Author: ahooli

Introduction

The visualization shares information on the population without health insurance during 2008 to 2015 across all states in the United States. Be it treatments, medications, consultations with doctors, hospitalization – all these charges could be wisely managed by selecting the right insurance package based on medical history. Being uninsured in a country like United States is a nightmare, when your hard-earned money goes into drain when you need any kind of medical attention!

Audience: Health Insurance Providers to ensure maximum coverage of the US population

Claim: Despite of free medical care, people prefer fines over premiums. If unable to pay medical bills, it can be waived off if a person is declared bankrupt or income is too low and so on. Hence, targeting right set of of population becomes important inorder to match them with the coverage that suits their requirements.

Analysis

What I like about the visualization?

1. Doesn’t require human intervention to see the changes over the years (but it doesn’t serve the purpose to the user)
2. Data is trustworthy.
3. The color palette gives it a good look and feel.

What I don’t like about it?

1. It’s too fast, just before the user could collect any information it changes.
2. Lacks depth.

What would make it more useful?

It shares very limited information with respect to population without health insurance coverage. It lacks depth as which age group, race and ethnicity are insured/uninsured. Some pointers on how it could be improved.

Addition of filters: Reveals more insights state-wise.

1. By Age-group
2. By Gender
3. By marital status
4. By Race-ethnicity
5. Type of plan (Private plan-Employment based, Direct purchase; Government plan-Medicare, Medicaid)
6. Median Annual Income
7. Employment Status
8. Education level
9. Number of health plans (One or Multiple coverage types)
10. Nativity (Native born citizen, Non-citizen, Naturalized citizen)

With addition of these filters, more insights will be revealed. For example, the employment rate in every state reveals what percent of people are insured under Private plan. Working-age adults are the most likely to be covered by private health insurance, which provided coverage to 71.1 percent of the population aged 19 to 64 years. And they might have lowest rate of coverage through the government.

On the other hand poor people or unemployed do not wish to be insured because of limited resources. Millions of Americans qualified for Affordable Care Act, but for whatever combination of reasons didn’t make use of the act.

Redesign

If I were to redesign I would also include bar graphs along with world map which will then depict more information on the above mentioned pointers.

https://drive.google.com/a/scu.edu/file/d/0B08_JvsnmpGTZXFJNDBKdWtvZzQ/view?usp=sharing

These bar graphs could be made interactive with the world map to see the trend across different states in United States.

Conclusion

Existing visualization needs to imbibe more details. By adding above mentioned filters, it is easier to find out which state and which set of people need to be focused on. In order to keep the premiums affordable for everyone else it is necessary the young people who are generally healthier and cheaper to insure should sign up for coverage. Hence, it becomes essential to know the age category that needs to be focused. Insurance providers would be then able to target right set of people to get them insured.

 

References:

https://www.census.gov/dataviz/visualizations/health_insurance/

33 Million Americans Still Don’t Have Health Insurance

https://www.census.gov/content/dam/Census/library/publications/2015/demo/p60-253.pdf

http://www.healthline.com/health-news/why-some-people-dont-buy-health-insurance-071315#4

 

Introduction

While I was browsing for jobs with high pay in bay area, I stumbled upon this website. It has some really great visualization enough to keep one hooked on to. Information is indeed beautifully captured and keeps the user engaged! This should take all you folks back to first week of the coursework, where professor mentioned what should be done to make your visualization appealing. I picked up the Gender Pay Gap which is the difference between women’s and men’s average annual pay. This is just a topic which pulled my attention and is not meant to offend anyone in any manner. So, let’s dig deeper and explore the visualization.

The line chart compares the yearly salary of both the genders across different categories of job and between two countries (US and UK). It displays the job types across Y axis and yearly salary (in $000) on above X axis.  Colors are used to differentiate gender, Green for men and Purple for women. The chart is made interactive in 3 areas – by country (US, UK), Plot by (Salary, Gap), Sort by(Job category, Widest Gap, Narrowest Gap, Highest Paid Men Job, Highest Paid Women Job, Ascending, Descending).

