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Research Paper on Data Domain and Visualization

  • What to Do
  • Specify Data Domain to be visualized.
  • Define Tasks to be supported by information visualization tools.
  • Research state-of-art approaches. Identify 3 to 5 distinct “clusters” of tools to highlight & analyze.
  • Explain which features you used to distinguish between the different “clusters.”
  • Select the “best” or “most representative” tool for each “cluster.”
  • Provide Analysis and apply “InfoVis Toolbox” for each selected tool.
  • Think of yourself as “information visualization tools curator” and provide your personal analysis and “point of view” of what matters.
  • Create Presentation
  • Create set of slides to showcase the 3 to 5 distinct approaches you have identified.
    Provide visual examples of the tools presented.
  • Create Report
  • 20 to 25 pages, written as a standard paper.
  • Include an appendix that lists and groups 20 to 30 relevant tools / papers 
    you found into the 3 to 5 “clusters” you identified.
  • Provide an URL or clear reference for the relevant tools / papers you found.

EXAMPLE

Introduction

                The visualization of financial data is a far reaching field utilizing many different approaches.  This paper will analyze twenty one distinct approaches to the visualization of financial information, including information relating specifically to personal finances and to the stock market.  The first section of this paper will define and detail what is meant by financial information and will overview the different types of data sets which are examined by the tools in question. This section will draw on literature reviews, technical reports and thesis reports written by experts in the field to distinguish financial data from other types of data and to show how and why visualization techniques are useful for looking at this type of data.

                The second through the sixth sections of this paper will consist primarily of brief, single paragraph annotations of the twenty one tools examined.  Each tool, found on the internet using either commercial search engines or the SearchCrystal engine, will be detailed with regards to its purpose, features, creator and availability.  A URL will be provided for each tool leading either to a demonstration of the tool, access to the tool itself, or to documentation describing the tool.  The tools will be arranged into sections according to groupings or ‘clusters’; groups of tools which share features or visualization methods.  These clusters are as follows;; ‘Charting software’, the largest category, which compromises software suites which produce multiple and interactive graphs or charts; ‘Dashboards’, or tools which use visualizations modeled after automobile dashboard displays such as tachometers and speedometers; ‘Geographic models’, or tools which impose financial data onto globes or maps; ‘Treemaps’, comprising tools which utilize the treemap visualization method; and a final category called ‘Other’ which comprises those tools which are entirely unique.  Each section will focus on one grouping, and a brief discussion of the common characteristics will precede the annotations of the individual tools.  Following the annotations, a single, representative tool for that cluster will be discussed at greater length and a checklist of visualization techniques will be filled out, indicating what sorts of tools or visualization methods/techniques are utilized by the tool.  For the ‘Other’ category, these checklists will be filled out for each tool.

                The seventh section will provide discussion on the findings of the preceding five sections, and in particular will attempt to relate the checklists for representative tools to different aspects of financial information as a data set, to show what sorts of tools are best used for what sorts of functions or information needs.  A final, concluding section will re-iterate the findings of all the preceding sections and attempt to provide a summary of the research performed.

Section One

Definition of the Data Set “Financial Data”

                Herein by finances we could mean any kind of data relating to money transactions, specifically those relating to personal or corporate finances or the international stock markets.  Pasha Roberts, in his masters thesis on the subject of financial information visualization (2004), sums up this simple definition as “a time series of price and volume values for a wide array of assets” (Roberts, pp.17).  All other sets of data which can be considered financial, Roberts says, can be derived from this information. 

                Herein by financial information we are primarily concerned with two types of financial numbers; stock market fluctuations and values and personal or corporate financial transactions.  Both types of information need to be updated in real time to track changes in the value of multiple items, either in the form of stocks changing their value as they are traded or in the form of accounts being monitored as transactions occur and money is spent or deposited.  In both cases time and value are the principle variables, and it is frequently necessary to track value and value changes over time.  Insofar as stock market information is concerned, other factors also need to be tracked; these being volumes, different ways of calculating the moving day average of stock values, and different markets.

                In both instances it is also necessary to track multiple, indeed, extraordinarily large sets, of data.  Individual stocks need to be compared to all other stocks in the market in order to identify and act on trends, and stocks need to be isolated into market segments which can in turn be compared to one another.  In terms of personal or corporate finance, multiple accounts will need to be compared and monitored, often encompassing hundreds of thousands of transactions a day in large financial institutions such as banks, investors, etc. 

                Miriam Lux, of the Fraunhofer-Institute for Computer Graphics, sums up these problems in a slightly dated study (1998) by noting the following: “Financial information can be characterized with the following attributes:  large amount, multi-dimensional and abstract nature, complex information structure, hidden information” (Lux, pp. 58).  She goes on to show that these factors combine to make financial information multi-dimensional and shows that it contains a complex information structure which is not immediately visible based simply on looking at the data in text forms.  Vast amounts of data needs to be examined to discover the structure which underlies financial transactions, and this data needs to incorporate the multiple variables of time, value, price and volume.  For these reasons, visualization systems should be utilized in order to efficiently examine financial information. 

Section Two

Charting Software

a. Introduction

                Charting software is easily the largest cluster of tools which deal with financial information.  The term ‘charting’ is here used in its broadest application to refer to tools which generate almost any sort of ‘traditional’ charts: tables, bar graphs, vertical and horizontal line graphs, time lines, or pie charts.  The majority of the tools in this cluster utilize more than one type of chart.  They usually utilize color in one or more ways to differentiate parts of the charts from one another, and almost all of them utilize details on demand in the form of mouse-over information or highlighting capabilities.  However, despite the great size of this cluster it also constitutes the least interactive of the tools located for visualizing financial information.  On the whole, tools in this cluster only create charts – the user is not given much control over the form of those charts and almost no ability to manipulate or change the charts. 

