Updated Dec. 3
Excerpts from The International Journal of Geographic Information Science, 13(4): 311-334]
Alan M. MacEachren, Monica Wachowicz, Robert Edsall, and Daniel Haug GeoVISTA Center, Department of Geography, Penn State University
and
Raymon Masters
Center for Academic Computing, Penn State University
Staff assistance from and access to equipment has been provided by the Penn State Center for Academic Computing
This web document is a supplement to a full paper that will appear in the International Journal of Geographic Information Science. Here, we include the introduction from that paper, provide an outline of the contents (with a few key paragraphs included), and use a set of annotated static and dynamic illustrations to provide a understanding (that goes beyond the limits of print) of our prototype geovisualization tools and how they work.
We present an approach to the process of constructing knowledge through structured exploration of large spatiotemporal data sets. First, we introduce our problem context and define both Geographic Visualization (geovisualization) and Knowledge Discovery in Databases (KDD), the source domains for methods being integrated. Next, we review and compare recent geovisualization and KDD developments and consider the potential for their integration, emphasizing that an iterative process with user interaction is a central focus for uncovering interesting and meaningful patterns through each. We then introduce an approach to design of an integrated geovisualization-KDD environment directed to exploration and discovery in the context of spatiotemporal environmental data. The approach emphasizes a matching of geovisualization and KDD meta-operations. Following description of the geovisualization and KDD methods that are linked in our prototype system, we present a demonstration of the prototype applied to a typical spatiotemporal dataset. We conclude by outlining, briefly, research goals directed toward more complete integration of geovisualization and KDD methods and their connection to temporal GIS.
Large environmental data sets represent a major challenge for both domain and information sciences. The domain sciences, most of which developed under data poor conditions, must now adapt to a world that is data rich &endash; so data rich that large volumes of data often remain unexplored while the media they are stored upon deteriorate or become obsolete. The information sciences, most of which developed in a pre-computer era or when batch processing by computer was the norm, must now adapt to a world that is not only digital but highly dynamic &endash; in which there is a potential for computers to produce answers in real time as an analyst explores data and poses "what if" questions. Much of the environmental data being generated today (e.g., from the Earth Observation System, from monitoring efforts in endangered ecosystems, from meteorological stations, etc.) includes georeferencing. The spatial aspects of these data are, in fact, often a primary focus of analysis&endash;for studies of pollutant dispersal, forest fragmentation, and other applications. Repeated observation is critical to answering the most important environmental science questions (those related to environmental process), thus environmental data sets typically have temporal as well as spatial components.
It is in this context of both increasing spatiotemporal data availability and rapidly evolving computing technologies that we see a substantial challenge for research in methods for spatiotemporal data analysis. The challenge is two-fold: to extend GIS, spatial analysis, and visualization methods (developed for application to static spatial data) into a spatiotemporal domain and to integrate these components of geographic information science to produce new methods (and associated tools) that facilitate environmental science and environmental policy decisions. This overall challenge is the focus of the Apoala Project underway in our laboratory.
In this paper we address one aspect of the problem, the development and integration of data analysis and visualization methods designed to facilitate identification and interpretation of spatial and spatiotemporal features. Toward this end, we focus on methods associated with the new but expanding fields of Geographic Visualization (geovisualization) and Knowledge Discovery in Databases (KDD). Geovisualization has been defined as "the use of concrete visual representations &endash; whether on paper or through computer displays or other media &endash; to make spatial contexts and problems visible, so as to engage the most powerful of human information-processing abilities, those associated with vision (MacEachren and others, 1992, p. 101). A primary focus of geovisualization research over the past decade has been the role of highly interactive visualization tools in facilitating identification and interpretation of patterns and relationships in complex data. KDD has been defined as: "the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable pattens in data" (Fayyad, et al., 1996, p. 6). As with geovisualization (see below) KDD is characterized as a multistep process, a process in which "data mining" algorithms (algorithms through which patterns are extracted from data) play a central role.
In the next section, we elaborate upon these definitions of geovisualization and KDD and build the case for their integration. Section three outlines our approach to an integrated knowledge construction1 system (GKConstruct), with emphasis on integration of geovisualization and KDD methods, and describes key features implemented in an early prototype. In section four, we demonstrate how the linked geovisualization and KDD methods can be applied to a sample time series of georeferenced climate data. We conclude, in the final section, with a brief discussion of our more ambitious effort to link these knowledge construction tools more directly to temporal GIS.
