We are all too familiar with the map visualisations in media depicting the spread and severity of COVID-19 across the world. The representation of statistical data on a map is a powerful tool that can effectively convey factors such as magnitude, density and spatial variations. Analysing data in this format can help identify trends (eg “hotspots”, “patient zero”) from large datasets. Whilst students outside the discipline of geosciences may be familiar with analysing a data map; constructing one would be a rare experience.
In our undergraduate environmental chemistry laboratory, students analyse the metal ion content and hardness of water samples collected on campus. We have used Google Maps Application Programming Interface (API)1 to allow students to geotag their results on a Google Map. The resulting bubble map is live and continually updated as students complete the lab and submit their results.2 This map is shared with the cohort so students can view the evolution of data, their contribution to the “project” and generate their own hypotheses as to why certain concentrations may be linked to certain locales (eg. age of building). This approach offers rich context-based learning that could be modified to address other datasets/contexts, locations, and disciplines
