It is the end of Dashboard Week. The challenge for today is to visualize a given data set without Alteryx. The dataset is about the UN Air Quality Index and contains different CSV files related to different years. However, by unioning them in Tableau, I found the data set is quite clean and suits me to make the dashboard without other tools.
Storytelling with Dashboard
One of the main reasons I would like to move to Australia is to avoid pollution. This dashboard compares the different air conditions and climate between Australia’s main cities and the city I used to live in. I prefer to move to the city, which is closer and more similar to my original city.
The left map shows the city I came from, Foshan, and the capital cities in Australia. You can tell the distance from Foshan and cities in Australia by looking at the distance number or hover over the cities. The colors by the index number of PM2.5 are considered the main hazardous air pollution to daily life. The darker the color, the higher of pollution with PM2.5.
How to choose the right city using this dashboard:
If I choose 4 nearer cities, I may choose Darwin, Brisbane, Perth, and Adelaide. I may not choose Canberra for its color is darker than Foshan’s. That means, in 2019, the air pollution in Canberra was even worse than the city I used to live in.
If you click on Canberra, you can also see different kinds of pollution. Luckily, except for the PM2.5 and PM 10, other types of pollution index in Canberra is lower than Foshan. The main reason for that could bush fire in 2019. You also can hover over the cities on the map to have a better picture of the trends of PM2.5 pollution in recent years.
After I chose 4 cities close to Foshan, I would like to see the climates different. By selectin the filter on the top of the Barbell charts on the right, I can tell the different kinds of climate in certain seasons with different measures. By clicking on different cities, I can easily find which city is more similar to Foshan. I found Sydney is the most similar city, and I decided to move to Sydney.
This use case is just for me to make the relocate decision from my located city; however, I can change it to suit other people’s needs next time.
Techniques for today
1.Get Geoinformation from spatial objects:
When Tableau calculates the distance or makes lines from one point to another, it needs 2 separate columns of spatial points. The original data set only contains one spatial point information for each city in each country. Besides that, as I mentioned in the previous blog, we can’t do fixed LOD to spatial objects. I need another table that includes spatial objects for Foshan and cities in Australia. There is a quick way to do it without any extra tool.
Double click City to have a map in the canvas. Select Australia cities and right-click it and select the ‘View data.’ You can see the latitude and longitude of cities. Like the picture below:
Paste it into Excel file and do the same thing to Foshan city and duplicate its lat and long for every row:
2. Make line and distance calculation:
After we have the table, create the Make line, and distance calculation becomes quite easy. Please see the pictures below:
Barbell charts are an alternative visualization choice for illustrating the change between two data points. It needs two same measures in the dural axis. One is the circle chart, and the other is the line chart. It also needs one dimension filtered down to two categories to put into color marks in both charts.
4. Parameter action allows interaction across data sets
To better visualize the data, I need the interaction between two charts to allow users to select different cities in Australia to compare with Foshan on the right chart. I tried ‘set action.’ However, ‘set action’ can come across data sets. So I decided to use ‘parameter action’ instead.
I create a ‘Selected city’ parameter which only contains the names of Australia cities.
Then I set the ‘Parameter action’ in the Dashboard action. If both charts include ‘City’ in the view, it will allow you to interact between charts based on different data sets.
This week was very busy but good for practicing all the knowledge we have learned so far. We also learned some new knowledge by taking a different kind of challenge:
- How to web scraping data using API and embed the youtube link into the Tableau dashboard–What would you prepare for dinner? Dashboard Week Day1
- Using the Pearson correlation tool can help with figuring out important features quickly–Figure Out Important Features Using Pearson Correlation Tool– Dashboard week Day2
- How to connect ArcGIS data using Tableau Eris ArcGIS Server–Find Your Nearest Park With Facilities You Need–Dashboard Week Day3
- How to build a good viz base on an old version of Tableau—Select Three Drinks to Get Drunk–Dashboard Week Day4
- How to visualize the spatial objects only use Tableau.