                Many of the tools increase data-density and the data to ink ratio by putting in more than one type of chart on a single display; SpotFire (see section 3.g and 3.k), for example, uses multiple types of charts on each of its tabbed pages, and does allow for a great deal of interactivity with its click and drag tagging/comparing interface.  However, it proves itself to be the exception rather than the rule[MSOffice1] .

                The tools found in this cluster range from use in personal finance, such as Expensr (section 3.b) corporate finance, such as SpotFire (sections 3.g and 3.k) and the stock market, such as Share Cracker (section 3.f).  Far and away, most of these tools are used for personal finance (six out of the nine tools listed), although this group also includes tools which can be used to model financial data but can also be used to model other types of information (GraphWise, section 3.d and WebFOCUS, section 3.I). 

b. Expensr (https://www.expensr.com/app.php)

                Expensr is a tool which allows users to track their personal finances using a variety of information tools, including charts, calendars, blog feeds, comparisons with other person’s spending patterns, and interactive graphs.  The primary function of the suite of tools is to monitor personal spending and to analyze how funds are spent by breaking expenditures into categories and graphing the size of those categories relative to one another.  The tool provides features which allow users to create budgets, track their purchases, and compare their spending habits with other persons in a ‘community’ section.  Users enter their own transactions into the charts and control how data is tagged and filtered.  The graphs support mouse-over details on demand, but are not manipulable or movable.  The display of the pages is customizable and interactive.  Like many of the tools discussed in this paper, Expensr is a subscription service and these comments are based on a live demonstration rather than the actual product.

c. Finesse (http://ieeexplore.ieee.org/iel3/4136/12180/00559222.pdf)

                Finesse is a spreadsheet based information visualization tool which allows for an interactive display of financial data in a variety of different formats – text, numbers, heat-maps, and three dimensional graphs.  The information on the tool is completely interactive, offering details on demand when the user mouses over or clicks on them and being updated in real-time.  The authors of the tool based it on the layout of a spreadsheet, utilizing the same sorts of features for calculations found in spreadsheets, because of their familiarity to the user base of financial analysts.  Cells can be related to one another using the common arithmetical relationships from regular spreadsheets or what the authors call ‘presentation relationships’, which creates a grouping from the cells based on some shared feature such as font, color scheme, or more complex relationships can be built.  Its primary use is to examine real-time financial data.

d. GraphWise (http://www.graphwise.com/)

                GraphWise is actually a search engine, first and foremost.  The tool crawls webpages within its domain and searches for charted information, which it then lists as results.  As with WebFOCUS (discussed in section 2.I), it can be used to chart financial data as well as other sorts of data.  For each result, the user can create an interactive graph or chart from the information found by the search engine, which allows for details on demand and zooming.  A color scheme can be selected, but individual colors cannot be assigned by the user.  It can make different sorts of graphs (pie charts, bar graphs, etc.) out of the information, and the user can select which type of graph is preferred (pie, bar, line, etc.).  It is free and available to the public.

e. Grisbi Graph (http://grisbi.weasel.nl/)

                Grisbi Graphs allows users to take personal financial information stored in the Grisbi financial manager tool.  The graphs tool imports information from Grisbi files and creates pie charts, bar graphs and time lines to visualize data in a number of ways.  Users are able to specify the time period which the graphs cover, the accounts to be visualized, the kind of graph used and can link directly from the graphs to the Grisbi files.  The graphs themselves are straight forward two-dimensional charts, but do allow for limited flexibility as users can aggregate and compare different accounts, and assign colors.  As a visualization tool it are fairly limited, but it is freely available to the public.

f. Share Cracker (http://share-cracker.roland-waddilove.qarchive.org/)

                Share Cracker, a tool used specifically for viewing information about the London Stock Exchange, uses simple charting software to create overviews of stock market information in real time.  It creates charts listing percentages of changes and current prices, pie charts indicating the state of portfolios broken down into rising stocks, falling stocks and static stocks, and integrates line graphs to show the change over time of individual stocks selected by the users.  The charts are broken down by market segment and industry type.  It automatically detects stocks which are rising or falling the fastest and displays information to the user about them and includes a ‘watch list’ which users can populate with stocks they want to keep track of.  It utilizes color minimally and has little to no interactivity.

g. SpotFire (http://spotfire.tibco.com/solutions/index.cfm)

                SpotFire, provided by Tibco, is a tool which uses a variety of graphing functions to display financial and other data in multiple ways.  Line charts, bar charts, pie charts and others can be created out of any data fed into the system.  It supports a great deal of interactivity, allowing users to select, tag, import, and compare sets of data instantly.  Most of these functions are performed by clicking and dragging items on the screen.  Data can be shown in multiple ways using different screens, with each screen featuring a different set of content and different ways of looking at it, in the form of lists, charts, graphs and others.  Interactive sliders allow users to filter the data based on any number of variables, which can be specified by the user.  Color and shading is used to separate different data sets on the charts the tool creates, but they do not appear to be customizable.  Like many of the tools listed so far, SpotFire is only available by subscription, and this description is based on a promotional video found on their website.

h. VisualCalc (http://www.visualcalc.com/products_cnstl_fc.htm)

                VisualCalc offers a suite of prefigured calculators for use in determining personal finances.  The calculators range in content from mortgages to auto-loans.  Each calculator has a sliding display of multiple variables, and allows you to see how a change in each variable will effect the others.  It also produces charts based on the values set by the sliders to indicate payment schedules and interactive charts.  The charts display the payment schedules in two ways, and allow for details on demand by showing precise figures when you mouse-over them.  The sliders on the calculators are interactive and allow you to set each variable independently or set all but one and determine what loans are feasible given your constraints (for example, you can set the loan period, interest rate and maximum monthly payment and the calculator will tell you how large of a loan you can afford).  The calculators are available for free to the public.

