Universität Wien
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290003 VU Introduction to Spatial Data Science (2024W)

5.00 ECTS (2.00 SWS), SPL 29 - Geographie
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

No class on:
Wednesday 11.12.2024 13:00 - 15:00
Wednesday 08.01.2025 13:00 - 15:00

  • Wednesday 02.10. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 09.10. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 16.10. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 23.10. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 30.10. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 06.11. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 13.11. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 20.11. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 27.11. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 04.12. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 11.12. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 08.01. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110
  • Wednesday 15.01. 13:00 - 15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110

Information

Aims, contents and method of the course

This course will introduce students to Spatial Data Science as a "fourth paradigm" of science. The course will outline all stages from the initial conceptualization and representation of data to their creation, entry, cleaning, and analysis, e.g., using clustering or classification. We will discuss classics such as DBSCAN, develop a first understanding of issues of computational complexity, and also cover FAIR principles (findability, accessibility, interoperability, and reusability) in (research) data management.
The course will also introduce foundational literature about different kinds of regions in geography and then introduce a novel, data-synthesis-based method to replicate the findings of these classical papers in order to show applications of Spatial Data Science.
Topics will include introductory materials in knowledge representation, data engineering, geographic information retrieval, clustering, classification, and so on. We will also touch on several application areas, such as social sensing and place recommendations.

Assessment and permitted materials

Active participation, assignments, midterm exam, final presentation

Minimum requirements and assessment criteria

Students will develop their own experiments (as a series of assignments) and present them during the class.

Examination topics

• Active participation (10%)
• Assignments (30%)
• Midterm exam (30%)
• Final Presentation (30%)

Reading list

Optional: O'Sullivan, D. (2024). Computing Geographically: Bridging Giscience and Geography. Guilford Publications.

Association in the course directory

(MG21 PF MOBIL)

Last modified: Mo 30.09.2024 10:26