Audience: Organizations working towards equal pay.

Claim: Race and ethnicity hampers gender wages in both men and women.

What makes it beautiful?

It’s easy to compare the earnings because of the easily locatable filters. The job categories are grouped and segregated by horizontal dotted line when sorted by job category. It includes exhaustive list of jobs for comparison. Type of Currency is clearly mentioned in both the countries. With line graph, the visualization gives good amount of information in a simple and effective way.

Areas of Improvement

All occupations: When sorted by Job Category, the visualization includes ‘All occupations’ at the bottom. This adds an element of confusion as it doesn’t align with definition of Job category.

Color code: I have been seeing the use of blue color is associated with men and pink with women. It’s good to have a color standard (or I just like to see it that way).

Gender Issues: Why are women paid lesser in almost every sector. One of the factor which is most talked about is LGBT community. Gender discrimination is a major issue when it comes to LGBT group of people.

https://www.pri.org/stories/2015-04-18/why-we-cant-forget-transgender-people-when-talking-about-pay-gap

https://www.americanprogress.org/issues/lgbt/news/2012/04/16/11494/the-gay-and-transgender-wage-gap/

Race: The visualization does not target any specific race and ethnicity to compare the salaries. Hispanic and Black earn lesser than white counterparts due to job market discrimination. If racism is one of the reason, what percentage and which race is bringing down the salary aspect as per gender. Filtering the salary based on race and ethnicity adds more importance to the existing visualization supporting the claim.

http://fortune.com/2017/04/03/equal-pay-day-2017-gender-gap-states/

http://www.huffingtonpost.com/entry/racial-wage-gap_us_57e05f86e4b0071a6e091153

http://www.pewresearch.org/fact-tank/2016/07/01/racial-gender-wage-gaps-persist-in-u-s-despite-some-progress/

Data validity: It’s shown that there is no job in US where women earns higher than men as per the data displayed. However, when I researched on this aspect, I found that there are actually few jobs where women are paid more than men. Social Worker is top one among them. This makes the visualization not so trustworthy.

http://money.cnn.com/2016/03/23/pf/gender-pay-gap/

http://www.cnbc.com/2016/11/25/10-jobs-where-women-earn-more-than-men.html

Open Interpretations: When plotted by Gap and sorted by Job Category, the X axis is displayed in percentage. But fails to say percentage of what? Is it comparing with the particular Industry standards? The data is left for the viewer to interpret. Also, when the job category is plotted across Salary, it’s better to have population information which was used to calculate the yearly salary. Another important point, is the hourly rate which appears at the bottom X axis which is confusing as what it relates to. I’m assuming it is for ‘All occupations’.

Experience Level: The most important characteristic of pay is Experience level. What is the experience level of workers. It would have been better if there was one more filter which gives out the Salary Gap based on individual’s experience level. This would attract larger masses from an Intern to highly experienced person.

Conclusion

One can concentrate on why there is the gap between the the gender pay. Is gender discrimination one of the reason behind it? If so, hiring and equality laws against LGBT workers should be strengthened. The author should validate the details before constructing visualization else viewer would doubt the truthfulness of the content. The graph doesn’t call for any change(Enlighten) and just provides information to the viewer. Overall, it’s a simple and informative visualization and could be made better if focused on improvement areas.

References: http://www.informationisbeautiful.net/visualizations/gender-pay-gap/

 

Introduction

We all are aware of the negative implications of smoking on our system. Smoking is one of the habits known to reduce the life expectancy. It causes cancer and numerous other health complications. A common belief is that longevity of chain smoker is less than that of a non-smoker. Cigarette smoking attributes to 443000 deaths each year in United States. One of the claim is that, the younger you are when you quit, the greater the health benefits. And quitting at any age adds years to life.

What I appreciate about the visualization?

