Curriculum

This course discusses the principles and application of geographic information systems. It emphasizes on the use of GIS to organize and manage spatial data, and perform spatial analysis with GIS. Topics include: hardware/software components, raster and vector data structures, spatial database, spatial analysis and application issues. (Part-time students are advised to take this courses in the first year of study.)

This course intends to increase students' understanding of the use of transportation systems in contemporary societies and their impact in structuring the geographic space of cities, regions, and the world at large. It aims to enhance students’ knowledge of theories, techniques, and models of transportation analysis and business logistics used by practitioners in the public and private sector of the economy.

This course discusses the principles and applications of remote sensing image analysis. Topics include principle of remote sensing, acquisition of satellite imagery, format of digital images, digital image processing systems, image display, geometric correction, radiometric adjustment, image enhancement, image classification, accuracy verification, change detection, and orthophoto applications. This course is taught with both lectures and laboratories. Lectures will be accompanied by laboratories for students to grasp both the theoretical and practical issues in remote sensing image analysis. (Part-time students are advised to take this course in the first year of study.)

This course aims at the concepts and applications of artificial intelligence in solving real-life problems in Smart-City management. Fundamentals of artificial intelligence methods for decision making will be discussed. It covers various paradigms for artificial-intelligence applications in spatial knowledge representation and inference, expert systems for building knowledge-based GIS, computational models and intelligent spatial decision support systems (SDSS). The enrichment of spatial decision support by data mining and knowledge discovery will also be examined. In-house software will be employed to facilitate discussion and to assist students to acquire hands-on experience.

This course provides an introduction to the basic and spatial statistical analysis methods that are commonly used to investigate geographically represented data. Topics covered include: (1) correlation analysis, (2) principal components analysis, (3) cluster analysis, (4) Geostatistical analysis, and (5) global and local spatial regression models. Students will develop their competency in using and applying GIS and statistical tools to solve real-world geographical problems through hands-on lab exercises and course projects.

This course introduces to students geospatial big data (e.g., social media and GPS trajectories) and their applications in urban planning, including land functionality classification, environmental monitoring, sentiment analysis, and transportation planning. The use of GIS in various urban applications relevant to big data will also be covered. These applications encompass urban form, public health, 3D GIS, and geodesign. Through hands-on lab exercises, students are expected to understand how geospatial big data can be applied to address real-world challenges in the urban environment, and to grasp more advanced skills relating to GIS applications.

Students enrolled for Master of Science of Geoinformation Science are required to select research topics independently under the supervision of related teachers. They will be required to present and submit their theses for examination.

This course introduces a variety of applications of remote sensing in environmental monitoring. It focuses on how image analytical techniques are used in different applications so as to answer research questions pertinent to these applications. Topics include land use and land cover mapping, urban and population analysis, vegetation studies, hydrological science and terrain analysis.

This course covers the basic principles of Global Positioning Systems (GPS) with emphasis on satellite positioning, communication, and accuracy and error correction. It also introduces GPS applications in navigation, Intelligent Transport Systems (ITS), mobile Geographic Information Systems (GIS), and Location-based services. Through hands-on exercises, students will learn how to use GPS devices to collect data in the field in support of various geospatial applications.

This course covers major concepts and methods for modeling spatial patterns and processes. Topics include multi-criteria evaluation, cellular automata, agent-based models, terrain modeling, spatio-temporal data modeling, integration of GIS and environmental modeling, and relevant applications. During the course, students will read about different modeling approaches, discuss applications of the models, and work on lab exercises and a course project.

This course covers the basic principles for GIS design and application system implementation using the mainstream techniques. Topics include requirement analysis, object-oriented design using Unified Modeling Language (UML), database normalization, and GIS customization within a GIS environment. Students will develop their competence in designing and customizing GIS tools through lectures, hands-on lab exercises, and a course project.