Jobtree

When considering a new job, most people only look at their potential salary. In the interest of a green future, we've expanded this consideration to commute time, housing price and income to expenses.

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Inspiration

Our goal is for employees and employers alike to transition to consider quality of life aspects when making job postings. We believe that the majority of travel is between home and work throughout a given, week, month and year. Therefore, our goal was to decrease travel time / encourage public transit options to move our society towards a green future. To sum it up, we want job searching culture to transition towards a greener future considering new options such as commute time.

What it does

When a user selects a job posting, we calculate the following for each Montreal neighborhood:

Median housing index : cost of housing in the neighborhood compared to Montreal on average. Salary index : Quantity of salary saved after paying median costs of living in Montreal and rent. The goal for this index is to save approximately 30% of the salary, since most financial advisers encourage this behavior. Commute time index : We use Google's distance matrix to calculate travel time from the office of the job posting for public transit and private vehicles. We weigh the public transit more in our calculation ( 70%) to further encourage a transition to a green future.

From these values, we've created a heatmap of Montreal to evaluate how different neighborhoods perform for different job postings. We display the results below the heatmap for each respective neighborhood so users can consider all the options at their disposal.

How we built it

The application was built using VSCode IDE and a GitHub repository. We separated tasks in frontend and backend and had two members for each. The frontend team created the interface, search bar, and the job posting sections, while the backend team created the google maps algorithm, search bar filtering algorithm and retrieved data to perform the calculations. We used JavaScript, CSS and HTML to quickly implement functionalities due to their flexibility. Furthermore, we used several Google Cloud tools to create the visual representation and for calculations.

Challenges we ran into

Job posting APIs were unusable since we are not employers. We tried to sign up to become Indeed Publishers to have access to their API, however it did not work out. This was our first experience with the Google Cloud platform and learning how to use them slowed down our progress throughout the project. Especially the Distance Matrix calculator, we send close to 30 queries in one search however to retrieve the data takes a few seconds therefore we had to change product design to allow the information to be gathered in time.

Accomplishments that we're proud of

We are very proud of our heatmap and the results it displays. Our tool is usable by a widespread audience and as a result, we believe that we've succeed in creating a tool that can truly change the job searching landscape.

What we learned

We learned how to use backend elements like: GeoJSON, Google Distance Matrix, Google Maps Embed, Google Places JS API, Google Geocoding. Additionally, we furthered our knowledge in tools like Bootstrap and Javascript.

What's next for Jobtree

We would like to implement Indeed API to our website. This would allow all job postings in Montreal to be considered rather than the select few we've created in our project's JSON.

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