The visualization does an amazing job at convincing what happens to our body at every phase after last puff. The whole idea of this visualization is to persuade smokers to think of benefits when they give up on smoking. The color scheme equipped with human anatomy and brief description gives handful of information at first sight. It does a decent job in explaining how the system improves by hours to days to months and years.

What I don’t appreciate?

It is a very generic visualization and doesn’t target any specific group of smokers to prove the claim. There are no numbers which can explain what population of the smokers saw this change after their last puff. While it provides details on overall risk factors, it fails to consider fertility aspect which is one of the growing concerns in both male and female population.

How it could be improved?

It’s essential to visualize the changes in the human system targeted to different age groups, gender, nicotine dependency and medical background. A smoker who is 25 years with no other health complications might respond differently than a smoker with asthma who might take longer time to return to normalcy.

To support the claim, visualization should include numbers on how far people went on to live after they quit smoking as per age group. And what are the health benefits they witnessed over a period of time.

References:

http://www.huffingtonpost.com/2014/12/05/effects-of-quitting-smoking_n_5927448.html

 

Introduction

McDonald is one the largest fast food chain introduced in 1940. Though it might be the largest, it’s not as popular as In-N-Out which was introduced in 1948. In-N-Out is located in most of the western part of United States and is not likely to expand towards eastern part sooner. Personally if I have choose between McDonald’s and In-N-Out, I would always give heads up for In-N-Out. They deliver the best burgers without using any preservatives. It’s fresh, cheap and employees are customer friendly.

Explanation: Business and Marketing Operations

First, all In-N-Out branches are private family owned and not franchised. This helps them maintain control over food quality and cleanliness. Second, they follow the policy of Make-To-Order and have no freezers and microwaves ensuring quality food is delivered to customers as and when the orders are placed. Serving only fresh food minimizes the kitchen equipment reducing the capital expenditures. They have limited menu and can entirely rely on fresh ingredients reducing wastage. Customization of orders are respected which means they never say no to customers. Third, every store is located within 500 mile radius of patty making facility and distribution center. It’s not unusual to say that, the taste gets inconsistent but the quality always remains consistent! Even the employees are paid better, $10.50 per hour and are provided with better benefits.  These are some of the factors which explains the reason behind lesser locations.

On the other hand, McDonalds follows franchise model. They have a wide range of menu with Make-To-Stock policy and very few common suppliers. With Make-To-Stock policy the process of delivering food becomes faster. To be precise, it’s lesser than a minute as they do not entertain order customizations. Employees are paid around $9 per hour. These are some of the important factors which explains its number of locations.

How visualization could be improved?

The whole idea of visualization is that it should convey more in less. The visualization shows the number of locations In-N-Out and Mc Donald has in two different bar graphs. To make it more effective, I would join the two bar graphs and compare the number of locations state-wise. The circular graph is redundant, even though it depicts number of locations city wise marked in same color if they belong to the same state. Instead, it could be better to incorporate these cities in bar graph. Also, geographically, Mc Donald’s locations should be shown along with In-N-out. Depending on the number of locations every state has, it could be shown by different sizes of circles. For example, if a state has bigger circle it means there are more burger locations.

 

References:

Visualization can be found at: https://www.linkedin.com/feed/update/urn:li:activity:6256732224734011392/

http://www.huffingtonpost.com/2013/02/25/lynsi-torres-in-n-out_n_2759920.html  

https://rctom.hbs.org/submission/in-n-out-the-freshest-friendliest-fast-food/

https://www.linkedin.com/pulse/in-n-out-burger-tableau-dashboard-nick-manley

https://www.bloomberg.com/view/articles/2014-10-02/in-n-out-doesn-t-want-to-be-mcdonald-s

http://www.triplepundit.com/2014/02/n-can-pay-lot-minimum-wage-cant-mcdonalds/

http://www.businessinsider.com/why-in-n-out-burger-wont-expand-east-2015-4