Domain Analysis: A Technique to Design A User-Centered Visualization Framework

Transcription

1 Domain Analysis: A Technique to Design A User-Centered Visualization Framework Octavio Juarez Espinosa Civil and Environmental Engineering Department, Carnegie Mellon University, Pittsburgh, PA oj22@andrew.cmu.edu Chris Hendrickson, Ph.D. Civil and Environmental Engineering Department, Carnegie Mellon University, Pittsburgh, PA cth@andrew.cmu.edu James H. Garrett Jr., Ph.D. Civil and Environmental Engineering Department, Carnegie Mellon University, Pittsburgh, PA garrett@andrew.cmu.edu Abstract Domain Analysis for Data Visualization (DADV) is a technique to use when investigating a domain where data visualizations are going to be designed and added to existing software systems. DADV was used to design the data visualization in VisEIO-LCA, which is a framework to visualize environmental data about products. Most of the visualizations are designed using the following stages: formatting data in tables, selecting visual structures, and rendering the data on the screen. Although many visualizations authors perform implicit domain analysis, in this paper, domain analysis is added explicitly to the process of designing visualizations with the goal of producing more usable software tools. Environmental Life-Cycle Assessment (LCA) is used as a test bed for this technique. Keywords Visualization framework, Life-Cycle Assessment, user tasks, computer-human interaction, domain analysis, economic input-output. I. INTRODUCTION The research on information visualization has been focused on graphic techniques as well as interaction techniques. Progress has been made by many groups in the following areas: Visualization of Multidimensional Data [1,2]; Automatic Design [3,4]; Interactive Techniques [5,6,7]; Focus and Context [8,9,10]; Tree and Network Visualization [11,12]; and Visualization Frameworks [13,14,15]; etc. Currently some research groups are working on the visualization of documents and information on the internet. In this paper, the focus is on developing data visualizations based on user tasks. It is our hypothesis that the study of a user s tasks should yield usable information for visualization design. The knowledge of tasks and user models can help to select the data structures, the visualization techniques to use, as well as the interaction techniques. The traditional design process starts with the data tables as shown in Figure 1. In this traditional design process, designers select the data visualization techniques used in their systems based only on the type of information to visualize [3, 4]. Other researchers design visualizations based on low level tasks such as search, lookup, verify, and computation [25]. Although most of the research papers do not include a domain analisis explicitly, the analysis has been performed for some of the visualizations designers. Tables Figure 1: Traditional Visualization Design Process The explicit addition of a new step, domain analysis, to the traditional design process allows for a better fit of the data visualizations to the user tasks. During domain analysis, the knowledge of users and tasks are gathered and structured to be used in the selection of visualization and interaction techniques. Figure 2 shows the recommended process for designing data visualizations. Tables Technique Selection Domain Analysis Technique Selection Figure 2: Augmented Design Process Rendering Rendering Explicit domain analysis is useful as a document that allows designers to evaluate effectiveness of visualizations based on the users requirements and needs. Also, explicit domain analysis is useful to store the knowledge about the

2 design process that may make it possible to reuse old visual designs. The modification of this process requires a systematic method to gather knowledge about tasks and domains as well as heuristics to guide the process of selection of graphical and interaction techniques. To achieve these tasks, a method to perform domain analysis is described in this paper. Also described is a set of heuristics to select the graphic techniques. II. Domain Analysis Domain Analysis for Data Visualization (DADV) produces a structured description of the domain, a group of user tasks, and a list of visualization needs. Domain analysis is an exploratory activity to gather knowledge about a specific domain. It is similar to the activity of knowledge acquisition used by knowledge engineers who gather knowledge when developing expert systems. Domain analysis practitioners gather knowledge about of the data and problem solving processes of a domain. The methods used to gather knowledge about a specific domain are expert interviews, reviews of literature about the domain, observation of users performing tasks, and problem solving in the studied domain. The output from domain analysis is the domain description, the description of the user tasks, and the list of visualization needs. A. Domain Description. The domain description includes only the objects related to the solution of problems: Entities. The entities are used to describe the phenomena in the model. These entities consist of a set of attributes that describe the objects in the world. Relationships between entities. The relationships could be mathematical or causal. Assumptions. The assumptions in the data model are needed to