Spatial and Temporal Analytics


Content, progress and pedagogy of the module

Disclaimer. This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.



The objective of the course is to equip the student to perform analyses that enable value creation from spatial, temporal, and spatio-temporal data. Such data occurs frequently in many important societal and industrial settings and applications, e.g., in relation to smart cities, transportation, social-media analytics, census data applications, predictive maintenance, and digital energy.

Learning objectives


  • knowledge of different classes of analytics related to spatial, temporal, and spatio-temporal data such as the following
  • methods for aggregation and the identification of patterns: Examples include spatial and temporal aggregation and the identification of temporal and spatial patterns; and motifs, trends and periodicity in time series

  • methods for the identification of similarity and clusters: Examples include nearest-neighbor querying of spatial and spatio-temporal data; clustering of spatial and spatio-temporal point data, such as point of interest data and other spatially and temporally annotated data; trajectory mining and clustering such as the identification of co-movement, convoys, flocks, and swarms; and similarity and correlations in time series

  • methods for the identification of outliers: for example outlier detection in point-of-interest data, time series, and trajectories

  • prediction methods: for example prediction of future states from present and past data in the form of time series, prediction of future locations of trajectories


  • To be able to model and represent a given spatial and temporal data set in an effective way. 

  • To be able to perform analytics on given spatial and temporal data using relevant methods and techniques.


  • to able to pick relevant methods and techniques for a given spatial and temporal analytics use case

  • able to understand and reason about the results of spatial and temporal analytics

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

It is expected that the student uses 30 hours per ECTS, which for this activity means 150 hours



Name of examSpatial and Temporal Analytics
Type of exam
Written or oral exam
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Additional information

Contact: The Study board for Computer Science at or 9940 8854

Facts about the module

Danish titleSpatial and Temporal Analytics
Module codeDSNDVK302
Module typeCourse
Duration1 semester
Language of instructionDanish and English
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Education ownerMaster of Science (MSc) in Data Science and Machine Learning
Study BoardStudy Board of Computer Science
DepartmentDepartment of Computer Science
FacultyThe Technical Faculty of IT and Design