. . "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" . . . . . . . . "F2F" . . . . . . . . . . . . . . . . . . "Statistics, calculus, linear algebra, analytics geometry, programming (Python)"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "4.0" . "3.0" . "CONCEPT (not yet confirmed by all GEO-AI staff):\nThis course is designed to guide you through the intersection of machine learning and geospatial sciences, providing you with the expertise to address pressing societal and environmental challenges. You will be introduced to the foundations of supervised and unsupervised learning algorithms, exploring their applications in the geospatial domain. You will learn popular learning algorithms to address various inference tasks, such as clustering, regression and classification.\nFrom satellite imagery to GIS datasets you'll master the tools and methodologies required to preprocess, analyze, integrate and visualize them. You will gain the skills needed to extract meaningful patterns and insights from these geospatial datasets.\nFeature extraction and engineering are critical steps in building effective machine learning models. You will explore techniques to transform raw geospatial data into relevant features enabling your models to learn and predict more effectively.\nClustering techniques, for exploratory spatial data analysis, will be introduced to help you to discover hidden structures and trends within geospatial datasets.\nClassification and regression methods like decision trees, random forests, support vector machines and neural networks are pivotal machine learning tasks that you'll apply to a wide array of geospatial problems. Whether it's land use classification, predicting environmental changes, or estimating spatial variables like temperature or population density, you'll develop models that provide precise and actionable insights.\nThroughout the course real-world case studies will demonstrate the transformative impact of machine learning on geospatial sciences. You'll work on projects that tackle contemporary issues such as urban planning, environmental monitoring, and disaster management.\nBy the end of this course, you will be adept at applying machine learning techniques to geospatial sciences."@en . "Machine Learning for Geospatial Sciences"@en . . "Machine Learning for Geospatial Sciences"@en . "Machine Learning for Geospatial Sciences"@en . . . . . . . . . . "Compare and summarize modelling methods and applications for urban futures modelling (e.g. logistic regression for urban growth, cellular automata, agent-based models, etc.)."@en . . . . . . . . "Statistical Modelling"@en . "Machine Learning for Geospatial Sciences: LU5"@en . . . . "MGEO 5.0 BoK"@en . . "logistic regression"@en .