SD5821 Advanced 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 T Mendo; Dr Charlottee Van der Lijn

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,” “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

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

  • 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

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 T Mendo; Dr Charlottee Van der Lijn

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,” “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

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

  • 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.