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DRPS : Course Catalogue : Edinburgh College of Art : Architecture and Landscape Architecture

Postgraduate Course: Urban Data Science with R for Sustainable Cities and Communities (ARCH11290)

Course Outline
SchoolEdinburgh College of Art CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryAs more than half of the world's population currently live in cities, a proportion that is projected to increase in the future, the UN has included among the Sustainable Development Goal (SDG) that of creating Sustainable Cities and Communities. This course provides students with key theoretical and methodological knowledge as well as technical skills to analyse spatial data with R and evaluate the environmental, economic, social and spatial sustainability of cities and communities.

R is a programming language and open source software originally developed for statistical computing. Over the years, R has been increasingly extended with packages to handle and analyse spatial data. Compared with GIS software, i.e. Qgis or ArcGIS, it allows students to better deal with big data programmatically. The course is meant to be introductory, therefore students do not need to have any prior knowledge of programming.
Course description Students are introduced to some of the issues that makes cities unsustainable, what solutions could be implemented to make them more sustainable and how urban data can be leveraged to provide policy-makers with evidence to help them make informed decisions and develop transformative plans.

Through seminars, tutorials and hands-on sessions students are equipped with knowledge and skills to independently carry out data-driven analysis for evidence-based urban planning in the context of sustainable cities and communities.

The first part of the course covers 1) the key concepts behind programming in R such as what variables, data types, data structures and functions are defined in R, and 2) the specificity of spatial data introducing coordinates and geometries as well as the most common R packages to deal with spatial data. At this stage, seminars consist of presentations providing the theoretical groundings to successfully engage with programming in R; during tutorials students are guided in reproducing R code step by step to understand how concepts presented during the seminars translate into computational instructions; finally, during hands-on sessions students will independently, but under the supervision of the teacher or tutor, carry out exercises to practice what they have learnt, i.e. loading or creating and transforming a data frame or a spatial data object.
The second part of the course covers the theme of sustainability in the context of cities and communities through official documents developed by the UN and state of the art research. It explores how urban data can be leveraged to obtain evidence on the 4 dimensions of sustainability introduced by the UN Urban Agenda: the economic, social, environmental, and spatial dimensions. Each of these dimensions are discussed during seminars through real world cases; related data and R notebooks are shown during tutorials to give students examples of how data can help uncovering how much sustainable cities and communities are, and how such evidence can help policy-makers designing new policies; hands-on sessions will leave students free to develop their own code. Indicative topics discussed at this stage are:
- Inequalities and spatial segregation in cities
- Air pollution and sustainable transportation
- Urban density and sprawl
Such topics may change year to year but they will always be related to the 4 dimensions of sustainability in cities and communities mentioned above.

The course is structured in three sequential stages. Each week consists of a 1 hour seminar which introduces the theories behind the topics discussed, followed by a 2 hour tutorial during which the teacher or tutor presents the R programming language in action step by step, and hands-on session where students are supervised while programming in R.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  1
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 196 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) This course comprises of 2 assessment components.
- Report (1000 words), 70%, due in the examination period.
- Computational notebook (R code file), 30%, due in the examination period.

Summative assessment is by submission of a 1000 word report accompanied by a reproducible computational notebook. The report will articulate the aims, present and discuss the methods and results of a data analysis project they need to identify and carry out. Computational notebooks are interactive documents that combine code, results, text and images. Students will be able to develop their own notebook through Noteable, a computing service provided by the University, to carry out the analysis presented in the report. Each notebook will be checked for reproducibility, meaning that the instructor has to be able to run the notebook without errors.

Assessment is based on all three learning outcomes, weighted equally.
Feedback Formative Feedback
Formative evaluation is provided through verbal feedback during hands-on sessions on general progress and during a formative seminar at the end of the course (week 11), when students will present an overview of their report's plan. Written feedback is also provided on a report's abstract (300 word) submitted in week 7.

Summative Feedback
Summative feedback will be provided via written evaluation of the work and communicated to students as per University regulations.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a critical understanding of the benefits and limitations of R programming languages for handling data, and the role of data in the context of sustainable cities and communities.
  2. Apply R programming skills to handle and analyse data as well as best programming practice to write efficient and reusable code.
  3. Utilise logical, analytical, and problem-solving skills to make informed decisions about the most appropriate data and methods to describe a real-word case study.
  4. Communicate the outcomes of complex dynamics based on data evidence through the configuration of data visualisations in a written report.
Reading List
David, S. (2016) Rethinking Sustainable Cities: Accessible, Green and Fair. Bristol: Policy Press
Dixon, T. J. (2022) Sustainable Urban Futures and Sustainable Urban Systems in the Built Environment: Towards an Integrated Urban Science Research Agenda. Journal of Sustainability Research, no. 4
Oyana, T. J. (2020) Spatial Analysis with R. Boca Raton: CRC Press
United Nation Human Settlements Programme (2020) The New Urban Agenda Illustrated Handbook. Nairobi: UN-HABITAT
Additional Information
Graduate Attributes and Skills At the end of this course students will be able to:

Research and Enquiry - apply analytical and critical thinking while working with data in relation to current issues affecting cities; carry out independent research by analysing quantitative data and handling the complexity related to the concept of sustainability; be familiar with a widespread programming language such as R and acquire confidence with manipulating data.

Personal and Intellectual Autonomy - apply appropriate solutions and implement effective methods to analyse urban data and draw conclusions.

Personal Effectiveness - apply personally driven data projects, with the ability to prioritise and effectively use resources to achieve the projects goals.

Communication - report on data analysis work, with the ability to communicate complex ideas through writing and data visualisations.
Keywordsurban data science,sustainability,R programming
Course organiserDr Alessia Calafiore
Course secretaryMr Daniel Jackson
Tel: (0131 6)50 2309
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