
Are you passionate about solving the world's biggest challenges, like climate change, food insecurity, or biodiversity loss? If you're ready to make a real difference, this is the course for you! The world needs Spatial Engineers—innovative thinkers who can bridge multiple disciplines and master the art of spatial data to tackle these wicked problems head-on. This dynamic course will empower you to become a Spatial Engineer by working on a real-world case study focused on climate change. Through a challenge-based approach, you'll not only develop an actionable plan for climate adaptation but also dive deep into the three core knowledge areas of Spatial Engineering: Spatial Information Science (SIS), Spatial Planning for Governance (SPG), and Technical Engineering (TE). Here’s what you can expect: 1. Challenge-Based Learning: Engage in a hands-on challenge that will enhance your ability to integrate knowledge across disciplines. This experience is an introduction into the learning environment of the master’s programme Spatial Engineering as well (https://www.utwente.nl/en/education/master/programmes/spatial-engineering/). 2. Core Knowledge Mastery: Choose one of the three essential pillars of Spatial Engineering (SIS, TE, or SPG) and build a strong foundation in that area, especially if it's new to you. With expert guidance you’ll choose one of the 3 core knowledge areas, and it will be one you’re not yet familiar with. Join us and start shaping a sustainable future today!
These are the teamchers you'll work with on the challenge.
Describe cycles of nutrients, water and carbon and underlying principles such as conservation of mass and energy (Process thinking and conceptualization).
Apply basic statistical methods to geographic data to gain insights.
Identify and describe gaps in your knowledge (related to the three core knowledge domains of spatial engineering, see below)
Plan, execute and reflect on the learning process needed to bridge the gap.
Apply probability density functions and concepts of correlations and regressions (Stochasticity) to estimations of relevant parameters of earth system processes.
Apply parameter uncertainty like variability, RMSE and R2, to make interpretations of outputs of models that simulate earth system processes (Parameter uncertainty).
Integrate knowledge formulating an actionable challenge using the Challenge Based Learning for M-SE approach
Apply schematizing complex systems using existing techniques to capture them in equations (System thinking and model conceptualization).
Theory and context - Describe the evolution of spatial planning theory and practice and how these relate to notions of sustainable development and resilience. - Explain the political, legal and institutional context of a spatial planning situation.
Analysis of a planning situation - Analyse a given spatial planning problem/situation, identifying key stakeholders and their interests. - Describe the trade-offs between competing interests in a given spatial planning situation.
Recommend evaluation and future needs - Elaborate on ways to measure to guide, monitor and evaluate the realisation of spatial plans.
Discuss how spatial analysis can assist in anticipating future needs and emerging issues in society.
Advise on a planning situation - Develop and present a concept plan for addressing a given spatial planning situation.
Conceptualize and represent the real world in digital geospatial data.
Identify and apply methods for the exploration, analysis, integration, synthesis and presentation of digital spatial data and imagery in a geographic information system (GIS).
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