Universität Wien

290003 VU Introduction to Spatial Data Science (2024W)

5.00 ECTS (2.00 SWS), SPL 29 - Geographie
Prüfungsimmanente Lehrveranstaltung
Moodle
Mi 23.10. 13:00-15:00 Multimedia Mapping-Labor, NIG 1.Stock C0110

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Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Active participation, assignments, midterm exam, final presentation

Mindestanforderungen und Beurteilungsmaßstab

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

Prüfungsstoff

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

Literatur

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

Zuordnung im Vorlesungsverzeichnis

(MG21 PF MOBIL)

Letzte Änderung: Mo 30.09.2024 10:26