The GAV Toolkit for Multiple Linked Views - IEEE Computer Society

The GAV Toolkit for Multiple Linked Views MikaelJern,SaraJohansson,JimmyJohansson,JohanFranzén VITA–VisualizationTechnologyandApplications,LinkopingUniversity,Sweden mikael.jern, sara.johansson, jimmy.johansson, [email protected]

Abstract Implementing InfoVis multivariate data tools, timelinked coordinated views and visual dynamic queries with conditioning from scratch is not a simple programming task. Our research objective is to develop a generic GeoAnalytics visualization (GAV) component toolkit, based on the principles behind visual analytics (VA), for dynamically exploring time-varying, geographically referenced and multivariate attributes simultaneously. GAV includes components based on a synergy of technologies from information visualization, geovisualization and scientific visualization. Our research concentrates on improving visual user interfaces (VUI) methods and trying to extend existing visual representation techniques. The effectiveness of our proposed component toolkit and framework is demonstrated in two customized applications GeoWizard analysing multivariate energy usage data for Swedish municipalities and MD-Explorer exploring multivariate data using novel interactive ternary diagrams. We use parallel coordinates with embedded visual inquiry methods that serves as a visual control panel for dynamically linked and coordinated views. Finally, discoveries made during the visual exploration process can be captured and organized in a format for later recall and communication to others.

1. Introduction The fast growing quantity of spatial-temporal and multivariate data calls for creative integrated information and geovisualization tools. While researchers have made substantial advances over the past decade, many challenges remain particularly for working with temporal and multivariate attributes simultaneouslyandhaveaspatialdimension. WeintroduceourgenericGeoAnalyticsvisualization (GAV) component toolkit. GAV is based on the

principles behind visual analytics (VA) [9] but with more focus on the explorative data analysis (EDA) aspect. GAV provides a broad collection of dynamic visual interaction components familiar from the InfoVis research domain and hopefully pertinent and potentially useful. Our research concentrates on improving visual user interfaces (VUI) methods. Users interact directly with the on-screen graphics without dependence on more traditional and space-consuming GUI controls and therefore valuable screen area is reserved for VA representations. Multivariate data are not best analysed through the useofasingletypeofgraphandview.Inordertodetect complex patterns within multivariate data it is more necessarytoviewitthroughanumberofdifferentvisual representation methods, each of which is best suited to highlight different patterns and features. GAV provides a framework for building tailor-made multiple-linked andcoordinatedviewsapplications. GAV tools utilize the advantages of human perceptionenablingtheusertotakeamoreactiverolein the exploration process; a sense of immediacy interaction is achieved. For example, in our GeoWizard application this is demonstrated through its ability to coordinate time-link views. Parallel coordinates (PC), time and trend graphs, choropleth maps are time-linked so that all of the views are synchronized to the same point in time. Animating time-linked views simultaneouslythroughtimeisalsoacommonfeaturein many VA tasks and enables users to dynamically compare spatio-temporal data [8]. In our second demonstrator MD-Explorer, we introduce the ternary diagramtotheInfoViscommunity. Whenanalystsexploredata,theyneedtocaptureand synthesize their results in ways that will be easy to return to at later time in a format that is open to collaboration with others. The GAV “snapshot” feature allows the analysts to explain how they arrived at a conclusion.

Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007) 0-7695-2903-8/07 $25.00 © 2007

Figure 1: GeoWizard–searchforspace-time-att

Our ongoing applied research efforts presented in this paperfocusonthefollowingattributes: • Synergyofvisualrepresentationandvisualinterfaces; • Methodsfordiscoveringconditionedtrendsin temporalbehaviours; • Dynamicallylinked,coordinatedandresizableviews fromdifferentperspectives; • Methodstoprovideinteractiveperformancethatcan supportvisualanalyticreasoning; • Designbasedoncognitiveandperceptualprinciples; • Techniquestointeractvisuallywithdatainawaythat supportsclearunderstandingoftheproblem,finding reasonablewaystosolveit,seeingprosandconsof eachpossiblesolution,andunderstandingthetradeoffs betweenvariousoptions; • Integratepresentationanddisseminationintothe GeoAnalyticsdiscoveryprocessthroughsnapshots;

ributepatterns–5highlightedregions.

