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A facet analysis of visualization for environmental management
Landscape and Urban Planning, 2 I ( 1992 ) 247-25 1 Elsevier Science Publishers B.V., Amsterdam
247
A facet analysis of visualization for environmental management Richard Chenoweth Center for Resource Policy Studies, School of Natural Resources, 240 Ag Hall, Universityof Wisconsin Madison, WI 53 736. USA (Accepted 10 December 199 1)
ABSTRACT Chenoweth, R., 1992. A facet analysis of visualization 251.
for environmental
management.
Landscape Urban Plann., 2 1: 247-
One approach to organizing the world of visualization is to try to reduce the many issues to just a few dimensions or facets. The purpose of this oversimplification is to create a map, a framework to keep us from being lost in the details and losing sight of the societal and scientific goals which lead us to create visualizations in the first place. When the impressive demonstrations of technology are over and we have all had a chance to find out what some of those lengthy acronyms mean, we will still be faced with complex decisions affecting the fate of the environment.
CE
SUA
N
Figure 1 represents one effort to organize the world of visualization. In this depiction, there are three kinds of questions which should be considered. First, what is the purpose of visualization? Why go to the time, trouble and expense (usually born by the public) to do it? Second, who are the intended consumers of the visualization? And third, what are the social, political and organizational contexts within which the visualizations are produced and/or utilized? Questions can be posed and research agendas can be set forth within each facet of this universe. A fourth set of issues, which might be termed universal issues, is applicable throughout the structure.
Correspondence to: R. Chenoweth, Center for Resource Policy Studies, School of Natural Resources, 240 Ag Hall, University of Wisconsin, Madison, WI 53706, USA.
There are many unanswered questions about the ability of different kinds of visualizations, varying in sophistication and expense to produce, to describe, predict or explain phenomena to different kinds of audiences. Questions, for example, about how abstract a representation can be while still serving a useful purpose, for example stick figures may be all that is needed to tell how many trees there are on an acre and where they are located, but it is doubtful that such a visualization would be useful in meeting the legal mandate for federal agencies to provide “. . .aestheticallyand culturally pleasing” surroundings (National Environmental Policy Act, 1969 ). An adequate research agenda for visualization will need to focus some attention on understanding how attributes of visualizations are linked to human visual information processing and decision making in the environmental arena.
R. CHENOWETH
248
Consumers of Visualization
Env. decision
maker
Scientific enterprise Applied environmental decision-making Persua sivc communication Federal Agencies
Priva tc Industry
State/ local gov’t
Etc.
Social, political and organizational context of the visualization 4 facet analysis of visualization
for environmental
management.
the LJniversity of Wisconsin we will soon be comparing rural residents’ willingness to accept land use controls under ‘traditional’ land planning scenarios versus incorporating the use of geographic infor imaging to depict the visual consequences of regulatory alternatives. Using Ajzen and Fishbein’s ( 1980) model of volitional behavior, we will examine the possibility of visualization effects on lay i;eople’s subjective judgements, effective evaluations of land use outcomes, and motivations tc comply with the recommendations of significant actors in the land use planning arena. Professionally trained environmental managers are among the most important potential consumers of data visualizations. Therefore, an understanding of their visualization needs will be essential to the usefulness of any program of research in visualization. The notion of an
‘informed’ needs assessment, whereby potential users are made aware of the capabilities and limitations of visualizations before soliciting perceived needs and potential applications will be a necessary brick in building a solid foundation for a research agenda. A research agenda focusing on consumer needs and reactions will be as important as technological advances. Indeed, Steinitz suggests a program of adaptive testing of visualization technology developed in other arenas, focusing more on the substance of environmental decisions and less on technology invention which is sure to progress irrespective of the environmental professions. Finally, given that environmental issues do not respect disciplinary boundaries, funding priorities should reflect the potential for visualization to foster cross-disciplinary comlmunicatlon and interdisciplinary research on environmental issues.
