FROM GEOJSON TO GEOPANDAS:
Using Python, I converted the GEOJSON file to a GEOPANDAS dataframe. The geometry column stores the boundaries or coordinates of each of the 150 neighborhoods as Polygons. I was able to generate a plot of all 150 neighborhoods. I also took it one step further and annotated each neighborhood with its corresponding name. The following is the annotated plot of the 150 neighborhoods in the City of Toronto obtained from the GEOJSON file found in Toronto's Open Data Portal.
ALL SCHOOLS WITHIN CITY OF TORONTO:
A geographical spatial (GEOJSON) file containing the address points of all of the schools within the City of Toronto was also obtained from Toronto's Open Data Portal. These points were plotted on the existing plot of the 150 neighborhoods of the City of Toronto. The following is an enhanced plot showing this additional information followed by the Python code used to generate it.
FROM FOLIUM TO INTERACTIVE LEAFLET MAPS:
Python contains many useful libraries for working with Geographical Spatial data such as GEOJSON files. One such library is GEOPANDAS which can be used to generate plots of Geospatial Polygons. Another library is Folium which can be used to create Interactive Leaflet Maps which can then be saved as HTML files. The possibilities are endless when using Python for Data Science.