3A: Introduction to Geospatial Problems in Health & Geographic Visualisation using QGIS - Part 1
In this session, we introduce some key concepts of geographic modelling and visualisation. This includes concepts such as projections, coordinate reference systems, and areas of geographic organisation within the UK such as Output Areas.
We also begin our exploration of the free and open source geographic visualisation software package QGIS, covering how to load in point data that is stored as a .csv file and add colours, labels and more to start telling a story with geographic data.
3B: QGIS - Part 2 and Creating Static Maps in Python with geopandas and matplotlib
In this session, we continue to develop our QGIS skills, looking at how we can create choropleths in QGIS and then prepare our maps for printing or exporting, adding legends and titles for our end users.
We then move over to Python, introducing the geopandas package for importing and manipulating spatial data. We then visualise both point and choropleth data using a combination of geopandas and the matplotlib library, enhancing our maps and exploring how we can use features of Python like loops or faceted maps to produce multiple maps from a dataset in a quick and reproducible manner.
3C: Interactive Maps in Python with folium, visualising travel time data in Python, and obtaining travel time data with routingpy
In this session, we take a look at creating interactive point and choropleth maps in Python using the folium package.
We also take a look at how we would visualise travel time data with a choropleth, and how we can use the routingpy Python package to pull travel time data for our own combinations of source and destination points at scale for free.
3D: Location Allocation Problems
In this session we talk about how you would tackle a problem where you need to most efficiently place a certain number of sites to minimise the travel time, population-weighted travel time, or distance for a population across a region. We construct and carry out the p-median location allocation problem in Python, using our mapping skills and travel time matrices from the previous sessions in this module.