Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in April 2013 regarding Visual Analytics specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.
Description:
Visual analytics is a broad category of modifiers that refers to the visualization of data in a manner that simplifies the comprehension of data and information and aids in the analysis and communication of results.
Strengths:
1. Provides visuals to identify patterns.
2. Can provide a tangible three-dimensional object to physically hold.
3. Provide an effective way of presenting intelligence to decision makers.
4. Provide more depth to the analytical product through referencing to the visual.
5. Has the potential to provide an interactive means to display the estimate or information.
6. Can display relationships visually that might have been overlooked with the utilization of other methods.
7. Output can be automated.
Weaknesses:
1. Can be difficult to make it understandable for the consumer or decision-maker.
2. This modifier is not easily defined.
3. Not as helpful for individuals who do not learn effectively visually.
Step by Step Action:
1. Be aware of your decision-maker’s needs and preferences for the product.
2. Determine which form of a visual is most suited to your product and the decision-maker.
3. Make your visuals simple and easy to understand for the decision-maker.
Exercise:
As a class we were given the question to analyze how many Jelly Belly jelly beans would fill a container 5 inches long, 2 inches tall, and 3 inches wide. We were asked as a class three separate times to estimate how many jelly beans would fit into the container. Additionally, we had to give an estimate of how confident we were on each estimate from Activities 1-3. Activity 1 an estimate was made just based on knowing the size of the container and how one perceives the relative size of a Jelly Belly jelly bean. Activity 2 an estimate was made by viewing a picture of the jelly beans in the container. Activity 3 an estimate was calculated by holding the actual container filled with the jelly beans.
The results as a class were interesting to see how each individual determined their analysis. For some of the class, viewing the container in two different levels of visualization forms increased their level of confidence on their estimate. For other participants in the class their level of confidence in their estimates did not change during each of the activities, but their estimates changed when more visuals were presented. Moreover, it was interesting to see during the first activity many participants in the class tried to draw out the probable size of the container or tried to visualize the size of the container with their hands to make their estimate.
Showing posts with label Visual Analytics. Show all posts
Showing posts with label Visual Analytics. Show all posts
Thursday, April 25, 2013
Summary of Findings (Green Team): Visual Analytics (4 out of 5 Stars)
Visual Analytics
Green Team
Rating (4 out of 5 Stars)
Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the 8 articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in April 2013 regarding Visual Analytics specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.
Description:
Visual analytics is a collection of modifiers and methods used to improve the analysis of information, as well as representation to increase the effectiveness of the decision making process. Both qualitative and quantitative information is depicted through the modifiers and methods. The visual representation of the information should increase comprehension of the material in addition to appealing to the viewer’s aesthetics.
Strengths:
- Decreases the level of ambiguity in conveying information
- Supports people who are visually oriented
- Gains interest of the decision maker much more than a written element
- Has the potential to increase analytic confidence
Weaknesses:
- Not everything has the possibility of integrating a visual component
- Visual analytic products can introduce bias into an analysis
- Not necessarily intuitive to an outside party, especially since there is a different level of input between those that created the visuals and those the information is being presented to
- Does not support people who are not visually oriented
How-To:
- Find a topic that is capable of having some kind of visual component
- Compare and contrast various ways in which a visual component can be integrated (ex: graphs, charts, information arranged in a particular fashion, etc.)
- Select the visual component that you feel best communicates the idea while being mindful of who the consumer is or decision maker the visual component is geared towards
Personal Application of Technique:
A written description of the dimensions of the container were provided to individuals. The class was asked to estimate the number of jelly-beans that were in the container along with recording their level of confidence in that estimate. In the next stage, a static image of the container filled with jelly-beans was presented to the individuals. A dime was used to provide a size comparison to the image of the container with the jelly-beans. Following another estimate and confidence level, the individuals were given the container with the jelly-beans to hold and look at in a three-dimensional manner. The participants were then asked to give another estimate and confidence level.
The introduction of visual and tangible elements to the estimate generally increased the level of confidence in three of the five participants. One participant raised their confidence only after being presented with the three-dimensional, interactive visual aid. The other two raised their confidence level after the introduction of the 2D, static image. All participants changed their estimates each time a visual was presented, getting closer to the actual number. Some changed drastically (originally estimating 900+, then decreasing the estimate to 350, actually 353), while others remained in the same ballpark but improved their estimate.
