Application of 3D Spiral Graph Visualization to the Psychological Data
Zhuang Heliang 1 , Chieko Kato 2 , Kensei Tsuchida 3
1 Graduate School of Engineering, Toyo University, Saitama, Japan
2 3 Faculty of Science and Engineering, Toyo University, Saitama, Japan
Abstract- We have developed and been enhancing a web counseling system, especially to help people working overseas these years. A great deal of data about users has been generated through these years. The counselor needs to grasp the user’s situation from these data. However, since there are various types of data, it is very difficult to compare among different kinds of data and extract features from them. To cope with these problems, it is useful and helpful to visualize data. Visualization of data is an important theme in various fields. There are many studies and tools concerned with visualizing data. Among them, Excel is a typical software used by many people to visualize data in 2D graphs.
However it is recognized that 2D graphs have various restrictions. On the other hand, now we are attempting to incorporate SCT (Sentence Completion Test) into the web counseling system. SCT is used as one type of psychological assessment by hospitals, schools, companies, and so on. Its effectiveness has been observed as a test that can grasp the internal status and external status of the subject in detail. However, evaluations of SCT have shown that it relies on professionals’ subjectivity. What is more desirable is a system that depends not merely on the professionals’ feelings or experiences, but is capable of carrying out universal evaluations backed by objective data. The purpose of this study is to provide counselors with a 3D visualization tool of psychological data. In this study we gather users’ data from the web counseling system and quantitatively analyze the data from sentential responses by subjects taking the SCT, visualizing the data or the results to an easily understood 3D graph. The 3D display is implemented in a spiral shape by the Processing project. Our visualization method uses the spiral feature to visualize the psychological data. The data is displayed on one cycle of the direction of width of the X-axis and the Y-axis and arranged on the standard which specified the direction of length of the Z-axis. Colored according to quantity, the overall tendencies and features can be grasped visually by rotating the entire spiral. In addition, when using the 3D operation and a compact display of the spiral, from the operation to move the center point of view of the cylinder of the spiral, it is possible easily to grasp the numeric value of each item for people with certain common features.
Keywords – Visualization, Spiral, 3D, Data, Web Counseling System, SCT, Text Analysis I.INTRODUCTION
Visualization of data is an important theme in various fields. There are many studies and tools concerned with visualizing data.
Among them, Excel is one typical software program used by many people to visualize data in a 2D graph. The data can be displayed in many kinds of graphs (Polygonal lines, Bar graphs, Pie charts, Donut graphs, etc.) and it is very convenient. However, 2D graphs have various restrictions. Although 2D graphs are ‘flat’, using the X & Y (horizontal and vertical) axis’, the image has only two dimensions and if turned to the side becomes a line. 3D adds the ‘Z’ dimension. This third dimension allows for rotation and depth. Moreover, 2D graphs have spatial restrictions; for example, the maximum of a doughnut graph is two-fold. However, since there is a third dimension in 3D graphs, depth is made possible.
We have developed and been enhancing a web counseling system, especially to help people working overseas these years.
Development of the web counseling system is based on open source software. In particular, each mail server, database server and application server was constructed in such a way that they are linked to each other, and the integration of the system was measured. The mail server used postfix, the database server used MySQL, and application server used Tomcat. However, it is not enough to understand only a part of the user’s needs. A part of the web counseling system was improved based on the results of factor analysis to investigate the needs of the system. And the study was carried out in China due to an increase in the number of Japanese living and working there.
There was not enough data about the design of the computer for the web counseling. Because Japanese people are working together with Chinese people, the needs of Chinese workers are also important. Therefore, the needs of both Japanese and Chinese were compared [1].
In recent years there have been many changes of users’ needs, but there is not enough support to counselors. The counselor needs to grasp the situation of the user of the system. So it will be very important to consider how to assist the counselor to understand more easily the user’s data.
On the other hand, now we are attempting to incorporate SCT (Sentence Completion Test) into the web counseling system. SCT is a projective technique bug test which shows the relatively short writing of text, and allows one to write the format freely. It is used as one type of psychological assessment by hospitals, schools, companies, and so on. Its effectiveness has been observed as a test that can grasp the internal status and external status of the subject in detail. However, evaluations of SCT have shown that it relies on professionals’
subjectivity. What is more desirable is a system that depends not merely on the professionals’ feelings or experiences, but is capable of carrying out universal evaluations backed by objective data [2].
