observations and sampling: common patterns
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Observations and sampling: common patterns. Simon Cox CSIRO Exploration and Mining 7 March 2007. Science relies on observations. Evidence & validation Involves sampling A cross-domain terminology and information-model. What is “an Observation”. - PowerPoint PPT PresentationTRANSCRIPT
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www.csiro.au
Observations and sampling: common patterns
Simon Cox
CSIRO Exploration and Mining
7 March 2007
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Science relies on observations
Evidence & validation
Involves sampling
A cross-domain terminology and information-model
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What is “an Observation”
Observation act involves a procedure applied at a specific time and place (Fowler & Odell, 1997ish)
Result of an observation is an estimate of some property value
The property is associated with the observation domain or feature of interest
The location of the procedure may not be the location of interest for spatial analysis of results
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Observed property
Sensible phenomenon or property-type
Length, mass, temperature, shape
location, event-time
colour, chemical concentration
count/frequency, presence
species or kind
Expressed using a reference system or scale
Scale may also be ordinal or categorical
May require a complex structure
“Sensible”, but not necessarily physical …
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Feature-of-interest
The observed property is associated with something
Location does not have properties, the substance or object at a location does
The property must be logically consistent with the feature-type, as defined in the application domain
E.g. rock-density, pixel-colour, city-population, ocean-surface-temperature
The proximate feature-of-interest may merely sample the more meaningful domain-feature
Rock-specimen samples an ore-body
Well samples an aquifer
Sounding samples an ocean/atmosphere column
Cross-section samples a rock-unit
… i.e. the Observation-target
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«FeatureType»Observ ation
+ quality: DQ_Element [0..1]+ responsible: CI_ResponsibleParty [0..1]+ result: Any
«FeatureType»Event
+ eventParameter: TypedValue [0..*]+ time: TM_Object
«DataType»TypedValue
+ property: ScopedName+ value: Any
«Union»Procedure
+ procedureType: ProcedureSystem+ procedureUse: ProcedureEvent
AnyIdentifiableObject
«FeatureType»AnyIdentifiableFeature
AnyDefinition
«ObjectType»Phenomenon
+followingEvent 0..*+precedingEvent 0..*
+generatedObservation
0..*
+procedure 1
+observedProperty1{Definition must be of aphenomenon that is a propertyof the featureOfInterest}
+propertyValueProvider
0..*
+featureOfInterest
1
A common pattern: the observation model
An Observation is an Event whose result is an estimate of the value of some Property of the Feature-of-interest, obtained using a specified Procedure
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When is this viewpoint interesting?
Primarily if the data-acquisition metadata is of concern
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Procedures
Instruments & Sensors
Respond to a stimulus from local physics or chemistry
Intention may concern local or remote source
Sample may be in situ or re-located
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Procedures are usually process chains
Procedure often includes data processing, to transform “raw” data to semantically meaningful values
Voltage orientation
count radiance NDVI
Position + orientation scene-location
Mercury meniscus level temperature
Shape/colour/behaviour species assignment
This requires consideration of “sensor”-models and calibrations
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Advanced procedures
Modelling, simulation, classification are procedures
“raw” data == modeling constraints (sensor-outputs, process-inputs)
“processed” data == simulation results (outputs)
“interpreted” data == classification results (outputs)
SensorML provides a model and syntax for describing process-chains
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«FeatureType»Observ ation
+ quality: DQ_Element [0..1]+ responsible: CI_ResponsibleParty [0..1]+ result: Any
«FeatureType»Event
+ eventParameter: TypedValue [0..*]+ time: TM_Object
«DataType»TypedValue
+ property: ScopedName+ value: Any
«Union»Procedure
+ procedureType: ProcedureSystem+ procedureUse: ProcedureEvent
AnyIdentifiableObject
«FeatureType»AnyIdentifiableFeature
AnyDefinition
«ObjectType»Phenomenon
+followingEvent 0..*+precedingEvent 0..*
+generatedObservation
0..*
+procedure 1
+observedProperty1{Definition must be of aphenomenon that is a propertyof the featureOfInterest}
+propertyValueProvider
0..*
+featureOfInterest
1
Application to a domain
feature of interest Feature-type taken from a domain-model
observed property Member of feature-of-interest-type
procedure Suitable for property-type
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Conceptual object model: features
Digital object corresponding with identifiable, typed, object in the real world
mountain, road, specimen, event, tract, catchment, wetland, farm, bore, reach, property, license-area, station
Feature-type is characterised by a specific set of properties
Specimen
ID (name)
description
mass
processing details
sampling location
sampling time
related observation
material
…
