58勛圖

GG4257 Creating Sustainable Cities with Spatial Data Science

Academic year

2026 to 2027 Semester 1

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

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

Urban areas house over half the global population and are at the core of the most pressing challenges facing modern society. Inequality, climate change impacts, and environmental degradation are just a few examples of the daily issues local authorities must address. Being well-equipped with technical skills has become a key advantage for geography graduates. The ability to propose solutions that highlight cities' key role in achieving the SDGs is the cornerstone of this module's design. It will prepare students for careers in the public and private sectors through specialised training in geospatial methods for analysing complex urban patterns. It extends coding and spatial analysis techniques from GG3209 to tackle real-world urban challenges. Students will learn how modern urban analysts combine GeoAI workflows with traditional geospatial methods to address critical challenges relevant to achieving the Sustainable Development Goals.

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

  • Conduct advanced spatial analyses of urban environments that extend beyond traditional GIS capabilities to investigate complex urban sustainability challenges at multiple scales
  • Apply the use of programming languages and geospatial data analysis tools to address urban challenges with proficiency
  • Process and analyse large-scale urban datasets from diverse sources (census, satellite imagery, sensors, crowdsourced data), applying appropriate analytical workflows to extract meaningful geographical patterns
  • Critically evaluate emerging technologies in urban geospatial analysis, understanding their appropriate application and ethical implications in professional practice
  • Design and execute independent quantitative research projects by developing spatial data science workflows, selecting appropriate methods, and creating professional outputs that demonstrate career-ready capabilities.