FACET ANALYSIS OF VISUALIZATION
Such an emphasis would be entirely consistent with the National Environmental Policy Act of 1969 which states: “. . . all agencies of the Federal Government shall: (A) use a systematic, interdisciplinary approach which will insure the integrated use of the natural and social sciences and the environmental and design arts in planning and decisionmaking which may have ar: impact on man’s environment; (B) identify methods and procedures. . . which will ensure that presently unquantified amenities and values may be given appropriate consideration in decisionmaking along with economic and technological considerations” [ Sec. 102, A and
249
There may be special problems with the use of visualizations aimed solely at persuasion, for example compelling visualizations which are misleading or are not accurate representations of environmental conditions or processes. In this regard, there needs to be research and scholarship which draws upon the special expertise represented in marketing and communication as well as an exploration of the difficult legal issues sure to arise. Yet another problem is ownership, access and who pays for visualizations. Conflicts have already surfaced in the -world of public land records and geographic information systems (Epstein, 199 1).
Bl* AL CONTEXTS It seems to me that it is all too easy to get caught up in the technological wizardry of visualization without having a focused understanding of one’s objectives. The tinkering around to substance ratio could probably be improved. In any event, agencies that fund visualization research should assure themselves that proposals have clear social or scientific goals and are not, when the rhetoric is reduced to its basics, simply efforts at creating bigger, better, faster or flashier visualizations. There are many possible taxonomies for classifying the purposes of visualization. Thek e is, of course, no ‘correct’ taxonomy, only usefdl or un-useful ones. In this facet analysis, visualizations can be viewed as supporting the traditional ‘generic’ scientific goals of description, prediction and explanation. The choice of representational models should be governed by the scientific goal within particular domains of knowledge. The important point here is that data visualizations, to be useful in environmental management, are not ends in themselves. We need to continually remind ourselves that visualizations should be driven by the scientific enterprise and funding for a research program ought to reflect that larger interest.
Without understanding these contexts, it seems unlikely that the transfer of visualization technology will be orderly or effective in meeting societal goals or environmental challenges from the local to the global. If we are to improve environmental decision making and institutionalize the use of visualization technologies in environmental evaluation, planning and implementation, then the research capacity of the social sciences and planning professions will have as much of a role to play as the natural sciences and computer sciences. An important element of the context facet is the potential for flawed communication between scientists who generate data, those who create visualizations of data, those who d;sseminate the visualizations for different purposes and end-consumers. Any examination af methodological issues such as ‘realism’ will be relatively shallow unless explicitly imbeddsd in an understanding of these contextual issues. Another implication of the third facet is that we need to understand current institutional arrangements. Who is doing what with respect to visualization and environmental management? Such an understanding is required if we are to avoid costly duplicative efforts and to
R. CHENOWETH
250
develop cooperative resource pools, consortia and the like. Yet another aspect of the third facet is illustrated by my own work (Chenoweth, 1991) which focuses on how citizens and local units of government might consider aesthetic values before irrevocable change occurs. I am exploring four areas. The first is the public’s legal right, esta’ lished under NEPA, various state ‘mini-NEPAs’ and specialized legislation (e.g. Wild and Scenic Rivers Act and the Wilderness Act) to anticipate and to modify the probable visual consequences of environmental action before irreversible change occurs. The second is how visual simulations might serve as negotiated legal documents, i.e. contracts between government regulators and those they regulate, concerning the future appearance of the environment. The third is how visual simulations might be of assistance in the development of performance standards or other land use controls designed to protect aesthetic values. The fourth is how, together with contingent evaluation techniques, image-processing might be used to assess the economic consequences of damage to the appearance of the environment. In addition to the questions that are sure to arise within this facet, one thing seems a sure bet; coordination and collaboration between agencies, vendors, universities and others will be an essential element of a successful longterm research and development program in data visualization for environmental management.
