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

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

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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)

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Presentation Overview Framing the problem

Desired output

Data sources

Data manipulations

Creation and attribution process

Results

Conclusions

Future work

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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.

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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

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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.

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Data SourcesData Layer Source

Slope Derived from National Elevation Dataset (NED)

Land Cover (incl. forests & crop lands) National Land Cover Dataset (NLCD 2001)

Wetlands National Wetlands Inventory & NLCD 2001

Federal (public) Lands ESRI data disks version 9.2

State & Local Parks ESRI data disks version 9.2

National & State Roads ESRI business analyst street map (TeleAtlas 2006)

Residential Roads Estimated from ESRI business analyst street map (TeleAtlas 2006)

Water bodies National Hydrography Dataset (medium resolution)

Airports & Railroads ESRI data disks version 9.2

Poultry Support Businesses ESRI business analyst

Non-Agriculture Businesses ESRI business analyst

Municipalities & Urbanized Areas ESRI 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)

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Data Sources (con’t)

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Tabular Data Census of Agriculture

Aggregation and cross-tabulation to create a single record for each county in the U.S.

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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)

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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.

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Combining Raster Surfaces Probabilities

0 0.20 1.00

0.20 0.20 1.00

1.00 1.00 1.00

1.00 0.50 0.50

0.50 0 0.20

0.50 0.50 1.00

0.20 0.20 0.50

0.20 0.50 1.00

0.50 1.00 1.00

X X

=0 0.02 0.25

0.02 0 0.20

0.25 0.50 1.00

Land Cover Slope Distance from Roads

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Combining Raster Surfaces

•Individual probability surfaces were combined on a state by state basis

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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

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Creation and Attribution (con’t) ArcGIS Modelbuilder

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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.

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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

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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

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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.

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Contact Info

Jamie Cajka

RTI International

(919) 541-6470

jcajka@rti.org

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ModelBuilder Model

Input Probability Surface Output Synthetic Locations