Graduate Courses
The Department's programme of courses for graduate research students can be accessed by clicking on this link. Each course listed on the programme should normally run at least once every two years. The list of courses and/or their scheduling may be updated from time-to-time. The Department will release information on the final scheduling of courses just before the start of each academic year.
Semester 2 - AY2025/26
PROFESSOR BRENDA YEOH
DR TAN WENN ER
Workload: 3-0-0-4-3
Pre-requisite(s)/Preclusion(s)/Cross-listing(s): Nil
This course challenges students to analyse the practical problems encountered in using the various methods available in human geography research. It builds upon the undergraduate course in research methods and includes an evaluation of the construction and design of research questions in various field contexts, weighing between the major methods of data collection (e.g. quantitative and qualitative), and the practical problems of data and information analysis. Common research methods such as surveys, case studies, interviews, focus group discussions and participant observation will be carried out so that students can benefit from first hand experience in the field. Students will also be exposed to archival and map materials. Students will also be taught what sponsors look for in research proposals. As the course is entirely project-based, students are expected to have full-scale participation in the course.
C.A.: 100%
C.A.: 100%
DR YAN YINGWEI
Units: 4
Workload: 1-1-3-3-2
Pre-requisite(s)/ Preclusion(s)/Cross-listing(s): Nil
This course provides state-of-the-art training in Internet GIS technologies and spatial theories for mapping and comprehending activities in virtual space, real space, and the intersections of the two spaces. It sees Internet as an integral part of social life and provides students a venue to explore the implications of the digital transformations brought forth by the Internet. Major topics that will be covered include 1) web-based GIS mapping, 2) Internet of Things, 3) social sensing and social web, and 4) social dynamics of the Internet.
C.A.: 100%
DR WEI LUO
Units: 4
Workload: 1-1-3-3-2
Pre-requisite(s)/ Preclusion(s)/Cross-listing(s): Nil
This course provides students with an opportunity to gain hands-on experience in applying geospatial big data analytics to complex spatiotemporal problems that challenges sustainability of our society and environment, including but not limiting to disease outbreaks, traffic patterns, urban dynamics, and environmental changes. Major topics that will be covered include 1) nature of spatial big data, 2) volunteered geographic information, 3) spatial analytical approaches for discovering patterns, 4) data-driven geography, and 5) big data ethics.
C.A.: 100%
DR NATHAN GREEN
Units: 4
Workload: 0-3-0-3-4
Pre-requisite(s)/ Preclusion(s)/Cross-listing(s): Nil
Political ecology is a vibrant interdisciplinary field for critically investigating complex relations between environment and society, paying close attention to power and politics. This course traces the foundations of the field, particularly within geography, and its diverse epistemological approaches, which address how capitalism, knowledge, gender, race, and more shape human interactions with biophysical natures. The course covers current themes such as decolonization, urbanization, and climate change and is intended for graduate students with and without a political ecology background. Students will gain the theoretical tools and analytical skills necessary to understand, and address, urgent contemporary environmental and social problems.
C.A.: 100%
DR LI HAO
Units: 4
Workload: 1.5-0-1.5-4-0-3
Pre-requisite(s)/ Preclusion(s)/Cross-listing(s): Nil
Focusing on geospatial problems, this course introduces students to machine learning (ML) techniques for analyzing and interpreting spatial data. Students will learn to apply ML methods to spatial issues, emphasizing a solid foundation in ML for spatial analysis. The course covers essential concepts, methodologies, relevant tools (Python libraries), and best practices for implementing ML studies. Building upon this foundation, students will explore real-world ML applications, providing them with a comprehensive understanding of the ML pipeline. This approach equips students with the skills and knowledge to tackle various spatial problems and adapt to emerging challenges in the geospatial field.
CA: 100%
ALL TEACHING STAFF
Units: 4
Workload: Minimum 10 hours per week.
Pre-requisite(s)/ Preclusion(s)/Cross-listing(s): Nil
The level 5000 Independent Study Course is designed to enable a graduate student or small group of graduate students to explore an approved topic relating to their planned area of research. Students should normally expect to meet with their mentor three times over the duration of the course.
A proposal must be drawn up between the student(s) and mentor and approved by the Graduate Coordinator/Deputy Graduate Coordinator before the end of week 3 of the semester. This study proposal must state clearly the obligations of the student, the agreed-upon mode of assessment, the relevance of the chosen topic to his/her studies, and provide a clear guarantee that the assignment is in addition to work envisaged as part of their thesis. A culminating piece or pieces of written work (report and/or essay) is/are required. Where students have worked as a group, members of the group may submit individual pieces of written work or, alternatively, may work collectively on a joint piece of written work, depending on the approved agreement.
C.A.: 100%;
To comprise written work with a length that, under normal circumstances, falls within the range 4000-6000 words (excluding references and any appendices but including tables and figure and table captions) for individual reports or essays, or 6000-8000 words (excluding references and any appendices but including tables and figure and table captions) for a group-based, single (collective) piece of written work.
All CA will be double-marked. Where there is a large and unresolved discrepancy between the marks awarded by the two markers (>10%), work may be evaluated by a third marker.
DR MUHAMMAD NAWAZ
DR BENNY CHIN
Units: 4
Workload: 1-0-3-4-2
Pre-requisite(s): GE5223 - Introduction to Applied GIS, or with lecturer's consent
This course familiarizes students with advanced spatial data science techniques and literature in the emerging field of digital geography. Topics examined include spatiotemporal data mining, geospatial simulation, spatial statistics and machine learning techniques, and spatial data quality. Upon completion of the module, students will be expected to be able to apply these spatial data science techniques to their field(s) of interest, and critically assess the analysis outcomes and implications to human everyday life and the physical environment. Students are required to undertake an independent project, and their work will be presented in a seminar format.
C.A.: 100%;
ALL TEACHING STAFF
Units: 4
Workload: Minimum 10 hours per week.
Pre-requisite(s)/Preclusion(s)/Cross-listing(s): Nil
The level 6000 Independent Study Course is designed to enable the student to explore in some depth a topic in Geography that is of relevance to their research interests. Unlike with GE5660, there is no provision for group work with GE6660. Students should normally expect to meet with their mentor three times over the duration of the course.
A proposal must be drawn up between the student(s) and mentor and approved by the Graduate Coordinator/Deputy Graduate Coordinator before the end of week 3 of the semester. This study proposal must state clearly the obligations of the student, the agreed-upon mode of assessment, the relevance of the chosen topic to his/her studies, and provide a clear guarantee that the assignment is in addition to work envisaged as part of their thesis. A culminating piece or pieces of written work (report and/or essay) is/are required.
C.A.: 100%;
To comprise written work with a length that, under normal circumstances, falls within the range 4000-6000 words in total (excluding references and any appendices but including tables and figure and table captions).
All CA will be double-marked. Where there is a large and unresolved discrepancy between the marks awarded by the two markers (>10%), work may be evaluated by a third marker.
ASSOC PROFESSOR LIN WEIQIANG
Units: 4
Workload: 3-0-0-0-7
Pre-requisite(s)/Preclusion(s)/Cross-listing(s): Nil
This is a required course for all research Masters and PhD students admitted from AY2004/2005. The course provides a forum for students and faculty to share their research and to engage one another critically in discussion of their current research projects. The course will include presentations by faculty on research ethics and dissertation writing. Each student is required to present a formal research paper. Active participation in all research presentations is expected. The module may be spread over two semesters and will be graded "Satisfactory/Unsatisfactory" on the basis of student presentation and participation.
C.A.: No C.A.; graded on S/U