Predspot

Overview 📖

Predspot is a Python library for spatio-temporal crime prediction and hotspot detection. It combines machine learning techniques with spatial analysis to help predict and visualize crime patterns across time and space.

Key features:

• Spatial and temporal crime mapping
• Feature engineering for time series data
• Machine learning-based prediction pipeline
• Crime hotspot detection using Kernel Density Estimation
• Visualization tools for crime patterns

⚠️ Important Notice

This project was developed as part of a master's thesis and is currently in an archived state. While the core functionality exists, you may encounter compatibility issues with newer Python package versions. The code can work with some effort, but please note:

• This is not production-ready software
• Some dependencies are outdated and may require specific versions
• You might need to modify some code to work with newer package versions
• The project was created for research purposes

However, we believe the methodologies and approaches used here are still valuable! If you're interested in crime hotspot prediction, feel free to:

• Use this as a reference implementation
• Adapt the code to modern dependencies
• Build upon these concepts for your own projects
• Contribute to modernizing the codebase

We welcome anyone interested in reviving or learning from this project! 🚀

How to use? 🚀

Documentation under construction.

Basic usage example:

from predspot import Dataset, PredictionPipeline
from predspot.crime_mapping import KDE, create_gridpoints
from predspot.feature_engineering import Seasonality, Trend, Diff

# Load and prepare data
dataset = Dataset(crimes_df, study_area_gdf)

# Create prediction pipeline
pipeline = PredictionPipeline(
    mapping=KDE(tfreq='M', grid=create_gridpoints(study_area, resolution=250)),
    fextraction=PandasFeatureUnion([
        ('seasonal', Seasonality(lags=12)),
        ('trend', Trend(lags=12)),
        ('diff', Diff(lags=12))
    ]),
    estimator=your_favorite_sklearn_model
)

# Fit and predict
pipeline.fit(dataset)
predictions = pipeline.predict()

Development ⚡

Predspot has four main modules:

dataset_preparation: Module for preparing and managing crime datasets and study areas.

crime_mapping: Module for spatial and temporal crime mapping, including KDE-based hotspot detection.

feature_engineering: Module for time series feature engineering, including seasonality, trend, and difference features.

ml_modelling: Module that implements the prediction pipeline and model evaluation.

Installation steps 🛠️

Create conda env and install requirements:

conda create -n predspot python=3.8
conda activate predspot
conda install -y rtree geopandas  # if doesnt work, do: `conda clean --all`
pip install pandas statsmodels==0.10.2 geojsoncontour stldecompose scikit-learn matplotlib descartes
pip install .

Required dependencies:

• pandas
• geopandas
• numpy
• scikit-learn
• scipy
• stldecompose
• matplotlib

Tests 🧪 (TO DO)

TO DO: Unit tests can be executed as follows:

bash
python tests/test_dataset_preparation.py
python tests/test_crime_mapping.py
python tests/test_feature_engineering.py
python tests/test_ml_modelling.py

Input Data Format 📊

The crime data should be a pandas DataFrame with the following required columns:

• tag: Crime type
• t: Timestamp
• lon: Longitude
• lat: Latitude

The study area should be a GeoDataFrame defining the geographical boundaries of interest.

Resources 📚

For more information on the methods used in Predspot, please search more about these methods:

• Kernel Density Estimation for crime hotspot detection
• Time series decomposition for feature engineering
• Spatio-temporal crime prediction techniques

License 📜

BSD 3-Clause

Contributing 💡

Contributions are welcome! Please feel free to submit a Pull Request.

Guidelines for contributing:

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

Cite us

If you use Predspot in your research, please cite us:

APA:

Araujo, A., & Cacho, N. (2019). Predspot: Predicting crime hotspots with machine learning. Master’s thesis, UFRN (Universidade Federal do Rio Grande do Norte), Natal, Brazil.

Araújo, A., Cacho, N., Bezerra, L., Vieira, C., & Borges, J. (2018, June). Towards a crime hotspot detection framework for patrol planning. In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 1256-1263). IEEE.

or bibtex:

@article{araujo2019predspot,
  title={Predspot: Predicting crime hotspots with machine learning},
  author={Araujo, Adelson},
  year={2019},
  school={Universidade Federal do Rio Grande do Norte}
}

@inproceedings{araujo2018towards,
  title={Towards a crime hotspot detection framework for patrol planning},
  author={Ara{\'u}jo, Adelson and Cacho, N{\'a}dia and Bezerra, Lucas and Vieira, Carlos and Borges, Jo{\~a}o},
  booktitle={2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)},
  pages={1256--1263},
  year={2018},
  organization={IEEE}
}