We begin the paper with a brief section about related work that has influenced our research, followed by a conceptual and technical description of the overall GAV system. Next we will discuss the visualization and interaction techniques that are implemented in two demonstrators GeoWizard and MD-Explorer. We finish by discussing our findings and present ideas for future work.

2. Related work Implementing InfoVis multivariate data tools, timelinkedcoordinatedviewsand visual dynamicqueries with conditioning from scratch is not a simple programming task. Toolkits such as InfoVis Toolkit [19] and Visualization ToolKit [12] can ease this burden. These toolkits are freely available. Some commercial InfoVis systems, such as SpotFire [18] provide limited tools for


customized and scalable applications. We propose a new GAV component toolkit that integrates with Microsoft .NETandVisualStudiodevelopmenttools. Visualization of spatio-temporal data has been the subject of several recent research papers [6]. The results include both conceptual models and extensive specialized applications. GeoVISTA Studio [13] is an open source Java-based visual programming environment and is commonly used for developing geovisualization applications. Another general system is CommonGIS [10], tools that support exploratory data analysis. VISSTAMP [4] tools leverage visual and computational methods to search for space-time and multivariate patterns. Andrienko and Andrienko have described interesting approaches in several papers [5], [6] including the impact of data and task characteristics. Carr et al. [24] describe a multivariate approach based on conditioned choropleth maps.However many systemslacksupport for analysing simultaneously multiple attributes data and spatio-temporal behaviour. In our research we propose dynamic GeoAnalytics methods to also include support for visual inquiries [7] based on conditioned space-time andmultivariatepatterns.

specific task in the overall VA process, into customized andtask-centricapplicationssuchasGeoWizardandMDExplorer. The layered component architecture enables broad applicability, customization, scalability, reusability ofcomponentsandshortensthedevelopmenttime. Interoperability is an idea that is invaluable to the development of the GAV toolkit since different developers, working almost entirely independently, can contribute VA components to the GAV component repository. Example of available components include scatter plot, PC, shaded graphs, table lens, time and trend series graphs, choropleth map, ternary diagram, 2D, 3D and 4D contour maps, colour legend and several data model,filteranddatatransformationcomponents.

3. Conceptual data and component methodology GAV is a development toolkit for tailor-made, multiple-linked views and task-oriented applications based on layered component thinking. We employ our own component-based class libraries and data model. The GAV components are developed in C# based on Microsoft’s low-level DirectX graphics library and fulfil many VA requirements. We have set the following generic requirements for our tool design and implementationofaGeoAnalytics’framework: 1. Layeredcomponentapproachforcustomization, scalabilityandreusability; 2. Shortendevelopmenttimebyutilisingalready developedandassessedcomponents; 3. Mechanismforintegratingexternalusercomponents; 4. A3Ddatamodelforspatio-temporalandmultivariate attributedataexploration; 5. Hardwarerenderingtechniquesforinteractive performance; 6. Visualspace-timeandmultivariatequeryingtools; 7. Component-embeddedcolouringinteractionand filtering;

3.1 Layered component architecture The GAV toolkit enables rapid development of customizedapplicationsbycombininglow-levelbasicand functional components, each one performing a small

Figure 2: The GAV toolkit architecture. The data flow pipeline is divided into the following three layers: the data access layer, the data transform/filter layerand the data consumer layer. This separation into layers simplifies and improves the pipeline modularitywhichfacilitatesthedevelopmentprocess.

3.2 3D Data Representation An array-based data cube model is used to store multivariate data or time-varying multivariate data sets. Two dimensions of the cube can be used to store a multivariate data set where the axes represent object and their attributes. In the three dimensional case the data model is extended with one more axes which can, for example, represent time. An application specific example of the three dimensional case is seen in figure 6 where an object is a Swedish municipality containing a number of energy consumption measurements over time. A value in thedatamodelisfoundbyathreedimensionalindex.


Figure 3: Layeredcomponentarchitecture

3.3 Data Flow

3.4 Resizable views

The GAV toolkit data flow architecture is divided into threelayers;thedataaccesslayer,thedatatransform/filter layerandthedataconsumerlayer,seefigure2.

