PROGRAMME SPECIFICATION
PROGRAMME SPECIFICATION
Programme title:
MSc Smart Cities and Urban Analytics
Final award (BSc, MA etc):
(where stopping off points exist they should
be detailed here and defined later in the
document)
UCAS code:
(where applicable)
Cohort(s) to which this programme
specification is applicable:
(e.g. from 2015 intake onwards)
Awarding institution/body:
Postgraduate Certificate (60 credits), Postgraduate Diploma (120
credits), MSc (180 credits) following the Built Environment
Regulations
Teaching institution:
University College London
Faculty:
Bartlett Faculty of the Built Environment
Parent Department:
(the department responsible for the
administration of the programme)
Departmental web page address:
(if applicable)
Centre for Advanced Spatial Analysis
Method of study:
Full-time/Part-time/Other
Full-time, flexible/modular
Criteria for admission to the
programme:
UK Bachelor’s degree in an appropriate subject, awarded with First or
Second class honours, or an overseas qualification of an equivalent
standard from a university or educational establishment of university
rank. Candidates who hold a professional or other qualification
obtained by written examinations and approved by UCL together with
at least three years of appropriate professional experience may also
be admitted. The language requirements for admission to an MSc at
the Bartlett accord with UCL’s standard requirements for English
Language proficiency.
One calendar year full-time.
Up to five years flexible/modular study.
Length of the programme:
(please note any periods spent away from
UCL, such as study abroad or placements
in industry)
Level on Framework for Higher
Education Qualifications (FHEQ)
(see Guidance notes)
Relevant subject benchmark statement
(SBS)
(see Guidance notes)
N/A
From 2014 intake onwards
University College London
www.casa.ucl.ac.uk
Masters Level (Level 7)
N/A
Brief outline of the structure of the
programme
and
its
assessment
methods:
(see guidance notes)
Students take 60 credits for the Certificate, 120 credits for the
Diploma and 180 credits for the MSc. The MSc is made up of 7
compulsory modules and one optional module. They are:

Smart Systems Theory (15 credits) – Term 1. Assessment by
2 hour unseen written exam (60%) and 1000 word
assessment (40%) – compulsory.

Quantitative Methods (15 credits) – Term 1. Assessment by
term long project (50%), 4x 200 words homework (40%) and
10 minute presentation (10%) – compulsory.

Geographic Information Systems and Science (15 credits) –
Term 1. Assessment by unseen written exam (40%) and
1000 word coursework (60%) – compulsory.

Introduction to Programming (15 credits) – Term 1. Practical
module assessed by coursework) – optional. May be
substituted for any other relevant M level 15 credit module.

Spatial Data: Capture, Storage and Analysis (30 credits) –
Term 2. Assessment by 3000 word coursework (100%) –
compulsory.

Smart Cities: Context, Policy and Government (15 credits) –
Term 2. Assessment by 3000 word coursework (100%) –
compulsory.

Urban Simulation (15 credits) – Term 2. Assessment by 2
hour unseen written exam (60%) and 1000 word assessment
(40%) – compulsory.

