Data Visualization in Health Psychology

3rd International Conference of Health Psychology ( ICIAHP - 2017 ) One day Pre- conference Workshops of ICIAHP-2017 on

Registration Process 

Data Visualization in Health Related Researches
WORKSHOP FEE: Rs. 1,000=00
(Including Tea, Lunch, Kit Bag and Certificate)
Preliminary:Bring with you SPSS or Statistica in your Laptop.
Workshop Director:
Dr. D. Dutta Roy
Associate Professor, Psychology Research Unit
Indian Statistical Institute , Kolkata
(November 11, 2017, Time : 11:00 to 16:30)

VENUE :
National P.G.College , Lucknow

Registration:  contact to:

Dr. P.K. Khattri

Organizing Secretary, ICIAHP-2017

Head, Department of Psychology

National P.G.College, Lucknow

Mobile: +919415004020 / +919721524865

E-mail: iciahp2017@gmail.com

Q1. What is Data Visualization  ?

Data visualization is a general term that describes any effort to help people understand the significance of data and to communicate by placing it in a visual context. It is important in health psychological research to describe the health condition, it's determinants and promotion. Data provide information about psychological and behavioral processes in health, illness, and healthcare. Besides, Visual data help understanding how psychological, behavioral, and cultural factors contribute to physical health and illness. 

Q2. What is the history of data visualization ?

Historically, data visualization has evolved through the work of noted practitioners. The founder of graphical methods in statistics is William Playfair. William Playfair invented four types of graphs:   the line graph, the bar chart of economic data , the pie chart and the circle graph. Joseph Priestly had created the innovation of the first timeline charts, in which individual bars were used to visualize the life span of a person (1765). That’s right timelines were invented 250 years and not by Facebook!

Among the most famous early data visualizations is Napoleon’s March as depicted by Charles Minard. The data visualization packs in extensive information on the effect of temperature on Napoleon’s invasion of Russia along with time scales. The graphic is notable for its representation in two dimensions of six types of data: the number of Napoleon’s troops; distance; temperature; the latitude and longitude; direction of travel; and location relative to specific dates

Florence Nightangle was also a pioneer in data visulaization. She drew coxcomb charts for depicting effect of disease on troop mortality (1858). The use of maps in graphs or spatial analytics was pioneered by John Snow ( not from the Game of Thrones!). It was map of deaths from a cholera outbreak in London, 1854, in relation to the locations of public water pumps and it helped pinpoint the outbreak to a single pump.

Q3. What is the process of data visualization ?

In health psychological research and care, data visualization is important for acquiring, storing, retrieving and using of health care information to foster better collaboration among various healthcare providers. 

Q4. Why do we study data visualization in the research on disease ?

Disease pattern is changing in structure and process. Data visualization can gauge this change by clustering symptoms over periods.

Q5. Does it help in Community services ?

Health infographics can be used for community service. Besides data visualization helps in health survelliance. By analysis of social media post outbreak and geographical span of any depression can be easily identified. Accordingly survelliance system provider can take measures to stop it. Read this article:

a)Method for the Development of Data Visualizations for Community Members with Varying Levels of Health Literacy

Q6. Is there any software ?

You can use Python here: https://www.youtube.com/watch?v=41qgdwd3zAg&list=PLS1QulWo1RIaJECMeUT4LFwJ-ghgoSH6n

Q7. What are the data visualization tools ?

a.Histogram: Histogram is basically a plot that breaks the data into bins (or breaks) and shows frequency distribution of these bins.  You can change the breaks also and see the effect it has data visualization in terms of understandability.

b. Line chart:  Line Charts are commonly preferred when we are to analyse a trend spread over a time period. Furthermore, line plot is also suitable to plots where we need to compare relative changes in quantities across some variable (like time). 

c. Bar chart:  Bar Plots are suitable for showing comparison between cumulative totals across several groups. 

d. Box-plot: Box Plot  shows 5 statistically significant numbers- the minimum, the 25th percentile, the median, the 75th percentile and the maximum. It is thus useful for visualizing the spread of the data is and deriving inferences accordingly.

e.Scatterplot: Scatter plots help in visualizing data easily and for simple data inspection.

Learn more plots from here:

https://www.analyticsvidhya.com/blog/2015/07/guide-data-visualization-r/

Q8. What are softwares of data visualization?

With ever increasing volume of data in today’s world, it is impossible to tell stories without these visualizations. While there are dedicated tools like Tableau, QlikView and d3.js, nothing can replace a modeling / statistics tools with good visualization capability. It helps tremendously in doing any exploratory data analysis as well as feature engineering. This is where R offers incredible help.

R Programming offers a satisfactory set of inbuilt function and libraries (such as ggplot2, leaflet, lattice) to build visualizations and present data. In this article, I have covered the steps to create the common as well as advanced visualizations in R Programming. But, before we come to them, let us quickly look at brief history of data visualization. If you are not interested in history, you can safely skip to the next section. Ref: 

https://www.analyticsvidhya.com/blog/2015/07/guide-data-visualization-r/

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