Transcript Fuzzy - Africa Geospatial Forum

Bayesian network-based predictive
analytics applied to invasive species
distribution
Wisdom Mdumiseni Dlamini
-PhD Student / Director of Nature
ConservationUniversity of South Africa /
Swaziland National Trust
Commission
Outline of the Talk
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Aims
Introduction
Invasive alien plant species distribution modelling
Bayesian networks (BNs)
Methods (Predictive analytics –data mining using BNs)
Findings
Conclusions and on-going research.
Aims
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Investigate suitability of Bayesian networks (BNs) for
species distribution (geospatial) data analysis
(Chromolaena odorata and Lantana camara cases in
Swaziland)
Apply BN learning for geospatial predictive analytics
(data mining) and ecological knowledge discovery
Demonstrate potential/usefulness of BN-based data
mining for geospatial analysis and decision-making
Introduction
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Invasive alien plants are problematic in Swaziland and the world
over.
80% of country invaded and about 400 invasive plant species in
total
Four plant species identified and declared a disaster in 2005 due
to threat the economy and food security in Swaziland
(Chromolaena odorata, Solanum mauritiunum, Caesalpinia
decapetala and Lantana Camara)
Degraded rangelands, reduced water flows in streams/rivers,
threat to native flora and biodiversity.
Estimate cost: ~3% of GDP to control these.
Need for geospatial information for control, planning and decisionmaking and understanding their ecology
Introduction
Chromolaena odorata
(Photos R. Mackenzie)
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Lantana camara
Photo: K Braun
Introduction
Photo: E.M. Ossom
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Invasive alien plant species
distribution modelling
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All species distribution modeling approaches model the
function approximating the true relationship between the
environment and species geographic
occurrences/distribution.
Objective is to estimate some function f = μ(Gdata, E) - i.e.
applying an algorithm to data given an environmental space
E to estimate G (distribution)
Used in ecology to:
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model present, past and future distribution of species
predicting disease spread
predicting invasive species spread
niche conservation
Invasive alien plant species
distribution modelling (ceveats)
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Many algorithms do not handle asymmetric data
Many don’t handle interaction effects
Some do not handle nominal/categorical environmental
variables (e.g. vegetation types)
Many stochastic algorithms present different solutions even
under identical parameterization and input data
‘real’ distribution of species not known, so we do not know
when models are making mistakes and when are filling
knowledge gaps.
Invasive alien plant species
distribution modelling (ceveats)
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Which factors determine the distribution of species:
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The answer is often complicated (but important)
Species have physiological tolerances, migration limitations
and evolutionary forces that limit adaptation
A starting point for physiology may be traits
A starting point for abiotic factors is often climate
Climate variables often also correlate with other variables (e.g.
elevation, land cover)
Invasive alien plant species
distribution modelling
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Need for algorithms that will address the issues in
previous slide
Additionally, conventional SDMs are correlative and do
not adequately capture causal species-environment
relationships and ecological knowledge
There remains a critical gap in the understanding of
processes that induce observed invasion spatial
patterns
Bayesian networks
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A BN is a graphical model that encodes probabilistic
relationships among a set of variables
Two components:
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Directed Acyclic Graph (DAG)
Probability Table
Variables depicted as nodes
Arcs represent probabilistic dependence between variables
Conditional probabilities encode the strength of
dependencies
Lack of an arc denotes a conditional independence
Bayesian networks
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Bayes theorem : the posterior probability for  given D
and a background knowledge  :
p(/D, ) = p( /  ) p (D/  ,  )
P(D / )
Where p(D/ )= p(D/ , ) p( / ) d 
Note :  is an uncertain variable whose value corresponds to the
possible true values of the physical probability
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Bayesian networks
Bayesian network
example
A
B
C
D
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A Bayesian network represents potentially
causal patterns, which tend to be more
useful for intelligent decision making
However, algorithms for constructing
Bayesian networks from data were not
designed to discover interesting
patterns
Combined novel feature selection and
structure learning is interesting by nature
Causality + interestingness
tends to improve Usefulness
Bayesian networks
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BNs can readily handle incomplete (missing) data
BNs allow one to learn about causal relationships
BNs readily facilitate use of prior knowledge
Bayesian methods provide an efficient method for
preventing the over fitting of data (there is no need for
complex pre-processing and data transformation)
BNs also handle uncertainty very well
Graphical nature readily allows for interpretation of
interrelationships/interactions between variables
Methodology
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Identify the modelling goals
Identify many possible observations/variables that may
be relevant to the problem
Determine what subset of those observations is
worthwhile to model
Organize the observations into variables having
mutually exclusive and collectively exhaustive states.
