With COVID-19 forcing many public spaces and recreational facilities to close, people have been spending time outdoors more than ever. It can be boring, though, to visit the same places in your neighbourhood all the time. We created Explore MyCity to generate trails and paths for London, Ontario locals to explore areas of their city they may not have visited otherwise. Using machine learning, we wanted to creatively improve people’s lives.

Benefits to Community

There are many benefits to this application to the London community. Firstly, this web app encourages people to explore London and can result in accessing city resources and visiting small businesses. It also motivates the community to be physically active and improve their physical and mental health.

What it does

The user visits the web page and starts the application by picking their criteria for what kind of path they would like to walk on. The two criteria are 1) types of attractions, and 2) distance of the desired path. From the types of attractions, users can select whether they would like to visit trails, parks, public art, and/or trees. From the distance of their desired path, users can pick between ranges of 1-3, 3-5, 5-7, and 7-10 kilometres. Once users have specified their criteria, they click the Submit button and the application displays a trail using a GoogleMaps API with the calculated trail based on the criteria. The trail will be close in length to the input number of kilometres.

Users can also report a maintenance issue they notice on a path by using the dropdown menu on the home page to report an issue. These buttons lead to Service London’s page where issues are reported.

How we built it

The program uses data from for the types of attractions and their addresses or coordinates and use them as .csv files. A python file reads the .csv files, parses each line for the coordinates or address of each attraction for each file, and stores it in a list.

The front-end of the web app was made using HTML pages. To connect the front and back-ends of the web app, we created a Flask web framework. We also connected the GoogleMaps API through Flask.

To get user input, we requested data through Flask and stored them in variables, which were then used as inputs to a python function. When the app loads, the user’s current location is retrieved through the browser using geolocation. Using these inputs, the python file checks which criteria the user selected and calls the appropriate functions. These functions calculate the distance between the user’s location and the nearest attraction; if the distance is within the distance input given, the attraction is added as a stop on the user’s path until no more attractions can be added.

This list of addresses and coordinates is given to the API, which displays the GoogleMaps route to users.

Challenges we ran into

A challenge we ran into was creating the flask web framework; our team had never done this before so it took some time to learn how to implement it properly. We also ran into challenges with pulling python variables into Javascript. Using the API to display information was also a challenge because we were unfamiliar with the APIs and had to spend more time learning about them.

Accomplishments that we're proud of

An accomplishment we’re proud of is implementing the GoogleMaps API and GeocodingAPI to create a user-friendly display like that of GoogleMaps. This brought our app to another level in terms of display and was an exciting feature to include. We were also proud of the large amounts of data parsing we did on the government’s data and how we were able to use it so well in combination with the two APIs mentioned above. A big accomplishment was also getting the Flask web framework to work properly, as it was a new skill and was a challenge to complete.

What we learned

In this hack, we learned how to successfully create a Flask web framework, implement APIs, link python files between each other, and use GitHub correctly. We learned that time management should be a larger priority when it comes to timed hackathons and that ideas should be chosen earlier, even if the idea chosen is not the best. Lastly, we learned that collaboration is very important between team members and between mentors and volunteers.

What's next for Explore MyCity

Explore MyCity’s next steps are to grow its features and expand its reach to other cities in Ontario and Canada.

Some features that we would add include:

  • An option to include recreational (ie. reaching a tennis court, soccer field, etc.) and errand-related layovers; this would encourage users to take a walk and reach the desired target
  • Compiling a database of paths generated by the program and keeping a count of how many users have gone on each of these paths. The municipality can know which paths are most used and which attractions are most visited and can allocate more resources to those areas.
  • An interactive, social media-like feature that gives users a profile; users can take and upload pictures of the paths they walk on, share these with other local users, add their friends and family on the web app, etc.

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