By Scott Malone
Previously I provided a glimpse into the world of data management and the various challenges associated with formatting and managing geospatial data. After explaining the process of data management and customization in my last post, let’s review my experience with creating a time series animation.
My animation, as I mentioned previously started with data. I used the modeled E Coli measurements as an overall indicator of each site’s water quality. Remember that modeled E Coli readings occur every fifteen minutes and I used a four month time period which meant more than 700,000 readings all together! Attempting to symbolize every reading within the animation would have been a classic case of too much information. With this in mind, I decided to use the daily maximum value of the modeled E Coli readings cutting down the volume of the data while still maintaining a representative of the daily water quality. Visualizing one value per day made the most sense for my time series animation and the observer’s sanity. To distinguish the varying states of water quality I used threshold values pulled from the Water Bug mobile application offered through KCwaters.org as a template.
You can find out more about Kansas City water quality and how
the KCWaterBug mobile app keeps the public informed here.
As a background for the time series animation I used a land cover map (2006 NLCD) in hopes of generally linking the extent of an areas development to stream water quality. Looking at the animation, you can clearly see the stream located almost fully in the heart of the urban core, Brush Creek, has some serious water quality issues. The two telemetry sites on the stream change from red to yellow only once over the four month time span of the animation. In no way is this a definitive statement about the link between urban development and water quality however it is interesting to note that streams considered “fringe urban streams” located in less developed areas such as Wolf Creek have a much more diverse range of water quality classifications during the same time period.
There is more to water quality than rain storms and E. Coli.
Find out about PAHs in Kansas City streams here.
Another interesting trend visible in the time series animation is how E Coli levels follow precipitation events. Using precipitation data from StormWatch.com a Johnson County, Kansas regional weather service I was able to compare date precipitation events with the modeled E Coli. After a rainfall of an inch or so, modeled E Coli levels elevate, often into the red zone indicating a stream with waters unfit for contact (see fig. 1). Within a day or two most of the streams readings return to a safer level. All this is to say it would be safest to give a stream a couple of days after a heavy rainfall before swimming.
Constructing a visually appealing and informative time series animation while not near as trying as the data management side of the project was not without its challenges. All of the classic challenges of constructing a static map combined with the unique trials a time series animation presents made this project a very interesting endeavor. Managing and properly formatting a massive amount of time sensitive data while presenting an understandable and informative final product was a complicated yet rewarding experience. However my course work up to this point was more useful in addressing the problems that arose relating to cartography and typical GIS quirks, as opposed to the data management side of the project which was eye opening.
As I began my internship here at the EPA’s Region 7 I considered myself a competent GIS user during my time here I was exposed to a wide range of “Information” issues that made me if only for a second question that assumption. However as I wrap up this experience I can say with confidence that I have a deeper understanding of the intricacies of data management and map construction. Working on a project intended to provide the public with a greater understanding of water quality issues on a local level was rewarding in its own right on top of which the experience and knowledge I gained will help me as I move forward toward a career. I would like to take this opportunity to thank Casey McLaughlin and all the fine folks here at region 7 for their help this summer and suggest that you take a look at the fruit of my labor.
Scott Malone recently graduated from the University of Kansas with a degree in Environmental Studies. He spent part of summer 2012 as a voluntary intern with the Environmental Services Division where he worked with LiDAR, land cover, and water quality telemetry data.