Particularly when applied to scientific data, geovisualization and KDD have similar goals. They differ, however, in the extent to which they rely upon human vision or computational methods to process data. In this section, we review, separately, the underlying principles and key developments of the past decade in both fields (each builds on longer traditions, but has been identified as a distinct research stream for about a decade).
A coherent theoretical framework for geovisualization is just beginning to emerge. That framework integrates the formalism of semiotics as an approach for understanding and modeling representational relationships with a cognitive perspective on the process of using visualization methods to facilitate scientific understanding (MacEachren, 1995). The goal for this integrated perspective is to develop a conceptualization of geovisualization as a process that involves humans achieving insight by interacting with data through use of manipulable visual displays that provide representations of these data and of the operations that can be applied to them. From semiotics, we gain tools for understanding abstract representations of phenomena and processes (i.e., representations in digital, visual, and other forms) as well as methods for explaining how meaning is brought to the representations by their creators and users. From the study of cognition we gain a perspective on the ways in which human information users conceptualize problem domains, process visual displays, and link mental schemata to actions through interface tools (figure 1). This integrated cognitive-semiotic perspective serves as a base from which to consider three categories of visualization meta-operations that are at the heart of the data exploration components of the Apoala Project: feature identification, feature comparison, and feature interpretation. These three operation forms are defined below, briefly.
2.1.1 Feature identification
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The development of KDD coincides with an exponential increase in data generated by and available to science, government, and industry, particularly data generated in digital form. The term "knowledge discovery in databases" was coined in 1989 in an effort to distinguish between the application of particular algorithms designed to extract pattern from data (the subprocess of "data mining") and the overall process within which data mining is a step in "extracting knowledge" from these patterns (Fayyad, et. al., 1996).Several KDD methods have recently emerged from the literature and they differ in the conceptualizations developed, reflecting (in part) their separate developments in fields such as database systems, machine learning, statistics, and artificial intelligence. We have grouped them into three categories of KDD meta-operations of increasing complexity: concept hierarchy and structure extraction: (a process in which data abstractions are derived and linked at multiple conceptual levels), categories extraction and classification (a process of deriving classes, clusters, rules, and/or patterns from target data and using the result to assign data to classes that result from the categorization process), and phenomenon extraction (a process of deriving representations of real-world phenomena from a target data set). Each is discussed below, briefly.
2.2.1 KDD methods for concept hierarchy and structure extraction
See Print Version2.2.2 KDD methods for categories extraction and classification
See Print Version2.2.3 KDD methods for phenomenon extraction
See Print Version2.3 Common themes and potential for integration
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The KDD literature contains frequent mention of the importance of visualization (e.g., Brachman and Anand, 1996; Uthurusamy, 1996). In most cases, however, visualization is considered only as a tool to facilitate the interpretation-evaluation stage of KDD. (Simoudis, et al, 1996 and Slortz, et. al., 1995 are two exceptions). Our goal, in contrast, is a more complete integration of geovisualization and KDD methods. In order to achieve this goal, we approach system integration at three levels: conceptual, operational, and implementational (Howard and MacEachren, 1997).
3.1 Conceptual level
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3.3.1 Geovisualization methods
The geovisualization operation categories detailed above should, perhaps, be considered meta-operations&endash;because each suggests an overall operational goal that is best approached through the iterative application of a set of more specific visual analysis operations. In GKConstruct, dynamic "interactors" allow users to apply these visual operations to particular representation forms and the representation forms are related to one another through dynamic linking. With dynamic linking, data objects that share some aspect (e.g., spatial location) are linked across display views (thus across representation forms in our system) so that an action on a data object in one view will be reflected by a complementary action on corresponding data objects in other views. In our initial GKConstruct prototype we have implemented three dynamically linked general representation forms: geoviews, 3D scatterplots, and parallel coordinate plots. Each is described below:
Figure 1. Extended feature ID model for map-based visualization. This diagram extends upon an earlier pattern ID model of geographic visualization (MacEachren and Ganter, 1990) to integrate what we know about human perception and cognition in the context of visual information displays. Emphasis is given to the iterative nature of human experts examining a representation and attempting to interpret that representation and use it as a prompt to insight (a process that cycles between seeing or noticing "features" in the display and interpreting those features by matching what is seen with what is known. The model suggest that knowledge is stored in the form of cognitive representations that are drawn upon to generate knowledge schemata (methods for matching what is sensed with prior knowledge) [reproduced from MacEachren, 1995, Guilford Press].
