spatial modelling of fst distribution in louisiana

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Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA Junpyo Hong, Krishna Paudel, and Mike Dunn Department of Agricultural Economics and Agribusiness CENREP Louisiana State University ***[The authors thank Jane Niu for GIS

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Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA. Junpyo Hong, Krishna Paudel, and Mike Dunn Department of Agricultural Economics and Agribusiness CENREP Louisiana State University ***[The authors thank Jane Niu for GIS assistance]. Formosan Subterranean Termite. - PowerPoint PPT Presentation

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Page 1: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Junpyo Hong, Krishna Paudel, and Mike DunnDepartment of Agricultural Economics and Agribusiness

CENREPLouisiana State University

***[The authors thank Jane Niu for GIS assistance]

Page 2: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Formosan Subterranean Termite

Soldiers (orange-brown, oval-shaped head) and workers Teardrop soldier head of the Formosan subterranean termite (left) and rectangular head of the native subterranean termite (right) (Courtesy of Gregg Henderson, Louisiana State University)

Formosan subterranean termite, Coptotermes formosanus Shiraki, is considered

one of the most destructive and aggressive species of termites in the world.

Page 3: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

U.S. Distribution

Graphic by: Nan-Yao Su, University of Florida

Page 4: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Damages

• FSTs attack railroad ties, pilings, utility poles, or even live trees.

• New Orleans French Quarter Historical District of Louisiana.

• Damage estimates exceeding $1 billion per year in those infested areas in US.

• These damage costs include property damage, repairs, and control measures

Page 5: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Damages

Page 6: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Objective

• To simulate a prediction of a spread of the Formosan Subterranean Termite (FST) across the state of Louisiana based on the information of the prior spread pattern and human commercial behavior.

• Rationale : From policy perspectives, issues about the

pattern of the spread initiate not only when and how much regulatory effort should be exerted, but also where control efforts should be applied over spatial landscapes

Page 7: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Newton’s Gravity Rule

212

21

d

mmGF

► The force of attraction, F, between two bodies is proportional to the product of their masses, m1 and m2, divided by the square of the distance(d12) between them.

► Gravity model considers not only the source of population, but also the spatial structures and nature of potential infestation areas.

► Gravity model estimates the force of attraction between an origin and a destination, with movement rates being a function of this force.

► Gravity model is more appropriate when the distance to and attractiveness of destination are known or are of interest.

Page 8: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

DataGeographic Unit : Zip Code.• FST infestation.

► Source : Wood Destroying Insect Eradication Report (2004, 05, and 06) in Louisiana Department of Agriculture and Forestry. ► Stack all infestation by each zip code. ► 48 infested zip codes, and 414 non-infested zip codes.

• Attractiveness in the gravity model. ► the number of housing units. ► Census 2000 Summary File 1 (SF 1) 100-Percent Data’

• Distances ► the reference zip-code area (71360) to the rest of areas. ► a referenced zip-code polygon centroid to centroids of rest of zip-code polygons in ArcGIS.

Page 9: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Assumption

• FST spreads via accidental transportation of infested wooden manufactures.

• Wood manufacturing companies are wholesale manufacturing companies, not retailers.

• Spread initiated from one origin.

• Zip code of 71360 was selected as the initial (reference) zip code.

Page 10: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Infestation by ZIP-code

ZIPNumbef of

infestation ZIPNumbef of

infestation ZIPNumbef of

infestation ZIPNumbef of

infestation

    71118 * 1 71346 * 13 71423 20

70124 * 1 71146 1 71350 4 71424 * 5

70512 * 1 71303 * 351 71351 8 71430 * 5

70570 * 20 71322 1 71354 1 71432 1

70577 * 3 71323 4 71360 * 253 71433 * 4

70586 2 71325 * 1 71362 1 71446 39

70589 * 3 71327 2 71373 1 71450 1

70634 33 71328 * 26 71403 8 71455 * 3

70653 2 71329 1 71404 1 71456 7

70656 2 71331 2 71405 16 71457 1

70659 * 3 71334 1 71406 1 71461 6

70747 1 71341 5 71409 * 28 71467 20

70750 * 1 71342 4 71417 15 71477 2

70764 1 71343 17 71422 1 71485 * 32

* Indicates Metropolitan Statistical Area (:MSA).

