Skip to main content


CEG2727 : Geospatial Data Analysis I

  • Offered for Year: 2022/23
  • Module Leader(s): Dr Nigel Penna
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semester 1 Credit Value: 10
ECTS Credits: 5.0


The module aims to develop the essential mathematical skills for understanding and undertaking the processing and analysis of geospatial data. Students are provided with comprehensive explanations of the reasons for inaccuracy in survey measurements and shown how to achieve and guarantee survey results adequate for their purpose using least squares estimation. Knowledge of the basic concepts will be related to the computation of vertical survey control schemes but the universality of methods for handling measurement data will be illustrated through several examples.

Outline Of Syllabus

Differentiation and partial differentiation;

Taylor series;

Matrix algebra;

Population and sample statistics;

Probability distributions;

Confidence intervals;

Statistical testing;

Propagation of random and systematic errors;

Observational weights;

Principles of least squares estimation;

Least squares regression;

Levelling networks.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture181:0018:00PiP delivery
Guided Independent StudyAssessment preparation and completion22:004:00Practical write-ups
Guided Independent StudyAssessment preparation and completion180:309:00Revision for exam
Guided Independent StudyAssessment preparation and completion12:002:00PiP end of semester exam
Scheduled Learning And Teaching ActivitiesPractical33:009:00PiP, computer-based
Guided Independent StudyIndependent study158:0058:00General reading and revision
Teaching Rationale And Relationship

Early lectures introduce essential mathematical concepts for the statistical analysis of measurement data. Later lectures discuss least squares estimation applied to regression analysis and levelling networks. The theoretical concepts presented in lectures are demonstrated through case studies. Practical sessions allow students to apply the underlying theory with the aid of both spreadsheet applications and a specialised software package. Extensive time is allocated to independent study, which includes background reading and the revision of lecture materials.

Should the public health situation require, all present-in-person sessions will be delivered as synchronous online sessions. If necessary, the end-of-semester summative examination will be converted to a 24-hour take home examination delivered via the Canvas learning management system.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Description Length Semester When Set Percentage Comment
Written Examination1201A70End of Semester exam
Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report1M15Error propagation
Practical/lab report1M15Levelling computation
Assessment Rationale And Relationship

The majority of factual and conceptual material relating to the learning outcomes is assessed within the unseen exam. The two coursework reports assess the practical skills of students and the ability to present, describe and compare numerical results in writing. The two reports also assess the use of spreadsheet and specialised software applications for various least squares computations.

Reading Lists