In our previous blog posts, we downloaded and analyzed the GHCN weather data. That leads us to the next step: Displaying the data!
Our goal is to display the climate change data so that the regional trends are clearly visible as well as the local weather history underpinning those trends. In order to do this we decided on the following graphics components:
- displaying graphs of the trends in local weather data. The D3.js library handles this.
- and displaying the data history for a given weather station as a heatmap. We draw directly to
We are inundated with environmental data – Earth observing satellites stream terabytes of data back to us daily; ground-based sensor networks track weather, water quality, and air pollution, taking readings every few minutes; and community scientists log hundreds and thousands of observations every day, recording everything from bird sightings to road closures and accidents. But this very richness of data has created a new set of problems.
This second post in our four-part series gives a high-level view of the challenges of portraying and communicating big data in the geosciences – and how these challenges are being addressed – loosely based on the Earth and Space Science Informatics sessions and town halls at the AGU fall meeting in Dec 2016.
One of the challenges facing geoscientists is simply how to wrangle meaning from big data and effectively communicate their findings to other interested scientists, communities, students, planners or policy-makers. Big data is challenging as it can have a large number of variables with complex, non-linear relationships among them. Scientists are turning to data visualization – which leverages the incredible pattern-recognition power of the human eye – to design graphics that effectively convey complex information.