The Big Earth Data Project
A suite of hands-on activities to engage and inspire students to look at current environmental challenges faced today, funded by the UK Space Agency.
Helping introduce young people to the many applications of satellite data
MEI has developed resources to help your students develop skills in exploring large Earth observation datasets while teaching them about the measurements satellites can take.
These resources will help students learn about satellite modelling and forecasting. Focused on engaging contexts such as climate change, the effects of humans on Earth, and emergency disaster response.
Three sets of resources will be released throughout the 2024/25 academic year. Each set will have activities designed for Key Stage 3-4 and A-Level students, using Data Science skills to analyse real satellite data. The best part is that all resources will be free for anyone to access.
Set 1 Atmosphere: The Hole in the Ozone Layer
Overview and Teacher Notes
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Activity 1: Averages and single variable diagrams
Lesson one will look at seasonal variations in different measurements of the Southern Ozone Hole, using averages, histograms, and box plots.
Activity 2: Time series
Lesson two will examine how the Southern Ozone Hole has varied over time, using time series and averages.
Activity 3: Scatter graphs and correlation
Lesson three will explore the correlation between different measurements related to the Southern Ozone Hole using scatter graphs, measures of outliers and correlation.
Feedback
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Overview and Teacher Notes:
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Activity 1: Diagrams for single variable data
Lesson one examines seasonal variations in different measurements of Southern Ozone Hole, using averages, histograms, box plots, cumulative frequency curves and frequency polygons.
Activity 2: Exploring outliers
Lesson two uses monthly data to explore how the Southern Ozone Hole has varied over time. Students will explore outliers and how we can use other data to decide if they are valid data points or errors.
Activity 3: Identifying regions in scatter plots
Lesson three will look at bivariate data by comparing the size of the Southern Ozone Hole with other measurements from ground and satellite data. Using scatter graphs, regression lines and correlation coefficients.
Additional resource: Introduction to using Python for data
The additional resource is an introduction to using Python for data. It introduces the data set and some Ozone facts as well as showing how to use Python Notebook to calculate averages and measures of spread.
Feedback
We value your feedback. After using the resources, we would appreciate it if you would provide feedback on these resources.
Whilst using these resources in your classroom, you may wish to link the maths students are learning with careers. In the video linked below, Mark, who works for CAMS/ECMWF, explains how a strong foundation in maths has been fundamental to the work he does forecasting atmospheric pollution and discusses the current analysis of the southern ozone hole.
Set 2 Climate change: UK weather
Overview and Teacher Notes
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Activity 1: Exploring distributions with box plots and outliers
Lesson one explores the measures shown on a box plot, and compares the information in a box plot to other representations of data.
Activity 2: Probability distributions and expectation
Lesson two shows how a frequency diagram can be used to create a probability distribution. Students are then asked to make decisions based on the expectation calculated.
Activity 3: Cumulative frequency curves
Lesson three shows how cumulative frequency diagrams are constructed; students then calculate and interpret values in context.
Activity 4: Time series
Lesson four enables students to understand how time series are formed, and then compare and interpret time series.
Feedback
We value your feedback. After using the resources, we would appreciate it if you would provide feedback on these resources.
Overview and Teacher Notes:
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Activity 1: Connecting area in a histogram to frequency and probability
Lesson one shows how to calculate probabilities from areas in histograms, and looks at proportions from different data sets.
Activity 2: Modelling data with the Normal distribution
Lesson two explores whether the normal distribution is suitable for sets of data, then uses probabilities from the normal distribution to compare models to measurements.
Activity 3: Hypothesis tests with the Normal distribution
Lesson three uses hypothesis tests to test whether temperatures have increased, comparing data from 1970-1999 to data from 2020-2024.
Activity 4: Sampling methods
Lesson four compares estimates of the mean using simple random samples and systematic samples.
Feedback
We value your feedback. After using the resources, we would appreciate it if you would provide feedback on these resources.
Whilst using these resources in your classroom, you may wish to link the maths students are learning with careers. In the video linked below, Rebecca, who works for the ECMWF, talks about how maths helps her analyse and represent forecast data.
Set 3 Emergency: Flooding
Overview and Teacher Notes
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Activity 1: Scatter diagrams, correlation and lines of best fit
Lesson 1 looks at scatter diagrams, students describe trends and add lines of best fit to diagrams. Students can calculate the equation of their line and use this to make predictions of values.
Activity 2: Frequency diagrams and box plots
Lesson 2 explores frequency diagrams and uses them to compare distributions. They are compared to box plots, and used to create summary statistics.
Feedback
We value your feedback. After using the resources, we would appreciate it if you would provide feedback on these resources.
Overview and Teacher Notes:
Download Overview and Teacher Notes PDF
Activity 1: Interpreting scatter diagrams and regression lines
Lesson 1 looks at box plots and histograms to understand the data, and then explores scatter diagrams to identify patterns.
Activity 2: Using regression lines to make predictions
Lesson 2 looks at different correlations, interpreting correlation coefficients and using the equation of regression lines.
Feedback
We value your feedback. After using the resources, we would appreciate it if you would provide feedback on these resources.
More about the motivations behind this project
Our key motivations for creating these resources are:
- For students to recognise maths as a fundamental tool for tackling environmental issues and its societal benefits.
- To link careers with mathematical skills and learning.
- To create resources that take aspects of the key stage 3-5 curriculum and present them in an interesting and fun way.
More about where the data has come from
All activities in the Big Earth Data Project use data analysis and data science to answer pertinent questions about Earth Observation. All the activities for the Big Earth Data Project will use real data from Copernicus (European Earth Observation space programme), The European Centre for Medium-range Weather Forecasting (CAMS/ECMWF), LandSat (NASA Earth Observation Satellites), and other relevant data sources, including models built from data measured by these satellite programmes.