Rents across the major cities surged due to higher net immigration.We wanted to build an app to get insights for the rental market with interactive visualizations.
Explore our app here deployed using fly.io
Building this app involved steps which are summarized in the diagram below
The data for the analysis was collected from Canada Mortgage and Housing Corporation (CMHC) can be found here. Additional geographical information was extracted using Geopify.
For the transformation cleaning was done using jupyter. Data was transformed for both relational and non relational databases. The datasets for these were converted in csv and json formats.
We used MongoDB Atlas cloud database and AWS as a cloud service proivider for our project.
Following were used for the building process :
- Flask
- PyMongo
- Python
- Javascript
- CSS
- HTML
- D3
- Charts.js
- Plotly
- Leaflet
Flask code for the app can be located here.
The dashboard for our app has click buttons for four tabs and html script can be located here.
The first one is yearly trend by years. It is built using charts javascript library and code for the same is here.
It provides the insights for the Average rents and Vacancy rates across provinces over the years 2018-2022. Clickable buttons allow to add or delete the province based on the selection.
The second is the number of units available across provinces over the years 2018-2022 using charts library.
The dropdown selection allows the user year selection.
The third visualization tab is the average rents and units available in the centres across provinces using plotly and code is here along with forth tab.
The fourth visualization is bulit using leaflet which has layers for the average rents and vacancy rates.The size of the markers are based on the data for the location.It allows the user to get the information using pop up feature as below
- https://www.chartjs.org/docs/latest/
- https://plotly.com/javascript/
- Leaflet
- Youtube videos