#MakeoverMonday is a weekly data visualization exercise led by Eva Murray and Andy Kriebel. They post a data set and original viz - and we improve upon it! To participate or for more information, visit the Makeover Monday website.
2021 week 4: Coal Production in India
Below is the original visualization. It depicts the coal production and number of mines by coal owners across all states in India.
First, two questions:
1) What works?
- The underlying data is well-researched and documented. It is clear where the data comes from, and there are many additional dimensions to the data, such as type of mine, government or privately operated, etc.
- The original viz shows the correlation between coal production and number of mines, it's relatively easy to see where coal production is highest.
2) What doesn't?
- There are a lot of rows which makes it difficult to assess the top and bottom mines in terms of production and number of mines.
- The sort order is unclear - looks like it is a combination of production and mines.
- What question does this data answer - which mines are most productive? Which mines have the best ratio of coal production to number of mines?
Click here for the full interactive viz on Tableau Public.
- Let's start by focusing in on a question - which mining locations are the most productive?
- There are multiple dimensions available within this dataset: mine location (custom latitude and longitude), state, district, coal miner owner name, and mine name.
- This week was an opportunity to work with mapping!
- Sets and parameters. For the "top 20 mines by production", a set and parameter were used to create the dynamic filtering.
- Dashboard actions. Actions are such a powerful interactive technique for Tableau. This week, three dashboard actions were created to highlight data across charts:
- When selecting a top mine site from the bar chart, the geographic location will highlight.
- When selecting the geographic mine location, the correlative mine site from the bar chart will highlight.
- When selecting the coal owner from the coal owner production scatterplot, the related mines will highlight in the bar chart.
- Note: to make some of this work, additional data needed to be added to the detail within a chart. For example, for the coal owner to be linked to the mines, the coal mine owner dimension was added to the detail for the mine production bar chart.
- Coal owner production. One of the most interesting insights from the data is assessing coal production rate - that is, the ratio of production to mines. It's easy to see which coal owners produce the most coal, but do they have the highest production rate per mine? The scatterplot shows that the coal owners with the highest coal production actually have a lower production rate per mine than other coal owners.
Required Citation (with links)
Sandeep Pai and Hisham Zerriffi. A novel dataset for analysing sub-national socioeconomic developments in the Indian coal industry, IOPSciNotes, https://doi.org/10.1088/2633-1357/abdbbb
SOURCE ARTICLE: A novel dataset for analysing sub-national socioeconomic developments in the Indian coal industry
DATA SOURCE: Sandeep Pai and Hisham Zerriffi via Harvard Dataverse