Using copilot for the rapid generation of a visualisation platform to aid geospatial analyses

The scale and heterogeneity of modern geospatial datasets, coupled with expanding suites of statistical and dynamical models, produce analysis outputs that are increasingly difficult to navigate and synthesise. We present a practical case study on using a large language model (LLM)-assisted coding tool (GitHub Copilot with GPT-5 mini within Visual Studio Code) to accelerate the development of a lightweight, HTML-based platform that visualises results from pre-calculated climate indicators.

Our starting point was a dataset comprising more than 130 climate indicators derived from gridded observations spanning over 60 years. These indicators originate from multiple meteorological variable groups (e.g., temperature, precipitation) and are aggregated at several temporal resolutions (e.g., annual, seasonal). Downstream analyses include spatiotemporal  statistics, extreme value analyses and statistical significance testing, yielding hundreds of figures that are difficult to navigate and analyse. To make these outputs tractable, we prompted Copilot to generate a simple web application for visualisation and analysis purposes. The pre-generated plots from the climate indicator workflow were displayed there in an organised way, allowing for quick filtering through all indicators and different temporal resolutions, comparing different plots next to each other and using a subpage to concisely display aggregated group plots.

The platform is embedded and deployed via a GitLab CI pipeline, ensuring reproducible updates and immediate web accessibility for collaborators and users, thereby enabling rapid and easy access to vasts amount of output results. Our process of prompting a LLM to generate a visualisation platform offers a convenient and transferable workflow to aid geospatial data analysis.