City of Toronto open data and business decision making

City of Toronto open data and business decision making

From Ad-hoc to App.

In my two previous blog notes I did discuss the three types of Analytics Ad-hoc , Software (App) and hybrid.

In this blog note we will do a little bit more of analysis on the business license data from the City of toronto to help someone who is not necessarily a python programmer to analysis on the city open data by creating an interface.


A tool for analyzing data that will be used by the public should not require knowledge of Python coding.


Creating a user interface

Python has a few libraries that allows the creation of some interesting user interface widgets, I have chosen ipywidgets just to illustrate the ability to create a user interface that allows the user to do the following on the Data.

  • Select a certain category of licenses to view
  • Select a 3 Letter postal code (representing the area) or ALL for the whole city.
  • Use sliders to decide the year range he wants to look at.

So let us look at the ‘Greek Resturant’ scene in Toronto by focusing our search on the Danforth area for example (using Postal code M4K , see map below).


The ipywidgets used to interact with the user and matplotlib diagrams
Borders of M4K postal code

Using the widgets “Interact” capabilities of python, Once you select the Category, range and Postal, you will immediately see the plots showing you the results.

  • Number of Issued licenses per year
  • Number of Canceled licenses per year
  • The total (issued-canceled) of new business open in the postal code per year.



Now compare that to analysis to the restaurants

Borders of M5V postal code

/eating establishments at the heart of the business area downtown for that Analysis we will use the postal code M5V (see map to the right).

And we will focus our search on the 10 years period between 2004 and 2014.

Downtown Core
The Danforth

The graphs tell a story of two areas that respond to different events in different ways, it is obvious that in 2005 there was a change of licensing requirements that increased ‘cancelations’ all over the city but the Danforth was less resilient to licensing changes, but when the Economic crisis of 2008 hit, the downtown core as severely affected while the danforth absorbed the shock relatively well, and most negative impact was also shifted 1 year later with a very strong rebound in 2010.

Numerical Analysis

One of the features that will be useful for such analysis is to allow the users to print the numerical data used for the graphs.


             issued  canceled  total
2004             75        46     29
2005             59        72    -13
2006             99        85     14
2007             62        90    -28
2008             53       114    -61
2009             61        59      2
2010             61        51     10
2011             66        45     21
2012             54        50      4
2013             50        53     -3
2014             63        49     14


Using the notebook and next steps.

If you want to run some of those analysis yourself you can download the zip file from github

Use the ‘User Ready Business License Explorer’ notebook.

You will need the following libraries installed (the easiest way is using pip).

  • Pandas
  • matplotlib
  • numpy
  • ipywidgets

But what about those who want to use these data ?

The next challenge will be delivering this solution without requiring the user to install iPython, Pandas, numpy, iwidgets, etc. and that will be the topic of my next post.


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