understand the limitations. Data sources. These data sources are the data used in the domain. B. User Tasks The list of user tasks is ranked by frequency. The task description includes: the user tasks when he/she solves a problem; the stages followed to solve a problem, which are needed to understand the whole process of data analysis and information processing; and the data analysis heuristics followed by the users. C. Visualization User Needs Users are interviewed about their visualization needs. Users suggest the types of displays that may make solving the problem easier. The visualization needs consists of: heuristics to perform the data analysis; graphic techniques suggested; and the level of detail needed. III. Domain Analysis: A Case Study The domain analysis method was used in the analysis of the Life-Cycle Assessment (LCA) domain, which is a data intensive domain. An existing software prototype to practice LCA was used as a starting point in this domain study. A group of LCA users provided the knowledge needed to design a visualization tool that supports their tasks. A. Domain Description Life-Cycle Assessment (LCA) is a tool used by environmental scientists to evaluate the environmental impacts of a product or service during its entire life-cycle [17]. The reason to evaluate the entire life-cycle of a product is that environmental emissions might occur during any lifecycle period, such as manufacturing, material extraction, or end-of-life. A life-cycle study is divided into the following stages: inventory analysis, impact analysis, and improvement analysis. In the inventory analysis step, the materials, energy, and emissions during the entire life are computed. The impact analysis consists of assessing the effects of emissions and discharges during the inventory step. Finally, the improvement analysis step looks for opportunities to minimize emissions, material usage, and environmental discharges [17]. Economic Input-Output Life-Cycle Assessment (EIO- LCA) is a specific method used to perform LCA [18,19,20, 21, 22] based on the economic method named input-output [16]. This method receives a change in the demand, in millions of dollars, for particular industrial sectors as inputs and it computes the environmental effects due to these changes for the entire supply chain. The data used in this method are publicly available. The economic data used is the commodity by commodity matrix that reflects the economic information for a particular country. The toxic release inventory (TRI) for the same year as that of the economic data, and other environmental vectors, such as energy, fuels, and ores consumed by every industrial sector, are components of the EIO-LCA data. Entities The entities considered in an EIO-LCA study are industrial sectors, materials, environmental discharges, changes in the demand, and LCA stages. Industrial Sectors. The economy of an area is divided in industrial sectors. Those sectors interact by exchanging goods. A matrix describes the interaction between sectors, where the coefficients characterize the exchange between the sectors. A column represents the inputs to the sector while the outputs of sectors are represented by a row. Materials. The materials used to produce a product or to offer a service are included in this category. The most important materials to evaluate are non-renewable

3 resources. Those materials are never accounted for in the social cost of a product. Environmental Discharges. These discharges are the toxic chemicals released into the environment. These discharges are accounted for based on the media into which they are released. The media reported by the companies include air, water, land, and underground. Changes in the Demand. These changes are purchases or sales in one or more economic sectors, used to assess proposed designs. LCA Stages. These stages are the components for the life of a product. The stages considered are materials extraction, manufacturing, use, and end-of-life. An EIO-LCA evaluation could address of one or more of these stages of a product life-cycle. Relationships The relationships between entities are illustrated in Figure 3. The entities represented by rectangles are sectors that interact by buying or selling products or goods to other sectors. Relationships are represented using diamonds, which join the entities. Arrows represent inputs and outputs to each sector. Every industrial sector uses materials, fuels and energy. The same sectors send environmental discharges to the environment. Fuels, Materials, Energy Industrial Sector Emissions Figure 3: Sectors relationships Change in the Final Demand Sales Purchases Fuels, Materials, Energy Industrial Sector Environmental Effects Emissions Figure 4 illustrates another important relationship, which links industrial sectors to toxic emissions, materials or energy consumed. Assumptions The major assumptions made for EIO-LCA users are described below. The environmental effects are proportional to the economic effects. A sector with more change in the economy will have more emissions than a sector with a lower economical change. The product or service evaluated is considered as an average product. For example, when a car is evaluated, the system considers a generic car without distinction in the architecture, prices, and any other differences. The economic data remains constant over 