i. WebFOCUS (http://www.informationbuilders.com/products/webfocus/data_visualization.html)

                WebFOCUS is a tool which can be used to create interactive charts out of any sort of data, including financial data.  The tool allows the user to create any number of interactive charts, including pie charts, bar charts, histograms, or multiscapes.  All of the charts are three dimensional and interactive.  The multiscapes feature creates a nodes and lines type layout showcasing the connections between data sets.  Users can control the use of color in the charts and the tool supports semantic zooming and interactivity, as elements in the charts can be moved and re-positioned.  As with many other financial visualization tools, this is a subscription service.  This description is based on a promotional website.

j. WireVis (http://www.viscenter.uncc.edu/TechnicalReports/CVC-UNCC-07-13.pdf)

                WireVis is a tool which is used to monitor banking transactions for large banks and locate instances of transactions which seem suspicious, as when money is being laundered.  It presents four views of information (a heat map, a keyword network view, a search-by-example view and a tool the authors call “strings and beads”, which shows how transactions occur in individual accounts over time).  The tool is highly interactive, and can be set to find only transactions of a certain sort and over a certain aggregated period of time.  The tool uses the four views to make sure all the necessary variables which need to be tracked are tracked in the single tool (relationships between keywords, between those keywords and accounts, similar accounts to those relating to the keywords and how all of these accounts are moving over time).  It allows for details on demand, semantic zooming (the heatmap and strings and beads, particularly, support this feature), and packs in an extraordinary amount of data. 

k. Checklist for ‘Spotfire’

                Note: the checklist provided here is simply a reproduction of the ‘Information Visualization Toolbox’, originally designed by Anselm Spoerri for use in a graduate level course on the subject of Information Visualization tools.  The toolbox is broken down into three groups; Perceptual coding, Interaction, and Information Density: a check next to the methods or techniques listed under each of these categories indicates that the tool uses this method or technique, while a blank box indicates that it does not.  Under ‘Perceptual Coding’, the checks indicate whether the listed feature of the human visual system is utilized to display any information in the tool; under ‘Interaction’, checks indicate whether the user can manipulate the tool in the way stipulated; under ‘Information Density’, checks indicate whether the tool performs the functions listed. 

                We might say on the whole that those tools which have more check marks in the toolbox are ‘better’ than those which have fewer, but it will be shown in the discussion and conclusions sections of this paper that clusters of tools which use certain types of methods or techniques, codified in the toolbox, are more suited to given tasks than others.  In the remaining sections the toolbox will be reproduce and filled out accordingly without this preliminary discussion.

Perceptual Coding Interaction 
Position Direct ManipulationX
SizeXImmediate FeedbackX
Orientation[MSOffice2]  Linked DisplaysX
Texture Animate Shift of Focus 
Shape Dynamic SlidersX
ColorXSemantic Zoom 
Shading Focus plus ContextX
Depth Cues Details on DemandX
Surface Output / InputX
Motion   
Stereo Information Density 
ProximityXMaximize Data-Ink ratioX
Similarity Maximize Data Density 
Continuity Minimize Lie FactorX
Connectedness   
Closure   
Containment   

                For the ‘cluster’ of chart making tools, Spot Fire was selected as the most representative or ‘best’ of the tools because it is practically the only one which offers any substantial form of interactivity.  We can see that it scores highly under interaction, offering everything except animation and semantic zooming.  The lack of zooming does not detract from the use of the tool, however, in light of the fact that the only displays are fairly well understood charts and tables where a zoom feature would not considerably add to one’s understanding of the display.  At the same time, however, we note that Spot Fire and, indeed, all the other chart making tools score very lowly on Perceptual Coding, which marks how well the tool uses pre-attentive visual features to pack in data.  On the whole, chart making tools do not utilize pre-attentive features beyond using color and size, as in the case of bar graphs or pie charts, to distinguish sets of data visually.  These drawbacks make the tools somewhat less useful for analyzing complex data in large amounts, such as when looking at the stock market, and may explain why so many of the tools are geared toward managing personal and corporate finance, which contains less data to be examined. 

Section Three

Dashboards

a. Introduction

                Dashboard tools share a common feature insofar as they display a hemispherical icon which resembles the speedometer and tachometer displays found in the dashboards of automobiles.  The ‘needles’ on these dashboard icons can display the relative placement of a data set between two extremes, which depend on what sort of tool is being used.  They can indicate, for example, the age of the selected sets of data, or the value.  Insofar as they are unique, the displays are interesting and make use of a familiar layout to display information.  However, in general I found them to be unwieldy, having a low degree of interactivity and being difficult to read with any degree of accuracy.  There was no use of semantic zoom to allow you to accurately display the value the needle was pointing at; instead, a mouse-over is generally utilized to highlight the number when you point to the icon. 

