Skip to main content

An ETL for fetching/cleaning St. Louis crime data sets.

Project description

geocrime-stl

License: MIT Python 3.10+

A Python utility for cleaning and standardizing St. Louis Metropolitan Police Department (SLMPD) crime data for spatial analysis.

✨ Features

  • End-to-End ETL: Fetch, clean, and standardize monthly SLMPD crime data files automatically.
  • Spatial Formats: Convert cleaned DataFrames seamlessly into GeoJSON, GeoPackage (GPKG), or CSV.
  • Analysis Ready: Access built-in methods for minimal data summarization and visualization.

🌐 Data Source & Disclaimer

This utility programmatically fetches publicly available crime data directly from the St. Louis Metropolitan Police Department (SLMPD) Stats Page.

⚠️ Data Disclaimer & Limitations:

  • Availability: This tool relies entirely on the upstream availability and hosting structure of the SLMPD website. If their servers are down or their URL layout changes, the extraction pipeline may fail.
  • Data Integrity & Spatial Boundaries: The data processed is strictly "as-is" from the published monthly files. To ensure high data quality for spatial analysis, this utility applies two strict filtering constraints:
    1. Temporal Filtering: Because the SLMPD frequently updates historical records retroactively, this utility isolates and preserves only the records belonging to the target month.
    2. Spatial Clipping: Records containing invalid, malformed, or erroneous latitude/longitude coordinates that plot completely outside the official St. Louis city boundaries are automatically dropped.
  • Project Status: This is an independent, open-source utility. It is not affiliated with, endorsed by, or officially maintained by the SLMPD or the City of St. Louis.

🚀 Quick Start

Install

pip install geocrime-stl

Fetch/Clean/Visualize Data

import geocrime_stl as gc

data_pkg = gc.run_pipeline(4, 2026)

gc.generate_monthly_metrics(data_pkg)

gc.plot_monthly_maps(data_pkg)

🛠️ Local Setup (Alternative)

If you want to run the demo notebook locally or explore the source code, you can clone the repository directly:

git clone https://github.com/lweber89/geocrime-stl.git
cd geocrime-stl
pip install -e .

📓 Demo & Tutorial

To see a step-by-step walkthrough of the entire ETL process, check out the interactive Jupyter notebook included in this package:

👉 View the Demo Notebook

Note: If you have cloned the repository locally, you can open this file using Jupyter Lab or Jupyter Notebook to run the cells yourself.

🤝 Contributing & Pull Requests

Thank you for your interest in the project!

⚠️ Please Note: This is a personal project I developed and maintain in an effort to better understand the implementation of Python in geospatial data engineering / solutions architecture. I am not accepting pull requests or code contributions at this time.

If you find a bug or have an idea, feel free to open an Issue to let me know, or feel free to fork the repository and adapt it for your own personal needs!

📜 License

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

🔗 Project Links

GitHub Repository: https://github.com/lweber89/geocrime-stl

PyPI Home: https://pypi.org/project/geocrime-stl/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

geocrime_stl-0.1.2.tar.gz (13.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

geocrime_stl-0.1.2-py3-none-any.whl (13.3 kB view details)

Uploaded Python 3

File details

Details for the file geocrime_stl-0.1.2.tar.gz.

File metadata

  • Download URL: geocrime_stl-0.1.2.tar.gz
  • Upload date:
  • Size: 13.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for geocrime_stl-0.1.2.tar.gz
Algorithm Hash digest
SHA256 ca57bc7e6559960dbd3456f7c0eeabfd1babac61c6e786d0ac6dd53a4b574d43
MD5 bd9471e152454182211a01b7039084f7
BLAKE2b-256 76ab1d386e14087577dcb86c90a9d44234f5f0f98d57f5f878f6d075646c9cc9

See more details on using hashes here.

File details

Details for the file geocrime_stl-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: geocrime_stl-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 13.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.13

File hashes

Hashes for geocrime_stl-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f193608a372cf32a3b34a1249b1de002838a3fc144a6fc516d48409b527a866b
MD5 f095b00da9909bc4eefaabf456fbb902
BLAKE2b-256 0f2ddac0abda2498e197dd55a7a37342a649e0f264d1828a551b9edaa2e3562d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page