It’s finally the last day of dashboard week. This week has certainly been the most intensive one ever since the training started three months ago and has definitely put our skills to a huge test.
Since we had to make the dashboard and present at 3 o’clock in the afternoon, the time available is very limited. Thus, we are given a relatively easy challenge to work on and it’s a data set about air quality.
The Data and My Plan
The data is provided in csv format so we can directly load it into Tableau. The data contains different measures of air quality for 95 different countries. Some of the measures include PM25, PM10, Ozone, etc. In light of time constraints, I decided to just look at one country – China, and my plan was to build a dashboard that could reflect the changes in air quality across the time periods available in the data. Since each data set contains only one year of data, the first step was to union them in Tableau so I could get all the years, as shown below:
As I started to inspect the data, I realized there were quite a bit of missing data. For example, the year 2014 only had data for December. And for several years, only data for the first half year was available. To make the comparison fair, I decided to only look at the air quality data in the first quarter of each year. But I will make use of the viz in tooltip functionality (today we are not limited to version 8.3 anymore) to show air quality trend averaged for each month over all years for each city, so that when the user hovers over the map, he/she can still see the situation for all months.
Building the Dashboard
Due to time constraint, I only created a simple dashboard for today’s challenge – a map showing China and all cities contained in the data set within China, and a scatter plot comparing the average minimum and average maximum PM25 pollutant level for each city. I put year on the Pages card to enable transition. Hovering over city on the map will show you the trend.
I spent the rest of time polishing the dashboard, such as tuning the color and adding some texts to provide more description. Here’s how my final dashboard looks like and the link to it:
If you play the animation on the dashboard, you can clearly see improvement in air quality over the years. PM25 levels have dropped to a large extent across most cities. But unfortunately, the average median value is still considered at an unhealthy level, which is defined as anything above 35.5 for PM25. As this trend continues, however, we should begin to witness more and more cities having healthy air quality, especially in those along the southern coast of China.