. . "201800314" . "GIP_0003" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "f2f" . "During this course, students create a model in a step-by-step way. This model will be further developed and enhanced with additional functionality (using different geo-computational methods) throughout this course.\nThere is a strong emphasis on critical reflection (via sensitivity analyses, model verification, validation of models) and comparison of geo-computational techniques. The student is encouraged to identify the innovative parts of analysis and models."@en . . . . . . . . . "Basic Programming skills, Basic understanding of programming (e.g. Python) is recommended. Students that do not have any experience in programming are recommended to contact the course coordinator.,Basic understanding of programming (e.g. Python) is recommended. Students that do not have any experience in programming are recommended to contact the course coordinator."@en . . . "4"^^ . "4" . "2B " . . . "2023-04-23T22:00:00Z"^^ . "Processes relevant to system Earth, whether natural or man-affected, commonly display variations in space and over time, yet our understanding of their behavior remains limited. The increase in available monitoring data provides handles for a detailed study of these processes. Unravelling the way these processes function and having a mechanism to test hypotheses as well as the possible impacts of interventions is key to contribute to more sustainable development. At course end, the student will have learnt to make use of the available data in process studies, by a variety of computational techniques.\n\nIn this course, we present various geo-computational approaches that help to improve our understanding of geographic processes and/or to extract actionable geo-information. Special attention will be paid to agent-based modelling and to data mining and machine learning analytical methods, and to the integration of different methods.\n\nAgent-based models (ABMs) provide the opportunity to consider both natural and social components when modelling geographic phenomena.\nData mining and machine learning methods allow innovative uses of heterogeneous datasets and have proven their value to solving a variety of social, environmental and scientific problems that were deemed wicked or, even, intractable. Cloud computing is revolutionizing the way we perform spatiotemporal analysis. It allows scaling up our work and designing robust applications for real-life problems."@en . "Spatio-temporal Analytics and Modelling"@en . . "Spatio-temporal Analytics and Modelling"@en . "Spatio-temporal Analytics and Modelling"@en . . . "Learning outcome"@en . . "Apply cloud computing approaches to support and/or realize the main modelling and analysis phases."@en . "Apply cloud computing approaches to support and/or realize the main modelling and analysis phases."@en .