Plot Multi-Variate Marine Data with Time Maps

Analyzing the many variables in a marine environment can be a challenge. Tidal patterns affect maritime traffic, coastal communities, and aquatic breeding periods; water temperatures affect algae growth which in turn affects marine animals and the fishing industry; the list goes on. To further complicate matters, an important aspect of marine data acquisition is time. Typically, data is gathered over time, as opposed to spatially, which results in large, multi-variate datasets that contain a wealth of information. However, the information is not very useful if it cannot be properly visualized.

Major patterns, such as the peak passages, are easy to distinguish, but smaller patterns such as seasonal variability are difficult to identify. Additionally, the above graph represents only five years of data. What if five years isn’t enough? One can see how messy the graph would quickly become when adding another 10, 20, or 50 years of plots.

Dr. Koehler’s alternative visualization technique plots this time-series data as a color relief map, also known as a raster map. The date is turned into two temporal coordinates where the X-axis is a short term time step such as days or minutes, and the Y-axis is a longer time step such as months or years. The Z-value is represented in the cell location.

Using this time map technique, the Chinook passage data is plotted in Golden Software’s contouring, gridding, and 3D surface mapping software, Surfer. In this map, an additional 71 years of data is displayed. Despite the extra information, it is quite easy to distinguish temporal patterns, both large and small, that would otherwise be missed in the 2D line graph. The smaller spring and summer run patterns are now easier to see in this display. Also notable is the steady increase of salmon in recent years during the August through September run.

Time maps are also useful when displaying ocean tide data. The Hawk Inlet on Admiralty Island in southeast Alaska is one of two ports in the Alaska Panhandle where ship arrival and departure is dependent on the tides. The timing of tides and daylight hours are critical factors for safe navigation; therefore, it is important for schedulers to know when the tides are favorable for vessel travel.

In these traditional views, high and low tides are distinguishable. But these displays are not sufficient for planning when ships can safely navigate in and out of the port that day, week or month. A significant amount of information is lost and obtaining a clear understanding of tidal patterns is practically impossible with these views.

However, by employing Dr. Koehler’s time map visualization technique, a whole new picture appears. Schedulers can quickly pinpoint days and times when tide levels are conducive for ship travel. This view also makes it easier to add extra information, such as the time of sunrise and sunset, which is represented by light grey lines. Despite having over half a million data points, this plot is clear and understandable. Not only do port schedulers benefit from tidal information, so do coastal managers preparing for nuisance flooding, recreational fishermen, surfers and boaters, and scientists working on habitat restoration projects, to name a few.

Multi-variate temporal analysis is another area in which time maps provide a clearer and more understandable visual representation of complex data. For example, several environmental factors must be considered when analyzing algae blooms. Such factors include sea surface temperature, sea surface salinity, air temperature, precipitation, stream flows, tidal height difference, upwelling, and wind speed. Individually, these factors do not have much of an impact on whether or not an algae bloom will occur. Instead, the factors must be analyzed as a whole which can be a troublesome task.

How can one recognize the windows of opportunity for these algae blooms? As noted before, traditional 2D and 3D views fall short for this type of task. Cue in Dr. Koehler’s time maps.

The first step is to display each environmental factor as a time map. In the example of the algae bloom, seven different environmental time maps will be created. Second, a binary filter is applied to the data that highlights days with favorable conditions for an algae bloom. A value of one equals days with favorable conditions and a value of zero equals days that are not favorable for an algae bloom. The binary filter is also displayed as a time map.

For example, one environmental factor is stream flow. Flows less than 350 cubic meters/second are favorable for an algae bloom. By viewing the binary color relief, one can quickly pinpoint when stream flows are less than 350 cubic meters/second. These typically occur between August and October. One can also see in a drought occurred in 2001 resulting in less than normal stream flows for the months of January through April. All days represented in black are favorable stream flow conditions for an algae bloom.

The final step is summing the binary data for the individual factors and displaying the resulting data as a time map. The summed data will contain values between zero and eight. Cells containing zero indicate on that particular day, none of the eight factors had conditions suitable for an algae bloom. Cells with a value of four indicate on that particular day, four out of the eight environmental factors had conditions suitable for an algae bloom. Cells with a value of eight indicate all eight environmental factors had conditions suitable for an algae bloom on that particular day. When displaying this combined data as a binary time map, values of eight means all criteria was met and values zero through seven mean at least one criterion was not met.

Of the 4,380 days between 1993 and 2005, there were 127 days where all eight environmental factors were favorable for an algae bloom.