                Both of the tools found in this cluster are used for different functions; one measures personal or corporate finance and the other tracks stocks in the stock market.  The dashboard tools are effectively mixed in both cases with chart making tools such as those described in the previous section and make heavy use of linked displays, using the dashboard icons to show changes in value of different variables as data sets are selected or changed.

b. iDashboards (http://www.idashboards.com/finance.shtml)

                iDashboards uses a unique layout to display information in a familiar way; using speedometers and tachometers like those found on automobile dashboards to show percentages and trends in financial information.  The tool uses conventional bar graphs and pie charts to display the data as well, and allows for instant upgrades as charts and graphs change to display new information as you mouse-over them.  Color and shading is used to differentiate different charts and different pieces of information, but the use of color is not customizable.  Some of the dashboard tools act as interactive sliders, showing how different sets of data relate to each other and to the financial situation as a whole (one such odometer slider shows, for example, what percentage of the total number of accounts fall within a certain age range, which is specified by the user).  Many screens have scrolling bars of information, displaying text versions of the data displayed in the various charts the tool creates.  There are multiple views supported by the tool, which is unfortunately rather clunky to use and is not very intuitive.

c. See Stok Run (http://seestokrun.com/)

                See Stok Run is a trading visualization tool which monitors the activities of stocks in the international market using similar modules to the iDashboard (see section 3.b) system.  Mock tachometers or speedometers are used to display the daily fluctuations of the stock value in percentage terms, using colors (green for increases and red for decreases) to indicate increases and decreases and to show the twenty and fifty moving day average changes.  Additional tachometers show the overall status of the market, which is also shown using number-lines and is indicated again by color.  Another visualization, an animation which depicts the relative change in a stock’s price as the height of an airplane marked with the ticker symbol flying across the screen, is included.  This display is not interactive and makes sparse use of color or other visualization techniques.  A second, similarly whimsical animation, depicts the volume of the NASDAQ market as a train, the visible length of which is related to trading volume.  These appear to be provided mostly for amusement.

d. Checklist for ‘iDashboards’

Perceptual Coding Interaction 
Position Direct ManipulationX
SizeXImmediate FeedbackX
OrientationXLinked DisplaysX
Texture Animate Shift of Focus 
Shape Dynamic SlidersX
ColorXSemantic Zoom 
Shading Focus plus ContextX
Depth Cues Details on DemandX
Surface Output / InputX
MotionX  
Stereo Information Density 
Proximity Maximize Data-Ink ratioX
Similarity Maximize Data Density 
Continuity Minimize Lie Factor 
Connectedness   
Closure   
Containment   

                The checklist for iDashboards, which was chosen as the representative for this group on the basis of its being more concerned with just the unique dashboard icons, scored relatively lowly in perceptual coding, as was found in the previous cluster.  There is virtually no use of the various methods of visually comparing data beyond the use of color and size, both of which are utilized primarily through the inclusion of charts and graphs.  Additionally, these types of displays fail to include a semantic zoom or animate shift of focus, usually abruptly changing to a new screen when data is selected rather than transitioning smoothly.  Because of the degree of uncertainty encountered when trying to ascertain specific values from the dashboard icons, the tool additionally scored poorly on information density, as the dashboard icons allow the user to ‘twist’ the interpretation of data. 

                Dashboard tools can be used for any of the types of data included within the financial data set, but it does not appear that the tools are a fruitful display medium compared to more fully interactive tools like those found in the charting tools cluster (see section 2) or tree maps (see section 5).

Section Four

Geographic models

a. Introduction

                Geographic tools share the common feature of using a visual display in the form of a map or globe.  The tools utilize color and position to display financial data for different countries or regions by mapping data directly onto an image of that country or those countries.  Color is the primary feature of these particular tools which, on the whole, do not utilize many other pre-attentive or interactive features.  Both tools are used to discuss global finances, relating both to the gross national product of different countries and to their interaction with the global equity market, meaning that such tools can be used to display data from most of the sets which are considered to be ‘financial’ for the purposes of this study.  Personal finances, however, are not well represented by geographic tools.

b. Finance Maps (http://www.financemaps.com/)

                FinanceMaps creates an interactive map view of the global equity market.  Coloring is used on a map of the world to indicate the change in the global market over a period of time which can be specified by the user.  A legend shows what range of change each color represents, and the user can pick from a variety of time frames ranging from daily to the past five years.  Beneath the map is a chart listing the numerical change of each country, including the current and past trading values, indicated by country name and an icon of the country’s map.  The map is interactive insofar as it supports zooming and manipulation of the map, although these features are clunky at best.  The user can specify which currency is used to determine the relative changes of the global market, with seven major currencies represented. 

c. Visualization of Financial Information (http://delivery.acm.org/10.1145/280000/275532/p58-lux.pdf?key1=275532&key2=5657957021&coll=GUIDE&dl=portal,ACM&CFID=23244455&CFTOKEN=49095553)

                This paper proposes a technique for visualizing financial/economic information on a global scale.  The authors premise that two dimensional graphs are insufficient for the display of multi variate data such as financial information, because financial information includes such a large amount of data which is in a highly abstract form, exists in a complex structure and is often hidden.  To that end, it is necessary to utilize sophisticated visualization techniques to display financial / economic information in any useful way.  The authors propose to display the GNP of each country in the world superimposed on a map or three dimensional globe, using a color map to display the value of the GNP directly onto the image of the country.  This data can then be translated into three dimensional bar charts for comparison purposes.  This fairly simple visualization system, according to the authors, reduces the complex task of analyzing economic information by allowing for the display of large amounts of data, a simple visual display of the complex information structure, and a method of comparing large data sets to reveal hidden data, thus resolving the problems inherent in analyzing financial data outlined above.

d. Checklist of ‘Finance Maps’

Perceptual Coding Interaction 
PositionXDirect ManipulationX
Size Immediate Feedback 
Orientation Linked Displays 
Texture Animate Shift of Focus 
Shape Dynamic Sliders 
ColorXSemantic ZoomX
Shading Focus plus Context 
Depth Cues Details on Demand 
Surface Output / Input 
Motion   
Stereo Information Density 
Proximity Maximize Data-Ink ratioX
Similarity Maximize Data Density 
Continuity Minimize Lie Factor 
Connectedness   
Closure   
Containment   

                As we can see by filling out the toolbox for Finance Maps, these tools use hardly any of the visualization tool features which have been found to be useful.  There is almost no perceptual coding beyond the use of position, more or less nothing more than the location of a given country on a map, and color, used in this case to indicate percentage of change.  The displays are not linked to any kind of details, forcing the user to scroll down and find information on a chart rather than clicking on the map.  The map is supposed to be manipulable, in so far as the user is supposed to be able to move it around, zoom in, and select items, but none of these features work with any reliability.