Rating: 4 of 5 stars
Tuesday, April 23, 2013
Intelligent Visualization and Information Presentation for Civil Crisis Management
Adrienko & Adrienko's paper "Intelligent Visualisation [sic] and
Information Presentation for Civil Crisis Management" describes research
conducted as part of the EU-funded project OASIS to develop methods for
effective visualization support for situation analysis and management
during times of crises, namely through the "Situation Manager" software module. The authors cite the major goals of the research
are "to reduce the information load of the analyst, decision maker, or
information recipient without omission of anything important and to
ensure quick and accurate comprehending of the information" (Adrienko
& Adrienko, 2007, p.889).
Summary:
The research behind "Situation Manager" aims to create a generic crisis management system to support response and rescue operations in the event of large-scale disasters. Like intelligence products, visual analytic products should be deliverable in a timely fashion, while the information is relevant, and presented in a way that it is easily understood and therefore can be used by decision-makers. The authors suggest intelligence visualization requires reducing the information load on the recipient, display choices and designs that ensure quick and accurate recognition of meaning, and account for the characteristics of the medium used to view the information.
The authors suggest that intelligent visualization is used for two different purposes: to support the work of an analyst, planner, or decision maker, or to build an information presentation to send to a specific recipient. It is unclear if the authors see these purposes as mutually exclusive, though it is unlikely. In the development of intelligent visualization, expert knowledge is needed in the emergency management domain ontology, generic roles involved in emergency situations and their information needs, and techniques and methods to manipulate and organize different types of data. The research uses literature on crisis management as a source of domain-specific knowledge and literature on data analysis, graphic design, and geographic visualization as domain-independent knowledge to create an expert system.
"Situation Manager," still in its early stages at the time of this article's publication, is a scalable vector graphics presentation that is interactive and includes the emergency management expert information and a problem-specific user interface. The system takes information about the crisis situation and the territory affected by the situation, essentially answering the what and where questions. The where aspect includes an impact zone which can be supplied or self-created. The software module then identifies the hazardous agents involved in the situation based on preexisting knowledge and potential secondary hazardous events caused by the first crisis event. This helps to locate objects or resources that need to be saved or protected in the impact zone to prevent further damage, including an estimate of the number of people living in the zone based on census data. If the object is people, the Situation Manager detects if people in the impact zone can escape danger without outside intervention. The module displays objects and dangers by degree of how critical each is through the sizes of symbols used on the maps, larger symbols representing a more critical object at the current time.
Critique:
This research is particularly applicable to the intelligence field considering the blatant similarities between what the authors conclude are requirements for intelligent visualizations and the requirements we at Mercyhurst accept as necessary for an analytic/intelligence product. When reading the article I could specifically relate it to the work a classmate is doing to identify the potential for nuclear exposure caused by earthquakes in Iran.
The article itself is only a description of research being conducted, rather than methodologically reporting the findings and methods of the research. While the article suggests different sources of information used to create the expert system for visualization for crisis management, it would strengthen the presentation of the research to formally address all areas of data collection. Still, the authors do a good job of explaining the research in developing the "Situation Manger" in general terms and use good examples to communicate the use of such a tool. Though the module was not complete at the time of the article's publication, the authors provide a roadmap of implementation upon completion and suggest likely real-world applications.
It is interesting to me that the article does not address why visualization is a useful communication tool for decision-makers in general, not even referring to the extant literature on the subject which is extensive. Further, because the module was not complete at the time of publication, there is no record of successful implementation or feedback by users. Further analysis of this type of visual analysis and data visualization is necessary to strengthen the argument for the Situation Manager module.
Source:
Andrienko, N., and Andrienko, G. (2007). Intelligent Visualisation [sic] and Information Presentation for Civil Crisis Management. Transactions in GIS 11(6), 889-909. doi:10.1111/j.1467-9671.2007.01078.x
Summary:
The research behind "Situation Manager" aims to create a generic crisis management system to support response and rescue operations in the event of large-scale disasters. Like intelligence products, visual analytic products should be deliverable in a timely fashion, while the information is relevant, and presented in a way that it is easily understood and therefore can be used by decision-makers. The authors suggest intelligence visualization requires reducing the information load on the recipient, display choices and designs that ensure quick and accurate recognition of meaning, and account for the characteristics of the medium used to view the information.
