MSc Data Science (Social Analytics) / Course details

Evidence-based policy-making is central to contemporary spatial and policy analysis. The consolidation of Geographical Information Systems (GIS) as widely used tools for data management and advanced analysis via intensive use of data and methods to process and map this data have changed the focus of traditional spatial analysis towards a fully fledge use of digital tools.

Spatial and policy analysis knowledge and methods, supported by digital tools, is a central set of knowledge and skills for professionals in urban planning and policy related areas.

Acknowledging the ongoing significant shift in the UK and worldwide urban agendas towards a comprehensive digital approach to planning and policy design and monitoring, the course addresses basic and complex issues and delivers an essential toolkit for graduates to be effective professionals in providing robust, intelligible and accessible evidence for all domains of spatial and policy analysis.

The course unit has the following aims:

• To enhance the understanding of trends, relationships and spatial patterns of development and to in order to provide a context for policy-making;
• To present various methodologies of spatial analysis used to carry out spatial policy monitoring and evaluation to assess the outputs and outcomes of policy action;
• To make use of databases and information technology to analyse and present analytical findings;
• To encourage students think critically about (spatial) data, different forms of analysis, and presentation techniques with regard to data and analytical findings.

At the end of this unit, students should:

Harris R. Quantitative Geography: The Basics. Sage Publications; 2016.

Wong C. Indicators for Urban and Regional Planning: The Interplay of Policy and Methods. Vol The RTPI library series. Routledge; 2006.

E. Innes J, Booher DE. Indicators for Sustainable Communities: A Strategy Building on Complexity Theory and Distributed Intelligence. Planning Theory & Practice. 2000;1(2):173-186. doi:10.1080/14649350020008378

Kirk A. Data Visualisation: a Handbook for Data Driven Design. SAGE; 2016.

The course will have synchronous lectures, some asynchronous lectures, some support readings and many activities that will be subsequently discussed in in-class synchronous discussion sessions.

This module will be delivered through a combination of teaching methods with a strong emphasis on the importance of hands-on experience and small group learning. 

The teaching will be largely based on introductory lectures and practical workshops that will be taken by students independently, with a follow-up QA session to consolidate that knowledge. 

Short lectures will be used to introduce the concepts and principles that underpin each method and technique. This will be complemented by workshops to provide students with the first-hand experience of carrying out data collection and analysis tasks. 

There will be a strong connection between the different components taught in the module and the individual and project work. These will involve the cumulative collection, analysis and presentation of data and findings throughout the course.

• Introducing students to the range of techniques and methods used to analyse, monitor and evaluate spatial policy issues and actions;
• Providing students with the knowledge and skills needed to understand and apply these techniques and methods in theory and practice;

• Have a critical opinion about the use of advanced quantitative tools in urban planning and policy analysis;
• Understand the potential and limitations of using quantitative methods in urban planning and policy analysis;
• Demonstrate an understanding of the principles of spatial data analysis and its use in supporting spatial policy issues and actions 

• Data collection and management skills;
• Use a range of IT resources to obtain, analyse and present information;
• Use of spreadsheets and GIS to present information in a professional manner;
• Competency in reporting data analyses to support policy and planning processes;

• Group work;
• Use of different spatial analyses methods to develop evidence-based analysis to support policy design and monitoring in the context of both P&EM course units and the professional practice 

Verbal feedback during weekly sessions, and final summative feedback via GradeMark provided within 15 working days of each submission.

Carley, M. (1980) Rational Techniques in Policy Analysis: Heinemann Educational Books, London

Davies, H. (1995) Demographic Projection Techniques for Regions and Small Areas: A Primer UBC Press, Vancouver.

Field, B. and MacGregor, B. (1987) Forecasting Techniques for Urban and Regional Planning, Hutchinson, London.

Hambleton, R. and Thomas, H. (1995) Urban Policy Evaluation: Challenge and Change, Chapman, London.

Innes, J. E. (1990) Knowledge and Public Policy: the Search of Meaningful Indicators, New Brunswick, NJ: Transaction Publishers.

ODPM (2006) A Framework for City Regions, ODPM (now DCLG), London.

(http://www.communities.gov.uk/pub/588/AFrameworkforCityRegionsResearchReportPDF814Kb_id1163588.pdf)

ODPM (2006) State of the English Cities, ODPM (now DCLG), London.

(http://www.communities.gov.uk/index.asp?id=1127498)

RTPI (2006) Uniting Britain: The evidence base - spatial structure and key drivers, RTPI, London

(http://www.rtpi.org.uk/resources/publications/spatial.html)

Wong, C. (2006) Indicators for Urban and Regional Planning: The Interplay of Policy and Methods, Routledge, London.

Wong, C., Baker, M., Webb, B., Hincks, S. and Schultze-Baing, A. (2015) ‘Mapping policies and programmes: the use of GIS to communicate spatial relationships in England’ Environment and Planning B.