Figure 2. Simple concept hierarchy for climate analysis – one (a) appropriate for study of northern hemisphere climate, the other (b) appropriate to the southern hemisphere.
Figure 3. Integration of geovisualization and KDD methods. With each pairing of operation categories, we cite possible outcomes of the merger as it relates to knowledge construction goals. The possibilities are intended as examples from a larger potential list. Examples cited are derived from a perspective of what we gain by adding geovisualization to KDD operations. A similar matrix could be derived taking the alternative perspective.
Figure 4. A geoview is a three-dimensional window in which geographic space is mapped to display space in at least two of the dimensions. The third dimension is used to represent either the third spatial dimension (elevation/depth) or to represent time (with time treated as a linear dimension). In this case, the base of the view represents geographic space and political boundaries to help provide context. The z-axis represents time. Here, time is represented as linear and discrete starting with day 1 at the surface. Glyphs highlighted in red represent precipitation events at those times and places.
Figure 5. 3D scatterplots represent relationships between three variables, with one variable plotted on each axis of a cube. 3D scatterplots are a simple form of "spatialization" in which non-spatial data "dimensions" are mapped to the two- or three-dimensions of a display space. Spatialization suggests, and takes advantage of, the metaphor of near=similar | far=different. Using linked views, the "mapping" of non-spatial aspects of georeferenced data to the spaces of a display can be grounded in more intuitive representations that map geographic space to display space (our geoviews). In this scatterplot, a sample of cases included in a data mining run is depicted in a space defined by three attributes: sequential date (number of days from the beginning of the data set), precipitation, and sea level pressure. Color represents a fourth attribute (three classes of surface humidity) and size of "glyphs" represent a fifth (humidity at 700m).
Figure 6. A Parallel Coordinate Plot (PCP) is a data representation that contains several parallel axes, one for each variable in the data set. The data are distributed along each axis and a line connects individual records from one axis to the next, producing a "signature" for each data record. Parallel coordinate plots (PCPs) are particularly effective in uncovering relationships among many variables (Inselberg, 1997). Most previous implementations, however, provided a limited perspective on data relationships because the assignment of variables to axes was fixed. In our implementation, the user can interactively adjust that assignment (see Figure 8). This typical PCP depicts each case from a sample of data used in a data mining run by connecting the position of that case on each variable axis. The pattern of lines that results represents relationships among the classes generated in a data mining run (the initial axes of the PCP) and each of the variables included in the run (including spatial, temporal, and attribute variables). Our variation on standard parallel coordinate plots includes several additions related to user interaction (detailed below) as well as the use of color to represent categories for one of the variable axes (in this case, the surface humidity axis is grouped into three equal value range classes depicted with shades of green). Not surprisingly, the sample cases depicted that have low surface humidity (light green) all also have zero precipitation. Considering precipitation, we see that most of these zero precipitation events occur when surface atmospheric pressure is high and the high precipitation events occur when pressure is low.
Figure 7. Integration of representation and interaction forms. Each of the three representation forms implemented (as well as others that we may implement in the future), is controlled through multiple interaction forms that allow users to manipulate various parameters to the data-to-display mapping. In most cases, linking among the representation forms results in an action applied by a user to one representation being reflected across the set of representations displayed. Each of our representation forms can be independently or simultaneously manipulated through applications of one or more interaction forms. Each interaction form can be considered the implementation of a visual analysis operation. The interaction forms we have implemented include assignment, brushing, focusing, colormap manipulation, viewpoint perspective manipulation, and sequencing.
Figure 8. Assignment.
Assignment allows the user to link data variables to graphic or dynamic variables of the display. An early version of this kind of assignment used to explore multidimensional data is Bertin’s (1981) concept of matrix manipulation in which users can iteratively reassign variables to columns and rows of a matrix in an effort to find relationships among variables. In our prototype, users can similarly assign variables to PCP axes. By doing so, the user can put any two variables next to one another, or place multiple variables in a series to search for a particular signature. They can even repeat variables in order to group a key variable with multiple potentially related ones. Similarly, the 3D scatterplot allows a user to compare any three variables by assigning them to axes of the cube. In addition to assigning variables to location, users can assign variables to color (PCP) or to glyph size and color (3D scatterplot). When assignment is used to match data variables to particular display positions (e.g., the axes of the scatterplots or of the PCPs), the action affects only the representation to which it is applied. When assignment is used to match data variables to other visual variables (e.g., line or glyph color), the action is reflected in all appropriate linked representation forms. User assignment of "date" to the second PCP axis is illustrated in the top figure, with the resulting change in the PCP is shown at the bottom. Note the menu choice above the axis assignment menu. Using this control, the user can assign the variable chosen to any of six attributes for the 3D scatterplot linked to the PCP – the three axes, the glyph color or size, or to sequencing (see Figure 13).
