Integrating GIS and Bayesian Belief Networks for Exploratory Analysis of

E i t l D t d D i i M kiEnvironmental Data and Decision Making

Jim Fox, Derek Morgan, Matt Hutchins and Karin Rogers, g , gFebruary, 2013

2/20/20131

Thanks to:

Climate Literacy Education and R h (CLE R) G tResearch (CLEaR) Grant

Eastern Forest Environmental Threat Assessment Center (EFETAC) Assessment Center (EFETAC)

Cooperative AgreementRenaissance Computing InstituteRenaissance Computing Institute

State of North Carolina

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Decision Making Building Blocks

Data - Values Linked to Decision-MakersData -

What do we know?

What do we care about?

Decision Makersat the Proper

Scale

Future What

NaturalHumanB il

Action What are What

could happen?

BuiltEconomic

we willing to do about

it?

Risk

UncertaintiesModels

Who is “we”?Who is willing to

accept responsibilityd i and assign

resources?3

Data Linked to Values and UncertaintyData Linked to Values and Uncertainty

Data -

Values What do

Future What

What do we know?

we care about?

could happen?

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Use Bayesian Belief Networks (BBN)s!

BBN’s provide a graphical way to display (and even interact with) related information. interact with) related information.

Ability to update understanding with new information.

Evidence can change the likelihood of something occurring.

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To briefly illustrate BBNs and their usage, consider the following example starting with the belief that temperature

Case Data BBN

following example starting with the belief that temperature is influenced by elevation and annual season.

Case Data BBNElevation Temp Season

Hi Cold WinterLow Mild WinterLow Mild Winter

Medium Mild SpringLow Hot SummerHi Cold Summer

ldHi Mild SummerLow Mild Winter

Medium Mild SpringLow Hot SummerHi Cold Summer

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Select the Low Elevation Node and Winter Season Node…

Case Data BBNElevation Temp Season

Hi Cold WinterLow Mild Winter

Case Data BBN

Low Mild WinterMedium Mild SpringLow Hot SummerHi Cold SummerHi Mild SummerHi Mild SummerLow Mild Winter

Medium Mild SpringLow Hot SummerHi C ld SHi Cold Summer

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Coastal North Carolina GIS Datasets and Values –How Can We Filter and Prioritize?

Landscape Conditions

NLCD 2006APNEP Strategic HabitatDistance to Roads

BioticSNHALongleaf PineSAMBI Waterfowl

Ecological P

SEF - SE Ecological Framework

Hydro/Geomorph

NLCD Wetlands SALCC Flow AlterationPriority WatershedsNHD Fl li (B ff d)Processes Framework

Cumulative NDVI

p

NLCD I i S f

NHD Flowline (Buffered)

Chemical / Physical

NLCD Agriculture Societal

NLCD Impervious SurfaceEnvironmental JusticeSEF RecreationalPublic Water Supply (Buffered)

Natural Disturbances MODIS

H

( ff )

CulturalNational Register of Historic

Hotspots Places (Buffered)

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Moving Data Between GIS & BBN

The connecting unit of the BBN to the GIS is the geopixel.

9 9

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BBN: Probabilistic Map AlgebraBBN: Probabilistic Map Algebra Involves the use of a Bayesian network as the

combinatorial function used on a cell by cell basis combinatorial function used on a cell by cell basis when combining raster layers

A Bayes net is a graphical way to display (and even interact with) nodes that are probabilistically related ) p yby some sort of causal dependency

We are using this approach because we want to come back out of the BBN and delineate specific areas (pixels) on the ground.

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Use multiple “stories” for Exploratory Analysis

Each of the essential attributes are not distributed evenly across the twenty counties.across the twenty counties. No one pixel can likely meet all of the conditions

Different combinations of the essential attributes can show how different groups of decision makers and local conditions will determine the most important resources.

C ! Context matters!