Universal issues are those that are applicable to, or in some way influence, all three facets of the structure depicted in Fig. 1. Many of these issues pertain to hardware and software and these issues have received substantial attention elsewhere. It may be possible for each of us t.o locate our own work somewhere in structure. How-
ever, it appears to me to be time to document and to monitor which visualizations have OCcurred, to what effect and with what obstacles, problems or opportunities? Collectively, what visualizations have been produced, for what purposes, for what consumers, within which contexts and to what societal or scientific advantage (or disadvantage)? Without an understanding of where we have been, it will be difficult to chart where we might go. Recommendations for comprehensive documentation of the current state of affairs in data visualization are especially appropriate if we are not to reinvent the wheel. Another universal issue pertains to the disparity between the semantic conventions which have evolved in scientific measurement and the same terms applied to visualizations. Terms such as ‘reliability’ and ‘validity’ have a long history in science and have come to acquire some shared definition. There are conventions in the sciences, for example the internal validity of research designs for determining the truth or falsity of written propositions, a.k.a. hypotheses. But what does it mean to say that a visualization is true or reliable or valid or accurate or real? This is a serious problem from the standpoint of communication between scientists, producers, disseminators and consumcrs of visualizations. A research agenda in data visualization for environmental management should incllde purely methodological investigations which draw upon the methods literature in the sciences. Campbell and Fiske’s ( 1959) multitrait-multimethod paradigm, although over 30 years old, might be extrapolated to serve as a useful beginning conceptual framework for methodological inquiry into visualization. CQNCLI_JSIQN Technological advances in data visuslization are a certainty. Wh?.t is less certain, is the extent to which society and those who are charged by society to act as managers of the en-
FACET ANALYSIS OF VISUALIZATION
vironment will utilize data visualizations in an effkient and constructive manner such that the environment will be better off. Reduction of that uncertainty will require careful consideration of and research on the consumers of visualization, the purpose of the visualizations and the social, political and organizational context within with visualizations are produced, disseminated and used.
251
REFERENCES Ajzen, I. and Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ. Campbell, D. and Fiske, D., 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull., 159: 8 1- 105. Chenoweth, R., 199 1. Seeing the future: Aesthetic policy implications of visualization technology. J. Urban Regional Inf. Systems Assoc., 3 ( 1): 6- 13. Epstein, E., 1991. In my opinion . . . J. Urban Regional Inf. Systems Assoc., 3 ( 1) : 2-4. Foa, U.G., 1965. New developments in facet design and analysis. Psychol. Rev., 72 (4): 262-274.
247
A facet analysis of visualization for environmental management Richard Chenoweth Center for Resource Policy Studies, School of Natural Resources, 240 Ag Hall, Universityof Wisconsin Madison, WI 53 736. USA (Accepted 10 December 199 1)
ABSTRACT Chenoweth, R., 1992. A facet analysis of visualization 251.
for environmental
management.
Landscape Urban Plann., 2 1: 247-
One approach to organizing the world of visualization is to try to reduce the many issues to just a few dimensions or facets. The purpose of this oversimplification is to create a map, a framework to keep us from being lost in the details and losing sight of the societal and scientific goals which lead us to create visualizations in the first place. When the impressive demonstrations of technology are over and we have all had a chance to find out what some of those lengthy acronyms mean, we will still be faced with complex decisions affecting the fate of the environment.
CE
SUA
N
Figure 1 represents one effort to organize the world of visualization. In this depiction, there are three kinds of questions which should be considered. First, what is the purpose of visualization? Why go to the time, trouble and expense (usually born by the public) to do it? Second, who are the intended consumers of the visualization? And third, what are the social, political and organizational contexts within which the visualizations are produced and/or utilized? Questions can be posed and research agendas can be set forth within each facet of this universe. A fourth set of issues, which might be termed universal issues, is applicable throughout the structure.
Correspondence to: R. Chenoweth, Center for Resource Policy Studies, School of Natural Resources, 240 Ag Hall, University of Wisconsin, Madison, WI 53706, USA.
There are many unanswered questions about the ability of different kinds of visualizations, varying in sophistication and expense to produce, to describe, predict or explain phenomena to different kinds of audiences. Questions, for example, about how abstract a representation can be while still serving a useful purpose, for example stick figures may be all that is needed to tell how many trees there are on an acre and where they are located, but it is doubtful that such a visualization would be useful in meeting the legal mandate for federal agencies to provide “. . .aestheticallyand culturally pleasing” surroundings (National Environmental Policy Act, 1969 ). An adequate research agenda for visualization will need to focus some attention on understanding how attributes of visualizations are linked to human visual information processing and decision making in the environmental arena.
