GG4257 Urban Analytics: A Toolkit for Sustainable Urban Development

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

30

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Module capped at 25 students

Planned timetable

Fri 10am-1pm

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr M F Benitez

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Fernando Benitez

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module explores the intersection of urban analytics and sustainable development, focusing on using Python to analyse and visualise urban data. The course will cover a range of topics, from the use of advanced spatial data analysis libraries to more advanced topics to estimate population characteristics and the examination of urban sustainability. The module’s goal is to equip students with the knowledge and skills necessary to use urban analytics to address the complex challenges facing urban areas. Students will have the opportunity to work on real-world case studies and hands-on projects that will allow them to apply their newfound knowledge and skills. It is structured around two methods which are propose-based and will are assessed through pre-defined lab projects and an independent final research project. It also integrates seminar activities and an annual event career event where students hear professionals about how they use Spatial data science in their work.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS 'GG2011, GG2012 AND GG3209' OR 'SD2001, SD2002 AND GG3209' OR 'GG2013, GG2014, SD2100 AND GG3209' OR 'SD2005, SD2006, SD2100 AND GG3209'.

Assessment pattern

100% coursework

Re-assessment

100% coursework

Learning and teaching methods and delivery

Weekly contact

1hr lecture (x10 Weeks) 2hr Laboratory Practical (x10 Weeks)

Scheduled learning hours

30

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

270

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • By the end of the module, students will be able to demonstrate a comprehensive understanding of the fundamental concepts and theories in urban analytics and sustainable development.
  • By the end of the module, students will be able to display a strong foundation in Python and geospatial data analysis tools to address urban challenges.
  • By the end of the module, students will be able to understand the use of techniques to study population characteristics at a fine-grain scale.
  • By the end of the module, students will be able to appreciate the role of spatial data science in addressing the challenges faced by urban areas.
  • By the end of the module, students will be able to display a comprehensive understanding of the United Nations Sustainable Development Goals and their relevance to urban analytics and sustainable development