Feature Figure 5: The first few lines of the presence/absence node table in the Netica network file. On the right, each node feeding into the presence/absence node has its own column. Each of its possible states is combined so that each row represents a unique combination of the variable states. On the left, the probabilities are represented for each state of the presence/absence node. state of the location variables has its own column. Each row represents every combination of the states of the location variables and the corresponding probabilities for each state of the presence/absence variable. This script also adds a new column to the aggregate raster, created by the Combine tool, for the MIVI presence probability values. 2. The script then goes through each cell in the aggregate raster, matches the combination of states for each of its constituent variables to that same combination in the Netica output, and inserts the corresponding MIVI presence probability value into the column created in the previous step. At this point, every cell of the survey raster has a probability for MIVI presence. When this field is symbolized and displayed, the result is a risk map for MIVI presence. Given the simplicity of the variables investigated, this risk map is probably not the most accurate assessment of where one might find MIVI. However, this method allowed NEMAC to successfully take geographic information, use Bayesian statistical analysis, and present the results in a geographic context. NEMAC is working with EFETAC to refine the belief network-GIS link and use it in other studies and upcoming CRAFT projects. Most significantly, this process is not limited to invasive species risk. NEMAC is investigating other potential uses for the process to ensure its generality and is also working to simplify and automate the process, more tightly integrating ArcMap and Netica. For more information, visit the NEMAC Web site (nemac.org) or contact the authors, Jeff Hicks at jhicks@unca.edu or Todd Pierce at tpierce@unca.edu. Figure 6: The output of the Python script in ArcMap: the risk map for MIVI presence Acknowledgment The authors thank Dr. Danny Lee and Dr. Steve Norman at EFETAC and Karin Lichtenstein, Jim Fox, and Alex Krebs at NEMAC for their assistance and advice. About the Authors Jeff Hicks is a recent graduate of the University of North Carolina Asheville Environmental Studies program. With a varied background in multimedia and graphic design, he was drawn to GIS because it combined his interests in technology and the environment. He began his work on the belief network-GIS link as a student intern for NEMAC and has gone on to become geospatial analyst at NEMAC. Hicks currently is a key contributor to a collaborative effort with the U.S. Forest Service in the production of the Western North Carolina Report Card on Sustainability. He also assists with research on creative ways of integrating geographic data in visualization environments. Dr. Todd Pierce has worked in GIS for more than 18 years and has specialized in GIS and Web programming for 12 of those years. He holds a B.S.E. degree in electrical engineering from Tulane University and a doctorate in geography from Oxford University in the United Kingdom. He is responsible for linking GIS and databases to the Web at NEMAC, where he leads the development of an online multihazard risk tool for mitigation planning. He also assists with development of geographic decision facilitation processes that use Web applications and data visualization techniques to support public policy decision makers with land-use planning; flood mitigation and response; forest preservation; and other community issues. www.esri.com ArcUser Fall 2009 23