Geographical Skills
Geographical information
a) Understand what makes data geographical
o Data becomes geographical when it is linked to location,
space, or place.
o Includes both physical features (e.g. river discharge, rainfall,
soil type) and human aspects (e.g. migration flows,
deprivation levels, land use).
o Spatial context is essential: not just “what” but “where” and
often “why there?”.
o Example: Air quality data is geographical when mapped across
an urban area, allowing for analysis of spatial patterns and
inequalities.
b) Ethical and socio-political implications of collecting,
studying, and representing geographical data
o Ethics: Respecting confidentiality (e.g. anonymising survey
respondents), gaining informed consent, avoiding bias, not
misrepresenting results.
o Socio-political issues: Who controls the data? Could it
marginalise or stereotype certain communities? Power
dynamics in data collection (e.g. researchers from outside a
community may misinterpret findings).
o Example: Census data—valuable for planning services, but
sensitive in terms of privacy, minority group representation,
and political decision-making.
c) Understand the nature of and use different types of
geographical information
o Qualitative: Descriptive, subjective data (interviews,
photographs, field sketches, oral histories). Good for exploring
perceptions and sense of place.
o Quantitative: Numerical, measurable data (population
figures, rainfall totals, crime statistics). Allows statistical
analysis and comparison.
o Primary: Data collected firsthand (field surveys,
questionnaires, river velocity readings).
, o Secondary: Data obtained from other sources (census
records, satellite images, academic studies).
o Images/maps/diagrams/graphs: Visual tools to represent
spatial patterns (choropleth maps, GIS maps, flow diagrams,
scatter plots).
o Factual text: Objective accounts such as reports,
government documents.
o Discursive/creative material: Literature, art, or media
representations of place. Useful for studying lived
experiences.
o Digital data: Remotely sensed data (satellite imagery, drone
surveys, LiDAR), open-source online datasets (Google Earth,
ArcGIS).
o Numerical and spatial data: Statistics tied to geographic
coordinates (crime density per ward, GPS data from
movement tracking).
o Innovative forms of data: Crowd-sourced (e.g.
OpenStreetMap, Twitter geotagging), big data (mobile phone
movement patterns, real-time transport data).
d) Collect, analyse and interpret such information
o Selecting appropriate methods (surveys for perceptions, flow
meters for river discharge, GIS for land-use analysis).
o Analytical approaches must fit the data: statistical tests for
quantitative; coding/interpretation for qualitative.
o Recognising limitations (sample size, representativeness,
accuracy).
e) Critical questioning of data sources, methodologies,
reporting and presentation
o Ask: Who produced the data and why? What are the
limitations or biases?
o Identify errors: sampling bias, equipment error, temporal
variations, misclassification.
o Recognise misuse: cherry-picking data, misleading graphs,
correlation vs causation.
Geographical information
a) Understand what makes data geographical
o Data becomes geographical when it is linked to location,
space, or place.
o Includes both physical features (e.g. river discharge, rainfall,
soil type) and human aspects (e.g. migration flows,
deprivation levels, land use).
o Spatial context is essential: not just “what” but “where” and
often “why there?”.
o Example: Air quality data is geographical when mapped across
an urban area, allowing for analysis of spatial patterns and
inequalities.
b) Ethical and socio-political implications of collecting,
studying, and representing geographical data
o Ethics: Respecting confidentiality (e.g. anonymising survey
respondents), gaining informed consent, avoiding bias, not
misrepresenting results.
o Socio-political issues: Who controls the data? Could it
marginalise or stereotype certain communities? Power
dynamics in data collection (e.g. researchers from outside a
community may misinterpret findings).
o Example: Census data—valuable for planning services, but
sensitive in terms of privacy, minority group representation,
and political decision-making.
c) Understand the nature of and use different types of
geographical information
o Qualitative: Descriptive, subjective data (interviews,
photographs, field sketches, oral histories). Good for exploring
perceptions and sense of place.
o Quantitative: Numerical, measurable data (population
figures, rainfall totals, crime statistics). Allows statistical
analysis and comparison.
o Primary: Data collected firsthand (field surveys,
questionnaires, river velocity readings).
, o Secondary: Data obtained from other sources (census
records, satellite images, academic studies).
o Images/maps/diagrams/graphs: Visual tools to represent
spatial patterns (choropleth maps, GIS maps, flow diagrams,
scatter plots).
o Factual text: Objective accounts such as reports,
government documents.
o Discursive/creative material: Literature, art, or media
representations of place. Useful for studying lived
experiences.
o Digital data: Remotely sensed data (satellite imagery, drone
surveys, LiDAR), open-source online datasets (Google Earth,
ArcGIS).
o Numerical and spatial data: Statistics tied to geographic
coordinates (crime density per ward, GPS data from
movement tracking).
o Innovative forms of data: Crowd-sourced (e.g.
OpenStreetMap, Twitter geotagging), big data (mobile phone
movement patterns, real-time transport data).
d) Collect, analyse and interpret such information
o Selecting appropriate methods (surveys for perceptions, flow
meters for river discharge, GIS for land-use analysis).
o Analytical approaches must fit the data: statistical tests for
quantitative; coding/interpretation for qualitative.
o Recognising limitations (sample size, representativeness,
accuracy).
e) Critical questioning of data sources, methodologies,
reporting and presentation
o Ask: Who produced the data and why? What are the
limitations or biases?
o Identify errors: sampling bias, equipment error, temporal
variations, misclassification.
o Recognise misuse: cherry-picking data, misleading graphs,
correlation vs causation.
