. . . "Design and set up a space-time data modeling problem, identity measurable objectives, and implement the modeling ideas in the R statistical software"@en . . "Design and set up a space-time data modeling problem, identity measurable objectives, and implement the modeling ideas in the R statistical software"@en . . . "Differentiate between the conceptualization of spatial correlation of the different kinds of spatial processes (geostatistical, lattice, and point pattern processes) and their significance in spatial and space-time prediction"@en . . "Differentiate between the conceptualization of spatial correlation of the different kinds of spatial processes (geostatistical, lattice, and point pattern processes) and their significance in spatial and space-time prediction"@en . . . "Describe the concepts and assumptions of stationarity (second-order and intrinsic) and its role in stochastic spatial and space-time prediction and simulations"@en . . "Describe the concepts and assumptions of stationarity (second-order and intrinsic) and its role in stochastic spatial and space-time prediction and simulations"@en . . . "Differentiate between the principles of deterministic and stochastic spatial predictions and simulations for the different spatial processes"@en . . "Differentiate between the principles of deterministic and stochastic spatial predictions and simulations for the different spatial processes"@en . . . "Quantify the concept of spatial correlation (second-order and intrinsic) and implement it for variant stochastic spatial and space-time prediction (kriging) methods"@en . . "Quantify the concept of spatial correlation (second-order and intrinsic) and implement it for variant stochastic spatial and space-time prediction (kriging) methods"@en . . . "Describe the conceptualization of spatial data for modelling spatial processes"@en . . "Describe the conceptualization of spatial data for modelling spatial processes"@en . . . "Apply the concept of spatial and space-time simulations (conditional and unconditional) and evaluate the overriding advantage over kriging predictions."@en . . "Apply the concept of spatial and space-time simulations (conditional and unconditional) and evaluate the overriding advantage over kriging predictions."@en . . "https://ltb.itc.utwente.nl/page/792/concept/152761" . . "Spatio-temporal variation"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152785" . . "Point patterns"@en . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152787" . . "Statistical sampling"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152802" . . "Spatio-temporal simulation routines"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152820" . . "External drift kriging"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152821" . . "Spatial variation"@en . . . . . "https://ltb.itc.utwente.nl/page/792/concept/152843" . . "Ordinary kriging"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152872" . . "Monitoring methods"@en . . . "https://ltb.itc.utwente.nl/page/792/concept/152873" . . "Probability maps"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152891" . . "Co-kriging"@en . . . . "https://ltb.itc.utwente.nl/page/792/concept/152895" . . "Area to point kriging for lattice data"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "The Master’s Programme Geo-Information Science and Earth Observation (M-GEO) is a two-year academic curriculum at MSc level, taught fully in English, dedicated to understanding the earth’s systems from a geographic and spatial perspective. The field of Geo-information Science and Earth Observation has, in recent years, witnessed fast scientific and technological developments. As a result, geographic information has become a vital asset to society and part of our daily life. The ubiquitous production and availability of spatial data require cloud computing and new technology to turn the increasing volume of ‘big data’ to good use. The growing range of global challenges, from climate change and resource depletion to environmental pollution and pandemic diseases, that our society and in particular the more vulnerable populations on our planet are facing, increases the demand for academic professionals who have the ability, attitudes and skills to design solutions that are sustainable, transdisciplinary and innovative with positive societal impacts. Our education focuses on addressing these global problems by means of advanced geo-information and earth observation applications."@en . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . "Master’s Programme Geo-Information Science and Earth Observation (M-GEO)"@en . . . . "R"@en . . . "Course"@en . "201800315" . "EOS_0008" . "7"^^ . "196"^^ . "10"^^ . "2023-07-06T22:00:00Z"^^ . "online" . "The delivery of this course is partitioned into two: teaching, which embodies lectures, feedback, and Q&A sessions. There are feedback sessions 15 minutes before the start of every days’ lecture except day 1. These involve presentations delivered by students (in groups) followed by “questions” from their colleagues. The objective is to ensure students have control over the subject and also develop/encourage the skills to work in multinational groups. The groups are predefined (by myself) to avoid biases to ensure internationalization.\n\nThe Q&A sessions are ensured after each lecture. Here, the students are encouraged to ask questions or share their experiences pertaining to the topic. \n\nTutorial sessions are critical to this course as they offer the opportunity to practice the theory in the class. The tutorials for the first three topics are designed to be supervised; the remaining are unsupervised. The reason being that after the three supervised tutorials students would have gained enough skills and experience to advance student-centered learning.\n\nCritical to the design of this course is the mapping exercise and the mini-projects which take 10 and 40 percent of the assessment, respectively. The mapping exercise is to ensure that students can take basic instructions per the materials developed. The mini-project is designed to primarily ensure that students “gain experience and understanding to design and setup a space-time data modelling problem, identity measurable objectives, the modelling ideas in the R statistical software”."@en . "In this course, students are required to have basic knowledge of descriptive and inferential statistics. Basic knowledge of the R statistical software will be an added advantage,In this course, students are required to have basic knowldge of descriptive and inferential statistics. Basic knowledge of the R statistical software will be an added advantage "@en . "2"^^ . "4" . "2B " . "2023-04-23T22:00:00Z"^^ . "The premise of the course is motivated by the recent advancements in geoinformation data acquisition and storage and their intended use for evidence-based planning and monitoring. The spatial references of geo-information data may be attributed to the exact locations of measurements or over a fixed set of contiguous regions or lattices. This course seeks to handle the three main classes of spatial data/processes: geostatistical data/spatially continuous process, lattice data/discrete process, and point pattern data/point process. Such data appear common in diverse application fields like environmental science, agriculture, natural resources, environmental epidemiology, and so on. The aim is to present methods that can be used to explore and model such data. Naturally, data vary in space and in time; hence data close to each other (either in space or time) are more similar than those farther. Geostatistical modeling based on the semivariance and/or covariances and interpolation (kriging) in space and time will therefore be introduced. The methods will be extended and applied to data aggregated over contagious regions. The uncertainty is quantified, and attention will be given to making maps showing the probabilities that thresholds are exceeded. Attention is also given to optimal sampling and monitoring. Further, data that arise out of the occurrences of events; thus point pattern data will be considered. The significance of exploring the first and second-order properties of point patterns in diverse application domains like environmental and disaster (like earthquakes) modeling will be explained and applied. The last focus will be on lattice data; in principle, this kind of data consists of observed values over a set of fixed contiguous regions. This kind of data is rather easy to acquire and is mostly applied in health studies where data aggregation is a standard form of protecting locational privacy."@en . "Statistics Spatial & Spatio-temporal Data"@en . "Statistics Spatial&Spatio-temp. Data"@en . "Statistics Spatial&Sp