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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• Advances– Empirical data to predict farm locations (n = 40,000)– Predicts unpublished aggregate Census of Agriculture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 How to forecast unpublished (i.e., aggregated at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 How to forecast unpublished (i.e., aggregated at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
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