National Academies Press: OpenBook

Effective Methods for Environmental Justice Assessment (2004)

Chapter: Chapter 11 - Visual Quality

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251 CHAPTER 11. VISUAL QUALITY OVERVIEW Visual quality is a very important component of human existence. Because we are continuously exposed to visual stimuli in the environment, visual quality helps shape our perceptions, attitudes, and general views of life. Our visual physical environments can range from the grand and inspirational, such as a mountain vista or a pristine lake in the woods, to the utilitarian and dreary, such as views of a refuse dump or a barren, surface parking lot. A positive visual environment can stimulate feelings of well being, whereas a negative one can diminish enjoyment and quality of life. One key component of improving quality of life is thus to improve and enhance the visual quality of our environments. Visual quality is one of the most tangible areas affected by physical improvement projects. Almost all transportation projects will in some way alter the physical landscape and thus the perceived visual quality of the community. Many people will accept basic alterations in their physical environment as the price of progress, and so these changes may not be controversial. Some alterations, such as adding landscaping to screen a transportation corridor from a residential area, may even be perceived as beneficial and a positive impact. Other types of changes, however, such as adding a screen wall that blocks views of adjacent businesses in a transportation corridor, may be perceived as having a negative impact and may be highly controversial. It is important to remember that visual quality is highly subjective. “Beauty is in the eye of the beholder,” as the saying goes, and perceptions and interpretations of visual quality can vary widely. This is especially true among populations with different backgrounds, ethnic origins, or cultural traditions. In developing improvement projects, it is imperative that you first gain a clear understanding of the standards and values of the affected population group or groups. It is equally important to clearly and accurately communicate to the affected populations the likely visual impacts of the various project improvements and their rationale. Care needs to be taken that negative visual effects do not disproportionately impact protected populations. Assessments of visual quality play a key role in the project development process. These assessments help not only to communicate and explain the visual quality impacts of projects but also to illustrate the nature and appearance of the improvement project as a whole. Visual quality design and assessment should not be delayed until the end of the design stage when most design decisions for a project have already been made; rather it should be viewed as an integral component of the total design and project development program. This is especially true where environmental justice issues are concerned. It is much more efficient and effective to identify and avoid negative impacts early than to try to offset or mitigate them at the end.

252 STATE OF THE PRACTICE This section is presented in two parts. The first part addresses the current state of visual quality assessment. The second part discusses contemporary techniques for designing and communicating visual quality effects, with particular focus on communicating with protected population groups. Visual quality assessment process Transportation projects can cause significant visual impacts in the surrounding built environment. These projects may alter topography, require the removal of existing structures or landscaping or add new structures or landscaping, cast shadows on sensitive uses, introduce new streetscape urban design elements, or alter or obscure views and vistas of the existing landscape or of unique or historic community features. Visual quality assessments for transportation projects need to address three major components: affected visual environment, visual impacts, and visual impact mitigation. Each of these components is discussed below. Affected visual environment. It is first necessary to identify and describe the existing visual environment in the project area. In his book Image of the City, Kevin Lynch (1960) identifies five major components that make environments legible and imageable: edges, paths, districts, nodes, and landmarks. These components provide a potential structure for grouping and identifying existing visual characteristics in the project area. Visual impacts. The second component involves identifying and describing the potential visual impacts of the proposed project. Visual impacts need to be addressed from two different points of view: that of the population that will have to look at the project (i.e., people living or working in the project vicinity), and that of the population that will use the project (i.e., people driving on the roadway or riding on the train or bus). In instances where protected population groups will be affected, it is important to understand the community’s perception of the visual impact and to balance that against the perceived benefit of the project. In addition, the visual effects should be assessed in terms of their distribution in the project area to ensure that negative effects do not disproportionately impact protected populations. Visual effects that typically need to be considered for transportation improvement projects include one or more of the following: • Removal of buildings where existing development needs to be cleared; • New buildings, such as new maintenance buildings or stations for transit projects; • New, removed, or changed structures, such as bridges or elevated roadway or track segments; • New or changed urban design elements, such as equipment at transit stations, street furniture along roadways, entry monuments, or signs; • New or changed landscaping, such as installation of new street trees or removal of existing landscaping;

253 • New or changed lighting, such as new lighting in a transit maintenance yard that may impact adjacent residential developments; • New screens, such as noise abatement or visual screen walls; • New or changed pavements, such as special pavement treatments in downtown areas; • New public art, such as free-standing sculptures; • Natural features, such as water bodies, streams, or natural areas; • Adjustments to topography, such as large-scale excavation or filling; • Shadows where shadows from buildings or structures may impact adjacent sensitive developments; • Views where improvements may block existing views or open up new ones; and • Visual relationships where improvements may not affect existing facilities directly, but, because of close proximity or critical view-sheds, may indirectly impact the visual environment around special uses or features, such as historical districts, historical buildings or structures, or community landmarks. Visual impact mitigation. This component involves identifying mitigation measures for ameliorating potential negative visual effects. Mitigation measures might include modifications to the basic infrastructure, embellishment of the proposed improvements, or enhancement of the visual environment of the project area. Where various population groups are affected, the mitigation measures might vary to provide the most appropriate solution for each affected group. Although visual quality issues are highly significant for maintaining or improving quality of life, assessments of visual quality frequently are not given the same attention or weight as other evaluation criteria, such as transportation safety, air quality, or noise impacts. One reason for this might be that visual quality issues are much more subjective and cannot be as easily quantified as other effects. Another reason might be that effects such as air quality and noise are much more direct and physical, whereas visual quality effects are more subtle and visceral. In addition, visual quality assessments are very rarely conducted to evaluate environmental justice issues. To ensure a high-quality visual environment, more attention needs to be paid to the overall visual quality of projects, as well as to how visual quality relates to environmental justice. Visual quality design and communication techniques The techniques described below can be used in all phases of project design to identify the most appropriate design solutions, to communicate potential visual quality impacts to affected populations, and to mitigate negative impacts. Characterizing the potential visual quality effects of a project and communicating those effects to the affected population are important design and planning components in their own right. They are also important parts of the process of evaluating the environmental justice aspects of a project. These techniques can be used to evaluate project design decisions, to communicate ideas to protected population groups and to obtain feedback from those groups. They can be used in

