Spatial and Temporal Analytics
2022/2023
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.
PURPOSE
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
- 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
Skills
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.
Competences
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
Exam
Exams
| Name of exam | Spatial and Temporal Analytics |
| Type of exam | Written or oral exam |
| ECTS | 5 |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Additional information
Contact: The Study board for Computer Science at cs-sn@cs.aau.dk or 9940 8854
Facts about the module
| Danish title | Spatial and Temporal Analytics |
| Module code | DSNDVK302 |
| Module type | Course |
| Duration | 1 semester |
| Semester | Autumn
|
| ECTS | 5 |
| Language of instruction | Danish and English |
| Empty-place Scheme | Yes |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Study Board | Study Board of Computer Science |
| Department | Department of Computer Science |
| Faculty | The Technical Faculty of IT and Design |