Page 11: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

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Termite diffusion 2004#S None MSA#S MSA

,2005, and 2006

Page 17: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Simulation Model

• A production-constrained gravity model.

• Socio-economic variables (housing units) can connect biological organism to human behavior.

• Probability of potentially infested vehicles’ moving from a reference area (71360) to the rest of areas in Louisiana

n

fRfR TP

1

Page 18: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Simulation Model (cont’d)

TRf : information on all vehicles which travel from a reference zip-code (:71360) to an area of FST source, f.

RffRRRf dHWAT

AR : a scalar, WR : the total number of wholesale wood manufacturing companies in the reference zip-code, which is ‘5’ in this analysis,Hf : a total number of housing units in location f, dRf : the distance from R to f, and α : the distance coefficient.

What is AR ?

Page 19: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Simulation Model (cont’d)

“Balancing factors” in the spatial interaction literature.

n

fRff

R

dHA

1

1

Hf : a total number of housing units in location f,

Now, we are ready for prediction of FST infestation on nun-infested areas.

Page 20: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Simulation Model (cont’d)

PR containing all the information on the interaction among infested areas now makes the second excursion when the FST infested vehicle move to non-infested zip-code areas.

RuuRRRu cHPAT

TRu represents the probability of infestation by these vehicles that travel from a reference zip-code to an non-infested zip-code .

Hu represents the total number of housing units in non-infested zip-code areas.

Page 21: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

01

23

4actu

al/T

if2.0

70000 70500 71000 71500zip

actual Tif2.0

alpha = 2.0

Comparison Between Actual Infestation and Simulated Infestation Adopting 2.0 as Distance Coefficient.

Page 22: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Actual Infested Areas

Infested Areas when using distance coefficient is 2.0.The darker places indicate that those area are more likely infested.

Page 23: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Potential Infested AreasAlpha = 0.1

Page 24: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Potential Infested AreasAlpha = 2.0

Page 25: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Infested areas and Non-infested areas when using distance coefficient is 2.0

Page 26: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Discussion

• Attractiveness of the sites, the total number of housing units, play more notable role in the pattern of the spread than the distance does.

• Distance coefficient (alpha=2.0) introduced at the original Newton’s gravity model assures that the simulation fits the best to the actual pattern of infestation.

• It implies that preventive control against FST on sites less than 30 km distant to the infestation reported area would be more effective than control with monotonous criteria.

Page 27: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

Limitations• Assumption supporting the gravity model in this study, i.e., FST

spread via accidental transportation of the wholesale wooden manufacturers needs to be verified by a field survey.

• Since the fact that FST spread via railroad logs is widely believed, another pattern would be discovered by analyzing a spread involving a railroad network.

• Weak connection of infested wooden goods between wholesale manufacturers and consumers’ individual houses should be developed.

• Survey of sales among wholesales or retail-sales can provide reliable parameters which can be incorporated in the gravity model.

Page 28: Spatial MODELLING OF FST DISTRIBUTION IN LOUISIANA

uninfested Degree of infestation The Number of ZIP The Number of Housing Units Distance (Km)

a = 0.1

1 224 889 156

2 84 3738 182

3 47 7469 184

4 45 11947 188

5 14 18632 230

a = 0.5

1 241 1025 161

2 79 4413 179

3 60 9264 188

4 33 15191 186

5 1 9913 9

a = 0.9

1 242 1086 166

2 81 4635 176

3 53 9207 184

4 36 14454 176

5 2 7992 9

a = 2.0

1 235 1331 179

2 96 5819 176

3 58 8887 149

4 22 11628 130

5 3 6620 26

Prediction on the Number of Zip-code Areas and Their Average Number of Housing Units and Average Distances from The Reference Zip-code (71360) Calculated by

Different Distance Coefficients in Potentially Infested Areas in the Future.