254 combination with the environmental justice assessment methods described later in the Methods section of this chapter to assess distributive effects. Following is a discussion of some of the more commonly used evaluation methods, ranging from the relatively simple to the complex, for illustrating various types of visual quality impacts and for responding to various public concerns. Table 11-1 summarizes the visual quality design and assessment techniques. Table 11-1. Summary of techniques for visual quality design and assessment Method Assessment level Appropriate uses Use when Data needs Expertise required 1. Existing condition photographs Screening/ detailed Document existing environment, illustrate special features Always Low Photography 2a. Illustrative plans or diagrams Screening Communicate size, location, and basic intent of elements 2b. Illustrative sections Screening/ detailed Illustrate vertical, horizontal scale 2c. Perspective or axonometric sketches Screening Convey massing, scale, image, and character of a project In early stages of design, when design resources are limited, when realistic background material is not available, when photo-realism is not essential, or when there are technical issues that are best represented in plan view Medium Drafting and/or computer- aided design and drafting (CADD) 3. GIS view-shed analysis Screening/ detailed Identify view-sheds and lines of sight Appropriate GIS terrain data are available or there are significant view-shed or line-of-sight issues High Geographic information systems (GIS) 4. Photo simulation or montage Screening Visualize proposed designs Existing condition photographs are available, the design is fairly advanced, or the audience is skeptical or poorly informed Medium Manual paste- up or digital photo editing (e.g., Photoshop) 5. Computer imaging Detailed Visualize proposed designs Existing condition photographs are not available, the design is fairly advanced, the project will radically alter existing environment, or the audience is skeptical or poorly informed High 3-D CADD 6. Computer animation or virtual-reality modeling Detailed Visualize proposed designs Changing views over time are required or the view as seen from, for example, a train window is required High 3-D CADD, Computer Animation 7. Three- dimensional, physical models Detailed Visualize proposed designs Inadequate budget for computer animation and/or virtual reality Medium Model- building 8. Videos Detailed Illustrate similar existing designs Whenever comparable, completed projects exist Low Video production and editing

255 It is important to illustrate visual quality impacts, whether they are positive or negative, in a realistic and accessible fashion. The illustration and analysis technique(s) selected for the communication process should be appropriate for the target audience and for the level of design that is being represented. Too much abstraction or introduction of stylistic design elements can distort the potential visual quality impacts and confuse the viewing audience. Technique 1. Existing-condition photographs. The most common illustration technique used in visual quality assessments is existing-condition photographs. These photographs are used to supplement text describing the current conditions in the project area. In most assessments, photographs are used in conjunction with one or more of the design and assessment methodologies that illustrate the proposed project improvements. Although they can be used by themselves, existing condition photographs are much more effective if they are referenced on a base map or aerial photo of the project area. Such referencing provides accurate information regarding where the photographs were taken and which areas of the project they illustrate. Figure 11-1 represents typical panoramic photographic images and a key base photo used for illustrating existing conditions in a project area. Figure 11-1. Example of panoramic photographic images and key map Existing-condition photographs are an essential component of virtually all visual quality assessments. The following data and equipment would be required: • Information regarding where the project is to be located, • Film or digital photo camera, and • Methods for copying or reproducing the images. Computer equipment and software provide a quick and easy method of taking, splicing, and reproducing the required photographic images. Existing-conditions photographs can be presented at meetings using printed copies or electronically, for example in PowerPoint presentations. Care should be taken that these photographs represent all of the typical conditions

256 in the project area. This is especially important on projects where protected populations have been identified. Technique 2. (a) Illustrative plans or diagrams, (b) illustrative sections, and (c) perspective or axonometric sketches. Illustrative plans, sections, and image sketches represent design and illustration techniques that can be used on most projects. This type of material is relatively easy to generate because the information needed is readily available for most projects and the tools used can be as simple as a sketchpad or conventional drafting equipment. This does not mean, however, that these techniques will always involve the least effort or be the least costly. Some image sketches may require a considerable amount of preparation, layout, and rendering time. Figures 11-2, 11-3, and 11-4 represent examples of typical illustrative plans, sections, and image sketches, respectively. Figure 11-2. Typical illustrative site plan Although illustrative plans, sections, and image sketches can be used for the design and assessment of most projects, they are especially appropriate in situations such as the following where: • Detailed designs have not yet been developed, such as in the early stages of the design process; • Design resources do not permit the use of more elaborate presentation techniques; • Realistic background material is not available, such as for aerial perspectives where aerial photographs have not been taken;

