FLAPS: A new tool for managing disease risks in

agriculture systemsBrian Kraus

Colorado State UniversityDepartment of Biology

Farm Location and Animal Population Simulator

(FLAPS)Chris BurdettBrian KrausSarah Garza

Colorado State UniversityDept. of Biology

Kathe BjorkUSDA-APHIS-VS

David OryangFDA-CFSAN

Acknowledgements

• Funding:– USDA, APHIS, Veterinary Services, Center for

Epidemiology and Animal Health – Food and Drug Administration, Center for Food Safety

and Applied Nutrition• Collaborators and SMEs: Eric Bush, Barbara Corso, Dave Dargatz,

Kim Forde-Folle, Lindsey Garber, Jason Lombard, Reginald Johnson, Katherine Marshall, Ryan Miller, Ann Seitzinger

• Student Technicians: Lauren Abrahamsen, Kaydee Cavender, Raquel Batista-Martinez, Wimroy D’Souza, Amelia James, Somtirtha Roy

What is FLAPS?

• Spatial microsimulation model (linked with geospatial distribution model) – Disaggregates Census of Agriculture data to

simulate locations and populations of individual livestock farms

– End users obtain FLAPS output from a web-based GUI

– End users customize simulations through GUI

FLAPS – Advances

• Advances– Empirical data to predict farm locations (n = 40,000)– Predicts unpublished aggregate Census of Agriculture

(CoA) data without microsample of “real” data– Production types (under development)– Generalized methodology adaptable to all CoA data– GUI

• A limitation– Validation of population estimates (common issue for

all spatial microsimulation models; geographic validation good)

Census of AgricultureData

(partial)

Presence/AbsenceData

INPUT DATA

IPF(Predict data withheld

at the state- orcounty-level)

ANALYSIS METHOD

DM(correlates with

presence orabsence of farm)

Census of AgricultureData

(complete)

OUTPUT DATA

Probability surface

POPULATIONSIMULATION

DemographicInformation

GeographicInformation

Three challenges for livestock population simulation models in U.S

• Geography1. Where to distribute individual farms across a

landscape?

• Demography2. How to forecast unpublished (i.e., aggregated at

county or state level) Census of Agriculture data?3. How to simulate (i.e., disaggregate) populations

on individual farms?

Three challenges for livestock population simulation models in U.S

• Geography1. Where to distribute individual farms across a

landscape?

• Demography2. How to forecast unpublished (i.e., aggregated at

county or state level) Census of Agriculture data?3. How to simulate (i.e., disaggregate) populations

on individual farms?

Presence-Absence Sampling• Stratified random sampling design

– n = 10,000 locations per species (4 species)– Stratified using Census of Agriculture over 1 km2

grid

Swine farm, presence/absence sample (n = 10,000)

Presence-Absence Sampling• Absence

Presence-Absence Sampling• Presence

Distance to forest

Distance to cropland

Presence/Absence ModelResults

Covariate P-value AIC AICΔ

Distance to Cropland 0.001 5031.5 0.0Distance to Roads 0.001 5064.2 32.7Ruggedness 0.001 5087.1 55.6Slope 0.001 5101.1 69.6Distance to Open Areas 0.001 5153.5 122.0Distance to Wetland 0.001 5222.0 190.5Distance to Urban 0.001 5241.7 210.2Distance to Barren Areas 0.001 5245.4 213.9Distance to Forest 0.025 5253.4 221.9Distance to Pasture 0.044 5253.8 222.3

Domestic swine probability surface Validation: R2 = 0.82

Three challenges for livestock population simulation models in U.S

• Geography1. Where to distribute individual farms across a

landscape?

• Demography2. How to forecast unpublished (i.e., aggregated at

county or state level) Census of Agriculture data?3. How to simulate (i.e., disaggregate) populations

on individual farms?

2007 CoA Swine population per county

= data unpublished atcounty level

Numberof farms

Number of animals

(population)

1 –24

25 –99

100 –249

250 –499

500 –999

1,000 +

Most livestock

from large farms

Most farms are

small

But…1 –24

25 –99

100 –249

250 –499

500 –999

1,000+

Farm/population size categories

? ?

STATE-LEVEL (CoA)

COUNTY-LEVEL (CoA)

IPF

Farm by population size categories

Populationsize

Challenge #2Forecast

unpublisheddata

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Example of CoA data, unpublished values highlighted.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Information aggregated at the state level.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Information aggregated at the county level.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Overall totals for state and county level data.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Aggregated population groups.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 ??? 5 736

CNTY2 41 23,489 3 14,303 3 ??? 5 ??? 30 5,456

CNTY3 6 1,850 0 0 1 600 2 ??? 3 ???