Figure 9. Brushing.
Brushing is a technique that allows the user to directly highlight any set of entities in a representation that seem related to one another, appear to be outliers, or are otherwise of particular interest. While brushing can be used in a single representation, it is particularly powerful when applied to one in a set of linked representations. For instance, by highlighting all or part of a cluster in a 3D scatterplot, a user can quickly determine where those particular records fall in geographic and/or temporal space (in the geoview) and can learn whether they have similar signatures in the PCP. Here, the PCP has been "brushed" to highlight a group of adjacent days. [see web supplement for a dynamic illustration]
Figure 10. Focusing.
Focusing is a technique that allows a user to interactively "focus" in on a particular range of values for a numerical variable (Buja, 1991). Rather than directly pointing at or outlining display entities to be highlighted (as in brushing), focusing tools allow users to manipulate the number and definition of categories displayed. The simplest focusing method allows a user to manipulate a threshold or break point of a two class display depicted by a binary color scheme -- with all data entities having values above threshold represented by one color or symbol and all values below the threshold represented by a contrasting color or symbol (see MacEachren, et al., 1993 for application to visualization of data reliability). Here we allow the user to focus on any data subset by adjusting a maximum and a minimum threshold – with data entities having values between these extremes being highlighted by a hue difference. Building on Haug, et. al. (1997), focusing tools in GKConstruct also allow users to manipulate any break point between adjacent categories on multi-class maps. Focusing, like brushing, tends to be particularly effective when used with linked representations. In the PCP, focusing allows a user to adjust bounds on a numerical scale, highlighting those cases having values within the specified range. Values within the range are distinguished by color. The color assigned to each case in this way is applied to that case throughout the display – along the trace of that case throughout the PCP and in that case’s glyphs in both the 3D scatterplot and the geoview. Here we show just the PCP, with all cases within a specified surface humidity range highlighted in yellow and other cases turned to the background color.
Figure 11. Colormap manipulation.
Colormap manipulation involves replacing one color scheme (to which data are mapped) with an alternative color scheme, or replacing the colors assigned to specific data classes with alternative colors. By swapping schemes, the user can emphasize different patterns in the data (Brewer, 1998). For instance, a divergent color scheme emphasizes deviation from a mean in two directions, while a sequential color scheme emphasizes a singular trend. Changing the colors of a particular class can emphasize or de-emphasize that class. We have taken the approach of embedding several useful color schemes in GKConstruct, while at the same time, allowing the user to customize those color schemes. Colormap manipulation is applied to all representations simultaneously through linking. Here, the user can select among various color maps to be applied in all views. In this case, the user has opted for a 3-step colormap with purple representing high, gray representing medium, and green representing low
Click here to see a VRML example.
Figure 12. Viewpoint manipulation.
Viewpoint manipulation is implemented through tools such as panning and zooming in both two and three dimensional representations, or rotation in a 3D representation. Viewpoint manipulation allows a user to dynamically explore different aspects of a representation. In a 3D representation, user controlled rotation can provide depth cues that allow the user to perceive relationships that are not otherwise apparent (Kaiser and Profitt, 1992). Viewpoint manipulation, as implemented here, does not effect change in linked representations.
The success of this research was dependent upon our ability to create a function link between TCL and IBM's Data Explorer. This sidebar provides a detailed explination this work. Included are portions of code and conceptual diagrams. Don't forget to return to this page. More...
Figure 13. Sequencing.
Sequencing, or use of the dynamic variable of order to display one time slice after another, has an obvious use in facilitating a search for trends over time. Applied to non-temporal orders (e.g., ordering views along geographic or attribute dimensions of data), sequencing is a powerful tool for understanding the interrelations among variables (Slocum, et al, 1990). As implemented in GKConstruct, sequencing allows the user to choose any variable dimension from the data set, sort on that variable, partition the data into equal interval bins along that dimension, then play a sequence of images depicting each bin. Sequencing. when applied, is reflected in other linked representations as well.
