ICT skills — AQA A-Level Geography
Test yourself on ICT skills with AQA A-Level practice questions.
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ICT skills explained
Remotely sensed data involves gathering information about the Earth's surface from a distance, typically using satellites, aircraft, or drones.
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Geographers use this data to analyse spatial patterns, monitor environmental changes, and assess hazards over time. For example, satellite imagery can track the retreat of glacial ice, measure rates of deforestation in the Amazon, or map flood extents following a severe storm. By comparing historical and contemporary images, students can quantify land-use changes and evaluate the impacts of human activity or physical processes. This data is often integrated into Geographic Information Systems to layer variables and enhance spatial analysis.
Use of electronic databases.
Electronic databases are structured collections of digital information that geographers use to access, filter, and analyse large volumes of secondary data. They are essential for investigating spatial and temporal trends across both physical and human geography. For example, using the Office for National Statistics (ONS) database allows students to extract demographic data for specific census output areas to evaluate urban inequality. Similarly, the Met Office database provides historical climate records to analyse long-term temperature anomalies. Effective use involves querying the database using specific parameters, downloading the data in formats like CSV, and integrating it into spreadsheets or GIS software for further statistical analysis and visual representation.
Use of innovative sources of data such as crowd sourcing and ‘big data’.
Innovative data sources have transformed geographical research by providing massive, real-time datasets. 'Big data' refers to extremely large datasets analysed computationally to reveal patterns and associations. For instance, analysing mobile phone GPS pings or smart card taps reveals complex urban commuting flows. Crowd sourcing involves obtaining information by enlisting a large number of people, typically via the internet. A concrete example is OpenStreetMap, where volunteers map local infrastructure, or using social media geotags to track public responses during a natural hazard. Geographers must evaluate these sources for representativeness, as they often exclude demographics with limited digital access, creating spatial biases.
Use of ICT to generate evidence of many of the skills provided above such as producing maps, graphs and statistical calculations.
Information and Communication Technology (ICT) is essential for processing and presenting geographical data accurately. Students must use software to generate evidence of their geographical skills. Geographic Information Systems (GIS) are used to produce layered maps, such as choropleth maps, linking spatial data to locations. Spreadsheet software is invaluable for creating complex graphs like scatter plots, allowing for rapid visual analysis. Furthermore, these programs facilitate complex statistical calculations, such as computing Spearman's rank correlation coefficient or standard deviation, reducing human error and handling large datasets that would be impractical to process manually.
Your focus
- Interpret geographical features and environmental changes using satellite and aerial imagery.
- Evaluate the advantages and limitations of using remotely sensed data in geographical research.
- Apply remotely sensed data to investigate spatial patterns and temporal changes in specific case studies.
Show all 12 objectives
- Select appropriate electronic databases to source secondary data for a specific geographical enquiry.
- Extract and filter relevant datasets using specific search parameters.
- Evaluate the reliability and limitations of data obtained from electronic databases.
- Define big data and crowd sourcing within the context of geographical research.
- Apply examples of innovative data sources to investigate human and physical geographical phenomena.
- Evaluate the reliability, representativeness, and ethical implications of using big data and crowd-sourced information.
- Select and apply appropriate software to produce accurate geographical maps and graphs.
- Utilise spreadsheet functions to perform complex statistical calculations on large datasets.
- Evaluate the advantages and limitations of using ICT compared to manual methods for data processing and presentation.
ICT skills exam tips
Marking Points
- Identify specific geographical features or changes over time using satellite or aerial imagery.
- Explain how remotely sensed data provides a broader spatial scale than ground-level fieldwork.
- Evaluate the limitations of remotely sensed data, such as cloud cover obscuring satellite images or the cost of high-resolution drone surveys.
- Describe how remotely sensed data can be integrated with Geographic Information Systems to analyse relationships between different spatial variables.
- Identify appropriate electronic databases relevant to the specific geographical enquiry being undertaken.
- Demonstrate the ability to filter and extract specific datasets using targeted search parameters or queries.
- Evaluate the reliability, validity, and potential bias of the secondary data sourced from the chosen database.