In this study we gather user’s data from the web counseling system and quantitatively analyze the data from sentential responses by subjects taking the SCT, visualizing the data or the results to an easily understood 3D graph. The 3D display is implemented in a spiral shape by the Processing project.
A spiral is a type of smooth space curve, i.e. a curve in three-dimensional space. It has the property that the tangent line at any point makes a constant angle with a fixed line called the axis. Examples of helixes are coil springs and the handrails of spiral staircases.
Helices are important in biology, as the DNA molecule is formed as two intertwined helices, and many proteins have helical substructures, known as alpha helices.
The purpose of this study is to provide counselors with a 3D visualization tool of psychological data. Our visualization method uses the spiral feature to visualize the psychological data. The data is displayed on one cycle of the direction of width of the X-axis and the Y- axis and arranged on the standard which specifies the direction of length of the Z-axis. When there is so much quantity or kinds of data to 2D graph, it is a stake at a display. Because of the space, one cycle can display one piece of data in a 3D spiral graph so that it is very easy to understand. Further, colored according to quantity, the overall tendencies and features can be grasped visually by rotating the entire spiral.
II.RELATED RESEARCH
A. Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map[3]
Timelines are used as the basis for representing events over time. When they are combined with a geographical map, they show the spatiotemporal pattern of those events. This spatiotemporal line depicts temporal patterns of events with respect to their spatial attributes.
To help users observe patterns with respect to different spatial viewpoints and periodical constraints, we propose an interactive visualization approach using spirals-based technique. A spiral has the geometric shape that could help to reveal periodical patterns and the timeline is the best source for it. When the user wants to observe patterns with respect to different spatial viewpoints on the map and periodical constraints, the spatiotemporal line can be decomposed according to specified locations to browse patterns.
B. Evaluating the Effectiveness of Spatial Memory in 2D and 3D Physical and Virtual Environments[4]
User interfaces can improve task performance by exploiting the powerful human capabilities for spatial cognition. This opportunity has been demonstrated by many prior experiments. It is tempting to believe that providing greater spatial flexibility--by moving from flat 2D to 3D user interfaces--will further enhance user performance. This paper describes an experiment that investigates the effectiveness of spatial memory in real-world physical models and in equivalent computer-based virtual systems. The different models vary the user`s freedom to use depth and perspective in spatial arrangements of images representing web pages. Results show that the subjects`
performance deteriorated in both the physical and virtual systems as their freedom to locate items in the third dimension increased.
Subjective measures reinforce the performance measures, indicating that users found interfaces with higher dimensions more `cluttered`
and less efficient.
C. Visual Community Detection: An Evaluation of 2D, 3D Perspective and 3D Stereoscopic Displays[5]
3D drawing problems of the 90s were essentially restricted to representations in 3D perspective. However, recent technologies offer 3D stereoscopic representations of high quality which allow the introduction of binocular disparities, which is one of the main depth perception cues, not provided by the 3D perspective. This paper explores the relevance of stereoscopy for the visual identification of communities, which is a task of great important in the analysis of social networks. A user study conducted on 35 participants with graphs of various complexity shows that stereoscopy out-performs 3D perspective in the vast majority of cases. When comparing stereoscopy with 2D layouts, the response time is significantly lower for 2D but the quality of the results closely depends on the graph complexity:
for a large number of clusters and a high probability of cluster overlapping, stereoscopy outperforms 2D, whereas for simple structures 2D layouts are more efficient.
D. An Overview of Interaction Techniques and 3D Representations for Data Mining[6]
Visual Data Mining (VDM), presented by Beliken & Spenke (1999) as an interactive visual methodology “to help a user to get a feeling for the data, to detect interesting knowledge and to gain a deep visual understanding of the data set”, can facilitate the discovery of knowledge in data.
