Dashboard – https://ourworldindata.org/
Description:
This area chart visualization presents the death rates across the world caused by air pollution from three sources namely indoor solid fuels, particulate matter, and ozone. The death rate numbers shown are per hundred thousand from 1990 to 2015 in steps of five years.
What I like about this dashboard:
- Area chart is effective for visualizing magnitudes of connected-series dataset as visible because of a filling between the line segments and the x-axis. So, a person can observe the change in growth effectively. This observation cannot be visualized so effectively in other visualization tools such as line graphs.
- This dashboard presents both the absolute and relative trend of death rate either in world or in any country.
- It is interactive in nature and includes a drop-down menu of several countries in the world. Either one can visualize the pattern of entire world or can also view the pattern in any single country by just select it from a drop-down menu.
- Another good feature it includes is that one can view the magnitude of death rate for any individual source of air pollution or in a combination of two or three. Such as visualizing death rate pattern only from ozone or in a combination of two sources such as particulate matter and solid fuels.
- It also gives the information of death rate in an absolute as well as in relative to the other regions also.
Cons of visualization tool used:
- Data in one segment is hidden behind the data in another segment. When I visualized the death rate pattern only from air pollution from ozone, the width of the green area was thicker as compared to its width when all the three areas are enabled. This is undesirable as it does not convey the actual trend.
- Generally, to get a value for a point on a curve, we look at its Y coordinate. However, in this case, to get a value, we need to subtract the upper and the lower Y coordinates of a point on an area. This makes it difficult to visualize the relative values of the three areas in first go.
- For the absolute trend, as one moves from left to right in the chart, the y coordinate of the green area (death from ozone) decreases which gives an impression that the actual value of the deaths from ozone is decreasing from 1990 to 2015. However, the deaths from ozone do not change over the years as reflected by the (almost) not-changing width of the green area. This is highly confusing. The confusion also arises from the fact that the green area is very thin making it appear somewhat similar to a line curve. Hence, change in y-coordinate gives an impression of change in magnitude.
- The shape of the entire chart (group of three areas) depends on the order in which the three areas (green, red, blue) are stacked vertically. Hence, a change in order will significantly alter the shape of the chart. For example, if the green area which is almost constant in width is kept at the bottom, the entire chart will look more stable. This is undesirable because visualizations for same data should look similar.
Let’s move on to the critical analysis:
- Does this visualization carry any goal, does it have any purpose?I believe that the visualization severely lacks a purpose and its goals are quite unclear. Hence, I would not categorize it as enlightening.
- Considering the domain, two things came in my mind, audiences and the needs.
Audience – I am unable to identify the target audience for this visualization. If it is intended for the worldwide social or environmental agencies, I do not think that the information provided is sufficient to fulfill their needs or can help them to decrease the level of air pollution caused by any of the sources.
Let’s take an example:
Knowing the trend of deaths rate from particulate matter over the years does not solve any purpose unless it provides further information about the types/categories of the particulate matter causing deaths such as if they are man-made or natural or both, and their respective proportions in the deaths. There can be various types of particulates like the ones resulting from dust storms, volcanic eruptions, or chemicals such as oxides, nitric acids, etc. Similarly, nearly half of the world’s population still relies on burning solid fuels such as wood, animal dung, crop residue and coal for their day-to-day household needs. Therefore to get a better picture and to know the root cause of air pollution, further details providing death trends from these sub-types of particulate matter should have been included. Hence, I feel that the visualization is not insightful.
- Claim: This visualization does not showcase any claim, either about any particular air pollution source or any region most adversely affected by any kind of air pollution. As there is no claim, there is no warrant that provides any reasoning behind the arguments.
- Rebuttal: The viewers cannot throw any counter argument as there are no arguments presented in the visualization. If the designer of this visualization thinks that this dashboard is sufficient to work on the death rate numbers for any health or environmental organizations, my rebuttal would be that no this not sufficient as evident from the points listed in this blog post.
- The data is in the scale difference of five years, so one cannot get the actual information about the condition in intermediate years. Subjects like death rates require continuous data to analyze the situation across years.
- The authors have completely missed the connection between sources of air pollution and death. And, that connection is a “disease”, which is caused by air pollution. Air pollution leads to death of a person through a disease. People just do not die by inhaling harmful particles. Air pollution caused by any of the listed sources can result into a lung failure, heart problems, etc. Hence, I do not find the visualization numbers “convincing”.
What could have done better –
- Use of multiple sources of data: Death rate is a very sensitive subject so designing a visualization from only one data source makes it less effective as compared to the visualization designed from using multiple data sources which includes root causes, effects and continuous information from 1990 to 2015. The continuous data would also help to make any prediction in coming years, which cannot be made currently.
- This visualization does not give any comparative analysis of the effect of air pollution in various regions. So, Bar graphs could have been used for this purpose.
Below are the links showcasing some similar visualizations in air pollution. I would not say that these visualizations are a perfect substitute or they address every weakness raised above, but it appears that they carry a purpose and can be helpful for fulfilling the goal of their audiences.
Redesign:
As this visualization does not carry any specific goal and any specific actions to meet that goal, it cannot be categorized in the category of visual confirmation. It can fit in a visual exploration quadrant though. I came upon some useful visualizations from the data provided in this chart.
Comparing similar visualizations:
- http://www.scoop.it/t/classroom-geography/p/4018472031/2014/03/27/infographic-deadly-air-pollution-where-and-how
- http://www.wri.org.cn/en/node/41165
- You can view my redesigned part here – https://docs.google.com/a/scu.edu/document/d/1X1XZyh1MgFW0B3VsvxKV1aehY2M391skNTudTDfAKg8/edit?usp=sharing
- Tableau public work: https://us-east-1.online.tableau.com/#/site/magarwalscuedu/workbooks/46729/views