SD5821 Advanced Spatial Data Science
Academic year
2024 to 2025 Semester 1
Curricular information may be subject to change
Further information on which modules are specific to your programme.
Key module information
SCOTCAT credits
15
SCQF level
SCQF level 11
Availability restrictions
Available only to students studying the PG Cert, PG Dip, and MSc in Data Literacy for Social and Environmental Justice
Planned timetable
Not Applicable
Module coordinator
Dr T C Mendo
Module Staff
Dr T Mendo; Dr Charlottee Van der Lijn
Module description
Building on “Welcome to Data,” “Statistical Foundations,” and “Introduction to Spatial Data Science,” students will learn how to use GIScience methods and spatial data science (SDS) software for applications in social and/or environmental sciences. At the end of this module students should be familiar with spatial statistics methods, methods for analysis of movement, and contemporary developments in GIScience. Topics may include spatial autocorrelation and how to apply Geographically Weighted Regression (GWR) to questions of social and environmental justice. While the important concepts are consistent and we can use the same analytical software, this module will have two pathways managed through the virtual learning environment – social science vs. environmental science – to ensure students engage with data that are appropriate for their subfield of study. The module will use free and open-source software (e.g., QGIS, R, Python, GeoDA), to be compatible across the Win/Mac divide.
Relationship to other modules
Pre-requisites
IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510, SD5511, SD5520 OR HAVE PERMISSION FROM THE PROGRAMME DIRECTOR
Assessment pattern
100% coursework
Re-assessment
100% coursework
Learning and teaching methods and delivery
Weekly contact
This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.
Scheduled learning hours
0
Guided independent study hours
145
Intended learning outcomes
- Articulate the fundamental concepts and theories related to the application of Spatial Data Science to social or environmental science;
- Demonstrate their capacity to use geospatial data analysis tools to address social and/or environmental challenges;
- Locate appropriate spatial data and independently perform spatial data analysis;
- Demonstrate advanced problem solving and troubleshooting skills applicable to spatial research projects.
SD5821 Advanced Spatial Data Science
Academic year
2024 to 2025 Semester 2
Curricular information may be subject to change
Further information on which modules are specific to your programme.
Key module information
SCOTCAT credits
15
SCQF level
SCQF level 11
Availability restrictions
Available only to students studying the PG Cert, PG Dip, and MSc in Data Literacy for Social and Environmental Justice
Planned timetable
Not Applicable
Module coordinator
Dr T C Mendo
Module Staff
Dr T Mendo; Dr Charlottee Van der Lijn
Module description
Building on “Welcome to Data,” “Statistical Foundations,” and “Introduction to Spatial Data Science,” students will learn how to use GIScience methods and spatial data science (SDS) software for applications in social and/or environmental sciences. At the end of this module students should be familiar with spatial statistics methods, methods for analysis of movement, and contemporary developments in GIScience. Topics may include spatial autocorrelation and how to apply Geographically Weighted Regression (GWR) to questions of social and environmental justice. While the important concepts are consistent and we can use the same analytical software, this module will have two pathways managed through the virtual learning environment – social science vs. environmental science – to ensure students engage with data that are appropriate for their subfield of study. The module will use free and open-source software (e.g., QGIS, R, Python, GeoDA), to be compatible across the Win/Mac divide.
Relationship to other modules
Pre-requisites
IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510, SD5511, SD5520 OR HAVE PERMISSION FROM THE PROGRAMME DIRECTOR
Assessment pattern
100% coursework
Re-assessment
100% coursework
Learning and teaching methods and delivery
Weekly contact
This module includes 5 1-hour synchronous tutorial sessions and at least 5 hours of pre-recorded content (e.g., lectures). Students should consider the amount of independent study time this module involves when planning their learning.
Scheduled learning hours
0
Guided independent study hours
145
Intended learning outcomes
- Articulate the fundamental concepts and theories related to the application of Spatial Data Science to social or environmental science;
- Demonstrate their capacity to use geospatial data analysis tools to address social and/or environmental challenges;
- Locate appropriate spatial data and independently perform spatial data analysis;
- Demonstrate advanced problem solving and troubleshooting skills applicable to spatial research projects.