R E S E A R C H T R I A N G L E P A R K, N O R T H C A R O L I N A

Where Are The Farms? A Synthetic Database of Poultry and Livestock Operations in Support of Infectious Disease Control Strategies Presented by Jamie Cajka ESRI Federal Users Conference, Washington, DC Feb. 21, 2008

3 Acknowledgements  This research is in support of the Models of Infectious Disease Agent Study (MIDAS) project which is funded by the National Institute of General Medical Sciences (US Department of Health and Human Services).  This work was also performed by:  Mark Bruhn (RTI)  Dr. Gary Smith (University of Pennsylvania)  Ross Curry (RTI)  Seth Dunipace (University of Pennsylvania)

4 Presentation Overview  Framing the problem  Desired output  Data sources  Data manipulations  Creation and attribution process  Results  Conclusions  Future work

5 Framing the Problem  Animal-borne disease modelers need to know:  Animal operation locations.  Proximity to other animal operations.  Composition of animal operation. – Types of animals. – Number of head.  This is necessary to model the spread and mitigate the effects of outbreaks such as avian influenza and foot-and-mouth disease.  Avian influenza is a serious human health threat.  Modelers desire to create and test strategies.

6  Actual farm locations and animal counts by type are NOT available nationally.  Grower privacy concerns  National security concerns  National Animal Identification System (NAIS) will not be the answer.  Currently voluntary with about 20% participation.  RTI created synthetic farm locations that can be used as inputs into animal-borne disease models.  This presentation will focus on poultry operations, as that is the animal type that is currently complete. Framing the Problem

7 Desired Output  A geographically referenced set of farms within an area, characterized by:  Type of animals.  Number of animals.  Mix of animals.  Format could be one of:  A spatial data layer such as a shapefile.  A text file with x and y coordinates.

8 Data Sources Data LayerSource SlopeDerived from National Elevation Dataset (NED) Land Cover (incl. forests & crop lands)National Land Cover Dataset (NLCD 2001) WetlandsNational Wetlands Inventory & NLCD 2001 Federal (public) LandsESRI data disks version 9.2 State & Local ParksESRI data disks version 9.2 National & State RoadsESRI business analyst street map (TeleAtlas 2006) Residential RoadsEstimated from ESRI business analyst street map (TeleAtlas 2006) Water bodiesNational Hydrography Dataset (medium resolution) Airports & RailroadsESRI data disks version 9.2 Poultry Support BusinessesESRI business analyst Non-Agriculture BusinessesESRI business analyst Municipalities & Urbanized AreasESRI data disks version 9.2 & US Census Bureau Sensitive areas (churches, schools, etc.) ESRI data disks version 9.2 (including Geographic Names Information Service – GNIS names)

9 Data Sources (con’t)

10 Tabular Data  Census of Agriculture  Aggregation and cross-tabulation to create a single record for each county in the U.S.

11 Rasterization  All vector data were projected into Albers (NAD 83, meters)  Buffers were created as needed  Polygons were attributed for rasterization  Vector data were rasterized to a 30 meter resolution (to match NLCD)

12 Assigning of Probabilities  Focused on farm building location rather than land parcel location.  Based on:  Research team’s experience  literature review  examination of “truth” data for selected counties  Idea was to multiply probabilities together, so that 0 probability on a layer made the cell impossible for farm location.

13 Combining Raster Surfaces Probabilities X X = Land Cover Slope Distance from Roads

14 Combining Raster Surfaces Individual probability surfaces were combined on a state by state basis

15 Creation and Attribution  The production process was a combination of VB and ArcGIS Modelbuilder  VB  GUI Front End  Opened up a cursor into the Census of Agriculture summary  Attribution of type of farm

16 Creation and Attribution (con’t)  ArcGIS Modelbuilder

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19 Results  RTI generated a synthetic poultry operation shapefile for every county in the United States.  The number of farms was correct.  The locations corresponded to the probability surface.  The size and type were randomized.

20 Results (Con’t)  RTI sent synthetic poultry operation locations to researchers at University of Pennsylvania, to compare against the complete set of truth data. Actual Locations Synthetic Locations

21 Conclusions  Synthetic locations matched up very well to actual locations.  Data is still being tested in the models to see how sensitive the various parameters are.  Inter-farm distance  Animal type  Number of animals

22 Future Work  Creation of different locations for broilers, layers, and pullets using surfaces created specifically for each.  Creation of all farms with animal operations nationwide.  Cattle (currently underway)  Sheep  Goats  Hogs  Creation of synthetic cattle operation locations for the UK (currently underway)  Creation of SE Asian synthetic poultry operation locations.

23 Contact Info Jamie Cajka RTI International (919)

24 ModelBuilder Model Input Probability SurfaceOutput Synthetic Locations