Figure 14. Typical interpretation/evaluation display that includes the three representation forms.
3.3.2 Categories extraction and classification with AutoClass
AutoClass is a public domain software package that provides unsupervised classification based on Bayesian statistics. AutoClass III, the most recently released version, combines real and discrete data, allows data to be missing, and automatically extracts the number of classes from a target data set.
In this section, we illustrate the potential of our integrated geovisualization-KDD approach to knowledge construction with an application of methods to a sample gridded regional climate data set for northern Mexico and southern U.S. The target audience for our demonstration consists of environmental scientists (particularly climatologists). Sample data examined represent climate phenomena that are continuous in both space and time. At a conceptual level, the analysis goal is to find both individual features and classes of feature in spatiotemporal climate data sets. A secondary conceptual level goal is to explicate the data mining algorithm applied to the data (so that we can make more informed decisions about setting model parameters and so that scientists can better interpret the meaning of entity classes derived). At an operational level, these goals are instantiated as a series of operations or data processing tasks. Emphasis is on row two, cells one and two, of the meta-operations matrix introduced above (figure 3) &endash; on the application of feature identification and comparison methods to the KDD operation of categories extraction and classification.
4.1 Data Selection and Preprocessing
Data sets for this case study consist of daily winter climate data from 1985 to 1993 (data from Cavazos, 1998). These data were selected because they are nearly exempt of noisy, incomplete, or contradictory information (due to extensive preprocessing using recognized climate data analysis methods). Hence they serve as a good test data set for demonstrating our initial steps toward the integration of geovisualization and KDD methods. The data were output from the Goddard Space Flight Center (GSFC) 4-D assimilation scheme based on both observational data and a Global Change Model (GCM) to produce daily gridded data at a resolution of 2 degrees latitude by 2.5 degrees longitude, covering an area bounded by latitudes 20-43 N, longitudes 110-90W. The winter months from November through March are the focus because most of the surface cold fronts that affect the study area occur during this season.
4.2 Data Transformation
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4.4 Interpretation/evaluation
An initial AutoClass run with our sample data results in a classification with over 40 populated classes and a maximum global influence value below 5.0, thus the classification is a potentially reliable one. The integrated set of representation and interaction forms described in section 3 are applied here to output from each AutoClass run on our data. When measures of class strength are examined, we find that those classes with highest strength are ones associated with particular years, thus classes that exhibit temporal dominance (the when component is the most critical for defining the category). Typical of these classes are ones with winters revealing el Nino (very wet/raining winters) and la Nina (very dry winters) years. On the other hand, classes with intermediate strength are characterized by cyclic patterns, with spatial dominance. For example, classes in this group show the location of very wet and very dry areas in the region of the case study over the eight years. Finally, classes with a very low strength are (not unexpectedly) difficult to interpret.
Figure 15. Geovisualization tool integration.
Links between Data Explorer and Tcl/Tk are established using the DXLink facility along with custom C modules that establish new Tcl/Tk commands. The sequence of events initiated as the system is launched is listed along the left side of the diagram. Although each interaction form is described above separately, the tools are expected to be most effective when used together (figure 14). For example, a user might sequence through a variable, see an interesting pattern, and stop the sequence, sort the order in which the variables are presented in the PCP, manipulate the colormap, and then restart the sequencer in order to see if a pattern is more apparent. In essence, these techniques all share the similar goal of facilitating pattern noticing. Whether this pattern noticing is used for feature ID, comparison, or interpretation or for understanding the data mining process, a key goal is to find relationships among features in attribute, temporal, of geographic space.
Figure 16. Here, we use focusing to highlight only cases occurring in 1987. Focusing on the PCP observations from 1987 creates a particularly informative perspective on these data. The eight sample observations identified are members of three classes: class 0, class 1, and class 24. By brushing (in the PCP) the lines representing each class, the query is narrowed and the prototypical "signatures" of each class represented in 1987 can be traced. As shown in this figure, the four observations in 1987 that were most likely to be in class 24 (highlighted in green) have nearly identical signatures. Each is not only a zero precipitation event but also is characterized by moderate to low surface humidity, low midlevel humidity, and relatively low sealevel pressure.
Figure 17. Here, cases in class 0 that occur in 1987 are highlighted (in green), with a different class signature apparent. In contrast to Figure 16, these do not have a "signature" that tracks as consistently through all climate variables: the lines diverge at the midlevel humidity axis, but reconverge to one particular position on the sea level pressure axis (relatively high).