Col Totals

Col Error

Iterative Proportional Fitting

Marginal totals for the rows and columns.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 393 5 736

CNTY2 41 23,489 3 14,303 3 2,100 5 1,600 30 5,456

CNTY3 6 1,850 0 0 1 600 2 699 3 450

Col Totals

Col Error

Iterative Proportional Fitting

Use population group information to generate seed values.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 393 5 736

CNTY2 41 23,489 3 14,303 3 2,100 5 1,600 30 5,456

CNTY3 6 1,850 0 0 1 600 2 699 3 450

Col Totals

Col Error

Iterative Proportional Fitting

Add across each row and down each column...

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 393 5 736 9,699

CNTY2 41 23,489 3 14,303 3 2,100 5 1,600 30 5,456 23,459

CNTY3 6 1,850 0 0 1 600 2 699 3 450 1,749

Col Totals 22,873 2,700 2,692 6,642

Col Error

Iterative Proportional Fitting

…to get the current row/column totals…

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 393 5 736 9,699 63

CNTY2 41 23,489 3 14,303 3 2,100 5 1,600 30 5,456 23,459 30

CNTY3 6 1,850 0 0 1 600 2 699 3 450 1,749 101

Col Totals 22,873 2,700 2,692 6,642

Col Error 0 175 28 135

Iterative Proportional Fitting

…and the marginal errors for each row and column.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 394 5 736 9,700 64

CNTY2 41 23,489 3 14,303 3 2,239 5 1,593 30 5,456 23,591 102

CNTY3 6 1,850 0 0 1 600 2 737 3 456 1,793 57

Col Totals 22,873 2,839 2,724 6,648

Col Error 0 36 4 141

Iterative Proportional Fitting

After one iteration of the IPF method.

FIPS Farm Total

PopTotal

Farms1000+

Pop1000+

Farms500-999

Pop500-999

Farms200-499

Pop200-499

Farms100-199

Pop100-199

Row Totals

Row Error

STATE 62 34,975 10 22,873 4 2,875 8 2,720 38 6,507

CNTY1 13 9,636 7 8,570 0 0 1 330 5 736 9,636 0

CNTY2 41 23,489 3 14,303 3 2,275 5 1,455 30 5,456 23,489 0

CNTY3 6 1,850 0 0 1 600 2 935 3 315 1,850 0

Col Totals 22,873 2,875 2,720 6,507

Col Error 0 0 0 0

Iterative Proportional Fitting

After multiple iterations of the IPF method.

? ?

STATE-LEVEL (CoA)

COUNTY-LEVEL (CoA)

INDIVIDUAL-LEVEL (Simulated)

IPF

Farm by population size categories

Populationsize

Challenge #2Forecast

unpublisheddata

Challenge #3Disaggregate binsto individual-level

IPF +assumed

distribution

Simulation model• Take inputs from Demographic and Geographic

models

• Use custom built Python package to:

• Disaggregate populations from the CoA• Assign geographic locations to populations

• Use ArcGIS Server and custom built Flex application to allow user to run simulation

http://flaps.biology.colostate.edu

Simulation Output

Sioux County, Iowa

Human-livestock-wildlife interfaceHow to integrate humans and food safety

into these models?

“crop FLAPS”

• Recall FLAPS utilizes CoA data in a general manner

• We can adapt FLAPS with minimal changes or assumptions to predict the distribution of produce crops

• Example with leafy greens

Adapting FLAPS to estimate cropsLeafy Greens

• Geography– Approach 1: Use livestock FLAPS data

Assumes distribution of crop farms similar as livestock farms

Adapting FLAPS to estimate cropsLeafy Greens

• Geography– Approach 2: Sample with NASS’s Crop Data Layer

using all vegetables (not just leafy greens)Methodological differences from livestock FLAPS• 1 km2 cells are

sample unit (not points)

• Area based covariates (not distance based)

Presence cellAbsence cell

Adapting FLAPS to estimate cropsLeafy Greens

• Geography– Approach 1: Use livestock FLAPS data– Approach 2: Sample with NASS’s Crop Data Layer

using all vegetables (not just leafy greens)– Approach 3: Remote sensing?

• Demography– Area not population– But much unpublished data – will require some

distributional assumptions

Interested in evaluating/using FLAPS?

http://flaps.biology.colostate.edu

Contact. Brian.Kraus@colostate.edu