Universität Wien

290137 VU Statistical Data Analysis with Focus on Spatial Statistics (2021W)

5.00 ECTS (2.00 SWS), SPL 29 - Geographie
Prüfungsimmanente Lehrveranstaltung
GEMISCHT
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine

Time: Wednesdays at 13:30-15:00 starting 06.10.2021
Room on-site: MM-Lab, NIG, Stiege III, 1. Stock, C110

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Statistics are important to understand or perform quantitative analysis and research. In the past, this used to be a tedious task, however, today’s environments are interactive with comprehensive graphical outputs. Furthermore, free and open-source software, tools, and textbooks are out there on the web and will be used in this course. Classical statistics describes and infers from data without considering its spatial component. In Geography, we clearly want to investigate how a location/place/area relates or even affects the information that we examine. This is where spatial statistics comes in. This course teaches fundamental spatial statistical methods. However, these are mostly extensions of their classical one-dimensional statistical counterparts. Hence, classic statistics are introduced too. The methods and concepts are explained intuitively and assisted with exercises without a theoretical approach that would require formal proofs. It is expected to learn “what” to use, “why” to use it, “how” it is should be implemented, and “how” it can be interpreted.

The course has a fairly equal amount of both lectures and practical work, in which theory and methods precede their application. The practical work consists of exercises of multiple tasks to be solved/answered given sufficient support material. During the practical work commercial as well as open-source or free software tools are used. It is emphasized that most statistical and spatial statistical methods can be performed without the use of commercial software. Furthermore, there are sessions locked for either tutorials or extra guidance to previous exercises.

Entry requirements
Knowledge of GIS software (e.g., ArcGIS – elementary level) is a prerequisite for this course.

ArcGIS software for students: https://zid.univie.ac.at/en/software-for-students/

Art der Leistungskontrolle und erlaubte Hilfsmittel

The assessment is conducted via three testing elements (T1-3). The first is an assignment that requires the application of two examination topics on a new and independently chosen dataset. It is assessed via a reproducibility file and an oral presentation. The second, a peer review, involves the evaluation of the presentation and reproducibility files of examination topics that were not used by a student in T1. The last one evaluates a student’s participation and contribution during in-class activities.

The weights of each element are:
- Individual assignment 55% (T1)
- Peer review assignment 35% (T2)
- Contribution 10% (T3)

Mindestanforderungen und Beurteilungsmaßstab

 A student shall attend at least 75% of the sessions.
 T1 is an obligatory test to pass the course.
 T2 and T3 are not obligatory tests to pass the course.
 The maximum scoring points that can be achieved for T1 is 55.
 The maximum scoring points that can be achieved for T2 is 35.

Prüfungsstoff

• Statistical data analysis
• Spatial statistical analysis
• Spatial autocorrelation & clustering
• Correlation & GWR
*The examination topics cover the entire content of the course and its learning outcomes.

Literatur

-- Bluman, A. G. (2009). Elementary statistics: A step by step approach. New York. McGraw-Hill Higher Education.
-- Brunsdon, C., Fotheringham, S., & Charlton, M. (1998).
-- Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician), 47(3), 431-443.
-- Fischer, M. M., & Getis, A. (Eds.). (2010). Handbook of applied spatial analysis: software tools, methods and applications (pp. 599-628). Berlin: springer.
-- Haining, R. P., & Haining, R. (2003). Spatial data analysis: theory and practice. Cambridge university press.
-- Lee, J., & Wong, D. W. (2001). Statistical analysis with ArcView GIS. John Wiley & Sons.

Zuordnung im Vorlesungsverzeichnis

(BA GG 5.2)

Letzte Änderung: Fr 01.10.2021 16:10