. . "5.0" . "140.0" . "10.0" . . . . . . "blended" . . . . . . . . . . . . . . . . "Basic statistical analyses, Spatial statistics"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "3.0" . "3.0" . "Understanding urban dynamics and urban growth is crucial for strategic long-term planning of infrastructure, economic development, environmental sustainability, social equity and overall urban resilience. At its core, the interaction between land use and transportation plays a pivotal role in shaping urban dynamics, and such interactions and dynamics can be most efficiently understood by modelling.\nModelling urban dynamics and growth involves the use of various theoretical frameworks that captures transportation infrastructure affects land use patterns and vice versa. In this course, the students will not only be introduced with theories about land use and transportation interactions, but also knowledges and techniques of implementing models that encodes the interactions quantitatively. Several modelling frameworks (to be specified) will be introduced to simulate travel decisions and behaviours, mobility and accessibility, land use land cover changes. On top of developing the modelling capacity, the students will also be trained to assess and interpret the modelled scenarios, so that to link the modelling into the practical context of urban planning and policy making."@en . "Urban Futures Modelling"@en . . "Urban Futures Modelling"@en . "Urban Futures Modelling"@en . . "5.0" . "140.0" . "10.0" . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "3.0" . "[CONCEPT] Monitoring of physical and chemical atmospheric, land and ocean variables with remote sensing requires an understanding of Earth’s ecosystems. Understanding of systems can be based on expert knowledge, experimental relations or physical relations, and this understanding can be captured in a descriptive model. Models are to understand, detect, predict, and describe interactions within and between ecosystems and the atmosphere across scales that range from local to global.\n\nRemote sensing can be used for parameter input in models, but also for spatial and temporal interpolation or extrapolation. This course provides an introduction to knowledge-driven, data-driven and physical modelling, starting with appropriate model selection given a specific problem or data availability. The course therefore deals with basic concepts and boundary conditions. Much emphasis is on integration of remote sensing observations into models, and selecting optimal object / pixel / time based mapping method for a given problem "@en . "Modeling & Mapping"@en . . "Modeling & Mapping"@en . "Modeling & Mapping"@en . . . . . . "Modelling techniques"@en . "Urban Futures Modelling: LU3"@en . . . . . . . . . . . . . "Model and model selection"@en . "Modeling & Mapping: LU1"@en . . . . "MGEO 5.0 BoK"@en . . "model types"@en