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.
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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.
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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.
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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
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Bibliographie
Bibliography |
- Brunsdon, C., & Comber, L. (2015). An introduction to R for spatial analysis and mapping. Sage.
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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). |