257 Figure 11-3. Typical illustrative section Figure 11-4. Typical illustrative perspective

258 • Photo-realism is not essential; and • The visual quality impacts evaluation involves technical issues, such as viewsheds that are best represented in plan view rather than as photographs or computer imaging. The data and equipment requirements can be quite simple for preparation of illustrative plans, sections, and image sketches. Illustrative images can be created using sketch pads or conventional manual drafting equipment. However, with the current advances in computer technology, most of these illustrations are just as easily, and sometimes more conveniently, prepared using computer programs. As a minimum, the following data and equipment would be required: • Base maps; • Existing conditions information; • Design information regarding the proposed improvements, such as location and siting, massing, dimensions, materials, textures, and color schemes; and • Manual drafting equipment or computer programs, such as AutoCAD, Freehand, or CorelDRAW, for drafting and rendering the drawings. This method produces schematic and illustrative drawings that represent the general intent and appearance of the proposed improvements and their visual quality impacts. Because this material is more technical and schematic than photographic images, it should be tailored to the target audience. The material should not be so technical and detailed that the general public will have a hard time understanding the intent of the design. Written or verbal explanations can help make the material accessible to the public. Technique 3. GIS view-shed analysis. Topographic base information can be used to identify view-sheds and lines of sight in the project area. These in turn can be used to establish which parts of the project would have visual quality impacts on which populations. GIS view-shed analysis is most appropriate at the macroscale level or for very large and/or tall projects where long vistas are important. Technique 4. Photo simulation or montage. The photo simulation or montage technique has evolved into one of the most widely used methods for illustrating visual quality impacts. This technique consists of superimposing images of the proposed improvements on photographs of the existing environment. Because the results of this technique are very realistic images, the design of the proposed improvements has to be advanced enough to permit realistic interpretation and representation. The primary benefit of this technique is that it illustrates proposed improvements in the context of existing conditions. The viewing public has a much easier time relating to images of known conditions than to more abstract drawn or computer-generated scenes. This technique is especially effective when the material is presented using “before” and “after” images, which allows for easy comparison between the two. An even better comparison can be made when the “before” and “after” images are presented in an interactive mode, such as on a Web page, where

259 a viewer can instantly click between them. Figure 11-5 represents typical “before” and “after” photo simulation images for a project. Before After Figure 11-5. “Before” and “after” photo simulation images

260 Photo simulation or montage techniques should be used in the following situations: • Existing condition photographic backgrounds are available and applicable; • Designs have been advanced far enough to clearly define the proposed improvements; and • Photorealism is very important to illustrate the visual quality impacts—especially to highly skeptical audiences or where the impacts may be dramatically different from what the audience is expecting. One drawback of the photo simulation or montage illustration technique is that each image is “frozen,” which means that it represents only a single viewpoint. If another viewpoint is desired, a totally new photo simulation image needs to be created. Technique 5. Computer imaging. Computer imaging is similar to photo simulation or montage in that it can be used to create realistic images and scenes of proposed improvements. The major difference between the two is that in computer imaging everything is artificially created, whereas photo simulation or montage uses actual photographs of project components and backgrounds. Another important difference between the two is that in computer imaging, a 3-D model is created of the project. This model provides much more versatility and flexibility than the still images that are created with photo simulation or montage. With computer-generated 3-D images, different views of the proposed project can be very easily generated and presented. Many examples of such images, including videos, are available on one of the URS Corporation’s Web sites (www.ursimaging.com/2002onlineportfolio/). Figure 11-6 represents a typical example of a computer-generated 3-D image. Although tremendous advances have been made in the development and refinement of computer imaging techniques, the technology has not yet reached a level where computer-generated images are indistinguishable from actual photographs. Therefore, this method is mostly used where photo simulation or montage techniques are not feasible. Computer imaging typically is used in cases such as the following: • Existing-condition photo backgrounds and/or images of comparable proposed improvements are not available; • Designs have advanced far enough to clearly define the proposed improvements; • The proposed improvements would alter the existing environment to such a degree that very little of the existing conditions would remain; and • Reasonably realistic images are important to illustrate the visual quality impacts—especially to highly skeptical audiences or where the impacts may be dramatically different from what the audience is expecting. This type of imaging product requires computer equipment and 3-D rendering programs. At a minimum, the following would be required:

261 • Data about the existing conditions, such as AutoCAD plans, massing of existing buildings and structures, materials, textures, and color schemes; • Design information regarding the proposed improvements, such as location, massing, dimensions, materials, textures, and color schemes; and • 3-D computer drafting and rendering programs, such as AutoCAD, 3-D Viz, or Photoshop. Figure 11-6. Computer-generated 3-D image This approach produces highly realistic images of the proposed improvements and their visual quality impacts. However, because everything is artificially created, there may be less credibility with this technique than with photo simulation or montage. This technique can also be more expensive and time consuming than the previous methodologies, although it does have the major advantage of built-in flexibility and versatility. Once a model has been created in 3-D, it can easily be rotated to illustrate various viewpoints or perspectives, as illustrated by the examples on the Web site listed under Resources. This ability to manipulate or vary viewpoints can be set up as an interactive process and can be very effectively used in meetings to respond to various questions or requests from the viewing audience. Computer images can also be made available on a Web site for easy access.