We know from published studies that sea level rise impacts and their related adaptation strategies can vary impacts, and their related adaptation strategies, can vary across this region. One solution will not fit every issue.

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Story 1 – Wetland & Biotic Habitat CorridorCorridor Riverine systems entering the estuaries and linked

wetlands – these are key parts of the natural system in wetlands these are key parts of the natural system in the AP. River corridors within are the focus of priorities for

waterfowl conservation being as they provide suitable habitat

SEFWaterFowlNLCD Wetlands SEFWaterFowlNLCD Wetlands

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BBN screen shot – Wetland & Biotic Corridor StoryCorridor Story

2/20/201314 14

Story 2 – Drinking Water Focus

A water story, but with primary focus on how the natural system is providing services for the human sectorsystem is providing services for the human sector

Consider multiple datasets that show how managed and natural conditions influence the drinking water supply

Public Water Supply NHD Flow linesWatersheds

2/20/201315 / 0/ 0 35

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Story 3 – Tourism & Recreation Linked to the Natural Systemto the Natural System Barrier islands, bays and

estuaries igh

(9)

estuaries their link to a beach economy

and transportation issues.

EF

Rec

Hi

Natural systems supporting a large part of the local

SE

economy.

aces

His

toric

PlaHere is the Wright

Brothers National Memorial in Dare County/

H

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Sea Level Rise

Sea Level Rise Sea Level Rise overlay in raster formatMaps Generated by Dr. Tom Allen, ECU

Sea Level Rise overlay in raster format

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Combined Stories with SLRwith SLR

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Tourism andTourism and Recreation

Wetland, e a d,Biotic Habitat

andDrinking Water

Wetland and Biotic Habitat

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But will it work for more complex issues?

National Fire Plan County resolution instead of geopixel County resolution instead of geopixel

2/20/201320

StateALARFLGA

5.782.727.9317.3

WetFact2-2.09 to -0.07 0.99

Percent Area Forested0 to 5 0 +

Mill Production0 9.35

Mech Potential (Forest)0 to 12 0

WarmFact1-1.15 to 0.08 21.9

Mechanical Treatment Forested Areas

Southeast Fires, Fuels, and Homes BBN Mechanical Treatment Forested Areas

GAKYLAMSNCOKSCTNVATX

17.313.52.3112.09.55 0 +

7.5012.25.883.32

-0.07 to 0.340.34 to 0.50.5 to 0.810.81 to 1.121.12 to 2.77

11.317.116.823.230.6

1.01 ± 0.75

5 to 2424 to 36.936.9 to 4949 to 62.262.2 to 92.9

0.4413.117.325.643.5

59.5 ± 19

Reported Annual Incidents0 to 2020 to 4040 to 5959 to 8989 to 128128 to 210210 to 1900

4.627.9914.916.617.218.620.1

284 ± 450

0 to 2.952.95 to 3.413.41 to 3.753.75 to 4.064.06 to 4.73

17.218.118.718.917.7

3.02 ± 1.4

12 to 3131 to 4545 to 53.853.8 to 62.462.4 to 84.3

0 0 0

49.250.8

65.8 ± 9

Mechanical Treatment00 to 0.320.32 to 1.71.7 to 4.74.7 to 8.58.5 to 23.4

1.9712.922.719.321.122.1

5.79 ± 6.2

Surface Fuel ClusterFuel Cluster 1Fuel Cluster 2Fuel Cluster 3Fuel Cluster 4Fuel Cluster 5Fuel Cluster 6Fuel Cluster 7

34.76.2822.06.147.807.3015.8

203.3 ± 2.2

Vegetation ClusterVeg Cluster 1Veg Cluster 2Veg Cluster 3Veg Cluster 4Veg Cluster 5Veg Cluster 6Veg Cluster 7Veg Cluster 8Veg Cluster 9Veg Cluster 10Veg Cluster 11

1.930.130.6427.10.460.770.5010.117.513.620 5

WUI Area Factor-3.25 to -0.69-0.69 to -0.25-0.25 to 0.140.14 to 0.690.69 to 1.361.36 to 5.94

