No abstract

No abstract

BackgroundCurrently there is a critical need for accurate and standardized wildlife-vehicle collision data, because it is the underpinning of mitigation projects that protect both drivers and wildlife. Gathering data can be challenging because wildlife-vehicle collisions occur over broad areas, during all seasons of the year, and in large numbers. Collecting data of this magnitude requires an efficient data collection system. Presently there is no widely adopted system that is both efficient and accurate.Methodology/Principal FindingsOur objective was to develop and test an integrated smartphone-based system for reporting wildlife-vehicle collision data. The WVC Reporter system we developed consisted of a mobile web application for data collection, a database for centralized storage of data, and a desktop web application for viewing data. The smartphones that we tested for use with the application produced accurate locations (median error = 4.6–5.2 m), and reduced location error 99% versus reporting only the highway/marker. Additionally, mean times for data entry using the mobile web application (22.0–26.5 s) were substantially shorter than using the pen/paper method (52 s). We also found the pen/paper method had a data entry error rate of 10% and those errors were virtually eliminated using the mobile web application. During the first year of use, 6,822 animal carcasses were reported using WVC Reporter. The desktop web application improved access to WVC data and allowed users to easily visualize wildlife-vehicle collision patterns at multiple scales.Conclusions/SignificanceThe WVC Reporter integrated several modern technologies into a seamless method for collecting, managing, and using WVC data. As a result, the system increased efficiency in reporting, improved accuracy, and enhanced visualization of data. The development costs for the system were minor relative to the potential benefits of having spatially accurate and temporally current wildlife-vehicle collision data.

No abstract

This study was conducted to investigate innovative solutions to a measurement problem pertaining to self-reported body weight data as a key component of the Stepped Approach Model (SAM) of service delivery. Subjects (n = 223) were randomly assigned to one of two conditions: Informed Group (of self-report and weight measurement) + six body weighing habit items (IG, n = 113) and Uninformed Group (of self-report and weight measurement) + one body weight item (UG, n = 110). A t-test indicated that IG subjects reported significantly more accurately, t(194) = 2.99, P = 0.002, and with significantly less variability than UG subjects, F(109,112) = 1.95, P < 0.0005. A multiple regression of absolute difference weight (observed--self-reported weight) on observed weight revealed consistent accuracy across the weight range for IG subjects, whereas UG subjects' accuracy decreased as body weight increased. The slope of the IG did not significantly differ from 0, t(218) = 1.44, P = 0.150, but did significantly differ from the slope of the UG, t(218) = 2.78, P = 0.006. The following conclusions are noted when IG conditions are used: (1) a three-component strategy designed for maximum effect size results in accurate reporting across the entire weight range, (2) self-reported body weights under prescribed conditions can be used as valid 'proxies' for observed measurements, and (3) SAM proponents can rely on the validity of self-report body weight as a credible basis for decisions about changing intervention steps and evaluating intervention efficacy.