Hello everyone, welcome to my Dashboard Week journey 4.0!

Our challenge today is to use an API to download the data we need and then use it for spatial analysis. The website we’re utilising this time is the City of Melbourne’s website, which offers a wealth of open data sources along with helpful instructions for API usage.

Initially, I had planned to use only the cafes and restaurants dataset to determine the best locations for opening a new cafe or restaurant. However, after delving into the City of Melbourne’s extensive data sources, I stumbled upon some new ideas. I decided to make this dashboard a challenging project – a dashboard that could assist me in selecting holiday destinations within Melbourne.

My strategy was to first identify a landmark I wish to visit and then find the nearest 10 restaurants, from which I could choose one to dine in. Simultaneously, I would explore on-street and off-street car parks to secure a parking spot. For on-street car parks, I only required their locations, while for off-street car parks, I selected commercial car parks with available capacity. To enhance my dashboard’s functionality, I also integrated pedestrian data to help me avoid areas with high pedestrian volumes. This is an additional function for those who would like to find somewhere without the need to line up a long queue. These are the datasets I used in my dashboard:


With my content plan in place, I commenced the process of using the API to download data from the City of Melbourne’s website. Here’s a glimpse of my API workflow:

  1. Retrieve relevant datasets using the API.
  2. Clean and transform the data to suit my project’s needs.
  3. Perform spatial analysis on the collected data.
  4. Generate a Tableau hyper file to power my Tableau dashboard.


This is how my spatial workflow unfolded:

  1. Gathering the desired data.
  2. Data cleaning and transformation.
  3. Conducting spatial analysis.


With all the data I needed in hand, I began constructing my Tableau dashboard. To enhance user-friendliness, I included additional information such as cafe and off-street car park locations, indoor and outdoor seating capacities, available parking spaces, and distances. This is what my final dashboard looks like:

Link: Where to spend your holiday in Melbourne?



This marks the culmination of my journey through this dashboard challenge week. I’ve learned a great deal about APIs and spatial analysis.  Using multi-layer maps in Tableau for spatial analysis has been an enjoyable experience. While this week has been a hectic week for me due to the tight timeframes, it’s always rewarding when you can learn new skills. I hope you’ve also enjoyed this dashboard week journey and like my dashboard designs.

Thank you for joining me on this exploration of Melbourne’s data-driven possibilities. Until next time, happy dashboarding!





The Data School
Author: The Data School