Geographic Information Science courses

Approaches and challenges to analysing spatial data. Specific techniques covered will include measures of spatial autocorrelation, geographical regression, point pattern analysis, interpolation, overlay analysis, and an introduction to some of the newer geocomputation methods such as neural networks and cellular automata.

This course is taught in: Second Semester, City Campus (S2 C)

Format: one 2-hour class each week
Points: 15
Assessment: 60% coursework, 40% final examination
Prerequisites: No formal prerequisite but an understanding equivalent to GEOG 318 will be assumed
Programme: this is one of the core options for the Geography, Earth Sciences and Environmental Science programmes
Course coordinator:  Jay Gao

Concepts and theories underpinning digital analysis of raster data, including remotely sensed data, LiDAR data and digital elevation models. Sources, nature and accuracy of raster data, analysis and integration of raster data from diverse sources, and applications of raster data analysis in hydrology and environmental modelling.

This course is taught in: First Semester, City Campus (S1 C)

Format: one 2-hour lecture per week, plus one hour labs
Points: 15
Assessment: 60% coursework, 40% final examination
Prerequisites: no formal prerequisite but an understanding equivalent to GEOG 317 is assumed
Programme: this is one of the core options for the Geography and Earth Sciences programmes
Course coordinator: Jay Gao

Introduction to field of cartography, drawing contrasts with new approaches to geovisualisation facilitated by information visualisation and statistical graphics. Human perceptual and cognitive systems as related to visual displays. Principles of sound perceptual and cognitive map design. Planning, creation and delivery of cartographic and visualization-based projects. Review of emerging and future trends in this fast-changing field.

*This course will not be offered in 2016.

Spatial databases, spatial data structures and algorithms and converting and handling spatial data. Introduction to programming (in Python). Principles of object- and component-oriented architectures including details relating to ArcGIS as an example. Open source and open standards, web-mapping as a case-study.

*This course will not be offered in 2017.

The following themes are emphasised: (i) building and using models to investigate environmental and social problems, (ii) understanding the utility of modelling in various disciplines, and (iii) appreciating how dynamic phenomena can be represented and analysed computationally. The course provides an understanding of modelling concepts, approaches and applications, and methods for determining the suitability of a particular modelling approach for a given task.

This course is taught in: Second Semester, City Campus (S2 C)

Format: One 2-hour lecture and one 2-hour lab per week
Points: 15
Assessment: 100% coursework
Prerequisites: no formal requirement, but knowledge equivalent to that covered in courses such as STATS 101, MATH 108, GEOG 250, BIOSCI 209, ENVSCI 310 will be assumed
Programme: this is one of the core options for the Environmental Science and Geography programmes
Course coordinator:  Giovanni Coco