                In general, geographic tools appear to fall short of the other forms of tools in terms of performance, but that is more through an inadequate execution than from a flaw in the theoretical aspects.  With the inclusion of details-on-demand or immediate feedback, a properly functioning semantic zoom, and the ability to link displays in order to compare data sets over time these tools could become formidable and much more useful.

Section Five

Tree Maps

a. Introduction

                Tree maps break data down into groups which are then visually arranged as rectangles, which are then colored and arranged onto a rectangular display so as to allow the user to visually recognize patterns which might not be apparent in a text list.  The different algorithms which determine how the treemaps are made depends on the sort of map being created.  Tree maps traditionally use color, shading, size, and position as pre-attentive features, and as a rule rely on the use of semantic zoom, focus in context and details-on-demand in order to be fully functional.  Given the use of all of these features and the necessity of ‘driving down’ into the tree map to get detailed information, treemaps are among the most interactive and data-dense tools examined in the financial data set.  They are used almost exclusively to display information from the stock market.

b. HeatMaps (http://www.ssctech.com/assetmanagement/heatmaps/)

                HeatMaps, a service of SS&C Technologies, creates an interactive map of market and database content in a layout very similar to that used by Market Maps 1000.  The color coding used by the tool is similar, and is used to intuitively show stocks which are ‘up’ or ‘down’ by using green to show up and red to show down.  The shading of the color indicates how far up or down the stock is (darker equals greater movement).  It provides direct, real time access to various market information websites and can access and interface with many different sorts of databases.  It allows users to highlight trends and patterns, allows for semantic zooming, utilizes color and shading, consolidates and groups stocks and information into groups, and is customizable in terms of content and analysis tools.  Like Market Maps, it is a subscription service.

c. Market Maps (https://ecommerce.smartmoney.com/ecommerce/login?url=%2Fmarketmap%2Findex.cfm%3Fselectpromocode%3D1%26adv%3Dtools%26advtype%3Dmarketmap1000&host=selectdl.smartmoney.com)

                Smartmoney’s “Market Map 1000″ provides an aerial view of one thousand U.S. and international stocks in the form of a treemap, providing access to real-time quotes for a variety of stock market segments.  It uses color and shading to show market blocks in shades of red and green to depict their movement in value in real time.  Semantic zoom allows the user to focus in on individual market blocks to examine individual stocks as they relate to the rest of their respective market segments.  The user can access stock charts and analyst data on individual stocks and determine what value range of stocks are displayed using sliders.  It also features add ons which allow the users to instantly screen for mutual funds and compare stocks based on multiple types of criteria.  Because it is a subscription service, only minimal information about the tool is available.

d. ‘Minority Game’ Agent Based visualizer (http://ideas.repec.org/p/sce/scecf5/468.html)

                This paper discusses how visualization techniques can be applied to an agent based model of real world financial market.  The Minority Game, a simple model of the financial market wherein an odd numbered group of agents select one of two options at given times and those whose selection is in the minority (IE was selected by the fewest of them) win ‘points’.  This simple model can be used to describe stock market transactions of ‘buy’ or ‘sell’ with ‘points’ representing earnings.  The authors of the study applied the Treemap technique to the Minority game, where each rectangle represents an agent and color is used to show earnings or losses.  They also applied the ‘Sunburst’ technique, where each segment of the sunburst represents an agent in the minority game and color is also used to represent earnings and losses.  It is shown in the paper that the use of the fish-eye lens on both the treemap and the sunburst minority game displays is one way of making them more useful, although experimentation did not prove the method to be statistically useful.  Treemaps, it is concluded, are more useful than sunburst displays for the domain of financial data, because users are more adept at comparing the rectangles than comparing the segments of the sunburst circles.  The use of fish-eye lenses increased speed of answers and confidence in the accuracy of answers, but did not statistically improve the actual accuracy of answers, and may have contributed to a sense of false confidence in the use of the visualization tool.

e. Checklist for ‘Market Maps’

Perceptual Coding Interaction 
PositionXDirect ManipulationX
SizeXImmediate FeedbackX
Orientation Linked DisplaysX
Texture Animate Shift of FocusX
Shape Dynamic SlidersX
ColorXSemantic ZoomX[MSOffice3] 
ShadingXFocus plus ContextX
Depth Cues Details on DemandX
Surface Output / InputX
Motion   
Stereo Information Density 
ProximityXMaximize Data-Ink ratioX
SimilarityXMaximize Data DensityX
Continuity Minimize Lie FactorX
Connectedness   
Closure   
Containment   

                Market Maps and the other tree-map tools are among the most interactive tools examined and contain the most use of pre-attentive visual coding.  The market maps tool uses color, shading, position, proximity and similarity of the colored rectangular blocks to display patterns and similarities between market segments at a quick glance.  By ‘driving down’ into the market map the user can find detailed information and bring out specifics quickly.  This makes market maps, and by extension tree map visualization tools, ideally suited for displaying very large financial data sets.  The ability to display large data sets at once allows for the recognition of patterns and trends within the large sets without losing the ability to get highly detailed information, a necessity in the financial data field.  Because of their aptitude for displaying very large amounts of data in a fairly small space, treemaps are highly suited to looking at the stock market rather than at personal finances or corporate finances.

