Enrolment options

GIS-E4040 - Spatial Data Science for Sustainable Development, Lecture, 7.1.2025-20.2.2025

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

After completing this course, the students:

  • Have improved scientific skills in conceptualizing complex location-related problems in the society related to sustainability.

  • Can identify how the sustainability problems can be studied with geospatial data and computational approaches.

  • Have technical, hands-on competencies to identify, use, assess, process, and enrich geospatial data to study sustainability related topics.

  • Have competencies to identify, use and assess spatial data science methodologies in their analysis, and can apply them in practice to a range of sustainability related topics with Python programming language.

  • Can plan and manage team work over several weeks with their own sub goals, milestones and deliverables, and recognize the roles and responsibilities of members in a project team, linked with the team deliverables.

  • Know the best practices of open and reproducible science and can use them in practice

Credits: 5

Schedule: 07.01.2025 - 20.02.2025

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): 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 geographical and computational analysis approaches to study sustainability related questions. During the course, the students are introduced to various modern spatial data science methods which are applied to data in different geospatial data formats. 

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Assignments and project work.

Workload

  • valid for whole curriculum period:

    Lectures (20 h), self-study (20 h), assignments (50 h), final project work (45 h)

DETAILS

Study Material

  • valid for whole curriculum period:

    Lecture notes, programming tutorials, and literature. 

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    1 No Poverty

    3 Good Health and Well-being

    4 Quality Education

    5 Gender Equality

    7 Affordable and Clean Energy

    8 Decent Work and Economic Growth

    9 Industry, Innovation and Infrastructure

    10 Reduced Inequality

    11 Sustainable Cities and Communities

    13 Climate Action

    14 Life Below Water

    15 Life on Land

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring III
    2025-2026 Spring III

    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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