Under Construction Commercial Real Estate in Toronto Market

GeoVis Project @RyersonGeo, SA8905, Fall 2021, Mirza Ammar Shahid

Introduction

Commercial real estate is crucial part of the economy and is a key indicator of a region’s economic health. In the project different types of Under constriction projects within the Toronto market will be assessed. Projects that are under construction or are proposed to be completed within the next few years will be visualized. Some property types that will be looked at are, hospitality, office, industrial, retail, sports and entertainment etc. The distribution of each property type within the regions will be displayed. To determine the proportional distribution within each region by property type. Software that will be used is Tableau to create a visualization of the data which will be interactive to explore different data filters.

Data

The data for the project was obtained from the Costar group’s database. The data used was exported using all properties within the submarket of Toronto (York region, Durham region, Peel Region, Halton region). Under construction or proposed properties above the size of 7000 sqft were exported to be used for the analysis. Property name, address, submarket, size, longitude, latitude and the year built were some of the attributes exported for each property project.

Method

  1. Once data was filtered and exported from the source, the data was inserted into Tableau as an excel file.
  2. The latitude and longitude were placed in rows and columns in order to create a map in tableau for visualization.
  3. Density of mark was used to show the density and a filter was applied for property type.
  4. Second sheet was created with same parameters but instead of density circle marks were used to identify locations of each individual project (Under Construction Projects).
  5. Third sheet was created with property type on x axis and proportion of each in each region in y axis. To show the proportions of each property type by region.
  6. The three worksheets were used to compile an interactive dashboard for optimal visualization of the data.
Figure 1: rows, columns and marks

Results

Density Map Showing Industrial Property type
All Under construction project locations
Regional Distribution by Property type

The results are quite intriguing as to where certain property type constriction dominant over the rest. Flex is greatest in Peel region, Health care in Toronto, Hospitality in Halton, Industrial in Peel, Multifamily in Toronto, Office in downtown Toronto, retail in York region, specialty in York region and sports and entertainment in Durham with new casino opening in Ajax.

The final dashboard can be seen below, however due to sharing restrictions, the dashboard can only be accessed if you have a Tableau account.

Click here to view dashboard

Conclusion

In conclusion, using under construction commercial real estate dashboard can have positive impact on multiple entities within the sector. Developers can use such geo visualizations to monitor ongoing projects and find new projects within opportunity zones. Brokerages can use this to find new leads, potential listings and manage exiting listings. Governments of all three levels, municipal, provincial and federal can use these dashboard to monitor health conditions of their constituency and make insightful policy changes based on facts.

Create a Quick Web Map with Kepler.gl and Jupyter Notebook

Author: Jeremy Singh

SA8903

GeoVisualization Project Fall 2019

Background: This tutorial uses any csv file with latitude and longitude columns in order to plot points on the web map. Make sure your csv file is saved in the same folder this notebook is saved (makes things easier).

I recommend downloading the Anaconda Distribution which comes with jupyter notebook.

There are 3 main important python libraries that are used in this tutorial

  1. Pandas: Pandas is a python library that is used for data analysis and manipulation.
  2. kepler.gl: This a FREE open-source web-based application that is capable of handling large scale geospatial data to create beautiful visualizations.
  3. GeoPandas: Essentially, geopandas is an extension of Pandas; fully capable of handling and processing of geospatial data.

The first step is to navigate to the folder where you want this notebook to be saved from the main directory when juypter notebook is launched. Then click ‘new’ -> Python 3, a tab will open up with your notebook (See image below).

Next, using the terminal it is important to have these libraries installed to ensure that this tutorial works and everything runs smoothly.

For more information on jupyter notebook see: https://jupyter.org/

Navigate back to the directory and open a terminal prompt via the ‘new’ Tab’.

A new tab will open up, this will function very similarly to the command prompt on windows. Next type “pip install pandas keplergl geopandas” (do not include quotes). This process will help install these libraries.

Below you will find what my data looks like the map before styling

With some options

KeplerGL also allows for 3D visualizations. Here is my final map:

Lastly, if you wish to save off your web map as an HTML file to host somewhere like GitHub or AWS this command will do that for you:

Link to my live web map here:

https://jeremysingh21.github.io/

The code and data I used for this tutorial is located on my GitHub page located here:

https://github.com/jeremysingh21/GeoVizJeremySingh