In 2D space, VDM has been studied extensively and a number of visualization taxonomies have been proposed (Herman et al. (2000), Chi (2000)). More recently, hardware improvements have led to the development of real-time interactive 3D data representation and immersive Virtual Reality (VR) techniques. Thus, aesthetically appealing element inclusion, such as 3D graphics and animation, increases the intuitiveness and memorability of visualization. Also, it eases the perception of the human visual system (Spence (1999), Brath et al. (2005)). Although there is still debate concerning 2D vs 3D data visualization (Shneiderman (2003)), we believe that 3D and VR techniques have a greater potential to assist the decision-maker in analytical tasks, and to deeply immerse the users in the data sets. In many cases, the user needs to explore data and/or knowledge from the inside-out and not from the outside-in, like in 2D techniques (Nelson et al. (1999)). This is only possible in using VR and Virtual Environment (VEs). VEs allow users to navigate continuously to new positions inside the data sets, and thereby obtain more information about the data. Although the benefits offered by VR compared to desk-top 2D and 3D still need to be proven, more and more researchers are investigating its use with VDM (Cai et al. (2007)).
In this study, various hints were taken from the above related research, and have referred very well. The data is displayed on one cycle of the direction of width of the X-axis and the Y-axis and arranges on the standard which specifies the direction of length of the Z- axis. When there is such a great quantity or or variety of data to graph in 2D, it is a stake in the display. Because of the space, one cycle can display one data in 3D spiral graph in a way that is very easy to understand.
III.PREPARATION
A. Seven Steps of Information Visualization and Processing[7]
The process of understanding data begins from a data set and a question. A route until it results in an answer consists of the following steps.
[1] Acquire: To get the data. It may come from the files on a disk and may use the source on a network.
[2] Parse: Structure is added based on the meaning of data and it kicks by category.
[3] Filter: All unrelated data is removed.
[4] Mine: The statistical technique and the data-mining technique are applied, and a pattern can be found, or it enables it to perform mathematical processing.
[5] Represent: Visualization Dell which becomes a base is chosen. A bar graph, a list, a tree structure, etc.
[6] Refine: It is clearer, and fundamental representation is improved so that it may become rich in visual charm.
[7] Interact: A means to operate data or to control what is displayed is added.
Processing is a programming language, development environment, and online community. Since 2001, Processing has promoted software literacy within the visual arts and visual literacy within technology. Initially created to serve as a software sketchbook and to teach computer programming fundamentals within a visual context, Processing evolved into a development tool for professionals.
Today, there are tens of thousands of students, artists, designers, researchers, and hobbyists who use Processing for learning, prototyping, and production.
Processing includes a sketchbook, a minimal alternative to an integrated development environment (IDE) for organizing projects.
Every Processing sketch is actually a subclass of the PApplet Java class which implements most of the Processing language's features.
When programming in Processing, all additional classes defined will be treated as inner classes when the code is translated into pure Java before compiling. This means that the use of static variables and methods in classes is prohibited unless you explicitly tell Processing that you want to code in pure Java mode. Processing also allows for users to create their own classes within the PApplet sketch. This allows for complex data types that can include any number of arguments and avoids the limitations of solely using standard data types such as: int (integer), char (character), float (real number), and color (RGB, ARGB, hex).
Figure 1 3D spiral using Processing Project
B. Projective Test
In psychology, a projective test is a personality test designed to let a person respond to ambiguous stimuli, presumably revealing hidden emotions and internal conflicts. In this study, SCT (Japanese Version) was used.
Sentence completion tests are a class of semi-structured projective techniques. Sentence completions tests typically provide respondents with beginnings of sentences referred to as “stems,” and respondents then complete the sentences in ways that are meaningful to them. The responses are believed to provide indications of attitudes, beliefs, motivations, or other mental states. There is debate over whether or not sentence completion tests elicit responses from conscious thought rather than unconscious states. This debate would affect whether sentence completion tests can be strictly categorized as projective tests.
Figure 2 Part of SCT
C. Personality Test
A personality test is a questionnaire or other standardized instrument designed to reveal aspects of an individual’s character or psychological makeup. In this study, GHQ 28 in Japanese and S-H Resilience Test were used.
The General Health Questionnaire (GHQ) is a widely used questionnaire to assess general well-being and distress. Several versions of different length are available. In epidemiological studies a 12-items version is mostly used.
The S-H Resilience Test clarifies the degree of the support or cooperation from surrounding persons, the grade of self problem solving, the degree of cooperativeness, etc.. Both sides of [action and a view] are taken up, the idea or the opinion measuring what kind of tendency is being shown.
D. MeCab
MeCab(http://mecab.googlecode.com/svn/trunk/mecab/doc/index.html) is a morphological-analysis engine of an open source, and was developed by Taku Kudo, a graduate of the Nara Institute of Science and Technology, and currently a Google software engineer and one of Google Japan’s input developers. Figure 3 is the result of analyzing the text of an exercise using MeCab.
