LEARNING OUTCOMES
The student comprehends basic theories of object and field based spatial data models, the role of spatial relationships and the related concepts of uncertainty.
The student can select appropriate object and field data models for various environmental applications.
The student comprehends the elementary spatio-statistical analysis methods and the special requirements in analyzing spatial data (such as: spatial autocorrelation, interpolation, graph analysis, raster based analysis).
The student can use GIS software and perform elementary spatial analyses on environmental data.
The student can select the appropriate visualization methods for presenting environmental data and prepare a good map.
The student comprehends the role of geoinformatics as a field of science and its relationships to environmental and natural sciences.
The student can explain the basics of spatial referencing by coordinate systems and projections and recognizes the basic principles, technologies and applicability of geodetic positioning, remote sensing, laser scanning and photogrammetry.
Credits: 5
Schedule: 10.01.2024 - 23.02.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Jussi Nikander
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: Finnish, Swedish, English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
This course deals with the specialties of spatial phenomena and objects from the point of view of modeling, analyzing and visualization in environmental applications. The course offers introduction to spatial referencing by coordinate systems and projections as well as the basic methods of geodetic positioning, remote sensing, laser scanning, and photogrammetric measurements. Course introduces the basic theory of object and field based spatial data models and the spatial relationships, elementary spatio-statistical analysis methods and most important visualization techniques. The course includes computer class exercises.
Assessment Methods and Criteria
valid for whole curriculum period:
Lectures and assignments. The course grading is primarily based on assignments and optionally an exam
Workload
valid for whole curriculum period:
Lectures 11 x 2 h, assignments 63 h (mainly teamwork), individual studying 50 h
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Spring III
2023-2024 Spring IIIEnrollment :
Registration for the course via Sisu (sisu.aalto.fi).