Go Figure: Tools and Techniques for Displaying Characterization and Remediation Information

Abstract

Introduction The methods for collecting, analyzing, and displaying data have changed over time; however, the underlying goal of visualizing data has not: enabling easy interpretation of complex data. The advent of statistical graphics, such as line charts in the 18th century, demonstrated that visualizations could convey results more quickly and clearly than prose (Tufte 2001). The early data visualizations were an important step toward clearly communicating results from complex data, but they were hand drawn and time consuming to create. They required significant planning and were not easily manipulated or modified after the figure was created. These limitations persisted in the environmental remediation field for certain data visualizations (e.g., geologic cross sections) until very recently. Remediation and characterization visualizations have also historically relied on relatively sparse data sets that necessitate significant interpolation which can lead to data misrepresentation. For example, older figures showing contaminant plume boundaries using limited data typically oversimplified plume shapes, and older geologic cross sections understated the importance of geologic heterogeneity. New data visualization tools have alleviated many of these issues and can more effectively display and communicate complex results. One reason these new digital tools are successful is that they are commonly interactive. For example, dashboards (i.e., interactive figures that convey related information in an easytounderstand format) allow analytes and date ranges to be selected in real time for a graph, which increases the data an audience can view on a single visualization. Some digital tools can also display data in multiple dimensions. This is particularly helpful in the remediation field where projects require understanding of contaminant distributions in space (three dimensions) and over time (a fourth dimension). New data visualization tools also allow the analysis of data from more locations (i.e., higher resolution) and displaying different types of data (i.e., multivariate). This is important as we collect more data than at any point in the history of the field. New field instruments measure new parameters, many of which report data at incredibly high time frequencies (e.g., multiple times a second). New site characterization tools can record data at small scales (e.g., centimeter spacing) for the entire length of a soil boring. Digital field data collection results in more data available for analysis compared to manual collection methods. These all result in a larger quantity of data that is reported near realtime, which was impossible to visualize until relatively recently. These new tools also enable us to customize our visualizations for the planned users or target audience. In the remediation field, stakeholder groups can be broad, with diverse technical backgrounds, project roles, and perspectives. The goals of displaying data vary depending on the audience and require a unique presentation method for each. The larger quantity of available data and new visualization methods can make deriving and conveying results significantly easier. However, they also introduce challenges that can lead to confusing or misleading results. The purpose of this article is to share a variety of practical tools and techniques to aid in creating successful data visualizations, specifically for site characterization and remedial information. To complement the written text, we have recorded an audio summary that is included as downloadable Supplemental Information where we summarize the main features of this article and discuss what we consider key takeaways and implications (Audio S1).