Crafting Better Decisions

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					  Crafting Better Decisions
   Creating a link between belief networks and GIS

  By Jeff Hicks and Todd Pierce, University of North Carolina,
  Asheville’s National Environmental Modeling and Analysis Center

  Considerable research has led to an increased
  understanding of how human activity influ-                    Is the bulb blown?                                             Is the lamp plugged in?
  ences the landscape and has provided more
                                                        True           100                                                  True           100
  options for managing forests in an ecologi-
  cally sound manner. With advances in GIS              False            0                                                  False            0
  technology, decision-making techniques, and
  environmental protection policies, more effec-                           1                                                                  1
  tive and integrated management approaches
  are available.
      The Comparative Risk Assessment Frame-
  work and Tools (CRAFT), one such approach,
  has been developed by the U.S. Forest Ser-
  vice’s Eastern Forest Environmental Threat                                                     Does the light work?
  Assessment Center (EFETAC) to improve the
                                                                                             True          0
  quality of decisions for forest and natural re-
  source managers. CRAFT is designed to help                                                 False       100
  planning teams focus on the most important
  issues, organize their analyses, and use the                                                          0+0
  right tools and data in a facilitated environ-
  ment. CRAFT has four phases:
                                                      Figure 1: A simple network that predicts the outcome of a light working based on real-world observations
  n Specifying objectives: What’s the
  n Designing alternatives: What to do?
  n Modeling effects: What could happen?
  n Synthesis: What to communicate?
      To better model the effects of different al-
  ternatives, CRAFT uses belief networks [also
  known as Bayesian networks, Bayes networks,
  or causal probabilistic networks] and influ-
  ence diagrams to model uncertainty about the
  world by combining both common sense and
  observational evidence based on the theory of
  Bayesian statistics.
      Essentially, a belief network includes a se-
  ries of variables that represents real-world at-
  tributes and each variable has several states.
  For example, a variable could be whether
  a lamp shines and its states could be true or
  false. An expert on those attributes connects
  the variables in a graphic network that shows
  how one or more variables cause a change in
  another variable (Figure 1).
      The primary feature of a belief network is
  its ability to “learn” and continually refine the   Figure 2: Location map for the study
  extent of a relationship between two variables
  by using conditional probabilities. Instead of
  making educated guesses between two factors,
  a user (or in the case of CRAFT, a group of us-

20 ArcUser Fall 2009                                                                                                                       

                           Elevation                                            Aspect

                                     How Much?                            How Much?
  Canopy Cover                                                                                          Distance to

                              How Much?                                           How Much?

                                                                                                   Figure 3: A basic conceptual model showing factors that
                                                          Invasives                                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?

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

  Stage 2: Survey Data Preparation
  1. Convert vector survey data to raster with
     the same snap grid as the location data.
  2. Reclassify survey data into three classes:
     known MIVI absence, known MIVI pres-
     ence, and unknown MIVI presence or ab-                                                Elevation
     sence (i.e., areas not surveyed).                                          Lowest         0
                                                                                Low            0
  3. Clip the MIVI presence/absence raster to                                   Moderate       0                      North           0
     the study area boundaries.                            Canopy Cover         High           0                      Southeast       0
                                                     Low          0             Highest      100                      South         100
                                                     Moderate   100                            5                      Southwest       0
  Stage 3: Data Combination and Export               High         0                                                                   3                  Distance to Stream
  1. Use the Combine tool (Spatial Analyst                        2
                                                                                                                                                     More than 60m 100
     Tools > Local > Combine) to create an ag-                                                                                                       Btwn 30 & 60m 0
                                                                                                                                                     Less than 30m   0
     gregate raster. (The Combine tool visits                                                                                                                        0
     every cell in the study area. For each cell,
     it records the value for the presence or ab-
     sence raster and the values for all location
     rasters.)                                                                                              Presence/Absence               Figure 4: Representation of the
  2. Use a Python script written by NEMAC                                                              Absence      83.3                   data inputs overlaid with a screen
     to export this dataset as a simple                                                                Presence     16.7                   capture from Netica. In this
     comma-delimited text data table where ev-                                                                    1.17 + 0.37
                                                                                                                                           example, the user has selected
     ery cell in the study area is a single row                                                                                            hypothetical states for each
     and each variable—all location rasters and                                                                                            variable and Netica has updated
     the presence/absence raster—has a unique                                                                                              the probabilities for the presence/
                                                                                                                                           absence node.
                                                    Stage 4: Export Data from ArcMap                              elevation state, had a south aspect, and was
                                                    and Import It into Netica                                     more than 60 meters from a stream. Netica
                                                    1. Import the comma-delimited text file into                  moves on to the next row where the conditions
                                                       Netica. Netica automatically creates one                   might have been different. After Netica goes
                                                       node for every column. Each node repre-                    through the entire study area, it calculates the
                                                       sents a single variable for all location ras-              probability of each state occurring in the pres-
                                                       ters and the presence/absence raster.                      ence/absence node given every possible com-
                                                    2. Configure each node so its states corre-                   bination of states in the location nodes. Net-
      300,000 ESRI Customers                           spond to the classification applied to the                 ica can assert that for the stated combination
       are within your reach.                          map in ArcMap. Note that the presence/                     described previously, there is a 16.7 percent
                                                       absence raster has a value for unknown. A                  chance that MIVI will occur in places with
          Advertise today!                             state should not be configured as unknown                  those conditions.
                                                       because this represents areas that were not                    In Netica, users can interact with the net-
       Maximum Exposure.                                                                                          work to model what-if scenarios. As a user
                                                       surveyed. Statistics should be generated
       Minimum Investment.                             solely on areas that were surveyed. By not                 clicks on different states in each node and sets
                                                       creating a state for unknown values in the                 them to 100 percent certainty, the probabilities
                                                       presence/absence node, Netica skips all                    represented in each other node (based on what
                                                       cases that represent areas that were not                   is known so far) are updated and displayed.
                                                       surveyed.                                                  These hypothetical situations do not alter the
                                                    3. Arrange and connect the nodes to represent                 probability tables. Rather, they show how oth-
                                                       the conceptual model.                                      er variables respond when one or more vari-
                                                    4. In Netica, select Incorporate Case File to                 ables are set to certain states.
                                                       go through the entire data file and record
                                                       the observations for each row (i.e., every                 Stage 5: Export Data from Netica and
                                                       cell in the study area).                                   Import It Back into ArcMap
                                                    5. The case file populates a table in every                   Netica stores the conditional probability tables
                                                       node, including the presence/absence                       as a Netica network file that shows the proba-
                                                       node, and determines probabilities based                   bility of each state in the response node for ev-
                                                       on these tables.                                           ery combination of node state combinations.
         For Rates and Media Kit, visit
                         For example, the first row might repre-                    1. Parse the network file to a text table using
         or e-mail us at              sent a location where MIVI was present, had                       another Python script written by NEMAC.
                                                    a moderate canopy cover, was at the highest                       Each presence/absence variable and each

