1. GIS Predictive Modeling for Determining ADR Attributed to Cancer in Nevada

2. GIS Predictive Modeling Predictive models:  Use existing statistical data to infer statistical probability of the studied phenomena occurring in an unknown area. Datasets for this model:  Annual Death Rate  Total Population  Land Use (Developed)  Chromium-6 Levels

3. Dataset Manipulation Problems with the Water Quality Dataset  Too many missing variables  Zero values could be missing data or actual zero.  Difference in Total Population and Total Served  Discrepancies make dataset misrepresentative  Standardization Formula to Reconcile  (Chromium-6 * Total County Population) / Total Served

4. Dataset Manipulation Total Population and Health Statistics were converted to percentage per county. Land-Use Development Data  Hexavalent Chromium is byproduct of industry so the area of interest is the developed area. (Openspace+Low+Med+High Intensity) = Developed % Total County Area

5. Predictive Model Construction Join and Relate each dataset. Create separate shapefiles.  Export data and Add layer to map Change shapefiles from vector to raster.  Conversion Function: Polygon to Raster

6. Predictive Model Construction Raster File Usage  Extract By Sample using all four datasets as inputs  Renders a new database with 50,000+ raster values  Perform Linear Regression in SPSS with this database  Renders Constant value for ADR and values for other datasets  Use in ArcGIS Map Algebra in a base E exponent equation  Exp (.061 + ([Chromium] * -.047) + ([Develop] *.115) + ([Pop] * -.011))

7. Final (Non)Predictive Comparison

8. Suggestions for Future Research Student groups focused on one state or all one type of dataset. Possibly only have two projects/semester  More TIME would allow for:  More detailed datasets.  A wider range of datasets for a more precise correlation comparison.