Author: rsjain

The above visualization depicts the food eating habits across the US states. The article suggests the higher consumption of fruits/vegetables, the lower the obesity rate.

What I like about this visualization?

The first visualization shows the fruit/vegetable consumption and the second visualization shows obesity rate across the US states. Both visualizations show what it says but it would have been better if there would have been one visualization that depicts relation between fruit/vegetable and obesity rate. Also, each graph has color and show color variation as per range.

What I didn’t like about this visualization:

1. It’s difficult to understand the relation between fruits/vegetables with obesity. One needs to look at both visualizations and then try to decipher the relation. Visualization should be easy to understand and interpret the correlation between food consumption and obesity.

2. The article has taken only one factor into consideration to decide the obesity rate. It fails to take other factors into consideration such as lifestyle, physical activity, social and economic factors. it should have collected more data to decide the which factor could lead to increase or decrease in obesity rate in different states of US.

3. One could interpret the visualization that Colorado is skinny state and as well as less meat consumption state, which is totally wrong.

Audience:

This article is written for rural American to show the trend of food consumption and its relation to obesity

Does it has good visualization properties:

1. Truthful: Yes, it is. Though it’s combination of two visualization to show relation between fruit/vegetable consumption to obesity rate, but, each visualization is truthful in what it says.

2. Functional: Yes, each visualization is functional and conveys the individual information.

3. Beautiful: It’s partially beautiful, it’s just the map graph with color variation but not that appealing to the eyes.

4. Insightful: This both visualization doesn’t give any insights. The visualization fails to show any trend.

5. Enlightening: It fails to enlighten from the audience perspective. It’s show the consumption of food and obesity rate but no enlighten about what action could be taken to reduce the obesity rate.

How this graph could have been better:

1. Instead of two visualization to show relation between fruit/vegetable consumption to obesity rate, one could have used Dual axis concept and depicted the relation in graph.

2. Use of Bar graph. One could use bar graph to depict food consumption across states and line graph to show the trend of obesity rate of each state.

3. Also, collecting data and depicting other factors that could relate to obesity rate would have been more insightful. Adding other factors would be very helpful to decide that only consuming fruits/vegetable could lower obesity rate or not.

References:

Daily yonder

I was googling about best places to visit in New Zealand and trying to gather tourism information about New Zealand. And I stumbled upon the official website of New Zealand tourism. And Market & Stats tab caught my eyes, the moment I looked at that tab, it showed the list of the countries and I got more curious what stats would it been showing related to New Zealand vs other multiple countries. I opened the stats for the Singapore. The moment this page loads, the first thing is the visualization of International Visitors – Total vs Holiday Visualization of International Visitors for Singapore . And I started wondering what this graph is all about.

What this visualization about:

Even after reading the whole article, I was still clueless. After doing a little research, I understood that this graph is showing the No. of Singapore Visitors that arriving in New Zealand over the time period of Mar 2016 – Feb 2017. One line graph shows No. of Singapore visitors coming to New Zealand for Total i.e all purpose of visits i.e business, tourism etc. Other line graph shows No. of Singapore visitors coming to New Zealand only for Tourism. In short, the graph is trying to convey the purpose of visits of Singaporean to New Zealand.

Did I like anything about this graph?
When I say I started wondering that what this graph is about – it’s clear that there is nothing I could like about this graph. But I was clear it trying to convey No. of Visitors from Singapore to New Zealand.

How this visualization could be better:
1. The title of the graph: The title of the graph is International Visitor Arrivals – Total vs Holiday. This graph is about the purpose of visits of Singaporean to New Zealand. Instead of International Visitor Arrivals, the title could be Singaporean Visitor Arrivals – Purpose of visit. Total vs Holiday makes no sense at all. The Total includes all types of visits of the visitors, but it wasn’t clear anywhere what “Total” means. For me, “Holiday” also took the time to understand that Holiday meant Vacation or Tourism. Now, depending on your targeted audience, the words used for the title matters. For a visitor like me, using “Vacation” instead of Holiday would make sense and “Tourism” if the graph was targeted for NZ Tourism Manager.

2. More line graphs on all major purpose of visit: The graph shows line graph for Total. It could have included all the bifurcations or types of the visits and shown line graph for them. For example, Purpose of visit (Total) could be divided into Business, Education, Medical. It could have shown trend and no.of visitors for each of type of visit.

3. Making it more insightful: This graph could be made more insightful by adding reasons or events that contributed to the trend in an increase or decrease in No. of Visitors. It could have recorded various events/reasons that would have helped to understand why there is an increase in No. of Visitors in the month of December.

