HGO4941 ¨C Advanced Spatial Data Analysis

Course content

This course delves deep into the realm of spatial data analysis, equipping students with advanced skills and knowledge for collecting, handling, exploring, and extracting insights from geospatial information. The course explores in-depth how Geographic Information Systems (GIS) can be used to study social processes. Through combined lecture-seminar sessions and an individual project, participants will engage with a diverse range of spatial data types, methodologies, and analytical tools.

Key topics covered include:

• Spatial data collection: Importing spatial data from primary and secondary sources, including digitized maps, satellite imagery, news reports, and public databases.
• Spatial Point Patterns Analysis: Investigate the underlying patterns and structures in spatial point data, uncovering hidden trends, clusters, and spatial arrangements.
• Neighborhoods and Spatial Relationships: Understand the significance of neighborhoods and spatial relationships in spatial analysis, including spatial weights and distance metrics.
• Spatial Autocorrelation and Dependency: Explore the concepts of spatial autocorrelation and spatial dependency, understanding how neighboring data points influence each other and their implications for analysis.
• Regression Models for Spatial Data: Learn how to develop and interpret regression models tailored to spatial data, accounting for spatial dependencies and spatial heterogeneity.
• Quantifying Distances: Develop proficiency in quantifying distances and spatial relationships, crucial for assessing proximity and connectivity in spatial datasets.
• Network Data Analysis: Investigate the movement and interactions of various groups through network data analysis, including commuting and migration patterns.
• Geostatistics and Interpolation: Master geostatistical techniques for spatial interpolation, enabling the estimation of values at unobserved locations based on known data points.

The course will mainly use R and QGIS. Students will also learn to collect and visualize qualitative data and prepare data for quantitative analysis outside GIS, such as in Stata and R.

Learning outcome

Upon completing this course, students will emerge with a comprehensive understanding of spatial data analysis, equipped with advanced skills and knowledge to harness the power of Geographic Information Systems (GIS) in studying complex social and environmental processes.

They will be proficient in collecting, managing, analyzing geospatial data. Students will master the art of developing and interpreting regression models for spatial data, quantifying distances, and spatial dependencies, and applying geostatistical techniques for spatial interpolation.

Furthermore, they will gain the ability to seamlessly integrate qualitative and quantitative data within GIS, preparing data for external quantitative analysis using tools like Stata and R. This course empowers students to critically evaluate spatial analysis results and effectively communicate their findings to diverse audiences, making them adept spatial analysts ready to address real-world challenges and research questions in the social sciences.

Knowledge

• Understand the special nature of spatial data and how they are different from non-spatial data.
• Learn how we can collect and manage spatial data from diverse sources.
• Learn more advanced spatial analytical methods, including spatial point patterns, spatial regression, network analysis, spatial multicriteria assessment and clustering.
• Learn more advanced visualization techniques and cartographic principles.

Skills and competence

• Develop skills to master GIS and spatial data analysis software.
• Learn, critically discuss, and directly apply spatial methods and techniques in combined lecture-seminar sessions.
• Complete a project where GIS will be used to study socio-spatial phenomena on master level.
• Discuss sources of uncertainty and error in spatial data.
• Create data models used to answer specific spatial questions

Admission to the course

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

This course is a part of the master's program in Human Geography. Students with admission to other relevant master¡¯s degree programmes can apply for admission as guest students.

Students with admission to the programme must each semester register which courses and examinations they wish to sign up for in StudentWeb.

Recommended previous knowledge

It is assumed that participants have some previous knowledge of GIS or spatial analysis, equivalent to the course SGO1910 ¨C Introduction to Geographical Information Systems (GIS)

HGO4941 is an advanced-level course in geographical information systems and spatial analysis. The course presupposes students' previous knowledge of basic concepts and methods in mapping, spatial analysis, and GIS. A basic understanding of computer systems is recommended.

An intensive preparatory lecture will be provided before the teaching starts to ensure that all participants have basic knowledge. The intensive preparatory lecture will provide an introduction to basic R, and a theoretical and practical introduction to GIS concepts with practical examples in R, including spatial data import, visualization in maps, and basic spatial data management

Overlapping courses

• 5 credits overlap with SOSGEO4023 ¨C Advanced spatial analysis.

Teaching

The teaching for this course is organized as a series of three-hour learning sessions that combine a short lecture and a seminar involving hands-on exercises where each student will work on solving spatial questions using GIS. The seminars will also function as a project helpdesk, where the students can receive assistance with their ongoing project work. In addition, the students will individually work on a larger project.

An intensive preparatory lecture will be provided before the teaching starts to ensure that all participants have basic knowledge - see prerequisites.

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Compulsory instruction and coursework

Handing in weekly assignments is mandatory in 100% of the seminars.

Completed and approved compulsory course work is valid as long as the course is offered. Students who have failed to complete the compulsory attendance cannot take the exam.

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Access to teaching

A student who has completed compulsory instruction and coursework and has had these approved, is not entitled to repeat that instruction and coursework. A student who has been admitted to a course, but who has not completed compulsory instruction and coursework or had these approved, is entitled to repeat that instruction and coursework, depending on available capacity

Examination

Assessment of the course is based on a 3,000 word individual project report on a socio-spatial phenomenon. This word limit includes tables (these count as text) but it excludes the reference list and any text that is appropriately included in any maps or other figures.

You submit your assignment in the digital examination system Inspera.

Examination support material

All support material is allowed. When using AI, you must explain and be transparent about its use: read more about the guidelines for AI and exams at?How to use AI as a student - University of Oslo

Language of examination

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

More about examinations at UiO

• Use of sources and citations
• How to use AI as a student
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.