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Dashboard Week Day 3: Making Sightseeing in Melbourne Easier

This post was originally published on The Data School blog between 2018 and July 2025, before our program was renamed to MIP’s Analytics Career Accelerator. References throughout this article to “The Data School” or “DS” all refer to what is now MIP’s Analytics Career Accelerator. The program, its people, and its commitment to launching outstanding analytics careers remain the same – just under a new name.

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In the third challenge of our dashboard week, we were tasked with creating a dashboard that uses spatial analysis and data from the city of Melbourne to help users make informed decisions. I decided to focus on train station and landmark data, as I have experienced the challenges of navigating an unfamiliar train system as a tourist.

Our dashboard is designed to help users find the closest train stations to their desired tourist attractions, while also considering important amenities such as lifts and real-time information displays. I understand that carrying heavy luggage or being exhausted from a day of walking or visiting make navigating public transportation even more difficult, which is why I offer the ability to filter train stations based on specific amenities.

Using the dashboard, users can filter their search to show train stations within a 500-meter radius of their desired attraction(the distance can be changed as well), and easily see which stations have lifts and Pids. This way, they can decide which train station they can go after visiting one spot. Whether you’re visiting museums, parks, or other tourist attractions, the dashboard can show you the closest train stations with the facilities you need.

Data preparation

Visualization

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