GATMobilityAnalysis

Applying GAT and LSTM to understand human mobility patterns and anomalies

Environment Setup

We use Conda to manage dependencies. The environment.yml file located in the repository specifies all the required packages for this project.

Create the Conda Environment from environment.yml

To create the environment using the provided environment.yml file, run the following command:

conda env create -f environment.yml

This will automatically create a Conda environment named deep-learning and install all required dependencies, including Python 3.9.19.

Activate the Environment

After creating the environment, activate it with the following command:

conda activate deep-learning

Add the Environment as a Jupyter Kernel

To use this environment in Jupyter notebooks, you need to add it as a Jupyter kernel:

python -m ipykernel install --user --name=deep-learning --display-name "Python (deep-learning)"

Once done, you can select the Python (deep-learning) kernel in Jupyter notebooks.