5 years. The Department of Commerce updates this data every five years. Materials and Energy Figure 4: Materials used and emissions of industrial sectors Data Sources The data sources are publicly available. The data are provided electronically and are explained below. Six digit commodity-by-commodity economic matrix. This table is a component of the 1992 Benchmark Input- Output Accounts. This document is produced by the Bureau of Economic Analysis in the US Department of Commerce [24]. Toxic Release Inventory. This database is provided by the US Environmental Protection Agency (EPA). The most recent published data is for This database only contains data from manufacturing industries and consists of more than 600 chemicals sent to different media [23]. Data about energy, fuels, ores, conventional pollutants, hazardous waste, and water were obtained from different sources such as the EPA and the Census Bureau. The data was integrated by industrial sector and the units used are metric tons by million of dollars. The data analysis components were obtained after interviewing users performing EIO-LCA evaluations. The ten users that were interviewed belong to the same research group, but they have different degrees of expertise. B. User Tasks Use Industrial Sectors Release Toxic Chemicals The study of user tasks was performed by interviewing EIO-LCA users. EIO-LCA users were asked to describe the way they define the problems and the way they perform the data analysis. Other tasks, which are not included in Table 1, were studied by interacting every day with the LCA research team. The tasks could be divided in the following groups: tasks for LCA practitioners and tasks for LCA researchers. As can be seen in Table 1, the task performed with more frequency by EIO-LCA practitioners was the comparison of alternative designs, policies, or processes. The users try to find answers to questions such as which design, process or policy is more environmentally friendly.

4 Tasks Number Comparisons of Designs 9 LCA of a Product 4 Relation Economic and Environmental Data 2 Effects of Changes in Policy 1 Effects of Industrial Sectors 1 Search the suppliers 1 Industry Level Analysis 1 Effects Changes Industry 1 Table 1: LCA Practitioners Tasks The second most common user task is the LCA of a product, which is a subtask of a product comparison. EIO-LCA Practitioners Tasks These tasks were gathered from user interviews. Ten EIO- LCA users were interviewed to have a clear understanding of how EIO-LCA is used. The tasks performed by EIO-LCA users are product comparisons and EIO-LCA evaluations. Product Comparison Frequently, EIO-LCA users wants to compare many alternative designs or many products. The comparison is performed based on environmental impacts. For example, two car models built with different materials can be compared based on environmental impacts through their entire life-cycle. In the same way, the environmental impacts produced by the changes in some industrial sectors could be compared. In the case of comparisons, an EIO-LCA evaluation must be performed for every product or design compared. EIO-LCA Evaluation An EIO-LCA evaluation is a subtask of a product comparison. An EIO-LCA evaluation estimates the environmental impacts of a product through its life-cycle. Many times, an EIO-LCA evaluation is practiced for every product life-cycle stage. For example, one EIO-LCA for manufacturing, and one EIO-LCA for recycling. Tasks for EIO-LCA Researchers The EIO-LCA researchers perform the same tasks as the EIO-LCA practitioners, but they perform additional tasks. Two of those tasks are described in the following paragraphs. Data Benchmarks Advanced users are aware that EIO-LCA is still a research prototype. As such, they perform validations of particular data sets. For example, if they were to receive the general statistics of electricity for the entire US, then they would want to compare the data with the information used by EIO-LCA to perform the computations. The validation and comparison of data sets is performed also with conventional pollutants and global warming potential. Other times, they might want to compare the environmental effects of performing an LCA with data from two different years. Data Navigation Some users want to investigate specific data values in the economic matrix or the environmental vectors when they do not agree with the EIO-LCA results. However, many times they need also to see the Make Table, and the Use Table, which are other economic matrices used to compute the commodity-by-commodity matrix included in the software. It is difficult to navigate over the complete information because these matrices are very large and they do not fit on the screen. C. Visualization User Needs The heuristics of analysis as well as the visualization technique suggestions, were obtained from user interviews. Heuristics to perform the data analysis Data analysis strategies are summarized in Table 2. The first column represents the name of the data set to be analyzed. Every column from the second to the tenth represents a user. The numbers represent the priority assigned for the users to review the data. While 1 represents the maximum priority, 7 represents the lower priority. As can be seen in Table 2, the average user assigned 1 to the economic data and 2 to the electricity data. Table 2 shows that no