262 Technique 6. Computer animation or virtual reality modeling. Computer animation is one of the most advanced methodologies for designing and illustrating visual quality impacts. It is also the most involved and expensive. Some computer animation can be relatively simple. By building upon the 3-D models produced with computer imaging, simple “drive-by” or “fly- through” sequences can be created for the proposed project. In these simple scenes, everything appears static. More advanced and complex computer animation, such as the animation of a proposed highway project, involves adding cars, people, and other objects and carefully choreographing their movement and timing in the 3-D animation sequences. Computer animation techniques can also be used to illustrate project staging and the impact of the proposed improvements upon the existing landscape. Examples of computer animation are presented on the URS Corporation Web site at www.ursimaging.com/2002onlineportfolio/. Computer animation should be used when it is important to illustrate the visual quality impacts of a proposed project in three dimensions, as well as in time. For example, animation may be the best way to illustrate the changing views of a corridor from a moving vehicle, the movement of a train through a neighborhood, or the impact of a project on existing uses and facilities. For computer animation, the following are required: • Data about the existing conditions, such as AutoCAD plans, massing of existing buildings and structures, materials, textures, and color schemes; • Design information regarding the proposed improvements, such as location, massing, dimensions, materials, textures, and color schemes; and • 3-D computer drafting and rendering programs, such as AutoCAD, 3-D Viz, and Photoshop, and computer animation equipment and programs. This method produces highly realistic animated sequences of the proposed improvements and their visual quality impacts. It is the most expensive and time consuming of all the techniques available. However, the cost may be justified due to the large amount of information that can be conveyed in a very short time and the dramatic impact it can have. Computer animation may be viewed as part of a PowerPoint presentation or on a Web site. In this sense, it does require special equipment (e.g., a projection system or computer) and thus is slightly more difficult to access than computer imaging or other visual presentations. Technique 7. Three-dimensional, physical models. Where issues of massing or spatial relationships need to be addressed, three-dimensional physical models can be useful in conveying a large amount of information in a very concise and direct way. Physical models are especially useful in illustrating conditions of extremely complex urban conditions. Technique 8. Videos. A technique that is sometimes used to convey information regarding the visual appearance of a proposed project, and to address issues such as noise impacts is to take a video of a comparable situation in a similar project. Videos are frequently taken of Light Rail Transit (LRT) systems to allow impacted populations to experience, as realistically as possible, how a proposed LRT system will work, look, and sound.

263 Review. There are two primary components for assessing visual quality and environmental justice. The first comprises techniques for designing and communicating the visual effects, described above. The second component involves determining how different population groups may perceive the effects differently and how effects are distributed among population groups. That is the topic of the remaining sections in this chapter. SELECTING AN APPROPRIATE METHOD OF ANALYSIS Proper design and communication of visual effects is an important part of environmental justice assessment, and the methods described above can be used for that purpose. However, that alone is not enough. The overriding objective of an environmental justice analysis is to ensure that protected populations are not subjected to a disproportionate share of the negative impacts of a project—or that negative impacts are counterbalanced by equivalently disproportionate benefits. In the case of visual quality impacts, this analysis is problematic due to the fact that it is difficult to quantify negative impacts on a population. The problem is made even more difficult when you consider that different subpopulations (e.g., ethnic groups or age cohorts) may vary in how they value the numerous aspects of visual quality. One approach to this problem is to survey the affected populations to determine how attractive or aversive the visual quality impacts of the project are perceived to be. Nelessen (1994) describes a well-developed methodology for rating visual quality aspects of public projects. This method is variously known as Visual Preference Survey™ (VPS), Image Preference Survey, and Community Preference Survey. In a VPS, respondents are asked to rate each of a set of photographs on a 21-point hedonic scale ranging from -10 (most aversive) to +10 (most appealing). The results can be used to estimate how a population values various aspects of visual quality or how effective particular mitigation measures would be. Another approach is to estimate the economic “value” of visual quality effects for each protected population impacted by the project and for the total affected population. In this context, the estimation of economic value is not meant to imply that monetary compensation might be made for negative visual quality impacts; the estimations are for comparative purposes only. For example, if it could be determined that individuals in the protected population place half again as much economic value on visual quality as the population as a whole (i.e., 150 percent), that estimate could be factored in to the analysis of distributive effects. The question of how to assign economic value to things such as visual quality has received considerable attention from economists. Visual quality is one example of what are called “nonmarket goods”—that is, goods (things of value) that are not traded in open market systems. Economists use two broad classes of methods to establish the value of nonmarket goods: revealed preference and stated preference methods. Revealed preference (RP) methods rely on the analysis of quantifiable behaviors that, while not involving direct monetary payment for goods, can be used to infer willingness to pay for the goods in question. The travel cost method uses the amount of time someone is willing to spend traveling to see a visually appealing feature as a measure of its value. Similarly, the amount of time a person is willing to spend traveling to avoid a visually unappealing feature is a measure of

264 its aversiveness. The hedonic pricing method is based on analysis of the effect of a nonmarket good on the actual value of market goods. For example, if you compared the values of homes that were close to a visually aversive feature with those of similar homes that were not close to the feature, you could derive an estimate of how much the visual feature would reduce the market value of the particular home. Stated preference (SP) methods rely on surveys to estimate how much value individuals ascribe to a non-market good. Two variants of SP have been widely used – contingent valuation and contingent choice. The contingent valuation (CV) method makes use of survey questions that directly address the respondent’s willingness to pay for a non-market good—for example, “How much would you be willing to pay in extra taxes to beautify the roadway?” Alternative survey formats for CV are closed-ended questions (e.g., “Would you be willing to pay $50/year in extra taxes to beautify the roadway?”) and multiple-choice questions (e.g., “I would be willing to pay $10 / $25 / $50 / $75 / $100 per year in extra taxes to beautify the roadway”). Contingent valuation is the most commonly used method for establishing a value for nonmarket goods. In recent years, however, CV has been criticized for several reasons, including the fact that it generally overestimates the true value of goods (Diamond and Hausman 1994; Hanemann 1994). The contingent choice (CC) method—also known as the “method of choice experiments”—is similar to CV in that it asks people to make choices based on a hypothetical scenario. It differs from CV in that it does not directly ask people to state values in dollars. Instead, values are inferred from the hypothetical choices or tradeoffs that people make. The CC method asks respondents to state preferences between a scenario with one group of attributes or characteristics and other scenarios with different sets of attributes. Usually, each item on the survey is binary; in other words, there is a discrete choice between two scenarios. If one of the attributes in each scenario is a dollar amount, CC can be used to estimate dollar values. However, the method may also be used to simply rank options, without focusing on dollar values. Because it focuses on tradeoffs among scenarios with different characteristics, CC is especially suited to situations where a proposed project might result in multiple types of impacts or a proposed policy may have tradeoffs that need to be evaluated. For example, a highway project might result in improved access to work places and shopping venues but at the same time reduce the visual quality of the corridor. Economists have used RP and SP methods extensively to estimate the dollar value of nonmarket goods such as visual quality. In the present context, however, the intent is to characterize the standards and values of impacted populations, not to estimate the dollar value ascribed to visual quality. A dollar value estimate is only of interest to the extent that it can reveal the underlying standards and values of a population. RP and SP methods can be used to analyze the standards and values of a population as long as the results are cast in relative terms. For example, if an analysis reveals that a given population

