MPlan Planning

This course has normally included an overseas field trip in year 4, which is an optional part of the course. Please note there is a student contribution towards the cost of the trip depending on the location.

We are committed to attracting and supporting the very best students from all backgrounds to study this course.  

You could be eligible for cash bursaries of up to £2,500 to support your studies. 

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Planning and real estate professionals frequently require the ability to understand and work with quantitative data. This course unit starts by introducing the ethical implications of working with quantitative data. The course unit then provides grounding in different data sources, exploring different data types and the processes required before any visualisation or analysis can occur. The course unit then explores different analytical methods that can be used to facilitate interpretation and presentation of outputs related to planning and real estate professions, including inferential statistics and the foundations of basic computer coding.

The unit aims to:

- Introduce foundational concepts about data (metadata, ethics, disclosure, anonymity), practical skills and methodologies needed to develop a critical and organised approach to data analytics for planning and real estate.

- Provide grounding on generating, retrieving, manipulating and visualising quantitative data.

- Develop quantitative data handling skills for use in planning and real estate.

- Enable students to understand quantitative data to facilitate the use of statistics.

- Introduce a range of basic coding skills and relevant software for data analytics.

On completion of this unit successful students will be able to:

Lecture-based sessions: 
Core content on ethical implications of working with data and an introduction to understanding and working with quantitative data is taught as an introduction to the course unit (alongside workshops sessions). E-learning content is provided on Blackboard including interactive material using a range of multimedia sources.

Workshop (computer cluster) sessions: 
The majority of the taught components of this course unit are taught in the computer cluster so students can apply their knowledge and skills as they are learning. The course unit teaches knowledge of digital methodologies throughout the course unit, including introduction to different software packages, and opportunities for creative visualisation of data.

Directed reading: 
Students are encouraged to extend their knowledge of specific research methods and to consider the ethical implications of these, ahead of interactive lecture sessions. Links to readings will be provided through appropriate e-learning tools, e.g. Reading Lists Online.

Assessment and independent learning 
Students will be assisted with independent learning through the provision of different multimedia sources available on Blackboard.

• Describe and summarise data using descriptive and inferential statistics.
• Demonstrate data literacy including knowledge of data types, distribution, visualisation and manipulation.

• Explain some of the ethical, scientific and technological issues related to the use of quantitative data for planning and real estate.
• Evaluate the suitability of data for different analyses, including interrogating sources, sampling and techniques for manipulation.
   

• Retrieve and manipulate quantitative data from a variety of sources for use in built environment research.
• Analyse data, including screening, cleaning and transforming data for use in a range of situations and applications.

• Read and write basic computer code.
• Identify and use appropriate software to perform basic quantitative methods of data analysis to help understand planning and real estate challenges.

Individual report - Length 2000 words Weighting 100%

Formative in class, summative via Blackboard

- Bissett, B.D. (2007) Automated Data Analysis using Excel. Chapman and Hall/CRC.

- Harris, R. (2016) Quantitative Geography: The basics, pp.1-328.

- McCormick, K. and Salcedo, J. (2017) SPSS statistics for data analysis and visualization. John Wiley & Sons.

- McKinney, W. (2012) Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media, Inc.