R. CHENOWETH
248
Consumers of Visualization
Env. decision
maker
Scientific enterprise Applied environmental decision-making Persua sivc communication Federal Agencies
Priva tc Industry
State/ local gov’t
Etc.
Social, political and organizational context of the visualization 4 facet analysis of visualization
for environmental
management.
the LJniversity of Wisconsin we will soon be comparing rural residents’ willingness to accept land use controls under ‘traditional’ land planning scenarios versus incorporating the use of geographic infor imaging to depict the visual consequences of regulatory alternatives. Using Ajzen and Fishbein’s ( 1980) model of volitional behavior, we will examine the possibility of visualization effects on lay i;eople’s subjective judgements, effective evaluations of land use outcomes, and motivations tc comply with the recommendations of significant actors in the land use planning arena. Professionally trained environmental managers are among the most important potential consumers of data visualizations. Therefore, an understanding of their visualization needs will be essential to the usefulness of any program of research in visualization. The notion of an
‘informed’ needs assessment, whereby potential users are made aware of the capabilities and limitations of visualizations before soliciting perceived needs and potential applications will be a necessary brick in building a solid foundation for a research agenda. A research agenda focusing on consumer needs and reactions will be as important as technological advances. Indeed, Steinitz suggests a program of adaptive testing of visualization technology developed in other arenas, focusing more on the substance of environmental decisions and less on technology invention which is sure to progress irrespective of the environmental professions. Finally, given that environmental issues do not respect disciplinary boundaries, funding priorities should reflect the potential for visualization to foster cross-disciplinary comlmunicatlon and interdisciplinary research on environmental issues.
FACET ANALYSIS OF VISUALIZATION
Such an emphasis would be entirely consistent with the National Environmental Policy Act of 1969 which states: “. . . all agencies of the Federal Government shall: (A) use a systematic, interdisciplinary approach which will insure the integrated use of the natural and social sciences and the environmental and design arts in planning and decisionmaking which may have ar: impact on man’s environment; (B) identify methods and procedures. . . which will ensure that presently unquantified amenities and values may be given appropriate consideration in decisionmaking along with economic and technological considerations” [ Sec. 102, A and
249
There may be special problems with the use of visualizations aimed solely at persuasion, for example compelling visualizations which are misleading or are not accurate representations of environmental conditions or processes. In this regard, there needs to be research and scholarship which draws upon the special expertise represented in marketing and communication as well as an exploration of the difficult legal issues sure to arise. Yet another problem is ownership, access and who pays for visualizations. Conflicts have already surfaced in the -world of public land records and geographic information systems (Epstein, 199 1).
Bl* AL CONTEXTS It seems to me that it is all too easy to get caught up in the technological wizardry of visualization without having a focused understanding of one’s objectives. The tinkering around to substance ratio could probably be improved. In any event, agencies that fund visualization research should assure themselves that proposals have clear social or scientific goals and are not, when the rhetoric is reduced to its basics, simply efforts at creating bigger, better, faster or flashier visualizations. There are many possible taxonomies for classifying the purposes of visualization. Thek e is, of course, no ‘correct’ taxonomy, only usefdl or un-useful ones. In this facet analysis, visualizations can be viewed as supporting the traditional ‘generic’ scientific goals of description, prediction and explanation. The choice of representational models should be governed by the scientific goal within particular domains of knowledge. The important point here is that data visualizations, to be useful in environmental management, are not ends in themselves. We need to continually remind ourselves that visualizations should be driven by the scientific enterprise and funding for a research program ought to reflect that larger interest.