user assigned a priority to every vector in the data analysis process. Users did not seem to assign priorities to the data about ores and fertilizers. Global warming potential, ozone depletion, and water data did not receive a priority from many users. Table 2 suggests that users only reviewed a short portion of the data displayed in the results and they had different criteria to prioritize which data to review in the process of analysis. EIO-LCA Users Data Set Economic Electricity Fuels Ores Fertilizers Toxic Releases Toxic Releases weighted by toxicity Conventional Pollutants Global Warming Ozone Depletion Water Hazardous Waste Social Costs Table 2: User heuristics for data analysis There is a possibility that every user had a different degree of confidence about the data. Some users seemed to have more confidence in TRI data, while others might had more confidence in other data sets. Visualization techniques suggested

5 The users interviewed suggested the visualization representations that they would like to use to represent the EIO- LCA results. Table 3 presents the suggestions given by the users. As can be seen in Table 3, more users preferred the presentation of ranks including percentages. Some users wanted to see several products in the same graphic display when they were doing comparisons. Presentation Techniques Frequency Suggested Collapse information to five numbers 1 Weighting the environmental effects 2 Normalize the environmental vectors 1 Comparison of information with national data 1 Display of data showing percentages 3 Summary Tables 1 Summaries of information organized by LCA 1 stage Network of suppliers 1 Graphics of several workspaces 2 Use of tables for details 1 Show only the top tens values for every 3 environmental vector (Ranking) Show different graphics of the same data 1 Show the top five values 1 (Direct and Indirect) Show the information of several products at the 1 same time Allow users an index definition 1 Show comparative tables of products 1 Show a matrix of environmental vectors with 1 histogram in the top Show several workspaces in one screen 1 Table 3: Presentation Techniques Suggested by Users Level of detail needed To evaluate the level of detail that users need when they perform a data analysis, a question concerning this issue was posed during the interview. Table 4 shows the EIO-LCA users opinions. The data is presented in two columns: names of the detail required and the data frequency required. Detail Required Frequency Show summaries 10 Show totals 10 Show direct and indirect 3 environmental effects Show sectors comparison 1 Show detail and summaries 2 Show detail only for specific questions 2 Table 4: Level of Detail Required by Users As can be seen in Table 4, users preferred summaries and ranks of data instead of detailed information. Detailed information seemed to be necessary only when a user had a very specific question. IV. VisEIO-LCA Design Without doing the domain analysis, the starting point to select the interaction and visualization techniques would be the data tables. However, the selection of visualization techniques can be improved by using the knowledge about the domain. The design of VisEIO-LCA was directed by the following user tasks: product comparisons and data navigation. A. Product Comparisons This task is performed by LCA practitioners using a matrix with 485 rows representing the industrial sectors and 51 columns representing the environmental vectors. To perform a comparison of several products, a user works with one matrix for each product. When designing a data visualization for this task, it is not necessary to display the whole matrix on the screen, because users do not want to see the complete matrix. The users select subsets of the matrix to visualize as can be seen in Table 2. The EIO-LCA users do not need the whole environmental vector (a column in the EIO results matrix). They usually see only the summaries (only totals are included in summaries) or the top five or ten elements in each vector. To perform perceptual comparisons, the best graphical property is length. For a human, it is more difficult to compare two objects based on color, shape, or orientation. However, when the objects are represented using length the perceptual comparison is easier when the objects are shown together. Chart styles, such as bars, lines, areas, and steps, can be used to encode the data to be compared. The data used in a comparison can be represented as a chain of sectors. This chain has several products at the root level. Every product has several environmental vector families. Every family contains several environmental vectors. The data navigation might be done using the hierarchy. Because trees are good to represent hierarchies, they could be used to represent the data used in the comparison. Based on the previous information, the following techniques were selected: trees to group the information of products as a hierarchy (view trees), and charts to represent the product data. The workbook metaphor is used to allow users to have different graphics for the same comparison. Only 2D graphics were selected because the task does not require more sophisticated techniques. B. Data Navigation Advanced EIO-LCA users want to navigate the data sets. For this goal, there is no information about how the users would like to see the data displayed.