Multivariate data containing both spatial and temporal information are difficult to analyze through the use of a single type of graph. In order to detect complex patterns within the data it is more convenient to view it through a numberofdifferentvisualrepresentationmethods,eachof which is best suited to highlight different patterns and features. It is also necessary to achieve balance between non-abstract and abstract visual representation. Some problems are best solved with one or the other, however many require the services of both representations. A common approach is to display each view in a separate window and allow the user to arbitrary arrange the windows. In this context a view is referred to as a clearly defined region on the screen. The notion of arranging viewsisgenericandexistsinmanyvisualizationsystems.

1. 2.

3.

The data access layer provides GAV with data from underlying data sources. The toolkit supports readingmanydifferentdatasources; The data transform/filter layer of the toolkit filters and/or transforms the data provided from the data access layer, examples are: down sampling, clusteringandprojections; The data consumer layer includes the GAV functionalcomponents;


The GAV toolkit employs Microsoft’s development tool, Visual Studio’s .NET hierarchical layout management to interactively design a GUI layout with dynamic embedded resizable views in a single coherent window.Byusingthislayoutmanagementenvironment,a visual interface can be divided into a number of views separated by interactive splitters, allowing the user to scaletheindividualviews. GAV functional components are attached to views through a view organizer class (figure 4). This class handles when and where functional component are rendered. A list of connections between views and GAV functional components is stored internally. Whenever a component is to be rendered, the organizer finds the view assigned to that component and tells the graphics adapter to use that view as rendering target. A re-rendering of a functionalcomponentcanbetriggeredinmultipleways. • The paint event is received from a view. This occurs, forexample,iftheviewisresized. • The programmer has updated some properties on a componentandrequestsanupdate. • The component itself requests an update. This happens, for example, if the user interacts with a component by means of filtering, rotation or translation.

The standard filtering method in the GAV toolkit is index/row based. The information whether an index/row is visible or not is stored in a separate class and is not a part of the data flow pipeline. This approach enables filtering in different functional components showing different transformed versions of the same data. An exampleofthisapproachcouldbeaPCandascatterplot, visualizing the same data, where the data for the scatter plot has been transformed using a logarithmic function andthePCshowsthenon-transformedversionofthedata. GAV provides the user with high interactive performance when exploring spatial-time multivariate data. If an application does not scale well with these dimensionalities, its performance in terms of execution time can degrade. Many computer games and visualization environments use a render loop, which means that the views are updated all the time. A GAV component is only rendered when an update is explicitly required.TheGAVtoolkittakesadvantageoftheGPUon the graphics adapter to enable high rendering performance. A sense of analytical reasoning and dynamic interaction is, for example, achieved in GeoWizard through its ability to time-link views. Parallel coordinates, time and trend graphs, choropleth maps are time-linked so that all of the views are synchronized to the same point in time. Animatingtime-linkedviewssimultaneouslythroughtime is also a common feature in many VA tasks and enables userstodynamicallycomparespatio-temporaldata[8].

4. GeoWizard – a GeoAnalytics application

Figure 4: Afunctionalcomponentisattachedtoaviewusing theVUIorganizer.

3.5 Linked views Linkedviewsenablerapidanddynamiccomparisonof different visualizations of data. Coordination in GAV is implemented using a data linking method where the visualization components can use the same data model andcolouringscheme,andwhereanydynamicfilteringor picking made in one of the linked visualization components easily can be propagated to all other components.

