n.MA.ENR.ASA.17HS (Advanced Spatial Analysis) 
Modul: Advanced Spatial Analysis
Diese Information wurde generiert am: 19.05.2022
Nr.
n.MA.ENR.ASA.17HS
Bezeichnung
Advanced Spatial Analysis
Credits
3

Beschreibung

Version: 4.0 gültig ab 01.08.2021
Studiengang
Degree Program
MSc in Environment and Natural Resources
Arbeitsaufwand
Workload
90 hours (35 h contact lessons, 55 h self-study)
Modulleitung
Module Coordinator
Patrick Laube
Dozierende
Lecturers
Robert Weibel (UZH), Peter Ranacher (UZH)
Eingangskompetenzen
Entry Requirements
The students are able to
  • understand basic methods of spatial analysis,
  • use R (Basics of R): We will be working with R throughout the course and we will assume that you have some basic knowledge of R when you come to this course: basic R data types and how to deal with them; reading data; using R functions, including spatial operations; plotting (spatial) data; finding help in the R help and on the internet.
Ausgangskompetenzen
Learning Outcome and Competences
The students will learn to
  • apply appropriate procedures, given a particular research question that requires spatial analysis, from the learned set of techniques,
  • suggest interesting research questions, given your available toolset and a point data set or a network data set,
  • isolate scale effects,
  • interpret spatial and attribute distributions,
  • to use R for spatial analysis, in particular for point pattern analysis, network analysis, and geometric problems.
Inhalte
Module Content
In this course, you will deepen your knowledge of spatial analysis techniques, while at the same time exploring some of the fundamental Geoinformatics topics — such as effects of scale, density estimation, topological problems, power law distributions — in more detail. Analysis techniques introduced will place an emphasis on geometrical problems and will include advanced point pattern analysis (clustering, density estimation), localized spatial analysis, network analysis (network measures, shortest path problems), and delineation of polygons from point sets.

All techniques introduced in the theory sessions will be also be demonstrated in the R environment for statistical computing and spatial analysis, along with short exercises. This will then provide the basis for the term project carried out in the second half of the semester.
Lehr-/Lernmethoden
Teaching / Learning Methods
Lecture, seminar, practical exercises
Leistungsnachweis
Assessment of Learning
Outcome
The assessment of learning outcome is composed as follows
  • term project with project report
Bibliographie
Bibliography
  • Brunsdon, C., & Comber, L. (2015). An introduction to R for spatial analysis and mapping. Sage.
Unterrichtssprache
Language
English
Bemerkungen
Comments
In collaboration with UZH, max. 10 participants.

All information about the organization of this module can be found in the UZH course catalogue: https://courses.uzh.ch/
Search for “Advanced Spatial Analysis I” or “Geo872” AND select the correct semester (Fall Semester 20YY).

Hinweis