Aaron Addison

Google Earth, GIS, and the Great Divide: A new and simple method for sharing paleontological data

Introduction The ease, efficiency, and speed of data communication and analyses are paramount to, and characteristic of, any mature science. GIS is an extraordinarily powerful tool for many aspects of (geo)spatial analyses (Longley et al., 2001), but while used routinely to solve complex spatial analyses problems in many disciplines, its adoption within paleontology has been lagging (Conroy, 2006). Part of the problem is that (a) GIS software is expensive (usually prohibitively so to the individual paleontological researcher) and (b) very few paleontologists are trained in its use. Here we show how paleontological data can be easily displayed and communicated in ways never before possible by combining Google Earth and Geographic Information Systems (GIS). Using paleontological field data, we demonstrate several examples that go far beyond the novelty of simply “find my house” that many Google Earth users are currently familiar with. Specifically, we show how GIS map layers of paleontological interest, including their associated attribute tables (e.g., field catalog data), can be freely and easily transmitted to anyone with Internet access and familiarity with Google Earth. Data organized in GIS layers can be exported to the keyhole mark-up language native to Google Earth (KML/KMZ), transmitted to colleagues (who may have no knowledge of or access to GIS) as an email attachment, and then simply “dragged and dropped” by the recipient onto their own desktop Google Earth display, where the map layers appear “draped” over the Google Earth landscape. The recipient has access to all the graphics and attributes of each map layer that has been exported from GIS as well as to all Google Earth tools [e.g., ability to adjust map layer transparencies, labeling, longitude/latitude (or UTM determinations), spatial measurements, and “tilting” of landscapes for enhanced 3D views]. These tools are often sufficient to allow the non-GIS user to obtain specific information of interest from the data.

Development and interval testing of a naturalistic driving methodology to evaluate driving behavior in clinical research.

Background: The number of older adults in the United States will double by 2056. Additionally, the number of licensed drivers will increase along with extended driving-life expectancy. Motor vehicle crashes are a leading cause of injury and death in older adults. Alzheimer’s disease (AD) also negatively impacts driving ability and increases crash risk. Conventional methods to evaluate driving ability are limited in predicting decline among older adults. Innovations in GPS hardware and software can monitor driving behavior in the actual environments people drive in. Commercial off-the-shelf (COTS) devices are affordable, easy to install and capture large volumes of data in real-time. However, adapting these methodologies for research can be challenging. This study sought to adapt a COTS device and determine an interval that produced accurate data on the actual route driven for use in future studies involving older adults with and without AD. Methods: Three subjects drove a single course in different vehicles at different intervals (30, 60 and 120 seconds), at different times of day, morning (9:00-11:59AM), afternoon (2:00-5:00PM) and night (7:00-10pm). The nine datasets were examined to determine the optimal collection interval. Results: Compared to the 120-second and 60-second intervals, the 30-second interval was optimal in capturing the actual route driven along with the lowest number of incorrect paths and affordability weighing considerations for data storage and curation. Discussion: Use of COTS devices offers minimal installation efforts, unobtrusive monitoring and discreet data extraction. However, these devices require strict protocols and controlled testing for adoption into research paradigms. After reliability and validity testing, these devices may provide valuable insight into daily driving behaviors and intraindividual change over time for populations of older adults with and without AD. Data can be aggregated over time to look at changes or adverse events and ascertain if decline in performance is occurring.

A Naturalistic Study of Driving Behavior in Older Adults and Preclinical Alzheimer Disease: A Pilot Study

A clinical consequence of symptomatic Alzheimer’s disease (AD) is impaired driving performance. However, decline in driving performance may begin in the preclinical stage of AD. We used a naturalistic driving methodology to examine differences in driving behavior over one year in a small sample of cognitively normal older adults with (n = 10) and without (n = 10) preclinical AD. As expected with a small sample size, there were no statistically significant differences between the two groups, but older adults with preclinical AD drove less often, were less likely to drive at night, and had fewer aggressive behaviors such as hard braking, speeding, and sudden acceleration. The sample size required to power a larger study to determine differences was calculated.

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device TM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven. Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance. Participants were aged 65+ years and had normal cognition. Measurements: Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations. Temporal components included number of trips taken during different times of the day. Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods: Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS. Results: Primary locations were confirmed to be 100% accurate when compared to known addresses. Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns. Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night. Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.

Forging Partnerships: Foundations of Geospatial Data Stewardship

Geospatial data stewardship fosters user-driven collection, management, access, reuse, and preservation of location-based data. Academic and research libraries are poised to be at the center of geospatial data stewardship, working with partners to develop a sustainable, geospatial data stewardship hub. This article argues for the essential role of libraries in leading collaborative partnerships to develop a user-driven, spatial data infrastructure that includes people, policies, standards, community perspectives, and operational workflows. The emerging model of partnership-based spatial data infrastructure described at the Washington University Libraries demonstrates working methods in practice.

Caves and karst of Ecuador – state-of-the-art and research perspectives

Abstract Current knowledge of caves and karst regions of Ecuador is scarce and broadly limited to discrete areas such as that of the Galápagos volcanokarst. In continental Ecuador, carbonate karst mostly outcrops in the Amazonian basin and accounts for 5–10% of the surface of the country. However, owing to the difficulties of access within the Amazonian rainforest, most surface and subterranean karst is yet to be revealed. In this review, we present an updated map of the solutional karst of Ecuador based on the most recent geological surveys and our own research. We describe the principal karst regions of Ecuador from the Amazonian basin (Napo and Santiago) as well as the Galápagos pseudokarst. We show that Ecuador karst research may be of considerable importance for both basic and applied research owing to its geographical position and intrinsic vulnerabilities. We discuss the main challenges of karst-related research in Ecuador, such as paleoclimatic studies, subterranean biodiversity, and archeology. We discuss the main vulnerabilities and hazards related to karst uses in Ecuador, considering the paramount importance of tourism for the country.

Teaching Users to Work With Research Data: Case Studies in Architecture, History and Social Work

Teaching Users to Work With Research Data: Case Studies in Architecture, History and Social Work

COMPARISON OF A NOVEL, NATURALISTIC DRIVING ASSESSMENT SYSTEM WITH SELF-REPORTED DRIVING BEHAVIOR IN A SAMPLE OF COGNITIVELY NORMAL OLDER ADULTS

topic at a time out of a repertoire of six total topics, including the specialised topic and five generic topis, such as ‘music’ or ‘good memories’. Participants were seated individually and shown how to chat with Harlie on a smartphone. Headphones were used and each participant spoke to Harlie for approximately 5 minutes. Audio and text recordings were securely saved to an online server for analysis by the research team and participants were encouraged to share feedback on the experience during a group discussion. Results:The feedback on the experience of using Harlie was positive in both groups. While the knitting module was successful in prolonging conversational engagement, the woodworking module was not eliciting longer engagement for this specialised topic. Conclusions: All community members were able to successfully use the Harlie application, feedback was positive and differential chat preferences could be identified. This is a promising finding with potential future applications for using this chatbot with isolated older adults including those with dementia. By following the approach described here, individual chat preferences can be explored for each potential user, which could further enhance the experience of chatting with Harlie.