SD5820 Introduction to Spatial Data Science

Academic year

2024 to 2025 Semester 1

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

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

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr T C Mendo

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

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

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

145

The number of hours that students are expected to invest in independent study over the period of the module.

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

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 11

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

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

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr T C Mendo

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr L Cole; Dr T Mendo; Dr E Olamijuwon

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

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

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

145

The number of hours that students are expected to invest in independent study over the period of the module.

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.