6 There are 3 economic matrices used as data sources in EIO-LCA. Those matrices are the make matrix, the use matrix, and the commodity-by-commodity matrix. The selection of interaction techniques and the graphic techniques was made for every type of data source. Matrix Visualization The complete matrix is presented on the screen. The matrix to display can be selected from a tree that contains every data set in the system as can be seen in Color Plate 1. The whole matrix is rendered on the screen. Color is used to represent the values in the matrix. The range of values is divided into categories and then a color is assigned to each category. A user can interactively change the color map and the limit values for each category. Also, a user can select only subsets to view or a specific point in the matrix to see in another window in more detail. Color Plate 1 shows the matrix visualization. In addition, a bar chart can be displayed for every row and column in the matrix. Environmental Vectors Visualization There are 51 environmental vectors that can be visualized in the system. Every vector is displayed completely on the screen. Color was used to represent each value. The values are divided into categories and then the user can assign a color to each category. The limits between categories can be changed by the users as well as the color map. Detail about the vector can be seen in another window, which magnifies specific vector areas as can be seen in Color Plate 2. V. VisEIO-LCA Architecture The architecture of VisEIO-LCA has the following modules as shown in Figure 5: Chart Visualization. This component allows the user to select a level of detail for the information. A user also can select the number of sectors to visualize. One or more products might be displayed on one screen. Scatter Plot Visualization. This component displays the data in detail. This view allows the user to see the whole environmental vector on the screen while the chart view only allows users to see a partial picture of the system. Data Visualization. This component allows the user to see the data matrices in detail. The user can investigate specific values as well as look for patterns in the data sets. Geographic View. This component allows the user to see the environmental impacts by geographic area. A user can see which areas are impacted by changing the final demand value and the environmental vector. The EIO-LCA module generates the data sets used by the visualization modules. Currently, there are three different ways to see the EIO-LCA results and only one way to view the data sets. The only communication between modules is based on the system data inputs or the data results. Module details are explained in the following paragraphs. A. Chart Visualization The following information with respect to the use of the chart view was obtained from interviews of experienced users: The most performed task is the life-cycle comparison of designs or products. EIO-Data Data Visualization Figure 5: VisEIO-LCA Architecture The LCA evaluation of a product is the second most frequent task. Users do not view the whole data set to make their decisions. LCA practitioners want to see data summaries. Users like to view the information ranked by specific values. They are only interested in the top five or ten commodities in the list. The data subsets selected to perform the analysis differ from one person to another. Based on the information obtained from users, the following data visualization design decisions were made: The use of charts was selected because users want to see summaries. The styles of charts that were selected were those that use bars, steps, lines and areas. They were selected because the use of length as a graphic property is more effective for performing comparisons of numerical data. A user can display several products at the same time using a particular chart style. The product information can be compared with the national values. For example, the comparison of energy required for one product and the the energy consumed in the whole country. B. Scatter Plot Visualization EIO-LCA Chart Visualization EIO-Results Scatter Plot Visualization Geographic Visualization This view was provided although users do not seem to look for details. However, it was considered important because sometimes they might require details on demand. This view presents the complete set of points for one environmental vector over the entire set of sectors. The horizontal axis contains the 485 industrial sectors and the vertical axis