265 places twice as much dollar value on the benefit of cutting travel time to their workplace as on the visual quality of a traffic corridor, that ratio can be factored into the analysis of the overall impact of a project on that population. Using relative valuation in this way obviates the problem caused by the fact that different populations may have different ideas about the value of a dollar, as might be anticipated when comparing, say, a low-income population with a high-income population. It also reduces any possible concern about the absolute accuracy of the estimates. The following sections contain detailed descriptions of the visual preference survey and contingent choice methods. Much of the material describing CC is derived from uncopyrighted material found on the Ecosystem Valuation Web site (King, et al. undated; see www. ecosystemvaluation.org). In addition, a method is described for analyzing distributive effects of visual quality using either objectively or subjectively weighted visual quality information. METHODS Table 11-2 summarizes the methods for analyzing visual effects described in the remainder of this chapter. Table 11-2. Summary of methods for analyzing visual effects Method Assessment level Appropriate uses Use when Data needs Expertise required 1. Visual preference survey Screening/ detailed Select among design choices or compare standards and values of populations Visual quality impacts are to be analyzed apart from other impacts Low Survey methods, statistical methods 2. Contingent choice experiments Detailed Compare standards and values of populations Visual quality impacts are to be analyzed vis-à-vis other factors or when a dollar estimate of visual quality impact is desired Medium Survey methods, statistical methods, economic analysis 3. Distributive effect analysis Screening/ detailed Analyze distributive effects Differences in population standards and values are deemed important Medium/ high Statistical methods, GIS Environmental justice assessment of visual quality effects consists of four major steps: 1. Identification of protected populations (covered in Chapter 2). 2. Identification of standards and values of the impacted populations. 3. Design and communication of the visual impacts to the affected populations (covered in “Visual quality design and communication techniques,” earlier in this chapter). 4. Analysis of distributive effects.

266 This sequence is recommended because in most situations the key to environmental justice will be making design and visual aesthetic decisions that meet the approval of the populations that will have to look at the project and of the populations that will use the project. Identifying whether or not these populations include protected population groups (Step 1) and, if so, identifying the unique standards and values of those groups (Step 2) is crucial to making the best design decisions (Step 3). Distributive effects assessment (Step 4) can then be used to evaluate various design alternatives and the distribution of adverse and beneficial visual quality effects. Our discussion of “Selecting an appropriate method for analysis” provides an overview of techniques for identifying standards and values of protected populations and a method for evaluating distributive visual quality effects. Three of these methods are described in more detail below. Method 1. Visual preference survey (VPS) When to use. The VPS method can be used to determine which of a set of possible design choices is most appealing from a visual quality perspective. It may also be used to estimate the value placed on particular aspects of visual quality or on changes in visual quality resulting from a transportation project. By conducting equivalent surveys on groups of respondents representing different protected and nonprotected populations, the standards and values of impacted populations can be characterized and integrated into subsequent analyses of distributive effects and environmental justice. Population standards and values may be expressed in terms of the relative appeal or aversiveness of a particular visual feature or design option. Analysis. This method consists of the following five steps. Step 1 – Define the valuation problem. Determine the visual quality impacts to be assessed, and the relevant protected and nonprotected populations. Use interviews or focus groups to broadly identify which aspects of visual quality are of concern to the affected populations. For these preliminary inquiries, images of similar projects or computer simulations of the proposed project may be used to elicit comments. Step 2 – Make preliminary survey decisions. Make preliminary decisions about the survey itself, including how images will be presented (slides, computer projector, video), how many respondents will be surveyed in each session, and whether photographs of similar, completed projects or computer simulations will be used for the actual survey. If any of the protected populations to be studied include a significant number of members for whom English is not their first language, conduct the survey in the preferred language of the population. Step 3 – Survey design. Select scenes or images that illustrate the visual quality issues of concern to the impacted populations. Furthermore, select a range of levels of impact for each aspect of visual quality. For example, if foliage was identified as an important aspect of visual quality, use multiple images showing different types and amounts of foliage. Generally speaking, no more than 80 images should be used. Step 4 – Survey implementation. The first implementation task is to select the survey sample. Ideally, the sample should be a randomly selected group of participants from each relevant population, gathered using standard statistical sampling methods.