Without understanding these contexts, it seems unlikely that the transfer of visualization technology will be orderly or effective in meeting societal goals or environmental challenges from the local to the global. If we are to improve environmental decision making and institutionalize the use of visualization technologies in environmental evaluation, planning and implementation, then the research capacity of the social sciences and planning professions will have as much of a role to play as the natural sciences and computer sciences. An important element of the context facet is the potential for flawed communication between scientists who generate data, those who create visualizations of data, those who d;sseminate the visualizations for different purposes and end-consumers. Any examination af methodological issues such as ‘realism’ will be relatively shallow unless explicitly imbeddsd in an understanding of these contextual issues. Another implication of the third facet is that we need to understand current institutional arrangements. Who is doing what with respect to visualization and environmental management? Such an understanding is required if we are to avoid costly duplicative efforts and to
R. CHENOWETH
250
develop cooperative resource pools, consortia and the like. Yet another aspect of the third facet is illustrated by my own work (Chenoweth, 1991) which focuses on how citizens and local units of government might consider aesthetic values before irrevocable change occurs. I am exploring four areas. The first is the public’s legal right, esta’ lished under NEPA, various state ‘mini-NEPAs’ and specialized legislation (e.g. Wild and Scenic Rivers Act and the Wilderness Act) to anticipate and to modify the probable visual consequences of environmental action before irreversible change occurs. The second is how visual simulations might serve as negotiated legal documents, i.e. contracts between government regulators and those they regulate, concerning the future appearance of the environment. The third is how visual simulations might be of assistance in the development of performance standards or other land use controls designed to protect aesthetic values. The fourth is how, together with contingent evaluation techniques, image-processing might be used to assess the economic consequences of damage to the appearance of the environment. In addition to the questions that are sure to arise within this facet, one thing seems a sure bet; coordination and collaboration between agencies, vendors, universities and others will be an essential element of a successful longterm research and development program in data visualization for environmental management.
Universal issues are those that are applicable to, or in some way influence, all three facets of the structure depicted in Fig. 1. Many of these issues pertain to hardware and software and these issues have received substantial attention elsewhere. It may be possible for each of us t.o locate our own work somewhere in structure. How-
ever, it appears to me to be time to document and to monitor which visualizations have OCcurred, to what effect and with what obstacles, problems or opportunities? Collectively, what visualizations have been produced, for what purposes, for what consumers, within which contexts and to what societal or scientific advantage (or disadvantage)? Without an understanding of where we have been, it will be difficult to chart where we might go. Recommendations for comprehensive documentation of the current state of affairs in data visualization are especially appropriate if we are not to reinvent the wheel. Another universal issue pertains to the disparity between the semantic conventions which have evolved in scientific measurement and the same terms applied to visualizations. Terms such as ‘reliability’ and ‘validity’ have a long history in science and have come to acquire some shared definition. There are conventions in the sciences, for example the internal validity of research designs for determining the truth or falsity of written propositions, a.k.a. hypotheses. But what does it mean to say that a visualization is true or reliable or valid or accurate or real? This is a serious problem from the standpoint of communication between scientists, producers, disseminators and consumcrs of visualizations. A research agenda in data visualization for environmental management should incllde purely methodological investigations which draw upon the methods literature in the sciences. Campbell and Fiske’s ( 1959) multitrait-multimethod paradigm, although over 30 years old, might be extrapolated to serve as a useful beginning conceptual framework for methodological inquiry into visualization. CQNCLI_JSIQN Technological advances in data visuslization are a certainty. Wh?.t is less certain, is the extent to which society and those who are charged by society to act as managers of the en-
FACET ANALYSIS OF VISUALIZATION
vironment will utilize data visualizations in an effkient and constructive manner such that the environment will be better off. Reduction of that uncertainty will require careful consideration of and research on the consumers of visualization, the purpose of the visualizations and the social, political and organizational context within with visualizations are produced, disseminated and used.
251
REFERENCES Ajzen, I. and Fishbein, M., 1980. Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ. Campbell, D. and Fiske, D., 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull., 159: 8 1- 105. Chenoweth, R., 199 1. Seeing the future: Aesthetic policy implications of visualization technology. J. Urban Regional Inf. Systems Assoc., 3 ( 1): 6- 13. Epstein, E., 1991. In my opinion . . . J. Urban Regional Inf. Systems Assoc., 3 ( 1) : 2-4. Foa, U.G., 1965. New developments in facet design and analysis. Psychol. Rev., 72 (4): 262-274.