MSc Dissertation (60 credits) – Term 3 – compulsory.
Board of Examiners:
Name of Board of Examiners:
Built Environment Board of Examiners
Professional body accreditation
(if applicable):
N/A
Date of next scheduled
accreditation visit:
EDUCATIONAL AIMS OF THE PROGRAMME:
The programme aims to provide training in the principles and skills of social and spatial research. Its aims
include a strong understanding of qualitative and quantitative research methodology and methods of data
collection and analysis to support and enable independent and group research projects. In addition to focusing
on research skills, subject specific modules provide students with the opportunity to develop an excellence in
spatial analysis with the specific skill set to engage and contribute to the current debates in urban and spatial
continuums.
Broadly the MSc programme has two goals:
• To provide high quality training to enable students to carry out future doctoral research;
• Provide a stand-alone qualification to develop fully trained and competent researchers in the field of
spatial science with a developed understanding of space, temporal dynamics, the simulation of spatial
behaviour, network, visualisation, communication and interlinking topics that share a similar need for
data, outreach and research skills.
Specifically the course aims enable the students to:
• Utilise desktop and web-based computational techniques for the effective analysis, communication and
visualisation of spatially related phenomena;
• Understand the need to relate research skill and analysis techniques to policy led issues as a means of
understanding and predicting future spatial patterns and understanding the consequences of such
techniques;
• Understand the need to combine social and computational science in order to explore the complexities
of spatial systems;
• Develop the ability to lead future research by creating, refining and putting into practice new
methodologies and techniques.
PROGRAMME OUTCOMES:
The programme provides opportunities for students to develop and demonstrate knowledge and understanding,
qualities, skills and other attributes in the following areas:
A: Knowledge and understanding
Knowledge and understanding of:
Teaching/learning methods and strategies:
(a) Spatial analysis and geographic
information systems; storing, handling
and analysing geospatial data.
The course is focused on gaining a balanced
knowledge and research toolkit relating to geospatial
data and analysis. Taught courses on GIS, Quantitative
Methods, Programming, Smart Cities and Smart
Systems Theory and Urban Simulation cover key
elements in a, b, c, d, and e. The Geospatial Data
Mining project covers f and g in the course of classwork
leading to small analysis projects.
(b) Quantitative methods and their
application to spatial phenomena.
(c) Programming and data analysis
methods as applied to large spatial data;
rudimentary visualisation.
(d) The overarching “smart city” concept it’s antecedents, current research, and
future opportunities.
In addition to formal teaching, learning will be
developed via the CASA Seminar Series which has
weekly seminars by PhD students and guest
speakers on topics relating to the built form and
spatial analysis.
(e) The use of urban models to
understand city behaviour.
(f) Working with databases; simple
database design, querying, connector.
(g) Data mining - automated tools for
pattern detection (basic statistical and
machine learning methods).
Assessment:
Assessment is via practical projects with presentation
components, a collaborative group research project,
written coursework, an examination, and a final
dissertation.
B: Skills and other attributes
Intellectual (thinking) skills:
Teaching/learning methods and strategies:
(a) Ability to work with complex datasets,
storing and interrogating them in a
sophisticated way.
(a) Through core elements of the database module, and
dissertation.
(b) Critically assess research conducted
via computational and mathematical
means.
(b) From taught course learning outcomes, transferable
skills modules, dissertation and research seminars.
(c) Approach research problems in an
innovative manner leading to new
methodologies.
(c) Through group work and student-centred projects
(including the final dissertation)
(d) Critical assessment of techniques and
methods relating to the interface of cities,
sensing and technology.
(d-e) Through detailed discussion in Smart Cities
module, understanding of urban systems theory in
relation to cutting edge techniques.
(e) The ability to develop research in the
wider context of social and economic
policies with a view of any future
consequences arising from the
presentation of results.
(f) Ability to source and critically analyse
a wide range of literature.
(f-g) Through individual coursework, group discussion
and planning sessions, and dissertation.
(g) Identify data sources and appraise
their strengths and weaknesses.
Assessment:
Assessment is via practical projects with presentation
components, a collaborative group research project,
written coursework, an examination, and a final
dissertation.
C: Skills and other attributes
Practical skills (able to):
Teaching/learning methods and strategies:
(a) Work with GIS packages and
database GUIs to create, handle and
view data.
(a-b) Students will have workshop and practical sessions
where they will work using relevant software packages,
as part of BENVGSA3, BENVGSC2 and BENVGSC4.
(b) Program scripts and code to query
databases, analyse and summarize data.
(c) The integration of the skills from the above modules
will result in this code-analysis link being formed. This
link will be stressed throughout the course.
(c) Develop code from mathematical,
statistical or analytical considerations.
Assessment:
Assessment is via practical projects with presentation
components, a collaborative group research project,
written coursework, an examination, and a final
dissertation.
D: Skills and other attributes
Transferable skills (able to):
Ability to work in a team of diverse
educational and international
backgrounds; lead research; use
technology appropriately; use data and
literature resources appropriately.
(a) Write concise, evidence-based,
theoretically grounded reports.
Teaching/learning methods and strategies:
(a) Through writing essays and reports, receiving
detailed feedback and the opportunity to submit
revised work.
(b) Through presentations and project work
throughout the year.
(b) Effectively communicate the results of
practical investigations through writing,
presentation, public talks and public
writing.
(c) Create new analytic techniques and
design processes based on sound
understanding of computational theory.
(c,d,e) Through dissertation work, project reviews
and seminar interaction.
(d) Synthesise research results in order to
address a specific problem.
(e) Form considered judgements about
the computational, spatial and social
qualities of design through an
understanding of their interrelationships.
(f) Manage projects, time and work to
deadlines.
(g) Lead research.
(h) Work in diverse teams
(f,g,h) Through module coursework through the
year, and the collaborative group project.
Assessment:
(a,d,e) Through a variety of written coursework and
final dissertation.
(b,c,d) Through presentation of researched material
and practical investigations.
(f,g,h,i) Through a series of projects throughout the
year and coursework presentation/participation in
dedicated skills modules.
The following reference points were used in designing the programme:
 the Framework for Higher Education Qualifications:
(http://www.qaa.ac.uk/en/Publications/Documents/Framework-Higher-Education-Qualifications-08.pdf);
 the relevant Subject Benchmark Statements:
(http://www.qaa.ac.uk/assuring-standards-and-quality/the-quality-code/subject-benchmark-statements);
 the programme specifications for UCL degree programmes in relevant subjects (where applicable);
 UCL teaching and learning policies;
 staff research.
Please note: This specification provides a concise summary of the main features of the programme and the
learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes
full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes,
content and teaching, learning and assessment methods of each course unit/module can be found in the
departmental course handbook. The accuracy of the information contained in this document is reviewed annually
by UCL and may be checked by the Quality Assurance Agency.
Programme Organiser(s)
Dr Andrew Hudson-Smith
Name(s):
Date of Production:
February 2013
Date of Review:
20 February 2015
Date approved by Head of
Department:
20 February 2015
Date approved by Chair of
Departmental Teaching
Committee:
Date approved by Faculty
Teaching Committee
6 March 2015
March 2015