0 0 0

31.634.733.7

1.72 ± 1.6

0.08 to 0.470.47 to 0.870.87 to 1.091.09 to 1.331.33 to 2.65

20.120.617.213.66.59

0.541 ± 0.74

Mech Potential (non-forest)0 to 3.63.6 to 11.411.4 to 18.618.6 to 2626 to 3535 to 68

52.715.313.610.16.691.65

9.28 ± 11

Conservation Partner

WUI Area Factor

Conservation Partner Area (%)0 to 55 to 1010 to 2525 to 5050 to 7575 to 100

54.714.820.18.441.550.43

10.5 ± 14

Modal HotspotsAgricDevlpNatMxNatNNNone

6.7011.871.97.642.01

Normalized P95 Area Burned0 to 8484 to 147147 to 230230 to 390390 to 720720 to 20002000 to 7.899e5

0 0 0 0

37.439.722.9

91300 ± 200000

Non-Ag Rx Hotspots00 to 1.11.1 to 1.71.7 to 2.32.3 to 2.862.86 to 4.38

27.28.4312.718.817.615.2

1.61 ± 1.3

Mean HARM Index0 to 1.331.33 to 1.561.56 to 1.781.78 to 2.012.01 to 2.242.24 to 2.582.58 to 3.79

9.8815.014.414.914.714.916.3

1.99 ± 0.73

284 ± 450

Mean Burn Prob.0 to 2e-42e-4 to 3e-43e-4 to 6e-46e-4 to 0.00110.0011 to 0.0020.002 to 0.00440.0044 to 0.0463

9.2110.616.217.116.211.918.7

0.00564 ± 0.011

Mean Flame Intensity1 to 1.291 29 t 1 44

13.715 4

203.3 ± 2.2Veg Cluster 11Veg Cluster 12

20.56.75

208 ± 3

Conservation PartnerDODEFWSNAmerNoneNPSOFedOGovPrvtStateUSFS

9.763.950.446.664.462.679.467.4239.216.0

Area Burned Index

Area Burned Index

LWF Acres Burned00 to 9292 to 250250 to 470470 to 10001000 to 29002900 to 30500

28.08.2610.712.415.013.711.9

2430 ± 6000

Hotspot Ratio (Rx/Wf)-3.78 to -1.94-1.94 to -1.11-1.11 to -0.68-0.68 to -0.37-0.37 to 00 to 2.4547

25.212.416.316.96.5822.7

-0.877 ± 1.5

Wildfire Hotspots0 to 1.21.2 to 2.212.21 to 2.612.61 to 2.932.93 to 3.283.28 to 4.22

11.111.317.120.321.219.0

2.61 ± 0.95

1.29 to 1.441.44 to 1.621.62 to 1.881.88 to 2.132.13 to 2.482.48 to 5.4

15.415.117.514.910.413.0

1.95 ± 0.89

WUI Area Factor

norm_p95_brn 0 - 85 85 - 148 148 - 233 234 - 395395 - 723 728 - 2033 2040 - 789834

Probability 0.0000 - 0.0000 0.0005 - 0.0005 0.0005 - 0.0007 0.0007 - 0.00090.0009 - 0.0012 0.0012 - 0.0015 0.0015 - 0.0027

Key Metrics for Decision Support Tools

Data must be accurate, current and scaled for the decisionVi li i k d f l d l ll l Visualizations make data meaningful and locally relevant

Storytelling by neutral facilitators is critical for policy makers to understand uncertainty and follow a decision making processy g p

Group Decision Making is a continuing, iterative process that needs to be actively supported with appropriate technologies and toolsets (GIS and BBN’s!)and toolsets (GIS and BBN s!)

Collaboration is key – the three sectors of government, academia and industry must work together to meet this challengechallenge

Thank you!           Questions?jf @ d

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jfox@unca.edu