Project status: DELIVERED

This project has been delivered and is no longer maintained. If you'd like to reproduce or improve on it, please contact OpenOakland's Steering Committee at [[email protected]](mailto:[email protected]). Let us know why you're interested and what you hope to accomplish. Consider using the [Project Exploration Worksheet](https://docs.google.com/document/d/1k24P9JiAUEzJLPFRDjVh7aRZexax6NUhfPFLSI3R80M/edit?usp=sharing) (required for all new OpenOakland projects).

Lovely Rita: Insights from Oakland Citation Data

Lovely Rita is set of tools for reading, cleaning, and saving parking parking citation datasets. The name pays homage to the song, Lovely-Rita, by the Beatles.

The project is a part of Oakland's Code for America brigade OpenOakland. You can read more about the project in this presentation.

With Lovely Rita, you can load historical parking citation data, clean the data (addresses and dates), geocode (turn addresses into geospatial coordinates), and save cleaned data to shapefiles for GIS analyses.

Check out our documentation for more detail.

Installation

It is good practice to use a virtual environment.

git clone https://github.com/openoakland/lovely-rita.git
cd lovely-rita
pip install -r requirements.txt
pip install . --user

Raw data format

Raw data should be provided in a .csv with the column names (in any order):

ticket_number

ticket_issue_date

ticket_issue_time

street

street_name

street_number

street_suffix

violation_external_code

violation_desc_long

state

city

badge_number

fine_amount

Command line interface

Several useful workflows can be run from the command line. Learn about the available workflows using lovelyrita --help. Learn about a specific workflow using lovelyrita <workflow> --help.

Python interface

There is also a python inferface if you want to dive deeper into the data. There are more involved examples in the notebooks folder.

Read in the data

from lovelyrita.data import read_data
citations = read_data(data_path)

Clean the data

Lovely Rita can also clean and parse addresses and dates.

from lovelyrita.data import read_data
from lovelyrita.clean import clean
citations = read_data(data_path)
citations = clean(citations)

Analyze the data

1. Number of citations per zip code
2. Time-series, number of citations
3. Type of violation by zip code

Save the data

There is also support for storing the data to shapefiles

from lovelyrita.data import write_shapefile
write_shapefile(citations, 'my-shapefile.shp')

Documentation

Clone the gh-pages branch

git clone -b gh-pages http://github.com/openoakland/lovely-rita.git lovely-rita-docs

Make changes to docs/source/*.rst in master branch.

Build the docs.

cd docs
make html

Docs are built to ../../lovely-rita-docs/html

git add -u git commit -m "docs message" git push origin gh-pages

Tests

There will be tests.

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

The many wonderful people who helped design and build Lovely Rita (* denote active contributors):

• Robert Gibboni aka r-b-g-b *
• Andrew Tom aka atomahawk *
• Ricky Boebel aka ricky-boebel *
• Joanna Jia aka jjia25
• Drew Erickson aka drewerickson
• Slav Sinitsyn aka slavster

License

This project is licensed under the MIT License - see the license file for details.

Acknowledgments

We would like to acknowledge the help of Danielle Dai and the Oakland Department of Transportation for providing the data and invaluable guidance for this project.