Figure 3 The result of text analysis using MeCab
IV.VISUALIZATION OF WEB COUNSELING SYSTEM’S DATA
These years, a web counseling system has been established and made operational. However, there is not enough support for counselors. The counselor needs to grasp the situation of the user of the system. It is, therefore, very important to make it easier for the counselor to understand the users’ data.
In this study, the web counseling system’s data will be displayed in 3D spiral shape using the Processing project.
A. Visualizing Data to a 2D Graph
Let us consider the following data: “User A’s number of times of web counseling system monthly consultation from 2008 to 2012”.
Figure 4 User A’s number of times of web counseling system monthly consultation from 2008 to 2012
From the picture, it may be seen that there are four items totaling five types of data: “suicidal ideation”, “spiritless”, “sleeplessness”,
“uneasiness”. First, we visualize the data to a 2D graph using Excel.
Figure 5 Visualization of data to a 2D graph
From the picture, we can see that only one kind of data can be displayed in one graph, and thus five kinds data require five graphs.
When comparing the data, the counselor must change between five graphs. This is very inconvenient, and it is also difficult to compare the all the kinds of data.
B. Visualizing Data to3D Spiral Graph
Next, the feature of spiral periodicity is used, wherein the total number of times of consultation of the each month is displayed numerically. The direction of width of the X-axis and Y-axis can be compared monthly within the same year.
Figure 6 Graph seen from the viewpoint of width direction
The direction of length of the Z-axis can compare each year of the same month. And in the each month, one color represents one kind of data. The red is the “suicidal ideation”, blue is “spiritless”, green is “sleeplessness”, and yellow is “uneasiness”. Height represents quantity.
Furthermore, expansion of a graph, reduction, rotation, and direction change can be interactively operated now using a mouse.
Figure 7 Graph seen from the viewpoint of length direction
From this picture, it is can see that the total number of times of consultation in September of every year is especially high. It may also be seen that the number of consultations for “suicidal ideation” is much higher in September, 2011 than at other times.
Figure 8 Number of times for consultation on “suicidal ideation” in September 2011
Here, five kinds of data are displayed in one 3D spiral graph, making it much easier to compare and understand than in a 2D graph.
V.TEXT ANALYSIS AND ITS VISUALIZATION OF SCT
In this study, the subjects are college students (54 girls and boys from 19 to 21 years old), and the consultation period is from the middle of October, 2012 to beginning of December, 2012.
A. Enforcement of Projective Test and Personality Test
First, the subject enters a reply by hand on the SCT questionnaire form on the first day.
The following week, the same subject who has replied to the SCT undergoes the GHQ 28 in Japanese, as well as the S-H Resilience Test.
B. Computer Analysis
After digitization by hand to each answer, SCT’s text data is digitized to space between word analysis to the word, the classification of each part of speech using Mecab
Next, number measurement for every item was performed using the KH coder (http://khc.sourceforge.net/), which is free software for content analysis (measurement text analysis), text mining, etc. and can analyze metrically various Japanese text type data obtained from a free answer item.
In the econometric analysis of the text, advice of the specialist of SCT was obtained, and categorizing and measurement of each category were performed for each part of speech. The noun, the verb, and the adjective were used in this experiment.
Noun: [people], [thing], [event], [emotion] and [other].
Verb: [action (one’s own action)] and [representation (Change of the state of a thing)].
Adjective: [negative] and [positive].
C. Standardization and Creation of Data
Each of above-mentioned nouns (people, thing, event, emotion and other), each verb (action and representation) and each adjective (negative and positive) were standardized using the STANDARDIZE function of Excel. After that, correction was made in order to make the standardized data easy to visualize.
The data of measurement of the standardized text, and result of each subject’s personality test obtained from GHQ 28’s element scale, and the S-H Resilience test, along with the sex of the subject, were used to create a three-point data file. The GHQ 28 contained the following elements: A (physical condition), B (sleeplessness and anxiety), C (social activity obstacle), D (depression trend); while the S- H Resilience Test included Parts A (Surrounding support, cooperation, etc. of a family, a friend, a coworker, etc. from people), B (Extent to which problem solving can be achieved by oneself), and C (Affinity and cooperativeness in association with the others). Figure9 is the data from scores of men’s S-H Resilience test’s A.
