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Society For Risk Analysis Annual Meeting 2009

Risk Analysis: The Evolution of a Science

Session Schedule & Abstracts


* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

T3-H
Symposium: Geospatial Risk Analysis Sponsored by RCSG

Room: Kent   1:30-3:00 PM

Chair(s): Dolores Severtson



T3-H.1  13:30  Same risk, different visual design: Public interpretations of hurricane track maps. Eosco GM*; University of Oklahoma   geosco@ou.edu

Abstract: Every media institution has its own branding whether it’s a TV station’s jingle to their station logo. When visuals hit the media, they too take on the many shapes and sizes of the station’s branding and marketing schemes. Risk visuals, especially those in the meteorological context, are an example of visuals that follow this branding trend. In essence, meteorological visuals communicate the same to similar science on every station in one geographic area, while representing this science, and its subsequent risk, in potentially drastically different visual ways. This paper will explore public interpretations of hurricane track maps to see how different designs shape individuals’ understanding of hurricane risks. In-depth interviews will be conducted in four different hurricane prone areas, including coastline cities in the following states: Texas, Florida, North Carolina, and New York. These states have been chosen for their varying levels of experience with hurricanes. Preliminary results show that different designs evoke a variety of understandings of the graphic from communicating hurricane size to showing hurricane strength to the correct answer of communicating the track uncertainty for the eye of a hurricane. In addition to individual understanding, preliminary results also show that shape, color, and map projection seem to alter individuals’ risk perceptions. The implications of varying visual designs of the same science will be discussed.

T3-H.2  13:50  Using GIS for environmental health decision-making in Wisconsin: Challenges and pitfalls in display and interpretation. Malecki K.M.*, Bekkedal M.Y.; Wisconsin Bureau of Environmental and Occupational Health, University of Wisconsin, Madison   Kristen.Malecki@wisconsin.gov

Abstract: The goal of Wisconsin Environmental Public Health Tracking (WI EPHT) is to integrate a wide variety of environmental health information (including disparate hazard, exposure, health effect and modeled risk data) and to assess spatial and temporal clustering of risks. WI EPHT aims to improve environmental health decision-making through enhanced monitoring and dissemination of information to a broad range of stakeholders including public and policy-makers. Geographic information systems (GIS) are used as tools to disseminate spatially distributed data and convey simple messages to the public. The WI EPHT program has been using GIS based methods to systematically analyze and display hazard and health outcome data on both a secure web-server and public website. Health outcome data are derived from existing administrative databases including hospital, vital statistics and cancer registries. Hazard data are mapped as actual concentrations or modeled risk estimates. WI EPHT has found each data set or model presents it own unique challenges related to spatial aggregation, timeliness, variation and uncertainty in risk estimates. For example, hospitalization and emergency department data are available at the zip-code level and vital statistics data at the address level; both are based on self-reported data. In addition, health outcome data must be displayed in such a way as to preserve the confidentiality of individuals, thus data are often spatially aggregated impacting the specificity and interpretation of data. Similarly, modeled risk estimates are built from multiple data sources each with their own levels of uncertainty and combined into a total estimate that can be displayed at various geographic levels using multiple color schemes. All of these features impact how data are can be presented on a map. This presentation will highlight WI EPHT programs challenges, proposed solutions and their implications for display and interpretation of environmental risk information.

T3-H.3  14:10  Assessing how proximity to hazards on a map influences risk beliefs and behavioral intentions and Testing a measure of perceived hazard proximity. Severtson DJ*, Burt JE; University of Wisconsin-Madison   djsevert@wisc.edu

Abstract: Maps are commonly used to provide information about environmental health risks to the public. Map viewers will likely infer their potential risk based on their perceived proximity to the environmental hazards displayed on the map. As it relates to map viewing, perceived hazard proximity (PHP) is a function of one’s perceived location on a map relative to the hazards depicted on the map. The purpose of this study was to explore how maps showing hazard proximity influence risk beliefs and behavioral intentions and to initiate the development of a useful measure of PHP. We devised an algorithm that assigns PHP based on hazard dose, inverse distance, and angles formed by location to the mapped hazards. Twenty four maps were developed that varied on angle, dose, distance, location in or out of a hazard cluster, and cluster density. Maps were systematically organized into 4 blocks of 6 maps. The maps depicted well water test results for private wells and a fictitious substance and a fictitious “you live here” location. Outcomes included specific (likelihood, severity) and global risk beliefs (problem seriousness, safety), and intentions to have one’s water tested for the substance and to drink less water. This randomized block trial was conducted among 447 undergraduate students. Data analysis is in progress. Initial findings show: a 10 point measure of perceived risk likelihood was most sensitive to map variations; hazard, distance, hazard by distance interactions, and the PHP measure had the largest effects on perceived likelihood; angle (narrow, wide), cluster size (2 versus 8 wells), density (tight, loose), and location in or out of a cluster had small effects; and the PHP measure explained variance in perceived likelihood better for some configurations than others. These and other findings will be shared in a presentation of key study results.

T3-H.4  14:30  Assessing the Association Between Public Health and Environmental Factors. Young LJ, Gotway CA, Xu X, Kearney G*, Hyman M; UNIVERSITY OF FLORIDA, FLORIDA DEPARTMENT OF HEALTH   LJYoung@ufl.edu

Abstract: Existing data from multiple sources (e.g., surveillance systems, health registries, governmental agencies) are the foundation for analysis and inference in many studies and programs. More often than not, these data have been collected on different geographical or spatial units, and each of these may be different from the ones of interest. Numerous statistical issues are associated with combining such disparate data. Florida’s efforts to move toward implementation of The Centers for Disease Control and Prevention (CDC)’s Environmental Public Health Tracking (EPHT) Program (EPHT) aptly illustrate these issues, which are typical of almost any study designed to measure the association between environmental hazards and health outcomes. In this presentation, we consider two approaches to drawing inference about associations between public health and environmental variables when a potential explanatory variable is measured on one set of spatial units, but then must be predicted on a different set of spatial units: spatial regression using aggregated data and case-cross over analysis. The strengths and weaknesses of each approach are discussed. Our focus is on relatively simple methods and concepts that can be transferred to the states’ departments of health, the organizations responsible for implementing EPHT.



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