Figure 5: GeoWizardsupportsvisualanalyticalreasoning throughthissense-makingloop


Complex official statistical data that contain geographic locations, time series and multivariate attributes are made available free of charge by National Statistics Institutes such as Statistics Sweden, SCB [14]. This data can be used for the purpose of making policy decisions, and to facilitate the appreciation of economic, social, demographic, environmental and other matters of interest to the governments, government departments, localauthorities,businesses,andtothegeneralpublic.For our GAV toolkit demonstrator, we select the environmental domain and the supply and use of energy data for controllingtheemissionofcarbondioxideamong 290 Swedish municipality regions during 1990-2004. Interoperability with SCB’s databases is achieved through a request for data in a SCB dynamic HTML form connected to the SCB server. Selected data is returned as anExcelfilethatisimportedintoGeoWizardwithaGAV component(figure5). In our previous publications, we first demonstrated an early prototype [1],[16] built on a commercial toolkit, which limited our preference for programming extensions nd and restricted distribution of the result. In our 2 attempt [2], we designed and implemented a complete application from scratch requiring a massive programming effort. In this 3 rd attempt, we introduce the GAV component toolkit and the data cube model as the programming platform for our application development. We can now implement tailor-made and verified applications, such as GeoWizard in1-2weeksinsteadofmonths. We use the data cube model optimized for efficiency and scalability in handling our spatio-temporal, multivariate attribute data sets. This conceptual data model can be seen as a cube filled with discrete values. The cube has tree axes: spatial, time and attributes. In GeoWizard, a region is a Swedish municipality and the attributes are various energy consumption measurements (transport, housing, etc). The time is the data acquisition period (figure 6). The general method for finding a value in the cube is by its position (space; time; attribute;). To obtain the housing energy consumption for Stockholm in 2004,findthevalueinthecubeatthatposition. Space-time-attribute awareness means that the GAV data cube can be analysed and visualized across all three dimensionssimultaneously.GeoWizardperformsthistask by integrating five functional components: parallel coordinates (PC), time graph (TG), time trend graph (TTG)andtwochoropleth maps(figure1).PCservesasa visual control panel “browser” for multivariate data and TGfortimeintheintegratedsystem.

Figure 6: GAVdatacubemodel

The PC, TG and TTG use the same PC basic componentbutwithdifferentassignedproperties.Theyall embed dynamic range sliders for defining visual inquiries such as thresholds and conditioning that constrains the dependent attribute data for selected time periods displayed to those meeting specified parameters on all attributes and time (figure 5). These events are processed with minimal cognitive overhead and virtually instantaneous response time. This can be an effective methodtoisolatepatternsthatsharearangeofvaluesofa particularattributeandtimestep. The GeoWizard’s VUI (figure1) is divided into five linked views separated by interactive splitters, allowing the user to adopt the layout by changing the size of individual views. Direct manipulation interaction allows maximum screen area to be reserved for the visualization while most of the traditional GUI controls are hidden and can be pulled out when needed in context-sensitive pulldownmenus. User-controlled events of interest can be expressed simultaneously in all three graphs involving multivariate attribute values for time varying data. Events can be defined for single time steps or in the complete temporal domain.Thisabilitytoexploreeasilythedataishelpfulin identifying specific patterns of interest, as well as in gainingunderstandingofthedatasetasawhole.


In addition to having a global view of patterns in the data cube, the VUI supports several methods to interactively select “regions” of particular interest including pick individual items, rectangle selection and selectionbasedondefinedconditionsandconstraints.

4.1 Parallel coordinates (PC) Parallel coordinates (PC) has been used in many multiple view geovisualization environments [13]. In GeoWizard, each municipality is represented by a string passing through the parallel axes. Each axis represents a dependent, single attribute data (one time step) in the data cube. A string forms a visual representation of the characteristics of one municipality. Differences between selected municipalities can be found by visually comparing the strings representing them. The number of attributes that can be visualized is restricted only by the horizontalresolutionofthePCview.VUIfeaturesinclude

range sliders for defining events such as exceeding of a given threshold, interactive axis labels for controlling visualizations and dynamic movable axis. Polylines outsidethegivenconditionaregrayedout.

4.2 Time graph (TG) The behaviour of the selected and constraint attribute data in the PC are represented in a time graph (TG) for a given time period. In figure 8, the attribute “Energy Total kWh/p” is selected in the PC. The corresponding attribute valuesforthetimeperiod1990-2004areshownintheTG (right).TheTGistime-linkedtothePCandupdatingtime in TG will simultaneously change the time step for all attributes in the PC (here 2004). Animating controls (top menu) will dynamically update the PC and linked map and focus users to relevant events. Interesting municipalities can be selected in the map, PC or TG and arehighlightedinalldisplays.

