Assignments

• Assignment 1
• Assignment 2 - [Slides]
• Marking criteria

Assignment 1

Raising awareness of multiple deprivation

• Type: Coursework
• [Equivalent to 2,500 words] Maps, code and 500 words.
• Due on Wednesday, November 18th-2015 (Week 8).
• 50% of the final mark
• Chance to be reassessed
• Submission channels will be specified in due course.

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)

• Choose a city/local authority in the UK that is not Liverpool, preferably one you know.
• Obtain the Index of Multiple Deprivation as well as census demographic data.
• Compose a map with different layers.
• Include a “zoom” of the global map by subsetting the original data.
• At least three and no more than five maps.
• Up to 500 words describing the patterns in the maps.

Optional suggestions (include at least one)

• Join deprivation indices from areas to building data to create a more aesthetic visualization.
• Discuss deprivation at different geographical scales.
• Compare the effect of different choropleth classification algorithms on visualizing deprivation.
• Explore the composition of the multiple deprivation as it relates to Census more basic variables (e.g. income, building age, etc.).
• Try to characterize the overall pattern found in the maps (is it concentrated, dispersed, agglomerated into different hotspots or something totally different?).
• Cross-check empirical findings with “common wisdom” about the areas where you have local knowledge.
• Begin to explore the underlying social processes for the empirical findings of your analysis.
• Exchange some of the maps for non-spatial graphics (scatter plots, bar charts, etc.).

Data

• CDRC Census Geodata pack.
• CDRC Census Data pack.
• 2015 Index of Multiple Deprivation.

Assignment 2

Targetting areas

• Type: Coursework
• [Equivalent to 2,500 words] Three maps/tables, code and 750 words.
• Released on Week 9
• 50% of the final mark
• Chance to be reassessed
• Final Assessment
• Due on Friday, December 18th-2015 (Week 12).
• Submission channels will be specified in due course.

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)

• Combine at least two datasets, potentially among those used in the course.
• Employ at least two techniques from the set of analytics covered in the course.
• Justify why you have chosen the methods you use and how they help you answer the question at hand. Critically discuss their limitations too.
• Provide a list of the top five areas that you recommend be funded for improvement.
• Explain clearly how you have arrived at the list and how the decision has been informed by the data analysis and the methodologies employed.
• Include documented code and data that allow the replication of the analysis presented.

Suggestive paths (optional)

• Combine a LISA analysis of deprivation with kernel density maps of Twitter activity to identify areas of high values at both.
• Combine several relevant variables into a geodemographic analysis to obtain candidate areas and display the results in an aesthetically pleasant choropleth.

Data

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:

• 0-15: the code does not run and there is no documentation to follow it.
• 16-39: the code does not run, or runs but it does not produce the expected outcome. There is some documentation explaining its logic.
• 40-49: the code runs and produces the expected output. There is some documentation explaining its logic.
• 50-59: the code runs and produces the expected output. There is extensive documentation explaining its logic.
• 60-69: the code runs and produces the expected output. There is extensive documentation, properly formatted, explaining its logic.
• 70-79: all as above, plus the code design includes clear evidence of skills presented in advanced sections of the course (e.g. custom methods, list comprehensions, etc.).
• 80-100: all as above, plus the code contains novel contributions that extend/improve the functionality the student was provided with (e.g. algorithm optimizations, novel methods to perform the task, etc.).