267 Because it is reasonable to anticipate that there may be wide variation in survey results within each subpopulation, a minimum of 50 respondents from each should be surveyed. It is best to divide the sample of respondents from each population into multiple small groups that are surveyed in separate sessions. The order in which the images are presented should be varied randomly across sessions to minimize order effects in the analysis. Respondents should be asked to rank each image on a 21-point (-10 to +10) scale, where the highest negative score indicates extreme aversion and the highest positive score indicates extreme appeal. Respondents should be given a score sheet on which to enter their responses. The score sheet should include a thumbnail of each image to ensure that the responses are properly matched with the images. If the order in which images are presented to each group is random, arrange the score sheets for each group in the same order as the images presented. Respondents should be instructed not to make comments or otherwise indicate their reaction to each image during the survey so that peer pressure does not bias scores. Step 5 – Compile, analyze, and report the results. VPS findings can be used for several different purposes, and different methods of analysis are used depending on the intended goal of the survey. For example, VPS findings could be used to determine the most attractive (or least aversive) design for noise abatement or visual screen walls for a particular population. VPS findings could also be used to establish relative attractiveness of, or degree of aversion to, a visual feature across two or more populations. Finally, VPS could be used to estimate differences in standards and values between protected and nonprotected populations—for example, to establish how highly each population values foliage as a visual quality asset. To determine the most appealing design for a visual feature, simply compute the mean score for each alternative design within each population. The design with the highest mean score is preferred by that population. If the objective is to establish a relative value for attractiveness or aversiveness of a particular visual feature, simply use each population’s mean score for that feature. However, you should take note if there is a low level of agreement among respondents within a population. Any major disagreements should be resolved through the use of focus groups or other methods for consensus gathering. Differences in standards and values between the various populations regarding broad aspects of visual quality may be analyzed as follows: • For each aspect of visual quality studied, group the images showing different levels of the aspect. Perform the subsequent analysis steps for each grouping of images. • Compute the range of each respondent’s scores for the group of images. This value reflects that respondent’s sensitivity to that aspect of visual quality. • Combining all respondents from all populations, rank order the sensitivity scores. • Divide the ranked sensitivity scores into three groupings—high, medium, and low, such that each grouping contains approximately 1/3 of the respondents. • For each population studied, compute the number of respondents exhibiting high, medium, and low sensitivity. • Cast these numbers into a 3 X n table (n is the number of population groups studied).

268 • Use the chi-square test to determine whether the distribution of high, medium and low sensitivities differs among populations. Chi-square is the preferred statistical method because it does not rely on an assumption of the normality of the underlying distributions (see pages 105-108). Data needs, assumptions, and limitations. VPS has relatively low requirements for external data because the majority of data are generated from the survey. Demographic data are required to determine if there are protected populations within the area of concern. Keep in mind the possible pitfalls associated with using VPS for estimating the relative appeal of a particular visual feature or for comparing the standards and values of populations: • There may be differences between populations in terms of the degree to which they express extreme likes or dislikes. If one population is relatively more stoic or more strongly opinionated overall, this will influence the outcome. • There may be a confounding of visual quality factors with other aspects of an image. For example, if you present images illustrating various levels of truck traffic, the respondents’ scores may reflect their attitudes toward noise or congestion in addition to their attitudes toward visual quality. • By using ranges within groupings of images as an indication of respondents’ sensitivity to different aspects of visual quality, any differences between individuals or populations in the average response to those groupings are lost. For example, one population may react more negatively to all images of a particular scene regardless of differences among the images in, say, the number of trees. Results and their presentation. Results are presented in tabular form or using bar charts to illustrate population differences. Parametric statistics such as the mean and standard deviation may be used for descriptive purposes, but nonparametric statistics such as chi-square should be used for inferential purposes. Assessment. Visual preference surveys involve asking a group of respondents to rate each of a set of images according to their perceived attractiveness or aversiveness. Ratings consist of scores for each image on a 21-point hedonic scale. Results may be used to select among design choices; to establish an absolute measure of attractiveness or aversiveness of a particular visual feature; or to evaluate population differences in standards and values regarding broad aspects of visual quality. Method 2. Stated preference/contingent choice (SP/CC) The CC method is a hypothetical method—it asks people to make choices based on a hypothetical scenario. It differs from the CV method in that it does not directly ask people to state their values in dollars. Instead, values are inferred from the hypothetical choices or tradeoffs that people make. There are a variety of formats for applying contingent choice methods, including:

269 • Contingent ranking – Contingent ranking surveys ask individuals to compare and rank alternate project impacts with various characteristics, including costs. For instance, people might be asked to compare and rank several mutually exclusive roadway beautification projects under consideration for a travel corridor, each of which has different outcomes and different costs. Respondents are asked to rank the alternatives in order of preference. • Discrete choice – In the discrete choice approach, respondents are simultaneously shown two or more different alternatives and their characteristics and asked to identify the preferred alternative. • Paired rating – This is a variation on the discrete choice format, where respondents are asked to compare two alternate situations and to rate them in terms of strength of preference. For instance, people might be asked to compare two roadway beautification projects and their outcomes, to state which is preferred, and to indicate whether it is strongly, moderately, or slightly preferred to the other program. Whatever format is selected, respondents’ choices are statistically analyzed using discrete choice statistical techniques to determine the relative values for the different characteristics or attributes. If one of the characteristics is a monetary price, then it is possible to compute the respondent’s willingness to pay for the other characteristics. When to use. The contingent choice method asks the respondent to state a preference between one group of environmental services or characteristics (a scenario) and another. A typical CC survey might comprise 50 to 100 such choices. If an estimate of the dollar value of each characteristic is desired, a monetary characteristic is included in the set for each scenario. Because it focuses on tradeoffs among scenarios with different characteristics, contingent choice is especially suited to situations where a project or policy might result in multiple different impacts on a population. For example, a highway project might impact accessibility to the workplace, noise levels, and safety, in addition to visual quality. While contingent choice can be used to estimate dollar values, the results may also be used simply to rank impacts, without focusing on dollar values. Analysis. This method consists of the following five steps. Step 1 – Define the valuation problem. Determine the visual quality and other impacts to be assessed and the relevant protected populations. Note that the size and complexity of the CC survey increases at roughly the square of the number of different impacts analyzed. For this reason, it is best to limit the number of impacts analyzed to five or fewer. Step 2 – Make preliminary survey decisions. The second step is to make preliminary decisions about the survey itself, including whether it will be conducted by mail, phone or in person, how large the sample size will be, who will be surveyed, and other related questions. The answers will depend, among other things, on the importance of the valuation issue, the complexity of the question(s) being asked, and the size of the budget. In-person interviews are generally the most effective for complex questions, because it is often easier to explain the required background information to respondents in person and people are more likely to complete a long survey when they are interviewed in person. In some cases, visual

270 aids such as videos or color photographs may be presented to help respondents understand the conditions of the scenario(s) that they are being asked to rate. While in-person interviews are generally the most expensive type of survey, mail surveys that follow procedures aimed at obtaining high response rates can also be quite expensive. Mail and telephone surveys must be kept fairly short, or response rates are likely to drop dramatically. Telephone surveys are generally not appropriate for CC surveys because of the difficulty of conveying the tradeoff questions to people over the telephone. If any of the protected populations to be studied include a significant number of members for whom English is not their first language, consider communicating (interviews, focus groups, questionnaires) in the preferred language of the population. Step 3 – Survey design. This is the most important and difficult part of the process and may take 6 months or more to complete. It is accomplished in several steps. The survey design process usually starts with initial interviews and/or focus groups with the types of people who will be receiving the final survey, in this case members of each subpopulation to be studied. In the initial focus groups, the researchers would ask general questions, including questions about peoples’ understanding of the issues related to the project, their familiarity with the area of impact (e.g., the travel corridor), and the value they place on the area and its attributes. In later focus groups, the questions would get more detailed and specific, to help develop specific questions for the survey, as well as to decide what kind of background information is needed and how to present it. For example, people might need information on the location and characteristics of the project and what impacts it will have on various environmental, social, and economic aspects of their lives. At this stage, the researchers would test different approaches to the choice question. Usually a CC survey will ask each respondent a series of choice questions, each presenting different combinations and levels of the relevant impacts, possibly including the cost to the respondent associated with each scenario. Each scenario might be described in terms of impact on travel time between two particular points, visual quality (e.g., billboards, trees, and beautification measures), tax burden, etc. The visual quality impact for each scenario should be illustrated using an image. Images may be presented as hard copies or projections. After a number of focus groups have been conducted and researchers have an idea of how to provide background information, describe the hypothetical scenarios, and ask the choice questions, they will start pretesting the survey. People would be asked to fill out the survey. Then the researchers would ask respondents about how they filled it out and let respondents ask questions about anything they found confusing. The researchers would continue this process until they had developed a survey that people seemed able to understand and answer in a way that made sense and revealed their values for the visual quality features being addressed. Step 4 – Survey implementation. The first task here is to select the survey sample. Ideally, the sample should be a randomly selected group of participants from each relevant population, selected using standard statistical sampling methods. Although CC surveys are sometimes conducted via mail or phone interview, CC surveys that include visual quality factors should be conducted in a controlled setting. In-person surveys may be conducted with random samples of respondents or may use “convenience” samples—asking people in public places to fill out the survey.

271 Step 5 – Compile, analyze, and report results. The final step is to compile, analyze, and report the results. The statistical analysis for CC is often more complicated than that for CV, requiring the use of discrete choice analysis methods to infer valuation or willingness to pay from the tradeoffs made by respondents. A full description of the analysis method is beyond the scope of this document; see Louviere et al. (2000) for a thorough discussion of the method. From the analysis, you can estimate the average value for each of the impacts of the project for an individual or household in the sample. This can be extrapolated to the relevant population to calculate the total benefits from the site under different scenarios. The average value for a specific action and its outcomes can also be estimated, or the different policy options can simply be ranked in terms of peoples’ preferences. Data needs, assumptions, and limitations. CC methods have relatively minimal requirements for external data because most of the data are generated from the interviews and surveys that comprise the study itself. To collect useful data and provide meaningful results, the CC survey must be properly designed, pretested, and implemented. However, because responses are focused on tradeoffs, rather than direct expressions of dollar values, contingent choice may minimize some of the problems associated with contingent valuation. Often, relative values are easier and more natural for people to express than absolute values. While CC generally is an excellent method for studying a population’s values, the following limitations should be addressed when designing a study: • Respondents may find some tradeoffs difficult to evaluate because they are unfamiliar. • The respondents’ behavior underlying the results of a CC study is not well understood. Respondents may resort to simplified decision rules if the choices are too complicated, which can bias the results of the statistical analysis. • If the number of attributes or levels of attributes is increased, the sample size and/or number of comparisons each respondent makes must be increased. • When presented with a large number of tradeoff questions, respondents may lose interest or become frustrated. • By providing only a limited number of options, respondents may be forced to make choices that they would not voluntarily make. • Contingent ranking requires sophisticated statistical techniques to estimate willingness to pay. Results and their presentation. The discrete choice analysis methods used to analyze the results of a CC study yield weights that represent the value placed on each factor studied relative to the other factors. For this reason, it is desirable to select one factor, such as dollar cost, that can be used as a standard for comparisons among the other factors. Results may be presented in tabular form or bar charts that describe the differences among populations in relative valuation of the factors studied.

