USGS Community for Data Integration Session 1

Presenter #1: Michelle Guy, USGS National Earthquake Information Center (303.273.8650; [email protected])

Title: Characterization of Earthquake Damage and Effects Using Social Media Data

Abstract:

The U.S. Geological Survey (USGS) operates a real time system that detects felt earthquakes, using only data from Twitter—a service for sending and reading public text-based messages, known as tweets. The detector algorithm scans for significant increases in tweets containing the word “earthquake” or its counterpart in several non-English languages and then sends internal alerts with the detection time, representative tweet texts, and the location of the population center where most of the tweets originated. The system has been running in real-time for over two years and finds, on average, three felt events per day with a false detection rate of 8%. The main benefit of the tweet-based detections is speed, with most detections occurring between 20 and 120 seconds after the earthquake origin time. This is considerably faster than seismic detections in poorly instrumented regions of the world. The detections have reasonable coverage of populated, earthquake prone, areas globally. The number of tweet-based detections is small compared to the number of earthquakes detected seismically, and only a rough location and qualitative assessment of shaking can be determined based on tweet data alone. However, the tweet-based detections are generally caused by widely felt events that are of more immediate interest than those with no human impact. We will provide a technical overview of the system and investigate the potential for rapid characterization of earthquake damage and effects using the 22 million “earthquake” tweets that the system has so far amassed. We will also investigate the potential use of other social media sources such as Instagram for rapid impact assessment. Additionally, this effort looks towards establishing a data feed of the tweet-based detections for sharing with collaborators and integrating derived products, such as event characterization, with seismically derived solutions for sharing with collaborators and USGS seismic monitoring and analysis systems.

From: SOCIAL MEDIA BASED EARTHQUAKE DETECTION AND CHARACTERIZATION: SYSTEM ASSESSMENT AND FUTURE DIRECTIONS. EARLE, P. S., USGS, Golden, CO, USA, [email protected]; GUY, M. R., USGS, Golden, CO, USA, [email protected]; HORVATH, S. R., USGS, Reston, VA, USA, [email protected]; TURNER, J. S., USGS, Golden, CO, USA, [email protected]; BAUSCH, D., FEMA, Denver, CO, USA, [email protected]; Smoczyk, G. USGS, Golden, CO, USA, [email protected]

Presenter #2: Joseph Long, USGS St. Petersburg Coastal and Marine Science Center (727.502.8024; [email protected])
Title: Online, on demand access to coastal digital elevation models
Abstract:

Scientists working in the coastal environment use bathymetric and topographic data to evaluate storm-induced coastal change, shoreline change, and ecosystem vulnerability. Moreover, forecast models for waves and water levels require gridded elevation surfaces that seamlessly span the land-water interface. The USGS performs copious amounts of lidar surveys and conducts geophysical surveys of the nearshore bathymetry to help address these needs. However, inadequate tools to merge these data which are collected at varying temporal and spatial resolutions, and from a variety of instrument platforms, limit the availability and applicability of the data. The focus of this work is to address the immediate need of integrating land and water-based elevation data sources so they are readily accessible to coastal scientists and decision makers requiring a seamless data surface that spans the terrestrial-marine boundary. The two primary products will be 1) a geoprocessing service that merges and interpolates the data sources; and 2) a map-based web interface and associated information management platform that allows users to identify an area of interest to implement the interpolation algorithms, access the final gridded data derivative, and save and document the configuration parameters for future reference. The resulting products and tools could be adapted to future data sources and projects beyond the coastal environment.

Presenter #3: Matt Neilson, USGS Southeast Ecological Science Center (352.264.3519; [email protected])
Title: NASWeb API – Web Services Access to the Nonindigenous Aquatic Species Database
Abstract:
The wealth of biodiversity data, including georeferenced records of species occurrences, held by public and private institutions represents an important resource to researchers, natural resource managers, and policy makers. Despite the growing prevalence of major distributed database efforts such as the Global Biodiversity Information Facility (GBIF) and Biodiversity Information Serving Our Nation (BISON) projects, providing and obtaining access to these biodiversity data stores represent a major challenge for both data stewards and users alike. The USGS Nonindigenous Aquatic Species (NAS) Database represents a unique biodiversity dataset containing over 150,000 georeferenced occurrence records from ~1,000 introduced aquatic species in the United States. The work proposed herein addresses access constraints to the NAS database by creating a publically-accessible Web Services API, providing an automated, machine-readable access point to NAS occurrence data. This open API (aligned with the recent focus on open access to government data) will increase exposure for this integral USGS data resource, enhance its integration with other internal and external data sources, and stimulate novel applications and analysis of NAS occurrence data.