Figure 9 Data from scores of men’s S-H Resilience Test A (after standardization)
D. Visualization and Consideration
First, we use the data to create a 2D graph using Excel.
Figure 10 Visualize the data to 2D graph
From the picture, the graph is hard to understand and compare, because there are too much data in it.
Next, each of the above-mentioned data files were automatically drawn in the 3D graph using Processing, so that display is implemented in a spiral shape.
Each subject’s measurement data of the text and each score of the subject’s personality test result was displayed in one cycle of the direction of width of the X-axis and the Y-axis, and was arranged on the standard which specified each subject in the direction of length of the Z-axis.
Thereby, each data can be visually compared between the subjects. Figure11 visualizes the results to a 3D graph using Figure 9’s data using Processing project.
Figure 11 Visualizing the results to a 3D graph using Processing Project
Moreover, every item of the text measurement and the standard score for each personality test’s result is represented by colors.
Regarding measurement of the text, the color is attached in four patterns of 0~25%, 26%~50%, 51%~75%, and more than 75%. For GHQ 28, the score of each item attaches the color in two patterns of more than 4 points, and 4 or less than 4 points. For the S-H Resilience Test, each score and the total score attaches the color in three patterns of “high”, “normal” and “low”. The trend can be read visually through its display in a spiral.
Figure 12 Scores from men’s S-H Resilience Test’s A (part of adjective with high evaluation)
From the picture, the feature of the difference by the score of item A can easily be read visually with regard to the “adjective”. It can be seen that the place of the [positive]’s colors is darker than the place of the [negative]’s colors at the place where the adjective is evaluated as “high”. That is, the subject with “high” evaluation of the item A has more quantity of [positive] than [negative] quantity.
Figure 13 Scores from men’s S-H Resilience Test A (adjective with low evaluation)
From Figure 13, it can be seen that the place of the [negative]’s colors is darker than the place of the [positive]’s colors at the place where evaluation of the adjective is “low”. This shows that the subject with “low” evaluation of item A has a greater quantity of [negative] than [positive]. Between subjects with “high” evaluation and subjects with “low” evaluation, it turns out that there is a tendency for the quantity of positive “adjective” and negative “adjective” use to be reversed.
Figure 14 Scores from men’s S-H Resilience Test’s A (adjective with high evaluation)
From Figure 14, it may be seen that the place for the [negative]’s colors is darker than the place of the [positive]’s colors, that the [positive]’s colors are darker than the [negative]’s, and also that the [positive]’s colors are the same as the [negative]’s at the place where the adjective of evaluation is “normal”. That means that the subject with a “normal” evaluation of item A has almost same quantity of [positive] and [negative].
VI.CONCLUSION
This study uses data from web the counseling system and quantitatively analyzes the data from sentential responses by subjects taking the SCT, visualizing the results to an easily understood 3D graph. Catching visually the concrete numerical value of each item of data or people with a certain common features made those values easier to comprehend.
Regarding data from the web counseling system, the feature of spiral periodicity is used; the total number of times of consultation in each month is displayed numerically. The direction of width of the X-axis and Y-axis can be used to compare various months of the same year. The direction of length of the Z-axis allows us to compare the same month of several years. And within the each month, one color represents one kind of data. Since there are five kinds of data to display in one 3D spiral graph, it is much easier to compare and understand than in a 2D graph. Thus, it can provide support to the counselor in grasping the situation of each user of the system.
The SCT quantitatively analyzes the data in the answer sentence of SCT of the subjects, and the results are visualized to a 3D graph. Each subject’s data of measurement of the text and each score of the subject’s personality test was displayed on one cycle of the direction of width of the X-axis and the Y-axis, and then arranged on the standard which specified each subject in the direction of length of the Z-axis. Moreover, each measurement of the text and each standard of the score of each personality test’s result for every item was colored. The information on each subject’s tendencies, which is difficult to grasp solely from the numerical values of the analyzed quantity data, is expressed in a direct and easily understood way, and thus becomes a means to support objective evaluation.
Future research will need to consider devices capable of minute and easier-to-use interaction. It is planned to introduce a more convenient function, ensure that graphs will be easier to see. This research also needs to be expanded for greater applicability to other problems.
VII.REFERENCE
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