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GY7702
Assignment
Introduction
The Centre for Cities (an independent charity and research centre) has identified housing1 as one of the key
current issues the UK is facing, and the UN-Habitat has named housing affordability as a global challenge2.
Many commentators and journalists3 have also been writing about this issue.
Housing has long been a topic at the centre of attention of studies in quantitative geography (Goodman
1978; Can 1992). In geographic information science, the past decade has seen a focus on exploring the spatial
variations in house prices using spatial statistical analysis (Páez, Farber, and Wheeler 2011; Fotheringham,
Crespo, and Yao 2015). More recently, researchers have also investigated the possibility of estimating house
prices based on new forms of big data, including remotely sensed imagery, social media and user-generated
images (Yao et al. 2018; Chen et al. 2022).
In completing this assignment, you will conduct a simpler analysis of house prices data. You will focus on
the relationship between house prices, deprivation and census data within a Local Authority District (LAD)
assigned to you in the Appendix. The aim is to explore how house prices are related to socio-demographic
variables at the local level, with the perspective of advising the local authority about key areas that might
be prioritised for support.
Instructions
Write an analysis report of no more than 750 words (excluding code, tables and references) focused on the
Local Authority District assigned to you in the Appendix and including at least three tables illustrating
the relationships between:
• house prices;
• the census sub-domain assigned to you in the Appendix;
• the deprivation index assigned to you in the Appendix.
1See the Housing section on the Centre for Cities website.
2See “Addressing the Housing Affordability Challenge: A Shared Responsibility” by Maimunah Mohd Sharif for UN-Habitat
3See, e.g., “We face peril because the UK economy relies on house prices. Here are three ways to fix that” by Fran Boait for
The Guardian, “Why a housing crash scares Britain like nothing else” by Annabelle Dickson And Esther Webber for Politico
EU, “Which areas in England are worst affected by the housing crisis? And what’s pushing up prices?” by Amy Borrett and
Carmen Aguilar Garcia for Sky News or “The housing crisis sits at the centre of Britain’s ills” by Hashi Mohamed for The
Financial Times
1
The report must include:
• the code used to generate the tables from the data outlined below;
• an introduction outlining and justifying your approach and a discussion of the results, drawing clear
links to the concepts and approaches to reproducible data science discussed in class.
The report must include the following paragraph:
This document uses data from from the Office for National Statistics, the Valuation
Office Agency, the Consumer Data Research Centre and the Ministry of Housing,
Communities & Local Government. Contains public sector information licensed under
the Open Government Licence v3.0; contains Ordnance Survey data Crown copyright
and database right 2015.
Submit your document on BlackBoard, using the form linked in the Assessment and Feedback section.
When writing up, remember that the submission should be anonymous. Do not include your name in the
document and use your Student ID as author instead. The assignment must be completed using the R
programming language.
Data
The data folder contains the five files listed below, which must be used for the sole purpose of com-
pleting this assignment – do NOT share these files.
To obtain the data to complete this assignment, follow the instructions below:
1. Access the University’s RStudio Server.
2. From the Files tab of the bottom-right panel, navigate to the /data/GY7702_CW1 data folder by clicking
the / character on the left of the address bar and then select data and finally GY7702_CW1.
3. Copy the files available in the folder to your home folder:
1. Select the check-box on the left of one of the files;
2. Select More > Copy To. . . .
3. In the Choose Destination panel, click on the three dots on the right side of the navigation bar.
4. Type-in the character ~ and click OK.
5. Click Save.
6. Repeat for each one of the files.