Transcript Cyber Infrastructure for Agro-Threats

Cyber-Infrastructure
for Agro-Threats
Steve Goddard
Computer Science & Engineering
University of Nebraska-Lincoln
Background
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In addition to the “normal” threats from natural hazards,
today we must worry about deliberate attacks on our
agricultural infrastructure, communities or economy
 Agro-terrorism becomes a new risk that must be
evaluated by decision makers
UNL scientists have already developed many of the models
needed to answer questions related to the risk of certain
agro-events occurring
 Much of the crop land cover, and agricultural census
information is already available
A cyber-infrastructure building on this data and existing
systems is needed to integrate the tools and data to
identify agro-threats
Our Mission
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To develop a decision support system of geospatial
analyses to enhance risk assessment
Our initial research in drought risk and exposure
analysis allows us to:
 Compute and map drought indices at increased
spatial and temporal resolutions
 Provide transparent access to distributed geospatial,
and relational databases
 Provide new algorithms (using data mining and
knowledge discovery techniques) that seek out
patterns between ENSO events and droughts or crop
yields
 Develop new geospatial analyses to better visualize
the emergence, evolution, and movement of drought
Current Tools
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Our current tools apply risk
analysis methodologies to
the study of drought
 Integration of basic
models with data
generates “information”
for analysis by decision
makers
 Information can be
gathered at any resolution
for which we have data
http://nadss.unl.edu
Layered Architecture
Presentation (User Interface)
e.g., Web Interface, Java applet
Data cache
Knowledge
Knowledge
Layer Layer
e.g., Data Mining, Exposure Analysis, Risk Assessment
e.g., Exposure Analysis, Risk Assessment
Data cache
Information
Layer
Information
Layer
e.g.,
Drought
Indices,
Regional
Crop
Losses
e.g.,
Drought
Indices,
Regional
Crop
Losses
Data cache
DistributedSpatial
Spatialand
andRelational
RelationalData
Data
Distributed
e.g.,
e.g.,Climatic
ClimaticVariables,
Variables,Agricultural
AgriculturalStatistics
Statistics
HTTP
IIOP
RMI
TCP
Building a Spatial View
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Data from information and knowledge layers are translated
spatially and interpolated to provide a “risk view” for a
defined area
Risk Indicators
Surfacing
Display
Drought Indices
Raster interpolation of data
points within various
windows
Re-summarization of
raster data
Soil Data
Climate Data
Inverse Distance Weighting
Spline
Kriging
Reported Yields
Generation of
displayable images
Building a Spatial View

Spatial data from information and knowledge
layers can be combined with various overlays to
create unique views of data
Risk Assessment in Practice
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By combining several domain specific factors from our
“information layer” we are able to create maps displaying
the risk of crop failure for states, regions or counties
The result is a
“spatial” view of risk
The user adjusts weight
factors for each variable
The risk calculator
combines the variables
Risk Assessment Applications
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By combining “information” from different sources we
create “knowledge”
We can project potential impacts for decision makers at
various levels
 State, county, farm an even field level projections
Total Market Value
Dairy Farms
Beef Farms
Benefits and Impacts
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Improving spatial and temporal analysis for risk
management
 State level to County level to Field level
Responding to risk events more effectively
 Predict risk levels for areas early
 Predict the effects of the occurrence of a risk
event
Application of our risk analysis research can
provide the same benefits to various domains,
including assessment of agro-threats
Conclusion
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A cyber-infrastructure for risk analysis can
provide experts and non-experts alike access to
tools to evaluate the risk and impact of an event
in real-time
Moving forward we hope to apply our expertise to
other agriculture risk factors including analysis of
agro-terrorism
Tools can be developed to help identify “safe
islands” -- locations that may be naturally
protected from the factors contributing to risk
 Identified regions could then be used to grow
crops if major growing regions are
compromised