Another graph that caught my attention was International Visitor Arrival – holiday by countries . This graph shows No. of Visitors by Holiday by countries. Comparison of No. of Visitors by countries is good KPI to understand from which country New Zealand had more no of visitors in terms of tourism. But this graph doesn’t really compare the data and trends of no. of visitors by tourism by countries. This could have been better as shown here: Overseas Residents’ Visits to Japan by Country and Region

Conclusion:

The author was trying to convey Singaporeans Visitors visiting New Zealand – Total vs Holiday. But the visualization didn’t do justice to the message or claim. A visualization should be simple to convey the story or message to any type of audience. One should in keep in mind to give importance to minute details such the words or title of the graph, even a title of the graph should match the message/claim of the graph. Adding more details by showing no. of the visitor by the different purpose of visit or just by telling what “Total” includes would add more sense to the graph.

References:

Tourism2025, Japan Tourism Stats, China Market snapshot

One is planning to move to another city, the first question in anyone’s mind is the new city going to be more expensive compared to current one. Will my income suffice to the average expenditure which includes housing, utilities, transportation, taxes etc? Recently, when I was looking for datasets and articles for my group project on which city is best place to live, I came across this article: Study says Washington is expensive than New York. The article made me wondering is that really true that Washington is expensive than metro cities such as New York and San Francisco. And most interesting about the article is the visualization used to draw the conclusion.

Here is the visualization:

The above visualization is a simple bar graph that shows average annual expenditures on various household items of selected cities.
Y axis – Selected cities
X axis – Categories of Household items & those are 1. Furnishing & Equipment 2. Housekeeping Supplies 3. Household Operations 4. Utilities 5. Housing

The best part about visualization is that it’s so simple. It shows the expenses of various categories with respect to the cities. For anyone who looks at the graph, it’s easy to come to the conclusion that Yes, Washington is expensive compared to all other major cities. Whichever city that has the biggest bar is the expensive one. But is that actually true? Does this visualization do justice to need or answer to the question i.e. Which is the expensive city to live in? And, I don’t think so.

Firstly, what is expensive? How you define your needs? If the income is high and people can afford to spend, does that make a city expensive? This logic goes with the above visualization. People in Washington have high income and spends major part in housing, but that doesn’t imply that Washington is expensive to live in. And also, above graph just tells us what people are spending on. What people are spending is no way correlated to an expensive city.

Secondly, I think data collected wasn’t enough to answer the need ( Which is the expensive city? ). Having or considering the data of expenditure on various household items can’t only be the determining factor in deciding which city is expensive. The data doesn’t give justification to the claim. It would have been better if the data was collected on following:
1. What is the various taxes of the selected cities?
2. What is the median household income?
3. What is the salary by profession or salary for the common profession?
4. What is the school & education cost?
5. What is the transportation cost?

After collecting data on above factors and many other ones, then it would be better to draw a visualization and draw a conclusion. Better would have been to compare and contrast the data on the above-mentioned factors. Comparing charts on various factors of various cities as shown here : SF versus NY helps in better understanding of which city is expensive.

This visualization made me understood how collecting limited data and how a simple graph could lead to a misleading conclusion. It is very important to define our needs correctly in correct context. Also, collecting enough data from multiple resources is also important. Validating the visualization to the question we want to answer is critical. It’s crucial to determine that have I drew correct visualization or not. Additionally, having evidence to support the claim makes it better visualization.

References:

Washington Post, Datausa.io

http://www.businessinsider.com/the-27-worst-charts-of-all-time-2013-6#wow-multicolor-3-d-cylinder-bar-charts-are-a-really-really-bad-way-to-articulate-relatively-simple-data-19

The above graph wants to see the trend of mortality rate in a relationship with epoetin dose and hematocrit group range. Within each hematocrit group, as the epoetin dose increases the mortality rate trend is increasing and the highest mortality rate being for hematocrit group range of <30%. Within each epoetin dose quartile, there is an increasing trend in the mortality rate as the hematocrit group range decreases.

Issues in the above graph:

1. Use of 3D chart: The use of 3D charts is confusing as well as deceptive to our human’s eyes. 3D graphs misrepresent the data which makes it difficult to determine the correct value. Analyzing the 3D charts requires additional brain processing which shouldn’t be the case. It’s not easy to understand the trend or insight of the mortality rate with respect to epoetin dose and hematocrit group. In more than 39 hematocrit group bar, Q2 and Q4 seem to be same.

2. Multiple colors: Use of multiple colors is distracting. Adding more colors makes it hard to read the graph.

How can be the graph improvised:

1. Convert 3D to 2D: Using bar graph, it helps to determine the values correctly as well as understand the trend of the mortality rate in a relationship with hematocrit group and epoetin dose. Deception about values for Q2 and Q4 for “>39%’ being same is eliminated.

2. Use one or two colors.

https://drive.google.com/open?id=0B_RLPSpuvXY1LUREek1vaG41Yjg