Figure 7: GeoWizard developed with GAV components. Four linked and coordinated views explore the emission of carbon dioxide (CO 2) kg/inhabitants for a selected region and county Stockholm in 2004. Six multivariate CO 2 attributes representing Total,Energy,Household,Industry,ServiceandTransportareexpl oredinthePC[ParallelCoordi nateview].AstringinthePC forms a visual representation of the characteristics of one municipality in the selected county. The user can follow the emissi on d in the map views. The di fferences between selected of CO 2 for each attribute and select which should be displaye municipalities can be found by visually comparing the (here 4) highlighted strings representing them. We compare the total cipalities are highlighted in all emission of CO 2 (left map) with the emission of household CO 2 (right map). Four specific muni views to point out interesting discoveries. Södertälje has the most problems with a high total of CO 2 emissions (fromindustry), while Danderyd - a high income area - has problems with CO 2 emissions from households, Upplands-Väsby shows the highest concentration of transport and finally the largest populated municipality Stockholm shows average emissions. A coordinated tableviewallowstheusertoreadCO 2values.


4.3 Time trend graph (TTG) Dynamic animating changes over time is a significant and important feature but there are more profound challenges.Evenifatrend orasharpchangeisrecorded in a data set, traditional visualizations of the data set’s structuraland dynamicproperties might not featuresuch trends and changes prominently enough to draw users’ attention. Therefore, it was necessary to provide a visualization component with built-in trend detection mechanisms connected to PC, TG and data modelling components. The time trend graph (TTG) represents Value (t) – Value (t - 1) in percentage, where t is the time period for the observation. The viewer can easily notice changes in particular years (TTG) but also observemunicipalitieswithsimilartrends(TG)although therevaluesareofdifferentamplitudes.

4.4 Choropleth maps GeoWizard provides two choropleth map views, where the left map is linked to the PC and visualize coloured attribute and for selected time step (TG) and the right map is linked to the TTG and is coloured according to the trend in % from TTG, taking constraints on any attributes into consideration. Any changes in classificationofthechoropleth map[11]alsochangethe colourofthecorresponding municipalitiesinthePCand TG. Map animation of events over time and geography isprovidedsimultaneouslyforrelevantviewsasthetime sliderismovedforwardandbackwardsintime.

4.5 Conditions and constraints GeoWizard provides visual inquiry methods based conditioned space-time and multivariate events (e.g.: “Find municipalities with a total energy usage in 2004 constrained by high Transport, Service and Housing energy – Search for extreme changes during 19902004”).AttributeEnergyTotalisselected(PCb- ottom). Time step is 2004 (TG - middle). With range sliders attached to each axis (figure 8), the user can interactivelyalterconstraintsonattribute,timeandtrend values. These dynamic queries will immediately reflect the visual contents in all views. Moving the handles at the top and bottom of the axis controls the range of a selected attribute. AND operations are performed by combining several range slides queries and corresponding subsets of the data is filtered out. In figure6,attributeEnergyTotalisselected(PCb- ottom). Time step is 2004 (TG - middle). Visual inquiry conditions are defined for three attributes Transport, Service and Housing – We are only interested in exploring regions with high energy usage for these three attributes. The range sliders are used to constrain the datacubetofulfilthegivenconditions.Aparticulararea withextremechangesintrendishighlighted.

Figure 8: Example of conditioned space-timeattribute inquiresdefinedbyrangesliders.

Figure 9: Themunicipalities that fulfil the given conditions in figure 8 are shown in a List View. The selected area Simrishamnishighlighted.


5. MD-Explorer – interactive Ternary diagram for multivariate data exploration MD-Explorer is an application developed for the exploration and presentation of aggregate data, which is multivariate data that at every combination sums up to 100 percent. The data visualized in MD-Explorer contains not only the variables that the aggregate is made up by, but also non-aggregate variables that vary duetotheproportionsoftheaggregatevariables,aswell as to the impact of uncontrollable variables. The nonaggregate variables presented in the application describe the behaviour of different combinations of aggregate variables as a mean value and a variance. The mean value is a measure of the result of that specific proportionofaggregatevariables,whilethevarianceisa measure of the variation of the result due to the uncontrollable variables. In figure 10 the data visualized in MD-explorer is presented using parallel coordinates (PC).Thedatasetis made up ofsixaggregatevariables, named Var 1 – Var 6, and the two non-aggregate variables,meanvalueandvariance.

Figure 10: Parallel coordinates visualizing the data set of MD-Explorer. The data set is made up of six aggregate variables, named Var 1 to Var 6, and two non-aggregate variables,namedMeanandVariance.