Figure 18. Focusing is moved to 1992 in order to compare class 0 cases from that year (again, highlighted in green) to those in 1987 (see figure 17). Focusing on 1992 shows the same "trace" for class 0: no precipitation, low surface humidity, high sea level pressure, and a wide variation of 700 mb humidity values. Class 0, then, is clearly more dependent upon sea level pressure than on midlevel humidity.
Figure 19. In this figure, the variable to which focusing is applied has been moved from "year" to "class#" to examine the characteristics of class 0 across all years (i.e., the full set of prototype class members). As was true for the specific years described above, observations in class 0 overall are characterized by zero precipitation, low surface humidity, high sea level pressure, and a wide range of midlevel humidity. This relationship can be confirmed using the 3D scatterplot, which shows the clustering of large glyphs (size scaled to sea level pressure) along the xaxis, which is, in this case, 700 mb humidity. A more dramatic result of the visualization of prototypical class 0 cases is the spatiality of this class, as displayed in the geoview; events in this class happen exclusively over land.
Figure 20. In this figure we focus on another class, class 24, and find it characterized by zero precipitation, relatively high (and more varied) surface humidity, and moderately low midlevel humidity and sea level pressure. This clustering is shown not only in the traces of the PCP but also in the 3D scatterplot. The spatiality of this class also shows a dramatic inverse of that of class 0: all of the instances of this class occur over the Gulf of Mexico. A climatologist would expect this result: high surface humidity and lower midlevel humidity is more common over open water than over land.
Figure 21. In this figure, exploration of Class 26 finds cases that have generally high surface humidity, zero precipitation, and a distinct temporal pattern (with two event clusters in time). The general pattern in the full data set is that the classes with high strength are distinguished more by temporal than spatial characteristics, particularly by patterns with similar events that are proximal in time (e.g., in a particular season of a particular year). The 3D geoview can incorporate time on the z-axis, and can be rotated so that these temporal associations are emphasized. Class 26, is a good example of a class that is characterized by temporal rather than spatial factors. In terms of climate variables, cases in class 26 have moderate surface humidity, low precipitation, and high midlevel humidity events. Events with this combination of characteristics are clustered temporally (several locations with similar attributes on the same day) as opposed to spatially (several days with similar attributes in the same region). This clustering is apparent if the geoview is rotated: events belonging to class 26 occur only on certain days of the data set.
Figure 22. Here, color hue is used to distinguish the 7 classes being explored. The distinct space, time, and attribute characteristics of the classes are clearly visible. The results of data mining can thus be visualized and interpreted effectively using our combined representation and interaction forms. As a confirmation of the above analysis, each of the seven classes in the sample data set (classes 0, 1, 3, 7, 12, 24, and 26) can be assigned a different color (using the spectral scheme, seven class choice in the PCP Classify menu). The clustering of these colors in the 3D scatterplot dramatically illustrates that the observations are classified according to the values of a combination of climate variables, in tandem with spatial and temporal characteristics.
The objectives of this paper have been to make the case for integration of geovisualization and KDD methods, to propose a conceptual framework for that integration emphasizing a merger of meta operations fundamental to each set of methods, and to describe an initial prototype knowledge construction environment and its application to a test data set. At this stage in our long term strategy for geovisualization-KDD integration, we have applied our knowledge construction methods to an isolated data set stored as flat files. We plan a subsequent coupling of the geovisualization-KDD methods with a temporal GIS. The goal here is to make the early stages in the knowledge construction process more flexible and facilitate interative exploration of user selected subsets of data (choice of which is prompted by prior analysis steps). In developing and implementing geovisualization methods, we have focused on methods that are particularly useful in the later stages of the KDD process (after data have been selected, preprocessed, and transformed into a format suited to applications of a particular data mining tool). We expect, however, that many of the geovisualization methods developed will also be useful for applications at the earlier KDD stages.
This research was supported by the U.S. Environmental Protection Agency (EPA), under Grant R825195-01-0 (Donna J. Peuquet and Alan M. MacEachren, Co-PIs). Support has also been provided by the Penn State Center for Academic Computing where MacEachren is a Faculty Fellow. We thank Tereza Cavazos for providing the Mexico data used in our demonstration and for help in interpretation of the data mining output as well as Mark Harrower for his work on both print graphics and on design and production of the web supplement to this paper.
Copyright © 1998 Pennsylvania State University