Assessing the Geographic Context of Risk Perception and Behavioral Response to Potential Ebola Exposure.

The 2014⁻2016 Ebola Virus Disease (EVD) epidemic outbreak reached over 28,000 cases and totaled over 11,000 deaths with 4 confirmed cases in the United States, which sparked widespread public concern about nationwide spread of EVD. Concern was elevated in locations connected to the infected people, which included Kent State University in Kent, Ohio. This threat of exposure enabled a unique opportunity to assess self-reported knowledge about EVD, risk perception, and behavior response to EVD. Unlike existing studies, which often survey one point in time across geographically coarse scales, this work offers insights into the geographic context of risk perception and behavior at finer-grained spatial and temporal scales. We report results from 3138 respondents comprised of faculty, staff, and students at two time periods. Results reveal increased EVD knowledge, decreased risk perception, and reduction in protective actions during this time. Faculty had the lowest perceived risk, followed by staff and then students, suggesting the role of education in this outcome. However, the most impactful result is the proof-of-concept for this study design to be deployed in the midst of a disease outbreak. Such geographically targeted and temporally dynamic surveys distributed during an outbreak can show where and when risk perception and behaviors change, which can provide policy-makers with rapid results that can shape intervention practices.

Terra Populus' Architecture for Integrated Big Geospatial Services.

Big geospatial data is an emerging sub-area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges to these and other cognate disciplines. First, raster and vector data structures and analyses have developed on largely separate paths for the last twenty years and this creates an impediment to researchers utilizing big data platforms that do not promote the integration for these classes. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio-temporally analyze big geospatial datasets. IPUMS-Terra, a National Science Foundation cyberInfrastructure project, begins to address these challenges. IPUMS-Terra is a spatial data infrastructure project that provides a unified framework for accessing, analyzing, and transforming big heterogeneous spatio-temporal data, and is part of the IPUMS (Integrated Public Use Microdata Series) data infrastructure. It supports big geospatial data analysis and provides integrated big geospatial services to its users. As IPUMS-Terra's data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses.

An analysis of ozone damage to historical maize and soybean yields in the United States.

Numerous controlled experiments find that elevated ground-level ozone concentrations ([O3]) damage crops and reduce yield. There have been no estimates of the actual yield losses in the field in the United States from [O3], even though such estimates would be valuable for projections of future food production and for cost-benefit analyses of reducing ground-level [O3]. Regression analysis of historical yield, climate, and [O3] data for the United States were used to determine the loss of production due to O3 for maize (Zea mays) and soybean (Glycine max) from 1980 to 2011, showing that over that period production of rain-fed fields of soybean and maize were reduced by roughly 5% and 10%, respectively, costing approximately $9 billion annually. Maize, thought to be inherently resistant to O3, was at least as sensitive as soybean to O3 damage. Overcoming this yield loss with improved emission controls or more tolerant germplasm could substantially increase world food and feed supply at a time when a global yield jump is urgently needed.

Spatial video geonarratives and health: case studies in post-disaster recovery, crime, mosquito control and tuberculosis in the homeless.

BACKGROUND: A call has recently been made by the public health and medical communities to understand the neighborhood context of a patient's life in order to improve education and treatment. To do this, methods are required that can collect "contextual" characteristics while complementing the spatial analysis of more traditional data. This also needs to happen within a standardized, transferable, easy-to-implement framework. METHODS: The Spatial Video Geonarrative (SVG) is an environmentally-cued narrative where place is used to stimulate discussion about fine-scale geographic characteristics of an area and the context of their occurrence. It is a simple yet powerful approach to enable collection and spatial analysis of expert and resident health-related perceptions and experiences of places. Participants comment about where they live or work while guiding a driver through the area. Four GPS-enabled cameras are attached to the vehicle to capture the places that are observed and discussed by the participant. Audio recording of this narrative is linked to the video via time stamp. A program (G-Code) is then used to geotag each word as a point in a geographic information system (GIS). Querying and density analysis can then be performed on the narrative text to identify spatial patterns within one narrative or across multiple narratives. This approach is illustrated using case studies on post-disaster psychopathology, crime, mosquito control, and TB in homeless populations. RESULTS: SVG can be used to map individual, group, or contested group context for an environment. The method can also gather data for cohorts where traditional spatial data are absent. In addition, SVG provides a means to spatially capture, map and archive institutional knowledge. CONCLUSIONS: SVG GIS output can be used to advance theory by being used as input into qualitative and/or spatial analyses. SVG can also be used to gain near-real time insight therefore supporting applied interventions. Advances over existing geonarrative approaches include the simultaneous collection of video data to visually support any commentary, and the ease-of-application making it a transferable method across different environments and skillsets.

Assessing American Red Cross First Aid mobile app user trends: Implications for resilience.

OBJECTIVE: Disasters have devastated communities, impacted the economy, and resulted in a significant increase in injuries. As the use of mobile technology increasingly becomes a common aspect of everyday life, it is important to understand how it can be used as a resource. The authors examined the use of American Red Cross mobile apps and aimed to characterize user trends to better understand how mobile apps can help bolster individual and community preparedness, resilience, and response efforts. DESIGN/MAIN OUTCOME MEASURES: Tornado data were obtained from the National Oceanic and Atmospheric Administration and the National Weather Service. Data for the mobile apps were provided by the American Red Cross. All data were reviewed for 2013, 2014, and three specific tornado events. Data were organized in Microsoft Excel spreadsheets and then graphed or mapped using ArcMap 10.2(™). RESULTS: Between 2013 and 2014, 1,068 tornado watches and 3,682 tornado warnings were issued. Additionally, 37,957,560 Tornado app users and 1,289,676 First Aid app users were active from 2013 to 2014. Overall, there was an increase in the use of American Red Cross mobile apps during tornado occurrences. Yet the increase does not show a consistent correlation with the number of watches and warnings issued. CONCLUSIONS: Mobile apps can be a resourceful tool. This study shows that mobile app use increases during a disaster. The findings indicate that there is potential to use mobile apps for building resilience as the apps provide information to support individuals and communities in helping before, during, and after disasters.