The MD-Explorer application exploits three GAV components; ternary diagram, 2D scatter plots and PC. The ternary diagram is a visual representation that is frequentlyusedindisciplinessuchaschemistry[20]and mineralogy [21] to analyse mixtures of ingredients or mineral compositions, as well as in population studies [22] for visualization of segregation in neighbourhood areas. A ternary diagram is drawn as an equilateral triangle where the corners of the diagram represent 100 percent of the variable positioned at that corner. Any point inside of the triangle represents a composition of the three variables. The interpretation of a ternary diagramis done drawing three lines through the point to be read, each one parallel to one of the triangle sides.

The proportion of that component is given by the distance between the triangle side opposite to that component corner and the line parallel to that side (figure11)[23]. The main limitation of a ternary diagram is that it is only able to visualize aggregates of three variables at a time. Due to this the ternary diagrams of MD-Explorer visualize a subset of the variables in the aggregate data. Positioned at the top and bottom left corners of the ternary diagrams in MD-Explorer is an aggregate variable, while the sum of all others is positioned at the bottomrightcorner. The ternary diagram functional component provides novel interactive features. Through colour legends positioned next to the diagram, connected to the mean andvariancevariables,the user canfilter out data that is of no interest, as well as perform a colour scheme thresholding where the span of a specific colour sequence can be expanded or reduced. Picking and highlighting of specific aggregate units is also implemented. To further simplify the interpretation of the display, the ternary diagram offers brushing in form ofguidelinesaswellastextinformation.Whenhovering over the diagram with the mouse the guidelines link out from the position of the mouse pointer to the specific proportions of the aggregate variables at the triangle sides. The text information shows continually updated information on the proportions of the aggregate units thatishoveredover. The ternary diagram is able to visualize data sets with regularly distributed aggregate data as well as with scattered data. When visualizing scattered data two possibilities is available. The first one is a nearest neighbour approach where the diagram is divided into areas based on the nearest aggregate unit in the data set. The second one is based on Shepard's method for interpolation of scattered data [26] and interpolates between the values of the four nearest aggregate units. Using this approach a smooth transition between the aggregate units is offered. In addition to these two possibilities a third one is available when visualizing regularly distributed data. In this third approach the diagram is divided into areas spanned by the aggregate unitsofthedatasetandcolouredaccordingtothevalues of those. Figure 12 shows examples of the three visualization possibilities. Using any of these approaches all of the triangular area of the diagram is used, facilitating the discovery of trends in the result. However, since all of the diagram area is coloured, although only some of the proportions of aggregate variables exists in the data set, there is a risk that the exactness of the data is lost. To prevent this and to preservetheunderstandingandaccuracyofthedataseta possibility of highlighting all points that represent an aggregateunitinthedatasetisavailable.


For screen area saving purposes both the mean and variance values of the aggregate units are shown in the same ternary diagram, using bivariate colouring. Hue is used to show mean values and the variance is represented by opacity. The colours are chosen on the basis of Trumbo’s principles ofbivariate colouring [25]. However, since there might be difficulties to assess both colour spans at once, the opacity can be varied by the user, thus adding an extra aid to help discovering high variance and to solely analyse the mean value. To further enhance the flexibility of the diagram the variance can also be visualized on its own, coloured in shadesofgrey,spanningfromwhitetoblack(figure13). The MD-Explorer visual interface is divided into a number of linked views separated by interactive splitters, allowing the user to scale the views of the layout. The three top views contain the ternary diagram component,visualizingdifferentsubsetsofthedata.The bottom left view is a scatter plot, visualizing the spread in mean and variance of the picked aggregates of the ternary diagrams,andthebottomrightviewcontainsthe PC component, visualizing all aggregate variables, as wellas meanandvariance.Coordinationisimplemented using a data linking method where the visualization components use the same data model and colouring scheme, and where any dynamic filtering or picking made in one of the linked visualization components propagates to all the others. The visual interface and coordination of the InfoVis components are shown in figure14.

