Welcome to day 2 of dashboard week for DSAU 17. We continue our daily dashboarding challenges & blogs today.

Day 2’s challenge was to use City of Melbourne data to find a location to open a cafe. The caveat on that was that it must be done in Power BI, and enriched with one other data set.

In Tableau, I am very familiar with utilizing map layers, so this presented a hurdle to overcome.

The data set we were given was this, the number of seats per cafe, restaurant & bistro within Melbourne, and the location of these businesses.

The First Attempt

I initially attempted to enrich the data with the pedestrian data to find the places with the most foot traffic, to work out foot traffic to cafe ratios that we could use to tap into hidden potential. I prepared this in Alteryx like so.

However, I ran into issues with attempting to use map layers in Power BI. My goal was to create a heat map, and overlay cafes on top of this, but I was unable to figure this out. I consulted with some colleagues, and did not pursue creating trade areas in Alteryx to import spatial objects, as this was also proving difficult.

Instead, I pivoted to Car Park data, to locate areas that had a large amount of car parks per cafe. I decided to use the off-street car parking data set for this purpose. In future iterations, I would also add in the On-street car parking data set.

I imported this directly to Power BI to begin our dashboarding.


Firstly, I created a basic view of the number of cafes per suburb, and followed this up with the number of parking spaces per suburb.

To follow this up, I created a measure for the sum of car spaces, to use to create our car spaces per cafe metric. I then made the # of car parks and car spaces per cafe into BANs.

Finally, I created a map to see where our potential competitors are, and put together the dashboard from there.

We can see that 62.44 car spaces per cafe is the number to beat. We also see that the CBD is extremely saturated, but Southbank may present an opportunity for expansion.

This was the dashboard prepared on day 2, stay tuned for another post for day 3 tomorrow!

The Data School
Author: The Data School