272 Assessment. The CC method can be used to establish how populations value a project as a whole, as well as the various visual quality attributes or visual quality effects of the project. The method allows respondents to think in terms of tradeoffs, which may be easier than directly expressing dollar values. In addition, respondents may be able to give more meaningful answers to questions about their behavior (i.e., they prefer one alternative over another), than to questions that ask them directly about the dollar value of a social or economic impact or the value of changes in environmental quality. Thus, an advantage of this method over the CV method is that it does not ask the respondent to make a tradeoff directly between environmental quality and money. Method 3. Distributive effects analysis When to use. Use distributive effects analysis when screening data indicate that there may be a disproportionate distribution of visual effects with respect to protected populations in the project impact area. Analysis. This method consists of the following three steps. Step 1 – Quantify the distribution of protected populations. Before proceeding with any more detailed analysis, determine whether any protected populations are disproportionately represented in the project area as a whole or whether they are unevenly distributed within the project area. Because visual quality impacts people in their homes as well as when they travel, both population distribution analysis and use analysis should be performed. Population distribution analysis may be performed using the Environmental Justice Index or one of the other methods described in Chapter 2. Use analysis may be performed using one of the transportation demand methods described in Chapter 7. If protected populations are proportionally represented in the project impact area as a whole and are uniformly distributed within the project area, no further analysis need be performed. If protected populations are not proportionally represented or are unevenly distributed within the project area, the maldistribution should be quantified. The project impact area should be divided into analysis areas that can be characterized with respect to the number of members of each protected and nonprotected population group that live in or use each area. Step 2 – Quantify the level of visual quality impact on each affected population. The level of visual quality impact on each affected population can be quantified using objective or subjective measures of impact. Objective measures can be based on counts of visual features, such as number of trees planted or number of billboards in each analysis area. An economic measure might also be used, such as the amount spent on beautification or visual screens in each analysis area. In either case, the estimate of visual quality impact is based simply on objective information and does not differ between protected and nonprotected populations. Subjective measures make use of information gathered using VPS, CC, or some other measure of the value placed on particular visual quality features by the various affected populations. The level of subjective impact may not be the same for all populations. For example, suppose that a VPS revealed that a protected population rated a particular visual feature (such as a screening

273 wall) as –3 on a 21-point hedonic scale, whereas the nonprotected population rated the same feature as +2. Those values could be used as direct measures of visual impact. It is also possible to combine subjective measures with objective measures. For example, if VPS or CC revealed that a protected population was 25 percent more sensitive than the nonprotected population to a particular type of visual quality effect (e.g., in their aversion to billboards), that could be used as a factor to estimate total aversiveness for each population. In this example, the impact of billboards on visual quality for the nonprotected population could be quantified simply as the number of billboards anticipated, whereas the impact on the protected population could be quantified as 1.25 times the anticipated billboard count. Step 3 – Combine the visual quality impact metrics with the population distribution data. This step involves computing the visual quality impacts—either objectively or subjectively measured—for each protected and nonprotected population within each analysis area. That is, for each area, the following calculations are performed: T I Npa p pa= × where Tpa = total visual quality impact on population p in analysis area a I p = level of visual quality impact on population p as calculated in Step 2 N pa = number of members of population p in analysis area a Values of Tpa for each population and area are cast into an n X m table, where n is the number of populations and m is the number of areas. A Friedman two-way analysis of variance or similar nonparametric test may then be used to determine whether there is a significant interaction effect between populations and areas in total visual quality impact. A significant interaction effect would indicate that the visual quality impacts were not proportionately distributed among protected and nonprotected populations. Data needs, assumptions, and limitations. These considerations depend on the methods selected to estimate the impact of visual quality factors on different populations. One limitation of this method is that it does not account for any additive effects, such as disproportionate distribution of counterbalancing benefits. Results and their presentation. Results and presentation of protected population information can rely on maps, tables, charts, or graphs similar to any other GIS-based census data technique. Many examples of results and their presentation are included in Chapter 2. Assessment. This method allows you to combine objective or subjective measures of visual quality impact with information about the demographics of protected populations.

274 RESOURCES 1) Guidelines for Landscape and Visual Impact Assessment. 2002. The Landscape Institute with the Institute of Environmental Management and Assessment. London, UK: Spon Press. Although this book is geared towards European projects, many of the ideas and concepts presented in it would also apply to United States projects. 2) The following Web site provides numerous examples of visual quality design and communication techniques for transportation system changes. Animation and video techniques that cannot be presented in this hardcopy guidebook, as well as photomontage and other techniques, are available for review at http://www.ursimaging.com/ 2002onlineportfolio/. REFERENCES Diamond, P., and J. Hausman. 1994. “Contingent Valuation: Is Some Number Better Than No Number?” Journal of Economic Perspectives, Vol. 8, pp. 45-64. Hanemann, M. 1994. “Valuing the Environment Through Contingent Valuation.” Journal of Economic Perspectives, Vol. 8, pp. 19-43. King, D., M. Mazzotta, and K. Markowitz. (Undated) Ecosystem Valuation. Available at http://www.ecosystemvaluation.org. Lynch, Kevin. 1960. Image of the City. Cambridge, MA: M.I.T. Press. Louviere, J., D. Hensher, and J. Swait. 2000. Stated Choice Methods: Analysis and Application. Cambridge, MA: Cambridge University Press. Nelessen, A.C. 1994. Visions for a New American Dream: Process, Principles and an Ordinance to Plan and Design Small Communities. Chicago, IL: American Planning Association. Visual Preference Survey is a registered trademark of A. Nelessen Associates, Inc., Belle Mead, NJ.

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TRB’s National Cooperative Highway Research Program (NCHRP) Report 532: Effective Methods for Environmental Justice Assessment is designed to enhance understanding and to facilitate consideration and incorporation of environmental justice into all elements of the transportation planning process, from long-range transportation systems planning through priority programming, project development, and policy decisions. The report offers practitioners an analytical framework to facilitate comprehensive assessments of a proposed transportation project’s impacts on affected populations and communities.

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