- Process and present extracted database information using appropriate graphical, statistical, or GIS techniques.
- Define and distinguish between big data (massive computational datasets) and crowd-sourced data (user-generated information).
- Apply innovative data sources to geographical contexts, such as using social media sentiment analysis for place perception.
- Assess the ethical implications of using big data, including privacy concerns and data ownership.
- Evaluate the methodological limitations of crowd-sourced data, particularly regarding self-selection bias and digital exclusion.
- Demonstrate the ability to select appropriate ICT tools, such as GIS or spreadsheets, for specific geographical data processing tasks.
- Produce accurate and appropriately scaled maps using GIS software, ensuring all standard map conventions are applied.
- Generate clear, accurately labelled graphs using spreadsheet software to represent complex geographical datasets.
- Execute statistical calculations using software functions to analyse data relationships, such as correlation or dispersion.
Examiner Tips
- 💡When asked to evaluate data sources, contrast the broad spatial coverage of remote sensing with the detailed, site-specific nature of primary fieldwork.
- 💡Use specific examples of remotely sensed data, such as Landsat imagery or LiDAR, to demonstrate a strong understanding of geographical techniques.
- 💡Always consider the temporal aspect; mention how comparing images from different dates allows for the calculation of rates of change.
- 💡Explicitly name the electronic databases you would use for a proposed enquiry, such as the Environment Agency for flood risk data.
- 💡Explain exactly how you would filter the database, mentioning specific variables, timeframes, or spatial scales.
- 💡Discuss the limitations of the database used, such as temporal gaps or changes in how variables are measured over time.
- 💡Provide specific examples of innovative data, such as Strava heatmaps for transport planning or USGS 'Did You Feel It?' for earthquake intensity.
- 💡When evaluating crowd-sourced data, explicitly discuss the digital divide and how it skews spatial representation.
- 💡Link the use of big data to specific geographical concepts, such as using real-time transport data to understand time-space compression.
- 💡When evaluating data presentation methods, explicitly mention the advantages of using ICT, such as the ability to easily update data or overlay multiple variables.
- 💡Be prepared to interpret maps and graphs that have clearly been generated using GIS or spreadsheet software, paying close attention to computer-generated legends.
- 💡Remember that while ICT reduces calculation errors in statistics, you must still be able to interpret the geographical significance of the generated p-value or correlation coefficient.
Common Mistakes
- Assuming remotely sensed data is always real-time; students must recognise that satellite passes occur at specific intervals and historical data represents a snapshot in time.
- Confusing remote sensing with Geographic Information Systems; remote sensing is the collection of data from a distance, whereas Geographic Information Systems are the software used to store, analyse, and display that data.
- Overlooking scale and resolution; students often fail to note that low-resolution imagery cannot be used to identify small-scale local features like individual buildings.
- Accepting database information without questioning its provenance; correction: always evaluate the source, date, and methodology of the data collection.
- Downloading excessive, unfiltered data; correction: use specific search queries and filters within the database to extract only the variables relevant to the research question.
- Assuming all electronic databases are entirely objective; correction: recognise that data categorisation and collection methods can introduce bias, even in official databases.
- Confusing big data with standard secondary data; correction: specify that big data involves volume, velocity, and variety that require computational analysis, unlike standard spreadsheets.
- Assuming crowd-sourced data is perfectly representative; correction: acknowledge that crowd-sourcing often excludes older or lower-income populations who lack digital access.
- Ignoring the ethical issues of big data; correction: always consider privacy, consent, and anonymity when discussing the use of mobile or social media data.
- Relying entirely on software defaults for graph axes and titles, resulting in missing units or unclear labels. Always manually format ICT-generated graphs to include specific, descriptive titles and axis labels with correct units.
- Using inappropriate map types in GIS, such as creating a choropleth map for discrete, absolute data. Select map types based on data characteristics, using proportional symbols for absolute values and choropleths for densities or percentages.
- Accepting the output of a statistical calculation without checking if the data meets the requirements for that specific test. Verify that the dataset meets the assumptions of the statistical test before using software to calculate the result.