Section Six

Other Tools

a. Introduction

                The fifth cluster identified for financial tools is also the second largest cluster.  Most of the tools come close to fitting in to one or more of the other sections, but because of important differences were considered separately.  Each will be discussed in greater detail below, and a perceptual coding toolbox will be filled out for each tool.

b. Financial Reminiscences  (http://ieeexplore.ieee.org/iel5/7989/22105/01028864.pdf?arnumber=1028864)

                The author of this study makes a case for using visualization techniques to create works of art out of financial information.  To illustrate this, the author describes the creation of the art piece “Financial Reminiscences”, which was created out of financial data which has been mapped into a ‘portfolio texture’.  This is done by distributing the data into a pie chart and then breaking down each sector of the pie chart into dots, the color of which corresponds to the sector of the chart it originated from.  These dots are then mixed into a plane randomly and packed densely, resulting in a sort of textured rectangular image.  These ‘textures’ represent the same information as the pie chart in a single plane and, it is claimed, are easier to compare in large numbers than pie charts because of the reduction of variables.  Additionally, the textures can be reduced substantially in size, allowing for them to be viewed and analyzed on PDA devices such as cellular phones.  As to their aesthetic value, that is an individual choice.

c. Checklist for ‘Financial Reminiscences’

Perceptual Coding Interaction 
Position Direct Manipulation 
Size Immediate Feedback 
Orientation Linked Displays 
TextureXAnimate Shift of Focus 
Shape Dynamic Sliders 
ColorXSemantic Zoom 
Shading Focus plus Context 
Depth Cues Details on Demand 
Surface Output / Input 
Motion   
Stereo Information Density 
Proximity Maximize Data-Ink ratioX
Similarity Maximize Data Density 
Continuity Minimize Lie Factor 
Connectedness   
Closure   
Containment   

                Financial Reminiscences hardly qualifies as a visualization tool, based on the listing of features from the toolbox.  There is no interactivity available with the tool, which really only is a method for creating a static display.  It does utilize pre-attentive visual features, but by randomly and densely mixing the data together much of the comparison value is lost, as is the ability of the viewer to get detailed information.  If perhaps the tool could be outfitted with some sort of zooming feature, it would seem much more useful.  It can be used either for the stock market or to look at personal or corporate finances, but on the whole it is considerably less useful in any of those fields than other tools coming from the chart making or tree maps clusters.

d. Spie Graphs (http://216.147.93.3/contact.htm)

                Spie Graphs creates interactive, three dimensional pie charts of financial data for companies.  Using a simple pie chart to render the data from a balance statement, which can then be surrounded by a ring representing the income statement, where the degree of overlap indicates the net income or net loss.  The diameter of the graph is a third variable, representing the growth of the company in terms of the numbers involved – the wider the diameter, the larger the company.  By stacking these pie charts over time, a three dimensional pie chart in the shape of a cone, allows the user to track the growth of a company.  Many of these graphs can then be compared by the user in a single setting, as when one is benchmarking companies or looking for companies to invest in.  The tool supports details on demand, allowing users to drill down into the graphs to get specific numbers for each chart.  As with other tools, Spie Graphs is a subscription service.

e. Checklist for ‘Spie Graphs’

Perceptual Coding Interaction 
Position Direct Manipulation 
SizeXImmediate FeedbackX
Orientation Linked Displays 
Texture Animate Shift of Focus 
ShapeXDynamic Sliders 
ColorXSemantic ZoomX
Shading Focus plus ContextX
Depth CuesXDetails on DemandX
Surface Output / Input 
Motion   
Stereo Information Density 
Proximity Maximize Data-Ink ratioX
SimilarityXMaximize Data DensityX
Continuity Minimize Lie FactorX
Connectedness   
Closure   
Containment   

                Spie Graphs is unique among the financial tools examined insofar as it is the first to use more perceptual coding features than interactive features.  On the whole, through the use of depth cues, color, and shape, Spie Graphs is able to pack in a great deal of data into a single chart.  These charts can then be compared, although there is no method that I am aware of for directly manipulating the charts.  As a chart creating tool, Spie Graphs could plausibly have been included within the cluster ‘chart making tools’ (see section two) but was not because it is the only tool examined which utilizes depth cues.  Like most of the chart making tools to which it is similar, Spie Graphs is primarily suited to be used to examine personal or corporate financial data.

f. Ticks (http://lineplot.com/expertise/thesis.html)

                Ticks, defined as the ‘atoms of the stock market’, are the individual motions of each individual stock, measured second by second in monetary value.  The Ticks visualizer plots this massive amount of data involved for each stock in a three dimensional line plot type graph, with the three axis’ representing time in seconds, the volume of the stock and the dollar value.  Color is used to represent the tick by tick price change visually.  Since the mapping is three dimensional, different tags in the shape of floating balls, diamonds, cubes and other shapes are used to indicate the value of positions on the vertical axis (the axis’ are invisible on the visualization).  A second display, showing the lines for each stock in a parallel projection, allows for a quick comparison of many stocks at once.  Aggregates of the open trades on each stock are represented in both views, as a cone whose shape and length indicates the size of trades and when they occurred for the single stock view and a rectangle next to the ticker symbol with size and position indicating the same.  Color is used in both instances to indicate increase or decrease in price at the time the trade was initiated.  The displays are all animated in real time to show how stocks are actually moving.  The platform gets points for ambition but is extraordinarily confusing to try to use.

g. Checklist for ‘Ticks’

