Lab 2 – The display of oceanographic data
This lab describes the ways in which some of the OOI data are displayed and has students work with some of the data products, graphs and maps.
OOI data are presented as collected, the data have not been massaged to remove outliers or gaps or to eliminate data that distract from a trend. In other words, these are real, and sometimes messy data.
How do oceanographers display data to help them answer scientific questions?
Much of the data scientists collect consists of numbers, measurements of something. These data need to be organized in a way to illustrate patterns in the data. For this reason graphs are frequently used to display data. Graphs are a pictorial way to visualize patterns that would be hard to identify in columns of numbers. Graphs often have x and y axes, and the variable (measured quantity) that one chooses to plot on these axes is chosen to answer a question. For example, oceanographers are often interested in how some measured property changes over time. The natural choice then is to plot time on the x-axis (increasing to the right) and the variable of interest on the y-axis. Sometimes oceanographers are interested in how a variable changes as one moves away from the beach, and in that case it would make sense to plot distance from shore on the x-axis. In this exercise we examine some of the ways in which data are collected and some of the common graphs used by oceanographers.
Interpreting data visualizations is a very important data skill! Often data are presented on X-Y graphs, because there is some expected relationship between two variables. Common relationships in oceanography are the variation of some measured property over time, over distance from shore or over depth in the ocean. When you become experienced at examining these types of graphs you will see that there is often a “typical” shape to a particular type of graph. Scientists get very excited when they see something different from the typical shape! That can mean they have made a discovery.
After completing this lab students will be able to:
- LO1. Identify key components of a scientific graph (axes scales, legends, multiple y-axes).
- LO2. Describe data patterns such as minimum and maximum values, trends over time or distance.
- LO3. Recognize graph types commonly used in oceanography (time series, station profile, vertical section contour plot, bubble or vector map).
- LO4. Discover that real-world data can be quite variable.
- LO5. Identify gaps in data and infer likely reasons for the gaps.
- LO6. Describe the oceanographic convention of plotting depth increasing down for station profiles and vertical sections.
X-axis, Y- axis, units, graphs, variability, temporal, positive trend, negative trend, no trend, inverse correlation, direct correlation, no correlation, causation.
Background on Data visualizations
Deriving the meaning from large data sets can be difficult especially if data is in a table format. You may not see the relationship between variables in a table, which is why scientific data is plotted in different data visualizations that help to organize and display the data. Data visualizations include maps, graphs, charts or diagrams that put data into a visual context that can make it easier to detect patterns, trends, and outliers in groups of data. In this exercise you are introduced to and asked to explore graphs and answer questions to interpret oceanographic data by looking at a variety of different oceanographic data visualizations that use large data sets. These data visualizations allow for you to interpret the meaning of the data
Tools of Science: Data as a Tool
Trends in data often are a change in some variable over space or time. Examples include the increase in water depth with increasing distance from the beach, the increasing in temperature with decreasing distance from the oven in your kitchen, or the increase in height as a child gets older. Patterns in data may be more complex than a simple increase or decrease in a variable (which is a trend). Air temperature measured over the course of a year shows a pattern of high temperatures in the summer and low temperature in the winter. River discharge typically also has an annual pattern of high discharge in the spring due to snow melt and lower discharge in the rest of the year or a similar pattern associated with wet (rainy) and dry seasons. Scientists may be looking at a temporal relationship such as spawning activity of a coral and the relationship to moon cycle or time of year.
X-axis, Y-axis variables
Trends or patterns in data presented on a graph should be easy to identify. The process includes first identifying the variables. Graphs typically have labels on the X and Y axes or scales that indicate both the variable that is plotted and the units of the variable. If more than one variable is plotted there is often a legend that helps you figure out what each of the plotted lines means. Sometimes a graph may have two Y-axes, with each axis representing a different variable and scale. In addition, graphs often have a title or caption that provides even more information. Maps and charts help visualize geographical location and spatial dynamics of the data. With maps you need to orient yourself to the location, look to see if there is land or bathymetric features and identify the variable that is plotted by seeking out the legend.
Identifying trends or patterns
Once you have identified key information from the graphic, you then need to look for patterns in the data, these may be similarities, differences, trends or other relationships. When identifying patterns in the data, you want to look for positive, negative and no correlation, as well as creating best fit lines (trend lines) for given data. The best fit line often helps you identify patterns when you have really messy, or variable data. A trend line is the line formed between a high and a low. If that line is going up, the trend is positive or increasing and if the trend line is sloping downward, the trend is negative or decreasing. You may have data or the trend(s) goes both up and down but on a different temporal (time) scale, such as seasons, daily, or based on tides. Take a look at these simple examples to help you identify these trends and relationships.
Click the graph on the left to view the three types of trends
Real data can sometimes be very messy. It often shows a trend that looks messy because of natural variability. What causes variability? It can be any number of things, for example, in the ocean water movement can have multiple sources. So there may be a wind driven current in one direction that when plotted shows variability, oscillations back and forth every 12 hours. These oscillations are due to the tide. If you were to plot the distance traveled by a floating object in the water you would see a gradual movement in the direction of the wind driven current with some sloshing back and forth. Analyzing such data requires identifying the trend and the variability, in this case the tidal oscillations.
The animation below shows the tidal current at a location in Maine. Positive values indicate the tide is flooding, or flowing into the harbor. Negative values indicate the tide is ebbing,or flowing out of the harbor. So the speed values show both how fast the water moves, in knots (nautical miles per hour), and the direction of the current. The animation also shows what the current record would look like at Portland Harbor if a 0.5 knot wind driven current was acting, in addition to the tidal current. The graphs look the same, but examine the y-axes. The tidal current graph shows that the current reverses direction (alternates between positive and negative numbers) while the wind and tidal current graph shows that although the strength of the current alternates the direction is always positive, into the harbor. Clearly the wind is blowing water into the harbor, but the tidal forces act to decrease the current when the tide ebbs, or flows away from the shore.
Once you have identified a pattern or trend, think about what the trend might mean by relating it to the concepts you learned in your course about oceanography. You may be comparing multiple different variables to each other, or you may need to compare different graphs. When comparing different graphs, be sure to pay attention to the scales, as they may be different. First, look to see if there is a correlation between the 2 or more variables on the graph, or spatial relationship on a map. The correlation could be direct where both variables increase and decrease together, or inverse where one variable increases and the other decreases. Sometimes there may be a correlation based on a temporal scale, seasons for instance. Remember, correlation does not always mean causation. A correlation indicates that there is an association between the variables, but doesn’t tell us why. Sometimes there is a reason and sometimes it is just a coincidence. (You can google “spurious correlations” and find all kinds of fun examples).
Figure 2.0.2. Simple graph diagram examples of Inverse, Direct and Seasonal Inverse Correlations of two generic variables. Remember that real data is sometimes messy, so graphs are not always this simple to interpret, however, this provides you with general patterns.
Why do oceanographers plot some data with the y-axis showing a depth of zero at the top and increasing depth as you go down the y-axis?
Show sample answer
Since oceanographers traditionally sampled the ocean from ships floating on the surface of the ocean, we think of properties changing downward in the direction of increasing depth below the surface. Station profile data are collected by lowering and raising instruments through the water. Therefore, the station profile graph typically displays depth on the vertical axis and another variable on the horizontal axis.
If you wanted to create a graph that showed how a dolphin has grown since it was born, what would you plot on the x-axis and what would you plot on the y-axis?
Show sample answer
This will be a time series graph. The x-axis should have units of time, such as the age of the dolphin in years. The y-axis should have units measuring growth, such as length in meters or weight in kilograms. Learn more about time series graphs in the next activity.