SD5820 Introduction to 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 L Cole; Dr T Mendo; Dr E Olamijuwon
Module description
Building on “Welcome to Data” and “Statistical Foundations,” this module will introduce students to the basic principles of spatial data science (SDS) for applications in social and/or environmental sciences. Students will learn the basics of how to use free and open-source Geographic Information Systems (GIS) software to perform analyses. At the end of this course students should be familiar with topics such as: why we need SDS; sources/types data, spatial data structures and georeferencing (linkages; coordinate systems); vector and raster data and point pattern analysis (characteristics, locational databases, basic algorithms, heat maps, interpolation); multi-criteria evaluation; and applications of MCE for 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 VLE – social science vs. environmental science – to ensure students engage with data that are appropriate for their subfield of study.
Relationship to other modules
Pre-requisites
IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510 AND SD5511 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
- Identify when to use different spatial analysis methods;
- Locate appropriate spatial data and independently perform appropriate spatial data analyses;
- Process, map, and analyse vector and raster data;
- Demonstrate problem solving and troubleshooting skills applicable to spatial research projects.
SD5820 Introduction to 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 L Cole; Dr T Mendo; Dr E Olamijuwon
Module description
Building on “Welcome to Data” and “Statistical Foundations,” this module will introduce students to the basic principles of spatial data science (SDS) for applications in social and/or environmental sciences. Students will learn the basics of how to use free and open-source Geographic Information Systems (GIS) software to perform analyses. At the end of this course students should be familiar with topics such as: why we need SDS; sources/types data, spatial data structures and georeferencing (linkages; coordinate systems); vector and raster data and point pattern analysis (characteristics, locational databases, basic algorithms, heat maps, interpolation); multi-criteria evaluation; and applications of MCE for 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 VLE – social science vs. environmental science – to ensure students engage with data that are appropriate for their subfield of study.
Relationship to other modules
Pre-requisites
IN ORDER TO TAKE THIS MODULE YOU MUST TAKE OR HAVE TAKEN SD5510 AND SD5511 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
- Identify when to use different spatial analysis methods;
- Locate appropriate spatial data and independently perform appropriate spatial data analyses;
- Process, map, and analyse vector and raster data;
- Demonstrate problem solving and troubleshooting skills applicable to spatial research projects.