The authors suggest that intelligent visualization is used for two different purposes: to support the work of an analyst, planner, or decision maker, or to build an information presentation to send to a specific recipient. It is unclear if the authors see these purposes as mutually exclusive, though it is unlikely. In the development of intelligent visualization, expert knowledge is needed in the emergency management domain ontology, generic roles involved in emergency situations and their information needs, and techniques and methods to manipulate and organize different types of data. The research uses literature on crisis management as a source of domain-specific knowledge and literature on data analysis, graphic design, and geographic visualization as domain-independent knowledge to create an expert system.
"Situation Manager," still in its early stages at the time of this article's publication, is a scalable vector graphics presentation that is interactive and includes the emergency management expert information and a problem-specific user interface. The system takes information about the crisis situation and the territory affected by the situation, essentially answering the what and where questions. The where aspect includes an impact zone which can be supplied or self-created. The software module then identifies the hazardous agents involved in the situation based on preexisting knowledge and potential secondary hazardous events caused by the first crisis event. This helps to locate objects or resources that need to be saved or protected in the impact zone to prevent further damage, including an estimate of the number of people living in the zone based on census data. If the object is people, the Situation Manager detects if people in the impact zone can escape danger without outside intervention. The module displays objects and dangers by degree of how critical each is through the sizes of symbols used on the maps, larger symbols representing a more critical object at the current time.
Critique:
This research is particularly applicable to the intelligence field considering the blatant similarities between what the authors conclude are requirements for intelligent visualizations and the requirements we at Mercyhurst accept as necessary for an analytic/intelligence product. When reading the article I could specifically relate it to the work a classmate is doing to identify the potential for nuclear exposure caused by earthquakes in Iran.
The article itself is only a description of research being conducted, rather than methodologically reporting the findings and methods of the research. While the article suggests different sources of information used to create the expert system for visualization for crisis management, it would strengthen the presentation of the research to formally address all areas of data collection. Still, the authors do a good job of explaining the research in developing the "Situation Manger" in general terms and use good examples to communicate the use of such a tool. Though the module was not complete at the time of the article's publication, the authors provide a roadmap of implementation upon completion and suggest likely real-world applications.
It is interesting to me that the article does not address why visualization is a useful communication tool for decision-makers in general, not even referring to the extant literature on the subject which is extensive. Further, because the module was not complete at the time of publication, there is no record of successful implementation or feedback by users. Further analysis of this type of visual analysis and data visualization is necessary to strengthen the argument for the Situation Manager module.
Source:
Andrienko, N., and Andrienko, G. (2007). Intelligent Visualisation [sic] and Information Presentation for Civil Crisis Management. Transactions in GIS 11(6), 889-909. doi:10.1111/j.1467-9671.2007.01078.x
Interactive Dynamics for Visual Analysis
Summary:
In the article Interactive Dynamics for Visual Analysis, Jeffrey Heer and Ben Shneiderman (2012) provide a taxonomic guide for analysts, researchers and other professionals to create visual analysis tools. They discuss the usefulness of visualizing data for comprehension in saying that "by mapping data attributes to visual properties, ... visualization designers leverage perceptual skills to help users discern and interpret patterns." The authors also stress the importance of ensuring that the visuals are appropriate and intelligible for the consumer.
The authors describe three dynamics for visual analysis, each of which include examples of task types or steps that fit into those descriptions. The three dynamics are Data and View Specification, View Manipulation, and Process and Provenance. Data and View Specification involve determining which data is to be shown and visualized with programs such as Microsoft Excel. Then it involves the filtering of data which shifts the focus among the different data subsets to isolate specific categories of values. Sorting the data can show surface trends and clusters and organize data according to a unit of analysis. The following image shows a more complex form of a matrix-based visualization of a social network.
The first matrix plot shows a social network when people are sorted alphabetically. The second plot shows a reordering by node degrees resulting in more structure and the third plot is permutated by network connectivity, showing underlying clusters of communities. The final step is to derive new attributes from existing values when input data is insufficient.
The second dynamic is View Manipulation which consists of highlighting patterns, investigation of hypotheses and revealing additional details. Selection allows for pointing to an item of interest, for example, dragging along axes to "create interactive selections that highlight automobiles with low weight and high mileage." Navigating is determined by where the analyst begins, such as in a crime map that depicts crime activity by time and region. Coordinating allows the analyst to see multiple coordinated views at once which can facilitate comparison. This can be done in histograms, maps or network diagrams. The following image shows a complex patchwork of interlinked tables, plots and maps to analyze outcomes of elections in Michigan.
