MAUP

Supporting codes for analysis conducted as part of The modifiable areal unit problem in geospatial least-cost electrification modelling by Babak Khavari, Andreas Sahlberg, Will Usher, Alexandros Korkovelos and Francesco Fuso Nerini

Content

This repository contains:

• An environment .yml file needed for generating a fully functioning python 3.7 environment necessary for the codes in this repository.
• The DBSCAN code used for the DBSCAN clustering.
• The code used in order to conduct the sensitivity analysis (based on SALib).
• And the OnSSET version used in order to generate the least-cost electrification results

Installing and running the notebooks

Requirements
NOTE: The requirements below refer to running the two notebooks on this repository (DBSCAN clustering and SALib). In case you wish to run the OnSSET codes instructions follows in a later section.

The MAUP module (as well as all supporting scripts in this repo) have been developed in Python 3. We recommend installing Anaconda's free distribution as suited for your operating system.

Install the repository from GitHub

After installing Anaconda you can download the repository directly or clone it to your designated local directory using:

> conda install git
> git clone https://github.com/babakkhavari/MAUP.git

Once installed, open anaconda prompt and move to your local "MAUP" directory using:

> cd ..\MAUP

In order to be able to run the codes available, you have to install all necessary packages. "full_project.yml" contains all of these and can be easily set up by creating a new virtual environment using:

conda env create --name MAUP --file full_project.yml

This might take some time. When complete, activate the virtual environment using:

conda activate MAUP

With the environment activated, you can now move to the clustering directory and start a "jupyter notebook" session by simply typing:

..\MAUP> jupyter notebook 

OnSSET Installation

Requirements

OnSSET requires Python > 3.5 with the following packages installed:

• et-xmlfile
• jdcal
• numpy
• openpyxl
• pandas
• python-dateutil
• pytz
• six
• xlrd
• notebook
• seaborn
• matplotlib
• scipy

Install with pip

Install onsset from the Python Packaging Index (PyPI):

pip install onsset

Changelog

24-June-2021: Original code made public
28-July-2021: Cleaned up the notebooks and added OnSSET codes used

Resources

Dataset can be found here: https://data.mendeley.com/datasets/jcr5rgt66j

Journal article can be found here: https://www.sciencedirect.com/science/article/pii/S2211467X21001371