22 ArcUser Fall 2009                                                                                                                                  

                                                                                                                         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 combi-
        nation 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
    2. The script then goes through each cell in
        the aggregate raster, matches the combi-
        nation of states for each of its constituent
        variables to that same combination in the
        Netica output, and inserts the correspond-
        ing 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     Figure 6: The output of the Python script in ArcMap: the risk map for MIVI presence
    a risk map for MIVI presence. Given the sim-
    plicity of the variables investigated, this risk     Acknowledgment                                        research on creative ways of integrating geo-
    map is probably not the most accurate assess-        The authors thank Dr. Danny Lee and                   graphic data in visualization environments.
    ment of where one might find MIVI. However,          Dr. Steve Norman at EFETAC and Karin                      Dr. Todd Pierce has worked in GIS for
    this method allowed NEMAC to successfully            Lichtenstein, Jim Fox, and Alex Krebs at              more than 18 years and has specialized in
    take geographic information, use Bayesian            NEMAC for their assistance and advice.                GIS and Web programming for 12 of those
    statistical analysis, and present the results in a                                                         years. He holds a B.S.E. degree in electrical
    geographic context.                                  About the Authors                                     engineering from Tulane University and a
        NEMAC is working with EFETAC to re-              Jeff Hicks is a recent graduate of the Universi-      doctorate in geography from Oxford Univer-
    fine the belief network-GIS link and use it in       ty of North Carolina Asheville Environmental          sity in the United Kingdom. He is responsible
    other studies and upcoming CRAFT projects.           Studies program. With a varied background in          for linking GIS and databases to the Web at
    Most significantly, this process is not limited      multimedia and graphic design, he was drawn           NEMAC, where he leads the development of
    to invasive species risk. NEMAC is investigat-       to GIS because it combined his interests in           an online multihazard risk tool for mitigation
    ing other potential uses for the process to en-      technology and the environment. He began              planning. He also assists with development of
    sure its generality and is also working to sim-      his work on the belief network-GIS link as a          geographic decision facilitation processes that
    plify and automate the process, more tightly         student intern for NEMAC and has gone on to           use Web applications and data visualization
    integrating ArcMap and Netica.                       become geospatial analyst at NEMAC. Hicks             techniques to support public policy decision
        For more information, visit the NEMAC            currently is a key contributor to a collaborative     makers with land-use planning; flood mitiga-
    Web site ( or contact the authors,         effort with the U.S. Forest Service in the pro-       tion and response; forest preservation; and
    Jeff Hicks at or Todd Pierce         duction of the Western North Carolina Report          other community issues.
    at                                 Card on Sustainability. He also assists with                                                                                                                                   ArcUser Fall 2009 23

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