7 contains the values for the environmental vector as can be seen in Color Plate 3. The following functions are provided with this data view: Change the range of values. This function allows users to select a subset of the data set. For example, a user wants to see the sectors that produce air emissions between value 1 and value2. View different data vectors in the same scatter plot. This allows the comparison of two environmental vectors. View the detail for an specific point selected. For example the name of the sector and the exact value for the specific point. The detail is presented in a different window. A user can have up to 10 vectors at the same time in one scatter plot as can be seen in Color Plate 3. C. Data Visualization This module was created because advanced EIO-LCA researchers sometimes want to verify or compare the data in the system. The tasks that users want to achieve with data are the following: Compare a data vector with a vector obtained from other information sources. This operation is called data benchmarking in this document. Navigate through the economic matrices and environmental vectors. This function is needed when users are trying to explain some results obtained from one EIO-LCA evaluation. To support data benchmarking, the visualization of vectors was selected. The vectors are displayed using color to represent values. A family of vectors is displayed in one workbook page. A user can change the color maps and the ranges to visualize the data. The user can see in detail specific sectors in the detail window. The matrices are encoded by color. The commodity-bycommodity matrix, the make matrix, and the use matrix can be navigated by users. A user can see the names and values for specific points in the matrix by using the detail window that magnify specific areas on the screen. A user can also display the values for an specific sector in a row or column as can be seen in Color Plate 1. Users can magnify the matrix cells in the detail window. For example, they can show the values for the interaction between sector 1 and sector 2. vector to visualize on the map where the affected areas are located. For example, a user selects air releases for the whole group of economical sectors. Users can zoom in on the map or they can get the information about specific states. Color Plate 4 shows the graphic view of the data set. This geographic data of EIO-LCA evaluations can be only seen by using graphic tools and is difficult to visualize using tables. VI. Implementation VisEIO-LCA supports four different views that include the chart view, the scatter plot view, the data view, and the geographic view. The graphic interface for the software prototype is shown in Figure 6. The information is presented to users using the following three windows: the Project Window, the Workspace Window, and the Detail Window. The Project Window that is shown in Figure 8 to the left of window that displays the chart includes the data sets that a user can visualize and includes three tabs that are useful to select the data views. The first tab allows users to display charts, the second tab allows users to display the data sets used for the EIO-LCA software to make the computations, and the third tab that allows users to view the data using scatter plots. The Workspace Window is the where the graphics are displayed. In Figure 6, The Workspace Window presents a bar chart. This window can be composed of many pages that compose a workbook with visualizations about the task performed in the project. Finally, the Detail Window, that is to the left of the CMU logo in Figure 6, allows users to see detailed information for the data and scatter views. D. Geographic Visualization The geographic view was not suggested by the EIO-LCA practitioners, but was by some advanced EIO-LCA researchers. This component allows users to see the graphic locations affected by the environmental impacts. A user can select an economic sector and a vector to visualize on the map. For example, a user can select primary batteries as the sector and air releases as the environmental vector. A user also can select all sectors and an environmental Figure 6: The graphic user interface for the software tool

8 VII. Summary and Future Research Explicit domain analysis helps to direct the process of designing a software system that provides data visualization. The design of visualization tools based on user tasks should produce more usable tools. Explicit domain analysis was useful for designing and implementing the visual EIO-LCA software. The advantage of using explicit domain analysis is that provides documentation about the user requirements, user tasks, and the process used to design the visualizations. This documentation can also be used to evaluate effectiveness of the visualization. Other engineering domains are being investigated so as to obtain a more robust and generic tool appliable to other domains. ACKNOWLEDGMENTS This research was supported by EPA (Environmental Protection