Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data

Aaron D Sweeney

doi:10.3389/fclim.2021.732174

Abstract

We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

Highlights

Timelines are an effective way of visualizing data inventory, leading to the improvement of both data curation and exploration (Kräutli, 2016; Shneiderman et al, 2017)
When we first started working at what was the NOAA National Geophysical Data Center in Boulder, CO, we wanted to understand the extent of water level data we were managing
Inspired by the data visualization work of Robert Aspinall at the NOAA Center for Operational Oceanographic Products and Services (CO-OPS, https://tidesandcurrents. noaa.gov/inventory.html?id=9410230) and our own experience with web development, we began to play with a number of open-source, Javascript libraries to display information about the data

Summary

Introduction

Timelines are an effective way of visualizing data inventory, leading to the improvement of both data curation and exploration (Kräutli, 2016; Shneiderman et al, 2017). The NOAA National Centers for Environmental Information (NCEI) is the long-term archive (hereafter, referred to as the Archive) for ocean-bottom pressure data (National Oceanic and Atmospheric Administration, 2005) and coastal tide gauge data (Center for Operational Oceanographic Products Services, 2007) collected in support of the NOAA Tsunami Program.

Results

Conclusion