The image shows a combination of tables, plots and maps. The final step, organization, involves arranging visualization views, legends and controls for more simplified viewing.
The final dynamic is Process and Provenance which involves the actual interpretation of data. Recording involves chronicling and visualizing analysts' interaction histories in both a chronological and sequential fashion. Annotation includes recording, organizing and communicating insights gained during analysis. Sharing involves the accumulation of multiple analyses and interpretations derived from several people and the dissemination of results. Guiding is the final step and includes developing new strategies to guide newcomers.
Critique:
This article was very effective in both showing many ways of visualizing data and also the importance of visualization in comprehension and facilitation of analysis. The inclusion of some well-known applications such as Google searches and crime maps and their many examples were beneficial in the authors' explanation of the taxonomy for those readers who don't have extensive experience with other specialized software or the field. It was also important that the authors distinguished between what may be visually intriguing but don't have much real-world application.
Some of the features explained within each dynamic were directly applicable to the intelligence field or anyone conducting an analysis of data, such as the investigation of financial markets or terrorists networks. I can foresee this field being a new avenue to explore for more effective communication between analysts and decision-makers. Analysts often learn that decision-makers prefer concise, clear and preferably visual information but may not know an effective manner to convey such information. This article provides a basic but helpful overview in how to go about visualizing data.
Source:
Heer, J & Shneiderman, B. (2012). Interactive Dynamics for Visual Analysis. Communications of the ACM, 55(4), 45-54. doi:10.1145/2133806.2133821
In the article Interactive Dynamics for Visual Analysis, Jeffrey Heer and Ben Shneiderman (2012) provide a taxonomic guide for analysts, researchers and other professionals to create visual analysis tools. They discuss the usefulness of visualizing data for comprehension in saying that "by mapping data attributes to visual properties, ... visualization designers leverage perceptual skills to help users discern and interpret patterns." The authors also stress the importance of ensuring that the visuals are appropriate and intelligible for the consumer.
The authors describe three dynamics for visual analysis, each of which include examples of task types or steps that fit into those descriptions. The three dynamics are Data and View Specification, View Manipulation, and Process and Provenance. Data and View Specification involve determining which data is to be shown and visualized with programs such as Microsoft Excel. Then it involves the filtering of data which shifts the focus among the different data subsets to isolate specific categories of values. Sorting the data can show surface trends and clusters and organize data according to a unit of analysis. The following image shows a more complex form of a matrix-based visualization of a social network.
The first matrix plot shows a social network when people are sorted alphabetically. The second plot shows a reordering by node degrees resulting in more structure and the third plot is permutated by network connectivity, showing underlying clusters of communities. The final step is to derive new attributes from existing values when input data is insufficient. The second dynamic is View Manipulation which consists of highlighting patterns, investigation of hypotheses and revealing additional details. Selection allows for pointing to an item of interest, for example, dragging along axes to "create interactive selections that highlight automobiles with low weight and high mileage." Navigating is determined by where the analyst begins, such as in a crime map that depicts crime activity by time and region. Coordinating allows the analyst to see multiple coordinated views at once which can facilitate comparison. This can be done in histograms, maps or network diagrams. The following image shows a complex patchwork of interlinked tables, plots and maps to analyze outcomes of elections in Michigan.
The image shows a combination of tables, plots and maps. The final step, organization, involves arranging visualization views, legends and controls for more simplified viewing.
The final dynamic is Process and Provenance which involves the actual interpretation of data. Recording involves chronicling and visualizing analysts' interaction histories in both a chronological and sequential fashion. Annotation includes recording, organizing and communicating insights gained during analysis. Sharing involves the accumulation of multiple analyses and interpretations derived from several people and the dissemination of results. Guiding is the final step and includes developing new strategies to guide newcomers.
Critique:
This article was very effective in both showing many ways of visualizing data and also the importance of visualization in comprehension and facilitation of analysis. The inclusion of some well-known applications such as Google searches and crime maps and their many examples were beneficial in the authors' explanation of the taxonomy for those readers who don't have extensive experience with other specialized software or the field. It was also important that the authors distinguished between what may be visually intriguing but don't have much real-world application.
