Poor surface air quality has a range of implications for human health and the economy. Analysing and interpreting the incoming data streams from air quality measurement stations is critical for tackling the problem and for developing early warning systems. I am using Python to develop a set of online analysis tools (ukatmos.org) to enable the public to quickly and easily plot air quality data in many ways, effectively freeing up information that is already publicly available but in awkward formats and often involves development of code. We anticipate these tools will also support data science classes at school, and can speed up scientific research by minimizing effort in repeating analyses.
The tools integrate numerous Python libraries (e.g. Pandas and NumPy), the Django web framework, the Plot.ly tools for creating interactive graphs, and SQL to address the large data volumes. Developing these Python tools in an adaptive and scalable way allows it to grow as more data become available, e.g. satellite observations. Adaptability also includes evolving user requirements. This talk will follow the processes I went through developing these tools and show a working example of the project so far.
