Contents:
New sources of data available from all kind of ‘smart technologies’ such as sensors, tracking-devices, crowd sourcing and social media open possibilities to create information and gain knowledge about our environment beyond that what is possible with ‘traditional’ sources of data. Especially analyses of spatial-temporal processes and interactions between people and their environment are accelerated by these new sources of data. Examples are the movements of people (tourists) through a city and the consequences for its accessibility or the perception of people about certain places.
The drawback is that these data often comes in high volumes, are often ill structured, and often are collected with a different purpose than that of environmental analyses. This means that (pre) processing, analyses, and visualization of such data requires specific skills. This includes, for example skills to create meaningful patterns from the data by applying (spatial) classification and clustering techniques, or applying sentiment and topic analyses techniques on for example social-media data. Knowing how to visualize these often-complex type of data is essential to effectively share and communicate the outcomes of analyses.
Moreover, making sense of these data and transform it to information useful for design, participation, decision-making and governance processes requires a critical attitude and good knowledge about the quality of the data, as well as critical reflections on the social and political implications of using smart technologies in environmental policy and decision-making. This course will pay ample attention to societal aspects such as citizen engagement in data gathering, ethical questions around big data and automation, and implications of using smart technologies on social and power relation in (urban) environmental policy.
To successfully follow this course knowledge about modern data-science concepts and techniques such as treated in Data Science Concepts (INF-xxxxx) or a data science minor is assumed.
Learning outcomes:
After successful completion of this course students are expected to be able to:
- understand the specific aspects of applying data-science for the environmental science domains;
- evaluate the quality and understand the limitations of data-sources from ‘smart technologies’;
- design procedures to solve an information need using data-science and visualization techniques;
- extract meaningful patterns/knowledge and synthesize it in an appropriate way such that is can be understood and used within an environmental design or planning process;
- apply appropriate data visualization techniques to complex environmental data;
- develop an attitude of responsibility by reflecting on the societal implications of using smart technologies and big data;
- identify boundaries between practices and develop and demonstrate the competences necessary for crossing these boundaries.
