Enrolment options

GIS-E1130 - Introduction to Spatial Data Analysis D, Lecture, 3.9.2024-28.11.2024

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

The student can identify appropriate analysis approaches for different geospatial tasks and describe data needs and suitable methods for the analysis process. The student understands the basic principles of spatial data structures and algorithms. The student can apply modeling techniques and algorithms, as well as spatial extensions of standard statistical, mathematical, and computational methods to solve spatial problems in Python. The student can evaluate the suitability of solutions and assess the quality of geographical data. The student can discuss the strengths and limitations of the methods.

Credits: 6

Schedule: 03.09.2024 - 28.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Jussi Nikander, Henrikki Tenkanen

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:

    The course introduces various mathematical, statistical and computational methods in their spatial forms. The contents cover spatial data modeling methods, data management and maintenance as well as spatial algorithms, data structures, and indexing methods. The course covers various methods and techniques related to processing and analysis of spatial data including measures of autocorrelation, spatial statistics, spatial interpolation, data classification, network analysis and clustering methods, map algebra, geostatistics, and geometric problem solving. In addition, different quality concepts and measures, as well as visual analysis and multivariate visualization techniques are covered in the course.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Examination and assignments

Workload

  • valid for whole curriculum period:

    Lectures (40 h), assignments (20 h), self-study (82 h), preparation for examination + examination (20 h)

DETAILS

Study Material

  • valid for whole curriculum period:

    Lecture notes and additional material

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    4 Quality Education

    9 Industry, Innovation and Infrastructure

    11 Sustainable Cities and Communities

    15 Life on Land

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn I - II
    2025-2026 Autumn I - II

    Registration:

    Registration for the courses via Sisu (sisu.aalto.fi).

Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof

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