Tim Negley, Nick Rose, and Jamie Combes shared a presentation on "Improving the Accessibility of Geospatial Data Evaluation and Visualization for Project Teams" at the tenth International Conference on Remediation and Management of Contaminated Sediments. 

Background/Objectives. The ability to easily visualize geospatial data for large and dynamic environmental systems is becoming increasingly important for information sharing and decision support. For example, data for sediment remediation sites—often collected by different entities—are used to support remedial design, environmental modeling, background assessment, chemical forensics, cost allocation, and human and ecological risk assessment. In these applications, the data end users may need to partition the data spatially and temporally to evaluate trends (such as by reach, depth, geomorphic areas, contaminant, and sampling events). However, large, complex and disparate data sets can be problematic to evaluate and visualize without specialized experience in data modeling, statistics, and geographic information systems (GIS). Recent technological advances in cloud-based analytics represent new and efficient opportunities to embed custom analytical and geospatial data visualization tools into centralized applications for use by project teams. The objective of this presentation is to provide a specific example of how advances in analytics technology can be used to transform raw data into insights across a project team.

Approach/Activities. Available sediment data collected for several hundred constituents, by multiple entities and over multiple decades, were extracted, transformed, and loaded into a central data warehouse for use across a project team. The final compiled data set contained several million rows of data. Working with the project teams, requirements for data analysis and visualization were defined to develop a custom geospatial data analysis and visualization application. The web-based application integrates ArcGIS Online, the R programming language, and Microsoft Data Analysis Expressions (DAX) to provide data end users a user-friendly solution to explore and visualize large datasets. End users can focus on specific subsets of the data relevant to their evaluation, drill down to the raw data in the data warehouse, and save the results for further evaluation. The application is served from the data warehouse and is available to data end users as a desktop and secured web-based application.

Results/Lessons Learned. The presentation provided an example of how recent technological advances can make exploratory geospatial data evaluation and visualization more accessible to multidisciplinary project teams working on large and complex environmental systems. The example highlighted the importance of proper planning— such as ensuring proper data governance, data cleaning, and data accessibility— to ensure usability and efficiency and demonstrates the importance of proper design to align the final work products with the project team’s objectives and needs. The presentation covered diagnostic tools and visualizations typically used across teams for exploratory data analysis, including spatial and temporal trend plots, exceedance plots, probability plots, interactive maps, box-and-whisker plots, and principal component analysis (PCA) as examples.