Duration
30h Th, 30h Pr
Number of credits
Lecturer
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Spatial analysis is a discipline that helps understand how phenomena are distributed and interact in space. It combines geography, environmental sciences, mathematics, and computer science to identify patterns, measure spatial relationships, and predict geographical phenomena. Using statistical and geostatistical methods as well as programming tools, it provides essential insights for resource management, territorial planning, ecosystem studies, and risk prevention.
This course aims to guide students through a scientific approach to spatial data analysis. Its objective is to enable them to understand and leverage the richness of spatial data, implement spatial analysis methods to test hypotheses, detect spatial structures, explore their possible causes, and model underlying geographical or environmental processes.
Theoretical topics covered include:
- Point patterns: Understanding and visualizing the spatial distribution of events, identifying clusters or dispersed areas.
- Area data: Measuring relationships between neighbors, detecting spatial patterns through autocorrelation, and testing the robustness of results using statistical tests.
- Spatial interactions and networks: Analyzing connectivity between places or objects, using graph theory to represent complex relationships.
- Continuous data: Describing and predicting phenomena through interpolation, understanding spatial variability via semivariograms.
- Modeling geographical phenomena: Incorporating the spatial dimension into statistical models, understanding errors related to scale or spatial structure, and applying geographically weighted regressions.
- Manipulate and prepare spatial data in R.
- Explore and visualize spatial structures.
- Implement statistical and geostatistical methods.
- Interpret results for real-world applications: environment, land-use planning, ecosystem monitoring, or natural hazard prevention.
To consolidate learning and promote collaborative work, students will be divided into groups to work on a case study applied to a spatial analysis problem. Each group will be expected to:
- Formulate hypotheses, prepare, and analyze spatial data.
- Identify spatial structures, test relationships, and explore possible causes.
- Present results in the form of maps, oral presentations, and a concise report, justifying methodological choices and interpreting the findings.
Learning outcomes of the learning unit
This course is designed to enable students to develop knowledge, practical skills, and critical analysis abilities, allowing them to understand and apply spatial analysis methods to real-world problems in environmental management, land-use planning, and territorial governance.
Knowledge
- Understand the fundamental concepts of spatial analysis: the distribution of phenomena in space, spatial autocorrelation, spatial interactions and connectivity, and modeling of geographical phenomena.
- Identify and characterize different types of spatial data (points, areas, continuous data) and the appropriate statistical and geostatistical methods.
- Be aware of the limitations and sources of error in spatial modeling, such as ecological fallacy, atomistic error, or scale-related biases.
- Understand the role of spatial analysis in ecosystem studies, resource management, territorial planning, and risk prevention.
- Manipulate, visualize, and explore spatial data using R.
- Apply statistical and geostatistical methods to detect spatial structures, measure spatial relationships, and predict values in unobserved areas (interpolation).
- Implement spatial modeling techniques, including spatial regression and geographically weighted regression.
- Produce clear and relevant graphical representations to effectively communicate the results of spatial analyses.
- Interpret the results of spatial analyses within an environmental or territorial context.
- Evaluate the relevance and robustness of chosen methods based on the data and study objectives.
- Develop critical reasoning about spatial structures and interactions between geographical phenomena.
- Work independently and rigorously in the preparation, analysis, and communication of spatial data.
Prerequisite knowledge and skills
To take this course, students should have a basic knowledge of:
-
Geographic Information Systems - GIS (geoprocessing, cartographic representation, and georeferencing) in order to manipulate and visualize spatial data. Students without prior GIS experience are encouraged to take the Introduction to GIS (GEOG0238) course offered at the beginning of the semester.
-
Statistics and probability, to understand spatial analysis methods and significance testing.
Planned learning activities and teaching methods
The course combines lectures, practical exercises, and a case study to enable students to acquire solid knowledge and operational skills in spatial analysis.
- Lectures: Presentation of fundamental concepts, statistical and geostatistical methods, and approaches to modeling spatial phenomena.
- Tutorials and practical exercises: Manipulation and exploration of spatial data in R, implementation of analysis methods, visualization of spatial patterns, and interpretation of results.
- Case study: Conducted in small groups (usually three students) with the aim of applying the methods studied. Includes discussions, presentations, and exchanges on analysis results to develop critical thinking and the ability to communicate scientific conclusions.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Further information:
Theoretical and pratical lectures are face-to-face (every week).
A case study will be carried out independently in groups of three students. Each group will present its work to their peers to encourage exchanges, critical discussions, and collaborative learning around spatial analysis challenges.
Course materials and recommended or required readings
Platform(s) used for course materials:
- eCampus
Further information:
The PowerPoint slides for the lectures are available on eCampus. At the end of each presentation (chapter), links to additional resources (videos, reference books, scientific articles) are also provided.
Please note that the slides presented during the lectures serve as a concise course aid. They cover the essential points but do not include the full oral explanations; taking personal notes is highly recommended.
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Written work / report
Further information:
The evaluation is based on a written exam (75%) and a case study (25% ? contingent on passing the theoretical part of the exam).
For each case study, a report must be submitted by each group within the deadlines set during the first class session. Each group will then present and defend their work in front of their peers. Completion and presentation of the case study are mandatory; students who do not comply will not be allowed to take the exam.
The purpose of the exam is to evaluate students' understanding of the concepts covered in the lectures. Students will be required to answer questions encompassing the entire course content.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Prof. François Jonard, francois.jonard@uliege.be
Earth Observation and Ecosystem Modelling Lab - EOSystM - www.eosystm.uliege.be
Office 2/48, Building B5a, Quartier Agora, Allée du six Août 19, 4000 Liège