Applied Data Analytics and Statistics for Spatial and Temporal Modeling (MOD550)

Statistics as a traditional science has considerable limitations when you apply it in spatial or temporal contexts. Key to these fields is the spatial and temporal aspects of the data. For example, a spatial sample or measurement is often attached to a spatial coordinate (x,y,z) describing where the sample was taken. Traditional statistics very often neglects this spatial context and simply works with the data as they are. However, from our own experiences (e.g. geological or environmental), we know that samples located close together are more "related" to each other, and this relationship may be useful to us when interpreting our data. In this course, we will deal explicitly with data distributed in space or time and aim at explicitly modeling the spatial or temporal relationship between data.


Course description for study year 2023-2024. Please note that changes may occur.

Facts

Course code

MOD550

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English

Content

The focus of this course is the basic science, technology and related assumptions involved in applying statistics in spatial or temporal contexts. The emphasis is on providing students with knowledge of the fundamentals of statistics most relevant for spatial or temporal data.

The core of the course is around data analysis and constructing static spatial and temporal models. Data sources, quality, relevance and choice of modeling techniques will be covered. This is followed by classical gridding, mapping and contouring. Kriging is introduced as a data-driven (variograms) form of classical mapping (estimation) and a means of data integration. Simulation techniques are introduced as a means of modeling heterogeneity and uncertainty. Machine learning techniques, such as regression modeling and analysis as well as multivariate data analysis, will be introduced and applied. Python and other programming tools will be used for modeling, preparing spatial and temporal data, scripting statistical workflows, and constructing visualizations to communicate model and analysis results.

What are the benefits of building and using spatial and temporal models, as opposed to relying on mental models or just "gut feel?" The primary purpose of modeling is to generate decision insight; by which we mean an improved understanding of the decision situation at hand. While mathematical models consist of numbers and symbols, the real benefit of using them is to make better decisions. Better decisions results from improved understanding, not just the numbers themselves.

Learning outcome

Knowledge:

• Basic understanding of data, statistics, and probability

• Exploratory data analysis for univariate, bivariate, and multivariate data

• Probability distributions and models thereof

• Fitting distribution to data

• Predictive modeling

• Regression modeling and analysis

• Multivariate data analysis including principal component, cluster, and discriminant analysis

• Applying machine learning techniques (e.g., random forest, gradient boosting machine, support vector regression, and kriging model) for predictive modeling

• Translating model input uncertainty into uncertainty in model predictions using Monte Carlo simulation

• Understand sensitivity analysis and the information it provides

• Visualization and reporting to provide input for a decision by transferring information to decision-makers.

Skills:

• Have the skills needed to build a good spatial or temporal model and to use it in generating powerful insights into the decision situation

• Have the skills needed to implement the basic statistical methods to analyze data and to estimate and simulate spatial and temporal properties conditioned on data by using Python and other programming tools

General qualifications:

•Students should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.

Required prerequisite knowledge

None

Exam

Form of assessment Weight Duration Marks Aid
Digital on campus exam 1/1 4 Hours Letter grades All

The assessment consist of a digital on-campus exam.

Coursework requirements

Portfolio containing 4-6 assignments that needs to be approved in to acccess the final exam.

The studetns may cooperate on the assignments.

Course teacher(s)

Course coordinator:

Reidar Brumer Bratvold

Course coordinator:

Aojie Hong

Head of Department:

Alejandro Escalona Varela

Method of work

The work will consist of 6 hours of lecture and scheduled tutorials per week. Students are expected to spend an additional 6-8 hours a week on self-study and assignments.

Open for

Admission to Single Courses at the Faculty of Science and Technology Petroleum Geosciences Engineering - Master of Science Degree Programme Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

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