DSN Bootcamp 2023

This repository contains material for producing analytics presented during the 2023 DSN Bootcamp. It contains Jupyter notebooks, with the code written in the Python language.

Running the notebooks

Locally using conda

To be able to run the notebooks on your local machine, you will first need to do the following (these instructions assume that you have conda installed):

1. Clone this repository
2. Create a conda environment to be able to run the notebooks, using environment.yml to install the required libraries.

You can run the following in a bash shell (or a git-bash terminal on Windows) to set everything up (in the folder your terminal is currently located):

git clone https://github.com/Flowminder/DSN_Bootcamp_2023
cd DSN_Bootcamp_2023
conda env create -f environment.yml

Start working with the notebooks

To run the notebooks on your local machine, you can do the following:

1. Activate the geo_python environment you previously created using environment.yml
2. Start a Jupyter Lab session

To do this, you can run the following in a bash shell (or a git-bash terminal on Windows):

conda activate geo_python
jupyter lab

Practical 1: Assessing health facilities coverage for maternal healthcare using GRID3 population estimates in Kaduna state

Using geospatial data:

• Population data for women aged 15-49
• Ward and state boundaries
• Health facility locations

We want to assess health facility coverage for maternal healthcare in Kaduna state, Nigeria.

Practical 2: Call Detail Records (CDR) analytics in Nigeria

Using a mobile phone dataset:

• Synthetic CDR data
• Cell towers
• Ward and region boundaries

We want to understand the dataset available and perform a first mobility analysis in Nigeria.

Links

You can find a rendered version of the notebook for Practical 1 here.
You can find a rendered version of the notebook for Practical 2 here.