Agency) and NSF (National Science Foundation) under the Title Environmental Input-Output Life-Cycle Assessment: A Tool to Improve Analysis of Environmental Quality and Sustainability. We thank Green Design Initiative for the support received for this research, including participation in the interviews and users that help to test the system. VIII. References [1] Feiner, Steven and Beshers, Clifford, Worlds within Worlds: Metaphors for Exploring n-dimensional Virtual Worlds, in Proceedings of the ACM SIGGRAPH Symposium on User Interface Software and Technology, pp , [2] Inselberg, A. and Dimsdale, B., Visualizing Multi- Variate Relations with Parallel Coordinates, in Proceedings of the Third International Conference on Human-Computer Interaction, Work with Computers: Organizational, Management, Stress and Health Aspects; Interface - Displays and Controls, 1, pp , [3] Mackinlay, J. Automating the Design of Graphical Presentations of Relational Information. ACM Transactions on Graphics, (Vol. 5 No. 2), April [4] Roth S. and Mattis, J., Automating the Presentation of Information, Proceedings of the IEEE Conference on Artificial Intelligence Applications, Miami Beach, FL, February 1991, pp [5] Ahlberg, C., Shneiderman, B. Visual Information Seeking: Tight coupling of dynamic query filters with starfield displays. ACM CHI '94 Conference Proc. (Boston, MA, April 24-28, 1994) [6] Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., Shneiderman, B. (1998) LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Department of Computer Science, University of Maryland, Technical Report CS-TR-3943, UMI- ACS-TR [7] Chuah, M.C., Roth, S.F., Mattis, J., and Kolojejchick, J. SDM: Selective Dynamic Manipulation of Visualizations, Proceedings of the ACM Symposium on User Interface Software and Technology, Pittsburgh, PA, November 1995, pp [8] Furnas, George W., Generalized fisheye views. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, ACM (April 1986), [9] Rao, Ramana and Card, Stuart K., The Table Lens: Merging Graphical and Symbolic Representations in an Interactive Focus+Context Visualization for Tabular Information, in Proceedings of ACM CHI'94 Conference on Human Factors in Computing Systems, Information Visualization, 1, pp , Color plates on pp , [10] Lamping, John, Rao, Ramana, and Pirolli, Peter, A focus + context technique based on hyperbolic geometry for visualizing large hierarchies, Proc. of CHI'95. [11] Johnson, Brian, and Shneiderman, Ben, Tree-maps: A space-filling approach to the visualization of hierarchical information structures, Proc. IEEE Visualization '91, IEEE, Piscataway, NJ (1991), [12] Becker R.A., Eick S.G., and Wilks A.R., Visualizing Network Data, IEEE Transactions on Visualization and Computer Graphics, 1(1), March [13] Roth S. et al. "Visage: A User Interface Environment for Exploring Information," Proceedings Information Visualization 96, IEEE Computer Society Press.his is the first reference. [14] 2. C. Ahlberg and E. Wistrand. IVEE: An environment for automatic creation of dynamic queries applications. Conference Companion, CHI 95, ACM May 1995, pp [15] Roth, S.F., Kolojejchick, J., Mattis, J., and Goldstein, J., Interactive Graphic Design Using Automatic Presentation Knowledge,Proceedings of the Conference on Human Factors in Computing Systems (SIGCHI '94), Boston, MA, April 1994, pp [16] Leontiff, Wassily Input-Output Economics. New York: Oxford University Press, [17] Vigon B.W. et al Life-Cycle Assessment Inventory Guidelines and Principles. EPA/600/R-92/245, February [18] Cobas E., Life Cycle Assessment Using Input-Output Analysis, PhD Thesis, Pittsburgh, PA, Carnegie Mellon University, [19] Horvard, A. "Estimation of Environmental Implications of Construction Materials and Designs Using Life-Cycle Assessment Techniques," PhD. Thesis Department of Civil and Environmental Engineering, Carnegie Mellon University, [20] Joshi, S. "Comprehensive Product Life Cycle Analysis Using Life-Cycle Assessment Techniques," PhD. The-

9 sis Heinz School of Public Policy and Management, Carnegie Mellon University, [21] Hendrickson, C. et al, Economic Input-Output Models for Environmental Life-Cycle Assessment, Environmental Science and Tecnology, April [22] Green Design Initiative, EIO-LCA software, [23] US EPA, Toxics Release Inventory. EPA 749-C [24] US Department of Commerce. "Six Digit Commodityby-Commodity Total Requirements," Industry Economics Division, Bureau of Economic Analysis BE- 51, [25] Casner, S. A Task-Analytical Approach to the Automated Design of Graphic Presentations, ACM Transcations on Graphics, Vol. 10, No. 2, April 1991.

10 Color Plate 1:Economic matrix and detailed view Color Plate 2: TRI vector data

11 Color Plate 3: Scatter Plot View Color Plate 4: Geographic View of the Impacts