Monitoring in Autopilot
The purpose of the presentation is to introduce an innovative way to collect and process environmental data using computer programming languages. Collecting large data sets, discovering valuable insights from the dataset, and then making informative management decisions are what we strive to achieve. Collecting and processing a large quantity of information can be labor intensive, if not impossible. With the assistance of computer programming languages, the data collection and data analysis procedures can be streamlined. A case study will be used to illustrate this concept with emphasis on end-user experience. In the case study, field crews managed to automate the data collection process by reprogramming the datalogger in the field. The datalogger was originally deployed inside a storm drain to measure water level. The reprogrammed datalogger automatically converted water level into flow rate. An email alert function was setup to inform the maintenance crew when the flow rate in the storm drain exceeded the critical level. The function allows maintenance crews to make informative operational decision without doing complex mathematical calculations. To take the automation one-step further, the datalogger was programmed to harvest weather data on the National Oceanic and Atmospheric Administration website and store it in its memory. The results were a completed set of flow and weather data stored in the datalogger ready for analysis. Once the data had been retrieved, data visualization and analysis were handled by the open-source software programs - R and Shiny. The data was automatically retrieved from the datalogger and processed in R; the data was then presented in an intuitive way on a web browser through the Shiny program. Since the data processing had been performed in the background, it dramatically reduced the data preparation time. Not only does it allow agency to re-allocate resources to focus primarily on data analysis and discover new insights from the data sets, but it also allow field crews to manage multiple monitoring locations at the same time. Additional data visualization functions for examination of water quality data will also be discussed. Our innovation supercharged the data processing procedures. The efficiency of data collection provides an added confidence for field crews to handle more demanding monitoring programs and data collection in the future.