Assignment 1

Raising awareness of multiple deprivation

In this assignment, you will take the role of the data editor of a local newspaper that wants to write about the geography of deprivation. In order to raise awareness of the problem among your readers, you will have to create a compelling visualization that is intuitive and attractive but also rigorous. In addition, in order to convince your most skeptical and data-savvy readers, you will have to provide the code used to create the visualization in a way that allows reproducibility.

Using data from the Index of Multiple Deprivation, as well as from the Census, create at least three and no more than five maps to display different angles and interesting patterns related to multiple deprivation in a British town other than Liverpool. Complement the maps with a short description of what they show, stressing the relevant aspects you would want your readers to focus on. Keep in mind this needs to be short and to the point, as the report will be passed to a journalist who will draft the final article for the newspaper. In addition to the figures and text, provide data and annotated code that allows to replicate the visualization.

Minimum requirements (complete all)

Optional suggestions (include at least one)


Assignment 2

Targetting areas

In this assignment, you will take the role of a real-world data scientist tasked to identify areas to direct investments. You are consulting for the City of Liverpool on a program to target investments towards particularly disadvantaged areas that are nevertheless popular or have the potential to become so. The Economic Development division knows that only five local super output areas (LSOAs) will be funded but would like to know which ones.

Choose one of the given questions, develop a data strategy, deploy it, and present the results in a rigorous but intuitive fashion, together with the code.

Minimum requirements (complete all)

Suggestive paths (optional)


This assignment can use any of the datasets employed in the course, and/or any other datasets you consider useful. If you are thinking of including additional datasets, or have ideas in this respect, please get in touch with the module lead (Dani Arribas-Bel).

Marking Criteria

This course follows the standard marking criteria (the general ones and those relating to GIS assignments in particular) set by the School of Environmental Sciences. In addition to these generic criteria, the following specific criteria relating to the code provided will be used: