Feature Elevation Aspect How Much? Canopy Cover How Much? Distance to Streams How Much? How Much? Invasives ers) can create a network, make observations on those variables, and compile the findings as cases. It is from these cases that the belief network software determines the conditional probabilities between two variables. While the theory underlying Bayesian statistics is complex, a software package commonly used for belief network modeling— Norsys Netica—is approachable, graphic, and intuitive. In addition, outputs are not as intimidating as the results generated by many statistical packages. Belief networks are useful for CRAFT and other risk assessment tools but have not been linked to GIS so variables can be placed in a spatial context. Although some networks, such as ones used to determine a likely disease diagnosis for a given set of symptoms, do not have an appropriate spatial context, for other networks, such as models used to determine likely forest health given a set of threats, spatial context is critical. This information can answer questions like, What areas of forests are most at risk? and Where can mitigation efforts be prioritized to leverage limited resources? Researchers at the University of North Carolina at Asheville's National Environmental Modeling and Analysis Center (NEMAC) looked for current solutions to tie belief network models to a GIS that would support the use of CRAFT but couldn't find anything that allowed for in-depth risk analysis or had a suitably generic process. It was critical that the process be general enough to apply to any spatial risk assessment from invasive spe- Figure 3: A basic conceptual model showing factors that lead to the occurrence of invasive species. The yellow boxes ask, What is the extent to which each of these factors contributes to suitable locations for invasive species? cies to wildfires to landslides. Consequently, NEMAC decided to write its own tool using the ArcGIS Desktop application ArcMap and incorporating Python scripts and Netica, a program for working with Baysian belief networks from Norsys Software Corp. As a test case to develop the method, NEMAC investigated the risk that an invasive species known as Japanese stilt grass, or Microstegium vimineum (MIVI), would encroach on an area near Hot Springs in the Pisgah National Forest in North Carolina. Invasive species data was collected by Equinox Environmental (equinoxenvironmental.com), a consulting and design firm. Within the study area (see Figure 2), Equinox Environmental collected GPS survey paths and marked every MIVI occurrence as a point feature. The paths were locations where MIVI was known to be absent and the points were locations where MIVI was known to be present. With the proposed process, this information could then be used to assess the risk of MIVI occurring in the rest of the study area that had not been surveyed. Simply put, the absence of evidence was not evidence of absence. First, a conceptual model (Figure 3) was created in consultation with scientists from EFETAC. [EFETAC, established by the U.S. Forest Service, uses an interdisciplinary approach in developing new technology and tools that anticipate and respond to threats to eastern forests.] While tracking the factors associated with the location of invasive species is incredibly complex, NEMAC simply sought to test a method for putting geographic information into a Bayesian statistical context and returning the results to geographic space. As a result, the location variables used were based on a trusted data source, The National Map Seamless Server, a data resource provided by the U.S. Geological Survey that is publicly available and easily accessed. The process for preparing data in ArcMap, exporting data to Netica, performing analysis in Netica, and importing the results back into ArcMap is summarized in the following five stages. Stage 1: Location Data Preparation 1. Obtain elevation, streamline, and canopy cover data from The National Map. 2. Derive aspect from elevation. 3. Create a multiple ring buffer around streams and convert the vector layer to raster. 4. Prepare location data so all rasters have the same projection and resolution and that each raster cell snaps to the same grid. 5. Reclassify all data to appropriate classes. Reclassification was an iterative process. (Initially, aspect data was classified equally based on the four cardinal directions. However, an EFETAC scientist pointed out that one class for north (270°–90°) and three equal classes for southeast, south, and southwest, respectively, were more appropriate classifications.) 6. Clip all data to study area boundaries. Continued on page 22 ArcUser Fall 2009 21 www.esri.com