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Presenting Environmental Data

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What can we do with environmental data to understand it and communicate with it?

There are some first things we can do with data to get an idea of what’s happening, like plotting a sensor measurement against time. Then there are ways we can ultimately present environmental data so that it communicates something meaningful, spurs advocacy, and avoids reducing real environmental harms to a data point.

Data visualizations like graphs, charts, maps, art, and other interactive media can tell a story. We can also use non-visual ways, like sound and touch.

We’re collecting resources below on kinds of environmental data and ways to present it--please add to this page to improve it and make it more helpful! Creative ideas that go beyond trend lines or bar graphs are super welcome and appreciated!


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Kinds of environmental data you might have

Environmental data can come in many forms depending on how and where you collect it. Here are some common examples:

Measurements from sensors

  • Data collected continuously over time from a sensor that stays in one place.
    • Example: an air quality monitor placed outside your home that measures particulate matter concentrations every 10 seconds 24 hours a day
    • Data you’ll have: date/time, environmental measurements. Potentially lots of data if the sensor is capturing data frequently.


  • Data collected intermittently (at certain times of the day) at a single location or at several different locations
    • Example: a portable water quality sensor taken to different locations along a stream every morning for several months
    • Data you’ll have: date/time, environmental measurements, location (maybe noted manually, separate from the sensor data).


Measurements from collected samples

  • Grab samples
    • Example: an air bucket monitor taken to a landfill site soon after nearby residents report strong odors. The monitor collects an air sample at a specific location and time and is analyzed in a lab.
    • Data you’ll have: environmental measurements for a sample, reported from the testing lab. Each sample measurement is associated with a sampling location and time (or, an average measurement if you’ve mixed samples from different locations and times).

If you haven’t started collecting data yet, check out some guidance on designing an environmental study---the design step is where you figure out where and when to collect data based on the specific questions you have about a local concern. There’s also more information on sampled data and continuous monitoring data in the “Types of Samples” section.


Ways to present environmental data

Scatter plots and trend lines

Shows how individual data points change against another kind of measure (called a variable). Often, this can be how something you measured in your environment changes over time.

If you have continuous data, plotting it against time is one of the first things you can do to start understanding the data.

scatter plot icon

Flaticon from Freepik


Example 1.
Scatter plot showing particulate matter (PM2.5) concentrations measured by an air quality sensor in an apartment building. Each black dot is an individual measurement. The PM concentrations spike during a fireworks celebration. Data from @LESBreathe.

LESBreathe_PM25_July4

Air quality data from @LESBreathe


Example 2.
A 2-day average trendline showing dissolved oxygen (DO) in treated wastewater, measured by a sensor. The DO goes up and down throughout the day, spiking when air is injected into the water and decreasing as sewage-cleaning bacteria use it up.

ORP DO sensor measurements

Dissolved oxygen (DO) and redox potential (ORP) sensor data from treated wastewater, by @jesseslone in this comment


More examples

Trends with respect to measures other than time


Bar graphs

Shows how measurements vary among different categories

bar graph icon

Flaticon from Dinosoftlabs

Example 1.


Histograms

Made by dividing data into several non-overlapping bins of the same size (x axis) and counting the number of values that fall into each bin (y axis). Histograms look like bar graphs but display numerical data rather than categorical data. Histograms are favorable because they are easy to interpret. However, you cannot distinguish between variables if they fall into the same bin, sacrificing the “true values” to see the overall distribution of data.

Histogram2.PNG


Smooth Density Plots

Similar to a histogram with very small bins and a curve that runs through the top of each bar. With a bin size of 1, the y axis of a smooth density plot displays the proportion of values within that bin. Unlike a histogram, frequency (or density) is computed and displayed on the y axis rather than count. Smooth density plots are useful for ease of comparison between two different distributions or data sets.

SmoothDensityPlot.PNG


Boxplots

Aka box and whisker plots, provide a 5 number summary of your data. Whiskers show the range of data with a box split along the quantiles (or 25th, 50th, and 75th percentiles). Outliers are shown as individual points outside of the range whisker.

Boxplots.PNG


Maps

Shows how measurements vary by location. Mapping can help you show geographic patterns in data, such as pollution hotspots, or how pollution moves across space.

Example 1.
A map of soil test results from around a pond neighboring a salvage yard. Data for each heavy metal detected is shown on the map as a number times the health standard for that metal (e.g., 2x higher than the standard). From Workshop guide: Mapping soil data by @kgradow1, adapted from Statistics for Action Air Quality "Mapping Data" and generously shared with permission.

SfA mapping soil data

Map of Hobbs Pond with color-coded samples for each chemical found in the soil


More examples:


Infographics

Combine images, text, and numbers.

Examples


Additional ways to present data

More data visualization

  • Using emojis to communicate air quality scores
  • Correlating sky color to air quality

Audio


Tools for data visualization and presentation


More resources on understanding and presenting data