Some of the features explained within each dynamic were directly applicable to the intelligence field or anyone conducting an analysis of data, such as the investigation of financial markets or terrorists networks. I can foresee this field being a new avenue to explore for more effective communication between analysts and decision-makers. Analysts often learn that decision-makers prefer concise, clear and preferably visual information but may not know an effective manner to convey such information. This article provides a basic but helpful overview in how to go about visualizing data.
Source:
Heer, J & Shneiderman, B. (2012). Interactive Dynamics for Visual Analysis. Communications of the ACM, 55(4), 45-54. doi:10.1145/2133806.2133821
Applied Visual Analytics for Economic Decision-Making
Summary:
Source: Savikhin, A.,Maciejewski, R., & Ebert, D.S. (2008). Applied visual analytics for economic decision-making. IEEE Symposium on Visual Analytics Science and Technology, 107-114. Retrieved from https://www.bioinformatics.purdue.edu/discoverypark/vaccine/assets/pdfs/publications/pdf/Applied%20Visual%20Analytics%20for%20Economic%20Decision-Making.pdf.
Savikhin et al. (2008) utilize the application of visual
analytics to improve an individuals' economic decision-making skills. The authors investigated the application of
visual analytics to common problems noticed in economics, the winner’s and
loser’s curse. The winner’s curse is
the individual who tends to overpay for certain items or services, either the
individual is worse off for buying the product or service, or the value of the
asset is less than the bidder perceived.
The loser’s curse is when an individual pursues an asset that is below
their profit-maximizing bid, or a competing entity attains the bid. The main
problems apparent is that economists are unable to see the potential for
creating a business strategy that is able to maximize profit, with most
economists are unable to consider all the information that could guide these
decisions. Thus, the authors apply
visual analytics to improve the decision-making abilities in both winner’s and
loser’s curse situations. The hypothesis by Sayikhin et al. (2008) for
their study was the subjects who participated in their interactive visual
analysis study would bid closer to the profit maximization decision as opposed
to those who participated in simple visual or tabular studies.
Sayikhin et al. (2008) conducted six different treatment groups, 3
for winner’s curse scenarios and 3 for loser’s curse scenarios. The three different visual aids the
participants looked at to help with their decision-making were an interactive
visual analytic model, a simple visual, and a tabular table. Each subject in the experiment acted independently from other subjects in the study. All were given the scenario of being a
decision-maker who had to decide how much to bid for a company.
Participants were given a possible data range that they could bid for
each company. Decisions on how much to bid were conducted on a computer generated program that would
randomly decide the value of the company and display the three different types of graphics.
Over the course of the experiment the participants switched between the
three types of visual aids and would base their bid value off their
interpretation of what the visuals portrayed.
In each of the three different visual representations individuals were given 30
different opportunities to bid on various companies.
Overall, subjects that were given the interactive visual
analytics treatment learned what the best bid/optimal solution would be more
often as compared to those individuals who were given simple visual or tabular representation of the bidding information. Moreover, for both the
winner’s and loser’s curse scenario groups, the periods of using interactive
visual analytics outperformed subjects given the other visual treatments. Overall, results were statistically significant in this regard. Each increased usage of the interactive visual analytics model allowed the
participants to learn from past decisions on bids and allowed these individuals
to make more optimal bids as opposed to other participants who received the other two visual treatments. It is also important to note that even a
simple visual aide provided more effective decision making capabilities as
opposed to viewing information with tabular formed displays.
Critique:
I found that this study was useful as it provided a way in
which to help individuals within the business realm to make more efficient decisions by analyzing their situation with interactive visual aids. It is important to avow that this study seems to suggest the usefulness
of showing information visually to overcome cognitive thinking judgments from
decision-making and improve learning capabilities. Moreover, it would be
interesting for a future study to demonstrate how interactive visuals seem to
engage our thinking more than just a simple visual does. One limit of this study was that the sample
size was small, so it would be interesting to conduct this study over a much
larger sample size to replicate the results. Another limitation
of this study other than sample size was that the authors only looked at bidding
patterns in winner’s curse or loser’s curse scenarios, not any other economic
conditions. Even though this is a topic
that would come up often within the business environment, it would be
interesting to see what other areas in business decision-making scenarios would
interactive visual analytics improve the process of decision-making. I would hypothesize that interactive visual
analytics would be able to be applied to multiple areas in the business
realm, especially for those individuals who learn more effectively visually.
