Using measures of Equity to predict Accessibility in Los Angeles County, a comparative study of Multilevel Modelling and Artificial Neural Networks

Goal: To compare the results of Multilevel Modelling and Machine Learning regression to analyse the suburbanisation of poverty using Equity and Accessibility data in Los Angeles County.

Authors

• Christina Last - University of Bristol
• Supervisor: Levi John Wolf - University of Bristol

Following this will require recent installations of:

• Python >= 3.5
• pandas
• geopandas >= 0.3.0
• matplotlib
• rtree
• PySAL
• scikit-learn
• mgwr
• cartopy
• Pystan = 2.18.1
• geoplot
• Jupyter Notebook

If you do not yet have these packages installed, we recommend to use the conda package manager to install all the requirements (you can install miniconda or install the (larger) Anaconda distribution, found at https://www.anaconda.com/download/). To instal Pystan 2.18.1, contextily, sklearn and mgwr, pip will be required.

Downloading the materials

If you have git installed, you can get the tutorial materials by cloning this repo:

git clone https://github.com/ChristinaLast/Equity-Accessibility-Los-Angeles.git

Otherwise, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/ChristinaLast/Equity-Accessibility-Los-Angeles.git) in your browser and click the green "Download" button in the upper right.

Test the environment

To make sure everything was installed correctly, open a terminal, and change its directory (cd) so that your working directory is the tutorial materials you downloaded in the step above.

cd "./Equity-Accessibility-Los-Angeles"

Once this is installed, the following command will install all required packages in your Python environment:

conda env create -f environment.yml