PROGRAMME SPECIFICATION
Programme title:
Spatial Data Science and Visualisation
Final award (BSc, MA etc):
MRes (180 credits)
(no exit award)
(where stopping off points exist they should be
detailed here and defined later in the document)
UCAS code:
N/A
(where applicable)
Cohort(s) to which this programme
specification is applicable:
From 2015 intake onwards
(e.g. from 2015 intake onwards)
Awarding institution/body:
University College London
Teaching institution:
University College London
Faculty:
Bartlett Faculty of the Built Environment
Parent Department:
Centre for Advanced Spatial Analysis
(the department responsible for the administration of
the programme)
Departmental web page address:
www.casa.ucl.ac.uk
(if applicable)
Method of study:
Full-time, flexible/modular
Full-time/Part-time/Other
Criteria for admission to the
programme:
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)
UK Bachelor’s degree in an appropriate subject, awarded with First or
Upper 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 MRes 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.
Level 7
N/A

Brief outline of the structure of the
programme
and
its
assessment
methods:

(see guidance notes)


Board of Examiners:
Individual Research Project: a 10,000 word dissertation
and 5,000 word research paper on a topic of the student’s
decision [90 credits, research module]
Digital Visualisation project: a group visualisation project
over terms 2-3, assessed by presentation (20%),
visualisation portfolio (50%) and written report (30%). [30
credits, research module]
Transferrable skills modules: Introduction to Programming
for Architecture and Design and Quantitative Methods [15
credits each]. Introduction to Programming for Architecture
and Design (BENVGACH) is assessed via programming
exercises (5x10%) and a larger programming piece with a
short (750 word) report (which counts 50%). Quantitative
Methods (BENVGSC2) is assessed through coursework.
Taught Modules: GIS and Data Science for Spatial
Systems. GIS (BENVGSA3) is assessed through
presentation (40%) and coursework (60%), the latter of which
includes a report and a software output. Data Science for
Spatial Systems is assessed through a software output
(iPython notebook) and a report. [15 credits each]
Name of Board of Examiners:
The Bartlett MRes 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 quantitative research methodology and methods of data collection and analysis to support
and enable independent and group research projects, and to take creative approaches to the visualisation and
communication of complex data-rich phenomena.
Broadly the MRes 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 data
science with a developed understanding of i) space, temporal dynamics and structuring, analysing and interrogating
spatial data; ii) communicating spatial data using networks, mapping, GIS, animation, interactive visualisation, apps;
and iii) 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 science.
The course is focused on gaining a balanced knowledge and
research toolkit relating to analyzing and communicating data
rich sociospatial research problems. The taught modules
provide a core structure of spatial analysis and modeling
through practical (creating maps, visualisations, and later tools
and workflows for creating visual outputs) and written
exercises to explore theoretical and practice-based questions
around their process (c,d,e,f,g,h). A central theme runs
through the course allowing students to develop data
collection, communication of research and visualisation.
These methods are developed in the visualisation project
(BENVGSA1) allowing students to plan and manage
data collection and techniques to visualise complex
phenomena (b,h) and assessed through visualisation outputs.
The integration of mathematical and computer modeling,
research techniques, interpretation and communication of data
with GI systems provide a unique understanding and insight
into spatial analysis and geographic information science
through taught and research modules (a).
(b) Visualisation in relation to the display,
communication and understanding of
complex datasets. Principles of visual
design for information.
(c) How GI systems and science emerged
as distinctive areas of research led
activity.
(d) Use of GIS for generating and
visualising summary statistics.
(e) The principles underlying the analysis
of spatial data.
(f) Knowledge of the fundamental
concepts of spatial sampling and spatial
autocorrelation.
(g) The theory and principles underlying
relational databases and their
interrogation using GUIs, programming
languages such as Python, and
visualisation tools.
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. The combination of taught courses,
seminars and existing teaching infrastructure has been
developed to ensure a strong, research led skill set for the
students which will be essential leading up to the final
research dissertation.
(h) Read-write-execute techniques for
storing, gathering and visualisating data
via web-based services.
Assessment:
Assessment is via practical projects with presentations, a
collaborative group research project, written coursework for
the taught modules, a research paper, and a final dissertation.
B: Skills and other attributes
Intellectual (thinking) skills:
(a) Ability to collate and effectively
communicate complex datasets.
(b) Critically assess research conducted
via computational and mathematical
means.
(c) Approach research problems in an
innovative manner leading to new
methodologies.
(d) Critical assessment of techniques and
methods relating to data communication
and policy.
(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.
(g) Awareness of information design
principles and pitfalls
Teaching/learning methods and strategies:
a) Through the taught modules and structured
research project in combination with the development
of transferable skills.
(b) Through exercises and discussion in the taught
modules, most notably GIS and Quantitative Methods, and in
discussions with dissertation supervisors.
(c) Through group and individual projects leading up to the
dissertation.
(d) Through guided analysis and case studies discussed in
taught modules.
(e) Via a combination of discussion sessions, practical projects
and individual research carried out through the year.
(f) Through reading and critique of key texts combined with
an analysis of ‘non academic’ literature available in the wider
context of online resources such as blogs and forums.
(g) Through material and regular feedback introduced in the
Digital Visualisation module.
Assessment:
Assessment is via practical projects with presentations, a
collaborative group research project, written coursework for
the taught modules, a research paper, and a final dissertation.
C: Skills and other attributes
Practical skills (able to):
(a) Use mathematical and programming skills
to structure, analyse and visualise data in a
variety of ways.
(b) Use specific software to visualise
modeling outcomes in a geographical
context (ArcGIS).
(c) Collect data using a wide variety of
methods, including those not traditionally
used in spatial analysis with an
understanding of levels of accuracy.
(d) Link methods and results to a broader
context as a means of adding value to
data and introducing a multi-disciplinary
view of the results
Teaching/learning methods and strategies:
(a) Through given examples discussed and
developed through the taught courses.
(b) Languages and software tools form a key part of teaching
in all taught and transferrable skills courses, as well as the
(c) Through the taught courses and mini project in
addition to active discussions and links via the online
‘Moodle’ system in addition to outreach to more
mainstream media.
(d) Through the taught courses, mini project and
online discussion as well as seminars.
Assessment:
Assessment is via practical projects with presentations, a
collaborative group research project, written coursework for
the taught modules, a research paper, and a final dissertation.
D: Skills and other attributes
Transferable skills (able to):
Ability to work in a team; lead research;
use technology appropriately; use data
and literature resources appropriately.
Teaching/learning methods and strategies:
(a) Through writing essays and reports, receiving
detailed feedback and the opportunity to submit
revised work.
(a) Write concise, evidence-based,
theoretically grounded reports.
(b) Through presentations and project work
throughout the year.
(b) Effectively communicate the results of
practical investigations.
(c,d,e) Through dissertation work, project reviews
and seminar interaction.
(c) Create new analytic techniques and
design processes based on sound
understanding of computational theory.
(f,g,h,i) Through module coursework through the
year, collaborative group project
(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) Use data and literature resources
appropriately.
(i) Work in a team.
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 GIS and Digital
Visualisation.
The following reference points were used in designing the programme:
 the Framework for Higher Education Qualifications:
(http://www.qaa.ac.uk/en/Publications/Documents/qualifications-frameworks.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)
Name(s):
Martin Zaltz Austwick
Date of Production*:
October 2015
Date of Review:
N/A
Date approved by Chair of
Departmental Teaching
Committee:
Date approved by Faculty
Teaching Committee
October 2015
October 2015