In this visualization, we'll be looking at the graffiti report
https://data.bloomington.in.gov/dataset/graffiti-reports
https://drive.google.com/open?id=1Ni0k_WGriS9Ss3T2JRlW_uDhD18AFEfzxVEQPkvsDb4
This approach leverages the free and open source Jupyter Lab Notebook system for documenting the steps taken.
I used pipenv to help manage the virtualenv.
https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html
If you've cloned this repo, there is already a Pipfile that will track dependencies.
pipenv install
pipenv shell
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-leaflet
jupyter lab
The jupyter lab command should launch the server and open a browser to navigate to something like: http://localhost:8888/lab
Now that we have data read in, we can show it on a map (geospatially). If using pipenv, ipyleaflet should have been installed with pipenv install
https://github.com/jupyter-widgets/ipyleaflet https://ipyleaflet.readthedocs.io/en/latest/
In this case, all rows have a lat/long associated with them. If they did not, we would need to geocode those addresses. geopy would help with this:
https://towardsdatascience.com/geocode-with-python-161ec1e62b89
Jupyter notebooks can be converted to Markdown, if that helps with sharing:
jupyter nbconvert my_notebook.ipynb --to markdown --output output.md