Perceptual Coding Interaction 
PositionXDirect Manipulation 
SizeXImmediate FeedbackX
OrientationXLinked DisplaysX
Texture Animate Shift of FocusX
ShapeXDynamic Sliders 
ColorXSemantic Zoom 
ShadingXFocus plus Context 
Depth Cues Details on Demand 
Surface Output / InputX
MotionX  
Stereo Information Density 
Proximity Maximize Data-Ink ratioX
Similarity Maximize Data DensityX
ContinuityXMinimize Lie FactorX
Connectedness   
Closure   
Containment   

                Ticks, used solely as a tool for mapping the motion of the stock market, uses an even mix of visual encoding and interactivity, although on the whole it suffers from a lack of specific values being displayed.  It does not show actual numbers, from what can be ascertained by reading the documentation, and does not allow for semantic zooming or mouse-over details.  The tool is also the only tool to utilize animation features in order to keep up to date in real time, a feature which is to be particularly valued in the volatile realm of financial data. 

h. Visual Trader (http://www.visualtrader.com/)

                Visual Trader is a subscription visualization tool which creates a dynamic map of the market by market segment.  Stocks are grouped into market segments and mapped by the user into circles, upon which is stacked three dimensional bar graphs representing individual industries.  The overall movement of these groups is then monitored and displayed using color and shading (red for down, green for up, darker colors indicating greater movement, as is common in this type of tool).  The bar graphs, rather than simply traditional bars, are made out of cylinders whose length, diameter and color all indicate information about individual industries.  More traditional charts with the actual prices and numbers for each stock are appended to the graphics, to allow for details on demand and limited interactivity.  These charts are arranged hierarchically, with prospective ‘good buy’ stocks listed at the top.  The three dimensional display and the hierarchical list are updated in real-time.  The tool comes with a suite of chart making tools, and the stocks displayed on the visualizer are linked to comprehensive information about the companies in question.  As in many previous cases, Visual Trader is a subscription service.

I. Checklist for ‘Visual Trader’

Perceptual Coding Interaction 
PositionXDirect ManipulationX
SizeXImmediate FeedbackX
Orientation Linked DisplaysX
Texture Animate Shift of FocusX
ShapeXDynamic SlidersX
ColorXSemantic ZoomX
Shading Focus plus ContextX
Depth CuesXDetails on DemandX
Surface Output / InputX
Motion   
Stereo Information Density 
ProximityXMaximize Data-Ink ratioX
SimilarityXMaximize Data DensityX
Continuity Minimize Lie FactorX
Connectedness   
Closure   
Containment   

                In terms of visualization of financial data, we have saved the best for last.  Visual Trader, out of all the tools examined, uses the most visualization features from any of the three examined categories and combines them into a highly interactive, visually appealing, and useful tool.  While it is only used for the stock market, a platform similar to this one could plausibly be developed for personal or corporate finances, perhaps incorporating some of the features of Spie Graphs.  The display features combines just enough pre-attentive visual cues with a very high degree of flexibility and interactivity to pack a large amount of data into a small space and to show that data in multiple ways, allowing users to find many different patterns and trends.  In terms of financial data, as discussed in section one, it fulfills all the tasks which a visualization tool should.

Section Seven

Discussion

                We may now make a few assumptions about the sorts of tools which are best suited to the different sorts of tasks associated with financial data.  We know from section one that financial data is, by nature, a multi variable set which includes very large amounts of data, all of which is time sensitive and the vast majority of which is updated in real time.  Tools which are to be useful in any of the financial sectors need to be able to display this large amount of data with all the relevant variables mapped in such a way that trends can be analyzed for all the different variables and across each of them, and which can account for the constantly changing nature of the data.  Of all the tools mentioned and discussed in the preceding sections, two stand out as notable: Spie Graphs and Visual Trader.  The first seems to be the best tool for analyzing personal and corporate financial data, while the latter seems to be the best for analyzing stock market data.  But why?

                Both of these tools, if they were to be placed in one of the many groups or clusters identified, would have been classified as chart making tools.  Yet this descriptor falls short for both of these tools because of the large number of pre-attentive visual features which they utilize.  Simply stated, chart making tools do not use pre-attentive visual features beyond the simple use of color and size to indicate and differentiate data sets produced on simple, basic charts and graphs.  Spie Graphs and Visual Trader both use a variety of visual features to pack information into a purely visual display, relying less and less on text, mouse-over details, and links to other charts in order to instantly and fruitfully compare and contrast data sets.  The use of pre-attentive visual features is what sets visualization tools apart from simple charts and graphs in the first place.

                It seems that many, if not all, of the tools examined in the cluster have foregone the use of pre-attentive visual features in the tools for the financial data set in favor of interactivity and linked displays.  Time and again the tools revealed themselves to be little more than collections of displays using the same kinds of lists, line graphs, scatter plots, pie charts and tables which any user could create and compare using software that comes prepackaged with every home computer on the market.  Instead of utilizing visualization features as such, these tools took advantage of the platforms to create multiple interactive displays and relied on the user to perform the work of comparing data and determining how trends and patterns should be isolated and displayed. 

                It is telling that the two tools which were deemed the best for their respective sets of data did not fall into any of the clusters which the other tools fell into.  This speaks to a wider trend of tools not fully utilizing the capabilities of information visualization within the field of financial data.  Instead there are many tools which think of themselves as visualization tools but do not make full use of the features which visualization tools are thought of as having.  Chart making tools, as noted above, do not make any use of pre-attentive visual features.  The geographic tools frequently failed to allow for interactivity or details on demand, forcing users to rely on tables and charts to find detailed information.  When users are forced to rely on charts and tables in this way, the entire purpose of using a visualization tool is defeated.  Dashboard tools likewise failed to provide a way to go from the visualization to concrete information, and again forced users to rely on tables, charts and graphs in order to find the details which are so necessary for work in the financial fields.

