A tool that finds anomalies in the data and plots them on the map to encourage further investigation for the causes of each anomaly.

Demo: Map of bushfires in Australia

process data.ipynb - combines data from the file with locations of the events of interest with the covariates from different files within the specified radius and within specified time window.

anomaly_detection.ipynb - Reads a csv file of locations of the evens of interest with the relevant covariates and finds outstanding entries.

CSV to KML & JSON.ipynb - converts longitude and latitude data to klm and json format for visualisation.

pip3 install virtualenv           # This may already be installed
virtualenv .env                   # Create a virtual environment
source .env/bin/activate          # Activate the virtual environment
pip3 install -r requirements.txt  # Install dependencies

jupyter notebook                  # Start iPython notebook

deactivate                        # Close session