The Great Flood of 1993
28 August 2008
USGS Water Data is a data goldmine that includes over 25,000 stations across the U.S. Each measures variables such as streamflow, water temperature, precipitation, and other hydrological and meteorological properties. Though sub-hourly readings are taken, daily data are available for many stations going all the way back to 1980. And all are available via automated retrieval. With just a bit of hacking, we can run the returned tab-delimited text files through our SpatialKey Data Importer, allowing us to load them into the visualization templates.
Droughts, floods, storm events — all are visible in our visualization templates utilizing this free government data. See, for example, the Great Flood of the Missouri and Mississippi Rivers in 1993 by visualizing streamflow (in cubic feet per second) measured at dozens of stations along these rivers.
The symbols on the map are sized proportional to the streamflow readings, allowing us to see the peak of the flooding in July and August. This dataset shows off many unique aspects of the SpatialKey visualization system. First, the templates excel at aggregating both spatially and temporally. In the screenshot above, the St. Louis and Grafton stations are aggregated together, because they are so close and would overlap, producing illegibility and reducing clarity, if they were displayed separately. The average of the two stations’ readings is symbolized; zoom in to reveal more detail, separating the St. Louis and Grafton stations’ symbols:
In addition to the spatial aggregation, this example also demonstrates temporal averaging. In the above screenshot, each of the symbols represents and average of one month of daily readings. Selecting different time ranges on the time chart instantly reveals the new average, and any range can be animated in the Playback template.
All of the SpatialKey visualization templates include extent-filtering, which updates the time chart to only display the temporal trend for points in the current map extent. Seeing the streamflow trend for an individual station is therefore as easy as zooming into that station:
The dataset shown off in this example is different from the others in our gallery. While the others demonstrated an ability to aggregate and visualize thousands of independent points across space and time (home sales, Wal-Mart store openings, arrests), the dataset presented here has only dozens of points, but includes thousands of streamflow readings (the dataset is over 12,000 rows long). This “remote sensor” style of dataset is easily accomodated by our templates. A third type of point dataset tracks assets (shipping containers, police cruisers) whose locations and attributes (speed, weight, etc.) may change over time. We have developed solutions in the past for this latter type of dataset, and will show off asset tracking in future visualization templates.
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