With the current scenario, everything is so uncertain, and it has already affected millions of people. With the lockdown, countries are losing their money, people are struggling to find a job, and the economy is falling. Apart from these, there is also some positive side to this lockdown.
India is one of the most populated countries in the world and is also majorly hit by COVID-19. As the number of cases was rising in India, it was on 24th March 2020 that the Indian government announced the nationwide lockdown. The whole nation stopped for the betterment of everyone, but along the way, it also overcame the rising issue of pollution in India. Post 16 days of lockdown in India, there has been a drastic change in the air quality. This topic visually informs the viewers about how the lockdown has effected the air quality in India.
Inspiration & References
Before going directly into making the visualization, I researched around similar topics and found out the best ones to take as inspiration.
I liked the use of colors on this, as it visually informs the viewer about the air quality within Southern California. All the variety of visualizations showcased here also suits this type of data.
With this exercise, I wanted to explore the option of the heatmap, as it would have been a great option to represent air quality data. I also liked the idea of a slider to compare two different dates.
I love the use of colors on this visualization, as it is easy to distinguish between different states with these colors. They also beautifully present the information with minimal visuals and by just using the line chart.
The information for this visualization was available to me through the data world. This Data informs about the Air Quality Index of India from 16th March 2020 to 9th April 2020, and it also highlights how the Air Quality Index has changed after lockdown. Information for this dataset is gathered from the official government website of Air Quality Management. Click here to view this specific data. However, it misses recording the air quality index of some states in India, such as – Jammu Kashmir, Uttarakhand, Goa, Himachal Pradesh, Manipur, Nagaland, Arunachal Pradesh, Chhattisgarh, Sikkim, and Tripura. With this limited information, this dataset is still more than enough to explore the boundaries of visualization.
After finalizing this dataset, I have used OpenRefine to refine the information. Most of this information was refine, but it still needed changes such as removing blank space from the text and using a standard format for dates.
Later, Tableau has been used to work for the visualization. It is a powerful software to create beautiful and endless visualization for the data. Tableau already comes with a preset of visualization, which you can choose from according to the information that you want to represent. This tool gives freedom to the users and allows them to create visualization as per their wish.
While creating this information visualization, the context of the data has to be kept in mind. I have tried to use the right set of visualization for each information. This data informs that air quality has improved in data post lockdown, and hence I have used colors that are fresh and send a positive message across. I have divided the whole visualization into three parts.
1 – Looking at the overall Air Quality Index of the states in India
2 – Looking at the air quality index of the most populated cities of a particular state.
3 – Looking at the recorded air quality index of different stations within a state.
This way, the visualization is not only divided, but it also becomes like storytelling.
1 – Avg. Air Quality Index of states in India
With this visualization, I wanted the viewers to get an overall impression of the air quality index of different states in India. The colors play a crucial part in this information, as they visually tell about how the air quality has been in a state of India. We can see how the color shift from yellow and orange to green and light green post lockdown, this notifies that there has been an improvement in the air quality. Prof. Chris Sula also mentioned using a map beside this chart so that non-Indian viewers can know more about the states in India. All this adds to the topic, along with the correct use of visualization and color.
2 – Avg. Air Quality Index of most populated cities of Maharashtra
This second visualization informs the viewer about air quality in the five most populated cities of Maharashtra. With this data, I wanted to show a progression over time, and hence I have used a line chart. This visualization beautifully represents how different cities have air quality recorded over time. The distinct bright colors represent the cities of Maharashtra in this visualization, and it also helps the viewer to distinguish different cities. Again these colors spread a sense of positivity and also subconsciously informs the viewer about the improvement in air quality. The map in this visualization also familiarizes viewers with the location of these cities in Maharashtra.
3 – Avg. Air Quality Index at different stations of Delhi
Delhi is the capital of India, and it also falls under one of the most polluted states in India. This information visualization tells about the Air Quality Index in Delhi on 16th March 2020 and 9th April 2020. There has been a mega improvement in the Air Quality of Delhi since the lockdown. Hence the left side is represented with dar shade of red and the right side with a light shade of red. It visually informs viewers about how the recorded air quality on the two different dates. A bar graph below also supports the visuals by informing users about the air quality index recorded at various stations in Delhi. The left bar being the one before lockdown and the right bar being the one post lockdown.
The final result for this information visualization is here! This visualization covers the topic of Air Quality in different states of India and how have lockdown affected it. With this information visualization, I have tried to cover three distinct subjects on the Air Quality Index in India. The purpose of this exercise was to choose the right visualization for the right set of data, and also the accurate message to convey from the information visualization.
This practice was a great way to explore different types of visualizations provided by Tableau. However, Tableau limits the actions for geospatial visualization. I wish I could explore more on that part by creating a heat map of India to show the Air Quality Index. With this information visualization, I got a good understanding of the different axis and where/how to use them. The ability to customize font, edit colors, and create a dashboard adds to the flexibility of visualization. If the information is available for every state in India, then it would have been a complete project. This project also opened up the boundaries for the scope of lockdown. For example – I can break down even into more details like, how many vehicles polluted the air during the lockdown, or how much are the factories responsible for the air quality during the lockdown. This project helped me to understand the data, refine it, and work on visualizations to convey the appropriate message.
- The Toxic Twenty Five: An Analysis of Southern California Air Quality, “https://www.vizwiz.com/2016/09/the-toxic-twenty-five-analysis-of.html“
- Murphy, J. (2020, April 10) “https://www.accuweather.com/en/health-wellness/air-pollution-way-down-over-northeast-nasa-satellite-images-appear-to-show/718578“
- Wright, R. (2020, April 1) “https://www.cnn.com/2020/03/31/asia/coronavirus-lockdown-impact-pollution-india-intl-hnk/index.html“
- Pollution in India 16thmarch2020-9april2020, “https://data.world/vizzup/pollution-in-india-16thmarch2020-9april2020“
- Central Pollution Control Board, “https://app.cpcbccr.com/ccr/#/login“