This module aims to develop students' knowledge of the mathematics, algorithms and techniques used to extrapolate 3D spatial data from a variety of incomplete, sparse, and/or irregular data sources.

The module is relevant to those interested in developing visualisation applications for data from a variety of disciplines such as criminology, geology, archaeology, anthropology, robotics, and medical imaging.

The module will build upon and expand the C++ graphical programming, mathematics, and scientific data visualisation skills developed during the students' course.

New skills developed will focus on both the mathematics and algorithms of data reconstruction and extrapolation.

On successful completion of this module, the student will be able to

• Determine an optimal data acquisition strategy for a given reconstruction scenario.
• Determine an appropriate destination data representation for a given reconstruction scenario.
• Create algorithms to perform 3D spatial data reconstruction from incomplete, sparse, and/or irregular data sources.
• Apply appropriate visualisation techniques to view reconstructed data.
• Critically evaluate solutions to a given reconstruction scenario via a written document.

Student achievement will be measured by an In-Course Assessment. This will involve creating a spatial data reconstruction and visualisation application to a specification chosen by the tutor.

Data Visualisation is a pre-requisite for this module.

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