Figure 11: A ternary diagram plotting variables A, B and C. The proportion of a point is read using straight lines that are parallel to the triangle sides. The red point contains 50 percent of variable A, 30 percent of variable B and 20 percent of variableC

uninteresting data and through expansion and reduction of the areas of the colour spans, thereby making the discovering of trends noticeably easier. On its own the ternary diagram can effectively display aggregate data madeupofthreevariables.Linkingittootherviewsthat are able to visualize more than three variables at the same time enhances the exploration of the data, overcoming one of the major limitations of the ternary diagram. Hence, the ternary diagrams make it easy to find trends in subsets of the data, and linking them to other views, such as PC, provides a possibility of pursuing the exploration of the results of all variables. Becauseoftheinteractionpossibilitiesandthelinkingto other views, aggregate units that seems to be interesting can be picked in the ternary diagram, the distribution of those, due to mean and variance, is then shown in the scatter plot and all lines corresponding to the aggregate proportionsinthePCishighlighted.

Figure 12: Ternary diagrams showing the three different visualization possibilities, the black points marking the position of the original data item s. The left diagram is divided into areas spanned by the aggregate units of a regularly distributed data set. The centre diagram is divided into areas using a nearest neighbour approach and the right diagram visualizes the same data set as the centre diagram using interpolation between the values of the four nearest aggregate units.

Figure 13: The different colouring possibilities of a ternary diagram visualizing two variables with colour. In the left diagram only the mean value, coloured by hue, is visible, whereas both mean and variance is shown at the same time in the centre diagram. In the right diagram only the variance valuesareshown.

The interaction possibilities in the ternary diagram anditscoordinationwithothervisualizationcomponents substantiallyincreasetheusefulnessofthisdiagram.The exploration is highly facilitated by filtering out


6. Tool for reporting using snapshots In VA practice [15], [9], tools are generally entirely separate from presentation and reporting tools. Results from using VA can be visual impressions, or mental images, which are hard to verbalize or express in any other form without referring to the discoveries from which they originate. The difficulty of recording and reporting the findings is a serious obstacle to wide recognitionanduseofVAtools.Thevisual presentation of analytic results needs to be clear and concise, and it must take place as soon as possible after the analyst reaches a conclusion. VA must therefore equip the analyst with tools that easily reveal what is going on already during the course of the analysis and be able to share with colleagues, visualizations and associated visual analytical reasoning that led to the resulting conclusion. GAV proposes a method of capturing, recording, commenting and organizing the findings of a visual analytical reasoning in a format that can be unwrapped by other users. We believe that our snapshots can help the analyst to highlight data views of particular interest and guide others to important discoveries. Colleagues canusethesedescriptivesnapshotstoquicklylocatekey information in the system by simply selecting the snapshot view they need. Consider, for example, figure 5 as a discovery and snapshot, where the analyst has selected two energy attributes to be mapped, time step, view properties, a region-of-interest and finally highlighted the discovery representing the behaviour of four municipalities. A Snapshot Manager remembers and records key system information such as space-timeattribute data cube; particular attribute filter constraints; selected views, regions, time step, and attributes; viewing matrix, colour scale, etc. Other users can later open GeoWizard and load the Snapshot Manager and returntoanysnapshotstage.

7. Conclusions This paper introduces a generic GAV component toolkit [17] that provides a broad collection of InfoViz and GeoViz components. GAV allows you to program structured GeoAnalytics applications without worrying so much about the low level details. GAV includes efficient interaction methods for event handling, brushing, zooming, drag-and-drop and more. Normally, you would have to write all of this code from scratch. Additionally, the GAV framework provides support for building dynamically linked, coordinated and resizable views where data also can be analysed from different perspectives. The advantages of the GAV component approachcanbesummarized:

• • • • • • •

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GenericInfoVizandGeoVizcomponentsthatcan supporttailor-madeandtask-orientedapplication design; Reuseofprovenfunctionalcomponentsinnew applications; Componentlinkingiseasy; Largenumberofpropertiesgivedevelopers flexibility; TakeadvantageoftheGPUonthegraphicsadapter toenablehigherrenderingperformance; Appropriateformultiple-linkedviewsapplications; Frameworkforthecreationofbothuser componentsandimprovedversionsofexisting componentssothatideascanbetriedoutrapidlyin afullyfunctionalusefulenvironment; Allowsdifferentmethodsforvisualizingdatatobe comparedwithinacommonframework;

