Learning Outcomes:
This module is as a core skills module and aims to deliver the following learning outcomes:
• increased awareness of geospatial research and its techniques, methods and tools;
• acquisition of mapping and map interpretation skills;
• understand fundamental techniques of data exploration, organisation and analysis;
• develop literature search, writing and referencing skills.
Indicative Module Content:
Week 2
Lecture 1 - Introduction to the module and SDGs: maps and sustainability
Lecture 2 - Reading, understanding maps, geography at our fingertips, mapping what we see, do, and cannot see
Week 3
Lecture 3 - Advancements in technology for mapping: remote sensing, drones, sensors, and GPS
Lecture 4 - GPS apps on mobile phones – demonstration
Week 4
Lecture 5 - Data types and measurement: quantitative versus qualitative – nominal, ordinal, etc.
Lecture 6 - Stats: mean, max/min, standard deviation, central tendency, weighted, etc.
Week 5
Lecture 7 - Introduction to hypothesis testing
Lecture 8 - Hypothesis testing for sustainability
Week 6
Lecture 9 - Sampling methods and interpretation: representativeness, group size, gender, age cohorts, random/targeted, etc. Factoring sample group parameters when interpreting answers.
Lecture 10 - Survey design and data management and interpretation: survey answer types (multiple choice, open ended, etc.), how to analyse qualitative responses, turning qualitative into quantitative, ethics, etc.
Week 7
Lecture 11 - Sustainability behaviours and infrastructure and their spatial distribution
Lecture 12 - Survey123 – signing in and demonstration and fieldwork preparation (Bring laptops if possible).
Week 8 [Fieldwork Week]
No Lecture - FIELD TRIP
Week 9
Lecture 13 - Field trip recap and data visualization: visual analysis versus tables and graphs, histograms, etc. Examples of available data and work through of data analysis and interpretation.
Lecture 14 - ArcGIS Online – demonstration and application (bring laptop if possible)
Week 10
Lecture 15 - Cartographic choices: practicalities of map-making and data quality checks
Lecture 16 - Mapping and statistical fallacies
Week 11
Lecture 17 - Ecological and social footprint of technology.
Lecture 18 - Using mobile app calculate personal footprint and changing a committed parameter, calculate it again. Class discussion on results.
Week 12
Lecture 19 - SDG issues in the context of the field trip case study
Lecture 20 - Ethical considerations: geoslavery, privacy, big brother, data protection, etc.