Source: Savikhin, A.,Maciejewski, R., & Ebert, D.S. (2008). Applied visual analytics for economic decision-making. IEEE Symposium on Visual Analytics Science and Technology, 107-114. Retrieved from https://www.bioinformatics.purdue.edu/discoverypark/vaccine/assets/pdfs/publications/pdf/Applied%20Visual%20Analytics%20for%20Economic%20Decision-Making.pdf.
Sunday, April 21, 2013
Investigative Visual Analysis of Global Terrorism
Summary:
The application of visual analytics to global terrorism. The authors, Miler, Smarick, Ribarsky and Chang, used an existing database with information on terrorist organizations to apply visual methods. The Global Terrorism Database (GTD) contains information on both domestic and international terrorist organizations. The authors applied their visual analytic system to this existing database to look at the five W's (who, what, where, when and why) of terrorist organizations in a manner that is easier for decision makers to understand.
Prior to this tool there were typically two groups of visual analytics: social network analysis and geo-temporal visualizations. The system implemented by the authors attempts to combine both social network analysis and geo-temporal visualization This tool has a number of layers that can be activated to look at the various elements of terrorist organizations. There are levels that show the location of attacks, which can be detailed on to see the specifics of what took place at that location. Through the different layers various elements can be visually depicted, which makes it easier to understand the data that is present.
The authors indicated that there are three types of individuals who typically go to the GTD website: the general public, investigative analysis, and terrorism experts. Through the visual analytics tool, individuals with varying levels of exposure to the subject matter can gain a significant level of understanding of the material. When the system was used by individuals in various organizations, they were all interested in applying the method to the various fields they were in.
Critique:
One element the authors identified in their conclusion was that there were certain elements that could be enhanced overtime. For example, there were instances of over-plotting data and geographic lines, which made it difficult to look at. Overall, it appears that this method is extremely useful to apply to existing data. Not only does it analyze various elements, but it also increases the ease of communicating with decision makers, as well as decrease the ambiguity that may be present in large data sets. This method is certainly something that should be incorporated when possible and certainly enhances the distribution element of information.
Wang, X., Miller, E., Smarick, K., Ribarsky, W., & Chang, R. (2008). Investigate visual analysis of global terrorism. Computer Graphics Forum, 27(3), 919-26. Retrieved from: http://ehis.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=74559524-5a41-4427-a79e-c5e026f76e72%40sessionmgr110&vid=3&hid=17
The application of visual analytics to global terrorism. The authors, Miler, Smarick, Ribarsky and Chang, used an existing database with information on terrorist organizations to apply visual methods. The Global Terrorism Database (GTD) contains information on both domestic and international terrorist organizations. The authors applied their visual analytic system to this existing database to look at the five W's (who, what, where, when and why) of terrorist organizations in a manner that is easier for decision makers to understand.
Prior to this tool there were typically two groups of visual analytics: social network analysis and geo-temporal visualizations. The system implemented by the authors attempts to combine both social network analysis and geo-temporal visualization This tool has a number of layers that can be activated to look at the various elements of terrorist organizations. There are levels that show the location of attacks, which can be detailed on to see the specifics of what took place at that location. Through the different layers various elements can be visually depicted, which makes it easier to understand the data that is present.
The authors indicated that there are three types of individuals who typically go to the GTD website: the general public, investigative analysis, and terrorism experts. Through the visual analytics tool, individuals with varying levels of exposure to the subject matter can gain a significant level of understanding of the material. When the system was used by individuals in various organizations, they were all interested in applying the method to the various fields they were in.
Critique:
One element the authors identified in their conclusion was that there were certain elements that could be enhanced overtime. For example, there were instances of over-plotting data and geographic lines, which made it difficult to look at. Overall, it appears that this method is extremely useful to apply to existing data. Not only does it analyze various elements, but it also increases the ease of communicating with decision makers, as well as decrease the ambiguity that may be present in large data sets. This method is certainly something that should be incorporated when possible and certainly enhances the distribution element of information.
Wang, X., Miller, E., Smarick, K., Ribarsky, W., & Chang, R. (2008). Investigate visual analysis of global terrorism. Computer Graphics Forum, 27(3), 919-26. Retrieved from: http://ehis.ebscohost.com/ehost/pdfviewer/pdfviewer?sid=74559524-5a41-4427-a79e-c5e026f76e72%40sessionmgr110&vid=3&hid=17
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