Gyms in Singapore are not convenient for people to go to.

We realised that we can solve this problem by making the gym a walking distance from people's house and also making the gym available to those who most use it.

What if we could predict the next best place to place the next gyms (future gyms) so that it would help to maximise the usage of the gyms and maximise the profits of the potential business owners looking to go into the gym business.

basically find the best point where most young people & gym goers & population with one of the highest density to look to build the next gym so that the gym is best optimized for usage and business profit.

What it does

Analyse population data for different areas and provide a recommendation based on factors such as age group, gender etc on the location to build gyms/ move mobile gyms.

How we built it

We used tableau to visualise the dataset and used the tableau servers to also host the dataset over there. We used HTML & Javascript to help to put all the visualisation into one dashboard.

Data Cleaning was done using Excel & python programming language to obtain essential data which we need.

Challenges we ran into

  • Cleaning raw population data
  • Using tableau to express the data properly
  • Accuracy of GPS markers
  • Population Density in housing areas

Accomplishments that we're proud of

  • it is our first time that we got the chance to work with geo-spatial dataset and explore the use of different data-layers to find out which is the best region within singapore to install the next gym within the area that most people need.

What we learned

  • How to use Advanced Tableau Functions in order to be able to display the area over the map analysis.
  • Using Excel & Python to hasten our data extraction and cleaning process.

What's next for TheNextGym

we aim to take the same solution and use it to be applied across other business verticals. such as, the next best place to do a

  • yoga/pilates event
  • sell healthy foodstand


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