This is a technical test of visualizing the investment in the neighborhoods of Toronto and the relative Tax Estimation on Residential Properties in those neighborhoods, using matplotlib/numpy/basemap/descartes in Python.

This project is a work in progress. The implementation is incomplete and subject to change. The documentation can be inaccurate.

Several geographical data sources of the Open Data initiative of the City of Toronto are used (taken to the WGS84 coordinate system, which most of them offer):

City Wards

Priority Investment Neighbourhoods for the Toronto Strong Neighbourhoods 2020

Business Improvement Areas

Current Value Assessment (CVA) Tax Impact Residential Properties

Capital Budget & Plan By Ward (10 Year Recommended)

The script download_investm_shapefiles_toronto.sh is given to download these GIS shapefiles and Excel budgets, and to prepare the GIS shapefiles to the WGS84 coordinate system (if necessary).

We need programs in the gdal yum package (RedHat) or gdal-bin (Debian) or gdal (brew in Mac OS/X).

 yum install gdal
 
 apt-get install gdal
 
 brew install gdal

(These belong to the Geospatial Data Abstraction Library)

For the visualization, we need the matplotlib and Basemap libraries in Python:

 http://matplotlib.org/users/installing.html

 http://matplotlib.org/basemap/users/installing.html

as well as NumPy.

For reading in Python the Excel spreadsheet Budget by Wards of the City of Toronto inside the program visualiz_investm_toronto_neighborhoods.py, the xlrd package must be installed.

This is the very first version of the .

Further work on this is coming.

As mentioned above, this is just a technical test of visualizing the investment in the neighborhoods of Toronto and relative Tax Estimation on Residential Properties: a more throughout Data Mining could be possible as to visualize why certain neighborhoods are chosen, etc, but these are social and urban planning projections for the future of Toronto, so Data Mining on them could be computationally expensive. (This could be interesting and useful on its own: the City of Toronto offers many more sources of information in its Open Data program.)