                The treemap tools represent a significant break from this trend, utilizing many ‘standard’ visualization techniques to represent financial information.  Yet this cluster, which one might expect to have been the largest, was on par with both Dashboard tools and Geographic tools.  One is led to wonder why treemaps and other tools which utilize pre-attentive features have not been more widely adopted in the realm of financial data representation.  Perhaps the need for detailed information, which appears to have led to the adoption of tools which include a wide variety of charts, graphs and tables, has erroneously led developers to believe that visual features cannot be used in this data set.  One needs only to look so far as Spie Graphs and Visual Trader, or to Market Maps 1000, to see that this is not the case.

Conclusion

                In this paper 23 separate visualization tools used for the display of financial information, ranging over personal finances, corporate finances, global finances, and the stock market were examined.  The tools were broken down into five clusters, and each of these clusters was examined on the basis of a representative or ‘best’ tool to see how well they utilize the features and components which are thought to make up a good information visualization tool.

                It was shown that chart making tools, which do not utilize pre-attentive visual features, account for the largest number of such tools, with the second largest cluster being those tools whose uniqueness prevents them from being classified in any single group.  It was also shown that those tools which are unique, which do not share the common characteristics of the other four clusters, had a greater incidence of making better use of the information visualization features.  Spie Graphs was concluded to be the best tool for visualizing personal or corporate financial data, while Visual Trader was seen to be the best for visualizing stock market data.  Both tools have similarities with chart making tools but differ from them significantly in their use of pre-attentive visual features and interactivity. 

                We can conclude then that financial data has not seen a great deal of action, so to speak, from the information visualization field.  Perhaps because of the need for detailed information updated in real time, financial data is more often described using conventional chart and graphing software and, where visualization methods are utilized, they predominantly do not take advantage of the pre-attentive features of the human visual system.  This represents a great opening in terms of future research and development of visualization tools, for those tools which do take advantage of these visual features are in a position to more quickly, efficiently and effectively monitor, track and display financial information.

Bibliography

All websites and electronic documents were retrieved on April 25th, 2008.

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Chang, R. et al. (2007).  WireVis: Visualization of Categorical, Time-Varying Data

From Financial Transactions. FromIEEE symposium on Visual Analytics Science and Technology, 2007,  pp. 155 – 167.  Retrieved from http://www.viscenter.uncc.edu/TechnicalReports/CVC-UNCC-07-13.pdf

Expensr, Inc. (2007).  Expensr.com: where did all my money go?  Retrieved from https://www.expensr.com/index.php.

FinanceMaps.com (2004).  Finance Maps: mapping the major equity market changes over the last 52 weeks.  Retrieved from http://www.financemaps.com/

GraphWise, LLC. (2007). Graphwise.  Retrieved from http://www.graphwise.com/.

iDashboards. (2007).  Financial Solutions.  Retrieved from       http://www.idashboards.com/finance.shtml

Information Builders (2007).  Data visualization tools from information builders.  Retrieved from http://www.informationbuilders.com/products/webfocus/data_visualization.html

Nirvana Systems. (2008).  Visual Trader.  Retrieved from http://www.visualtrader.com/index.asp

Page, J. (2008).  Spie Graphs.  Retrieved from http://216.147.93.3/contact.htm

Roberts, P. (2007).  Information Visualization for Stock Market Ticks: toward a new trading interface.  (Masters Thesis, MIT University, 2007).  Retrieved from http://lineplot.com/expertise/thesis.html.

Saito, Y. (2002).  Financial Reminiscences:  An example of art based on information visualization in finance.  In the Proceedings of the Sixth International Conference on Information Visualisation, 2002. Retrieved from http://ieeexplore.ieee.org/iel5/7989/22105/01028864.pdf?arnumber=1028864

SmartMoney.com. (2008).  Market Maps 1000.  Retrieved from https://ecommerce.smartmoney.com/ecommerce/login?url=%2Fmarketmap%2Findex.cfm%3Fselectpromocode%3D1%26adv%3Dtools%26advtype%3Dmarketmap1000&host=selectdl.smartmoney.com

SS&C Technologies, Inc. (2008).  Heatmaps.  Retrieved from http://www.ssctech.com/assetmanagement/heatmaps/

Varshney, A. & Kaufman, A. (1996).  FINESSE: A Financial Information Spreadsheet.  Retrieved from http://ieeexplore.ieee.org/iel3/4136/12180/00559222.pdf.

Tibco. (2008).  Guided Enterprise analytic solutions: Tibco – SpotFire.  Retrieved from http://spotfire.tibco.com/solutions/index.cfm

VisualCalc, Inc. (2008).  Visual Calc: Pre-Figured calculators.  Retrieved from   http://www.visualcalc.com/products_cnstl_fc.htm

Waddilove, R.  (2008).  Share Cracker reviews and free downloads.  Retrieved from http://share-cracker.roland-waddilove.qarchive.org/

Wei Jiange (2005).  Information Visualization Of An Agent-Based Financial System Model.           Computing in Economics and Finance, 468.  Retrieved from http://ideas.repec.org/p/sce/scecf5/468.html

Wessel, W. (2007). Grisbi Graphs.  Retrieved from http://grisbi.weasel.nl/


 [MSOffice1]Spotfire represents “state-of-art” and its founder used to be an infovis researcher. Spotfire is a good example of how infovis principles have been translated into a usable and adopted tool.

 [MSOffice2]Plays an important role in charts: how can you visually tell that stock is gaining rapidly? By visual orientation of the graph line.

 [MSOffice3]How is this supported in Market Map?

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