Dot map for Canada ( GIS, Python)

Help with creating a map for Canada similar to - [url removed, login to view]

or similar map for Toronto - [url removed, login to view]

This is methodology for US map [url removed, login to view]

Python was used to read the 50 state and District of Columbia shapefiles (with the merged SF1 data). The GDAL and Shapely libraries were used to read the data and create the point objects. The code retrieves the population data for each census block, creates the appropriate number of geographic points randomly distributed within each census block, and outputs the point information to a database file. The resulting file has x-y coordinates for each point, a quadkey reference to the Google Maps tile system, and a categorical variable for race. The final database file has 308,745,538 observations and is about 21 GB in size. The processing time was about five hours for the entire nation.

The database file was then sorted by quadkey and converted to a .csv format. SAS was able to do this within an hour without crashing.

Processing 2.0.1 for 64-bit Windows was used to create the map tiles. The Java code reads each point from the .csv file and plots a dot on a 512x512 .png map tile using the quadkey reference and x-y coordinates. The racial categorical variable is used to color-code each plotted dot. This process used the default JAVA2D renderer, but other platforms may work better using P2D. Map tiles were created for Google Maps' zoom levels 4 through 13 to make the final map. A non-color-coded map was also produced to help add more contrast for lightly populated areas. In total, the color-coded and non-color-coded maps contain 1.2 million .png files totaling about 7 GB. Producing all of the map tiles in Processing took about 16 hours for the two maps.

The Google Maps API is used to display the map tiles. Map tiles with zero population are never created using the above method. Therefore, an index was used to tell the map application whether a tile exists in order to prevent 404 errors.

The entire code is up on GitHub and was adapted from code developed by Brandon Martin-Anderson and Peter Richardson in order to account for the racial coding and errors in reading the shapefiles.

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Geospatial Python

Project ID: #6846368

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