Jan 1, 0001 12:00 AM

date = “2018-10-26T11:30:00” title = “Using a Large GPS Dataset to Enhance Survey Matching” abstract = “NOAA Fisheries is the United States’ federal steward of their nation’s ocean resources. To this end, they currently collect data on catch and “effort” in the recreational fishing sector in the charter boat modality. Catch is estimated with the Access Point Interview Survey (APAIS), in which biologists wait at randomly assigned docks, marinas, and slips and interview all passengers of any charter boat they intercept. The caught fish are inspected, identified, measured, and counted at the passenger level to produce an estimate for catch per unit effort (CPUE). “Effort” is estimated via a costly telephone survey with low response rate. CPUE and effort are then combined to produce an estimate of total catch per species.

In an effort to reduce costs, improve accuracy, and increase timeliness, NOAA Fisheries are experimenting with an alternative data collection procedure in which charter boat captains report the total catch per species at the end of each trip with a GPS-enabled electronic device (currently, Thorium). The captains’ reports are used as auxiliary data to the probability sample of intercepts, resulting in an estimator that has a similar form to a capture recapture estimator. This estimation procedure requires matching intercepted trips to reported ones. Since intercepted trips are a probability sample, this allows estimation of a reporting rate.

However, since charter boats may take multiple trips in a day, the trip report to APAIS interview matching procedure requires a time component. The times reported in the captains’ log are often misleading or erroneous, and thus the author sought an alternative source of data to enhance matching. Each vessel produces a GPS position report on a periodic basis – to date, there are over 2.5 million such reports.

We describe how the periodic GPS position data is used to improve shore arrival time estimates. Initially, locations at which vessels are stationary for extended periods of time and which match the NOAA Site Register (sites at which APAIS interviews may take place) are identified. Then, trips to and from these locations are identified. From these trips, we can estimate arrival time to be as accurate as half of the report resolution. Additionally, duration of trip is estimable. For vessels whose GPS reporting is turned off, we can then use previous trip data to enhance arrival time estimates. Finally, the relationship between catch and trip duration is explored. We discuss the obstacles with the dataset size and home base and trip identification. Our findings may be extended to other surveys which may be augmented with GPS data.” abstract_short = “” event = “BigSurv18” event_url = “" location = “Barcelona, Spain”

selected = false math = true

url_pdf = “” url_slides = “" url_video = “”

Optional featured image (relative to static/img/ folder).

[header] image = “talks/BigSurv18.png” caption = “BigSurv18”

#Embed your slides or video here using shortcodes. Further details can easily be added using Markdown and $\rm \LaTeX$ math code.

Ryan McShane
Visiting Assistant Professor of Statistics

My research interests include intransitivity in paired comparisons, sports analytics, nonparametric methods, and interdisciplinary research.