The customized GeoWizard application [17] demonstrates time-linked views comprising of coordinated PC, time and trend graphs that can detect and visualize multivariate spatio-temporal geographical clusters during a given time period. Visual inquires with virtually instantaneous response time, allows the analyst to dynamically detect and locate trends with specific features such as continuous decrease or increase for a time period. Our second demonstrator comprises a ternary diagram component that supports novel interactive features and could turn into an innovative multivariatedatatoolforexploringaggregatedata,when linkedandcoordinatedwith,forexample,aPCandmap. Our next step includes a comprehensive user task analysis [15]. Can our customary GAV applications be sufficiently powerful and flexible for exploration among domain specialists? Can simplicity and visual appeal tools help unskilled users? Can snapshots be used to communicateandreportGeoAnalyticsdiscovery? Finally, we are going to embed GAV components and our snapshot technology into communication tools, such as Microsoft Office or Adobe PDF creating “interactiveelectronicVAdocuments”. Acknowledgements This work has been funded by Norrkoping Foundation for Research and partly funded by the Swedish Foundation for Strategic Research, grant A3 02:116.


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[12] W. Schroeder, K. Martin and B. Lorensen The Visualization Toolkit: An Obje ct Oriented Approach to 3D Graphics.Kitware,Inc.,2003. [13]Geovistastudio. http://www.geovistastudio.psu.edu. [14]SwedenStatistics. http://www.scb.se [15] G. Andrienko et al, Reacti ons to geovisualization: an experience from a European project, Int. Journal of GeographicalInformationScience,2006. [16] http://www.openviz.com/ [17] http://vita.itn.liu.se/ [18] C. Ahlberg and E. Wistrand, IVEE: An Information Visualization & Exploration Environment. Proceedings of the IEEE Symposium on Information Visualization '95, (1995),IEEEPress,66-73. [19]Jean-DanielFekete, The InfoVis Toolkit,inProceedingsof the 10th IEEE Symposium on Information Visualization (InfoVis'04),IEEEPress,2004,pp.167-174. [20] R. Takahashi, H. Kubota, M. Murakami, Y. Yamamoto, Y. Matsumoto, and H. Koinum a. Design of combinatorial shadow masks for complete ternary-phase diagramming of solid state materials. Journal of Combinatorial Chemistry, Vol.6,No.1,pp.50–53,2004 [21] P.A. Sabine and R.J. Howarth. The role of ternary projectionsincolourdisplaysforgeochemicalmapsandin economic mineralogy and petrology. Journal of Geochemical Exploration, Vol. 63, No. 2, pp.123–144, 1998. [22] B. Plewe and S. Bagchi-Sen. The use of weighted ternary histograms for the visualization of segregation. The Professional Geographer, Vol. 53, No. 3, pp. 347–360, 2001 [23] W.D. Bancroft. A triangular diagram. The Journal of Physical Chemistry,Vol.1,No.7,pp.403–410,1897. [24] D. Carr, D.White, and A.M. MacEachren, Conditioned choropleth maps and hypothesis generation. In Annals of the Assoc of Am. Geographers, vol 95, no. 1, pp.3253,2005.[6]Muller,W.&Schumann,H.2003 [25]B. Trumbo. A theory for coloring bivariate statistical maps. The American Statistician, Vol. 35, No. 4, pp. 220– 226,1981. [26]Donald Shepard, A two-dimensional interpolation function for irregularly-spaced data,inProceedingsofthe196823rd ACMnationalconference,AC MPress,1968,pp.517–524.


Figure 14: The multiple-linked views user interface of the MD-Expl orer. The top three views all use the same ternary diagram(TD)componentandvisualize(1)vari ableoneandvariabletwo,(2)variableoneandvariablethree,(3)variableone and variable four respectively. The bottom two views includes a scatter plot (SP) to the left, with mean and variance on its axes, and to the right parallel coordinate s (PC) visualizing all six variables of the aggregate data as well as the mean and variance. In the left TD three aggregat e units are picked – with proportions 30 pe rcent and 50 percent, 20 percent and 60 percent,and20percentand50percentofvariableoneandtwo respectively.Thelinescorrespond ingtothoseaggregateunits arehighlightedinthePC,andtheirdistributioninmeanandva rianceinadatasetcontainingallaggregatevariablesisshow n intheSP.Aggregateunitswithameanvaluebelow6.2isfilteredoutinallviews.


The GAV Toolkit for Multiple Linked Views - IEEE Computer Society

The GAV Toolkit for Multiple Linked Views - IEEE Computer Society

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