Build a Directed Acyclic Graph that encodes the
assertions of conditional independence
Use the graph to describe the ecology species invasion
patterns and processes
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Methodology
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“Knowledge Discovery in Databases (KDD) is the
non-trivial process of identifying valid, novel,
potentially useful, and ultimately understandable
patterns in data” (Fayyad et al., 1996)
Focus on the quality of discovered patterns
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A lot of research on discovering valid, accurate patterns
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Little research on discovering potentially useful patterns
Data Mining consists of extracting patterns from data,
and is the core step of the knowledge discovery
process
Methodology
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Species distribution data obtained from 2009
aerial survey (~50m altitude flight throughout
country) – GPS coordinates from experts.
115 geospatial data sets covering biophysical,
climatic, socio-economic and topographic data.
All processed to rasters/grids of uniform size
(~1km)
Raster geodatabase created and exported to
CSV file
Methodology
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CSV file imported to Weka (open source machine
learning/data mining package) for analysis
Most species occurrence data was imbalanced (i.e. too
many absence (-ve) than presence (+ve) instances) Sampling variation and/or noisy data may mislead the
BN construction method, further contributing to the
discovery of a sub-optimal BN.
Data balancing implemented using Spread Subsample
approach
Discretization (using Minimum Description Length
(MDL) criterion with Kononenko correction)
Methodology
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The problem of constructing the optimal net is too
complex in large datasets
Feature selection
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Hybrid approach: GainRatio Attribute Evaluation followed by
Peng’s maximum Relevance minimum Redundancy (mRmR)
subset evaluation algorithm based on Correlation-based
Feature Subset (CFS) selection and Symmetric Uncertainty
The CFS search was done via particle swarm optimization
(PSO)
Done to reduce data dimensionality and redundancy whilst
simultaneously ensuring that only relevant, predictive and
uncorrelated features (variables) are selected
Methodology
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Various structure learning approaches being
implemented and tested on final subset of variables.
Both local and global search strategies were
implemented using Bayes score.
Methods based on search guided by a scoring function
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Iteratively create candidate solutions (BNs) and evaluate the
quality of each created network using a scoring function, until a
stopping criteria is satisfied
Sequential methods consider a single candidate solution at a
time
Population-based methods consider many candidate solutions
at a time
Methodology
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Conditional independence based algorithms also used
(CI and Inductive Causation (ICS) to extract causal
relationships.
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Not scalable to datasets with many variables (attributes)
Markov blanket applied in all cases (i.e. all variables
constitute the set of parents and children and parents
of children of the class variable).
Methodology
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Examples of sequential method
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Hill climbing algorithm starts with an empty network and at
each iteration adds, to the current candidate solution, the edge
that maximizes the value of the scoring function
K2 algorithm requires that the variables be ordered and the
user specifies a parameter: the maximum number of parents of
each variable in the network to be constructed
Both are greedy methods (local search), which offer no
guarantee of finding the optimal network
Population-based methods are global search methods,
but are stochastic, so again no guarantees
C. odorata BN
NB: the probabilistic
dependencies between
variables
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Legend
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Note the complexity on spatial
distribution highlighting a complex
interplay of factors
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Probability
Identified
invasion
hotspots not
identified
by training
data but
verified
with
independent
tree atlas
data
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Findings
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C. odorata
ROC
Recall (Sensitivity)
Minimum
0.85
0.90
Mean
0.87
0.94
Maximum
0.88
0.99
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L. camara BN
NB: the probabilistic dependencies between variables
Legend
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:0
Lo
w
H
ig
h
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Probability
Identified
invasion
hotspots not
identified
by training
data but
verified
with
independent
tree atlas
data
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Findings
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L. camara
ROC
Recall
Minimum
0.80
0.90
Mean
0.83
0.93
Maximum
0.85
0.98
Findings
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Distinguishing properties of BNs:
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their ability to reduce the joint probability distribution
of the model into a set of conditional probabilities
their capability to express model uncertainties,
propagate information quickly,
represent complex topologies,
combine domain knowledge with hard data, and
update model parameters as new information
becomes available.
Conclusions
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We proposed a method for integrating feature selection
and BN learning algorithms in non-spatial and
geospatial data mining
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Algorithms for constructing Bayesian networks
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Discover potentially causal, more useful patterns
Discover surprising patterns, potentially more useful
Hopefully, combining the “best of both worlds”,
increasing the chance of discovering ecological
patterns and processes useful for intelligent decision
making and invasion plant species management
Ongoing research: computational implementation of
the proposed method and ecological knowledge
Conclusions
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Geospatial predictive analytics: an emerging field in
‘big data’ era.
Applicability of our method to broader natural resource
management and geospatial analysis in particular
where both prediction and decision-making are
paramount.
Accessibility and sharing are crucial if we are to reap
maximum benefits from geospatial data
(A)Spatial data repositories/SDI could act as good data
mines from which to extract patterns to solve various
socio-economic/NRM problems.
Questions ??
Thanks you for
listening!