why use bayesian networks for poverty analysis
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Presented at the Basin Focal Project Poverty Mapping Workshop, November 2007, Chiang Mai, ThailandTRANSCRIPT
BN, BBN, CPN, ‘Bayesian Networks’
Why use BN• To estimate quantities which are unobservableq• Modelling – building models, ‘elicitation’• Mixed data, probabilistic relationships
– medical diagnosis– inference, risk, decision support– estimation of physical statep y– ‘data integration’
Example : Dryland Salinity WA ………….[ land condition, forest changes]
SALINITY : Information Gap, Policy & Management Problem :– where is it, where changed, where will it.. MAP, MONITOR, PREDICT
BIG AREA~230,000 sq km
Knowledge about SALINITY PROCESS – rising saline groundwater as the result of clearingrising saline groundwater as the result of clearing
Sample ‘truth’Observationaldata – spatial Y/N (date?)
Knowledge about PROCESS
Knowledge ??Landscape position importantLandscape position important- more likely in valleys
Salinity affects vegetation- Visible effects ? – images
G d t l l S il tGroundwater levels Soil type, Vegetation type, etc ???
Network diagram – dryland salinity – FIRST VERSION
Is each location (likely to be) saline or not ? - Not observable directly
Meaning of network, then
- A. How do we observe (get data) on ‘Landform Position’ ? everywhere- B. How do we observe ‘vegetation condition’ ?
(A from processing DEM; B [surrogate] classification from Landsat)
Network – dryland salinity – FIRST VERSION - getting the data
DEM IMAGE
processing task
classification task
DEM
‘Raw data’
IMAGE
‘Raw data’?arrows??what happens?
H d l i t t di NOT BNHydrologists concept diagram - NOT a BN
SalinityGround water depth and rate of rise
?
XHydrological model- deterministic Data
Model Parameters
XModel Parameters
Water Poverty ‘Network’
limitations for agriculturevolume, critical supply gap,uncertainty supply
?
uncertainty supply
Opportunity
??
WPPoverty measureor surrogate
Opportunity cost labour
g
Water-related health costs
?
Education/Investment constraints
Land Monitor – Information Gap
• The three highest priority environmental issues- Land salinisation, - Salinisation of inland waters, and
Maintaining biodiversity- Maintaining biodiversity (Western Australian State of the Environment Report, 1998)
• About 1.8 million ha in WA are already salt-affected, and this area could double in the next 15 to 25 yearsand this area could double in the next 15 to 25 years.
• Effects on Vegetation
• No Accurate map, No spatially explicit information on change, or prediction
Salinity Problem & ImpactResource Problem affects peopleEconomic & Social ProblemEconomic & Social Problem
Prediction 25% - 35% land lost
$$ - 40% Australia’s grain
Farming is not subsidised in AustgBusiness, Land value, Banks $
Built infrastucture : road networkMaintenance;Town Buildings ‘Rescue Towns’
Land Monitor ComponentsLand Monitor - Components
I Institutional support (agencies)I. Institutional support (agencies).
2. Demonstrated Technical Capacity (CMIS) Define necessary data (Landsat TM 1988-2000 DEM)Define necessary data (Landsat TM 1988-2000, DEM)and methods
3 Funding Support (National Govt)3. Funding Support (National Govt)------------------------4. Public Interest
LANDSAT TM – Complete Australian Archive since 1988
CMIS Methods and technical developments
• Rectification & Registration, Calibration (robust regression)
(C )• Discriminant Analysis (CVA etc)
• Enhanced ML classification (PP – uncertainty)
• DEM (pre)Processing – derived variables
• Data Integration - CPN, Decision Trees
• Trend summary and representation (vegetation condition)
( th NN LD D i i T )(others e.g. NN, LD, Decision Trees …)
Salinity Mapping & Monitoring Ground DataSalinity Mapping & Monitoring Ground Data