he received DAAD ( German Academic Exchange Services ) scholarship and ISNM (International School of New Media Scholarship) to complete a m aster degre e in d igital m edia and g raphics from the Digital Media School U niversity of Luebeck, Germany In 2005, s he returned to Palestine to teach in the Interior Design and Graphic Design Department at Annajah National University, Palestine In 2007 she received OSI (Open Society Institute) and USAID (United States Agency of International Development) scholarship to complete her PhD in u rban and r egional p lanning at the University of Florida, United States. During h er time at the University of Florida Alma Othman worked as Teaching and research assistant with Prof essor. Ilir Bejleri in urban design and GIS analysis courses as well as different research projects in transportation and urban planning Between 2013 and 2016 Alma worked as Consultant in various planning assignments ranging from historic pr eservation to development of m aster plans and sectorial analysis for local government agencies to development of international reports about planning in Palestine. In 2017 she defended her dissertation at the University of Florida to complete her d o ctorate degr ee and to be ready to pursu e a career in p lanning at the USA