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Geology domain model - feature type catalogue
Borehole collar location shape collar diameter length operator logs related observations …
Fault shape surface trace displacement age …
Ore-body commodity deposit type host formation shape resource estimate …
Conceptual classification
Multiple geometries
Geologic Unit classification shape sampling frame age dominant
lithology …
License area issuer holder interestedParty shape(t) right(t) …
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Water resources feature type catalogue
Aquifer
Storage
Stream
Well
Entitlement
Observation
…
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Meteorology feature type catalogue
Front
Jetstream
Tropical cyclone
Lightning strike
Pressure field
Rainfall distribution
…
Bottom two are a different kind of feature
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Spatial function: coverage
(x1,y1)
(x2,y2)
Variation of a property across the domain of interest
For each element in a spatio-temporal domain, a value from the range can be determined
Used to analyse patterns and anomalies, i.e. to detect features (e.g. storms, fronts, jetstreams)
Discrete or continuous domain
Domain is often a grid
Time-series are coverages over time
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Features vs Coverages
Feature
object-centric
heterogeneous collection of properties
“summary-view”
Coverage
property-centric
variation of homogeneous property
patterns & anomalies
Both needed; transformations required
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“Cross-sections” through collections
Specimen Au (ppm) Cu-a (%) Cu-b (%) As (ppm) Sb (ppm)
ABC-123 1.23 3.45 4.23 0.5 0.34 A Row gives properties of one feature
A Column = variation of a single property across a domain (i.e. set of locations)
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Some feature types only exist to support observations
class Figure: samplingBase
«FeatureType»Observ ation
«FeatureType»Surv eyProcedure
«FeatureType»SamplingFeature
«estimatedProperty»+ property: TypedValue [0..*]
constraints{each relatedObservation.observedProperty must match a self.property.propertyType}{relatedObservation.featureOfInterest=self}
«DataType»SamplingFeatureRelation
+ role: GenericName
«FeatureType»AnyFeature
«DataType»TypedValue
+ propertyType: PropertyType+ value: Any
Substitutability of SamplingFeature f orAny Feature is implied by the class stereoty pe <<FeatureTy pe>>
propertyValueProvider
0..*
featureOfInterest
1
relatedObservation
0..*
surveyDetails 0..1
Intention
sampledFeature
1..*
relatedSamplingFeature0..*
source
0..*
target
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Extensive sampling feature types
Station
+ elevation: DirectPosition [0..1]+ position: GM_Point
SamplingFeature
+ responsible: CI_ResponsibleParty [0..1]
Trav erse
Flightline
Profile
+ begin: GM_Point+ end: GM_Point+ length: RelativeMeasure [0..1]
Shape3D
SurfaceOfInterest
+ area: RelativeMeasure [0..1]
Interv al
Shape2D
SolidOfInterest
+ volume: RelativeMeasure [0..1]
Shape1D
SamplingFeatureCollection
constraints{count(member)>=1}
Swath
Section
Surv eyProcedure
Sounding
LidarCloud
Specimen
+ currentLocation: Location [0..1]+ mass: Measure+ material: CV_Coverage
+shape 1+shape 1+shape 1
+member 0..*
+surveyDetails
0..1
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Assignment of property values
For each property of a feature, the value is either
i. asserted
name, owner, price, boundary (cadastral feature types)
ii. estimated
colour, mass, shape (natural feature types)
i.e. error in the value is of interest
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Variable property values
Some property values are not constant
colour of a Scene or Swath varies with position
shape of a Glacier varies with time
temperature at a Station varies with time
rock density varies along a Borehole
Variable values may be described as a Coverage over some axis of the feature
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RockSample-A :Specimen
DensityItA :Observ ation
Density :Phenomenon
Densitometry :Observ ationProcedure
2610 kg/T :Measure
2006-11-23 :TM_Instant
Leederv ille, WA :Location
RockSample-B :Specimen
DensityItB :Observ ation
2580 kg/T :Measure
2005-12-23 :TM_Instant
West Leederv ille, WA :Location
+time+result
+procedure+observedProperty
+featureOfInterest
+sampl ingLocation
+density
+sampl ingLocation
+time
+procedure+observedProperty
+featureOfInterest
+result
+density
ProbeItA :Observ ation
Material :Phenomenon
Microprobe :Observ ationProcedure
MineralDistribution :CV_Cov erage
2006-11-24/2006-11-26 :TM_Period
RockSample-A :Specimen
Leederv ille, WA :Location
+observedProperty +procedure
+result+time
+material
+featureOfInterest
+sampl ingLocation RockSample-A :Specimen
2610 kg/T :Measure
Leederv ille, WA :Location
+density
+sampl ingLocation RockSample-A :Specimen
DensityItA :Observ ation
Density :Phenomenon
Densitometry :Observ ationProcedure
2610 kg/T :Measure
2006-11-23 :TM_Instant
Leederv ille, WA :Location
+featureOfInterest
+observedProperty +procedure
+result
+density
+time
+sampl ingLocation RockSample-A :Specimen
2610 kg/T :Measure
Leederv ille, WA :Location
RockSample-B :Specimen
2580 kg/T :Measure
West Leederv ille, WA :Location
+density
+sampl ingLocation
+density
+sampl ingLocation
ProbeItA :Observ ation
Material :Phenomenon
Microprobe :Observ ationProcedure
MineralDistribution :CV_Cov erage
2006-11-24/2006-11-26 :TM_Period
RockSample-A :Specimen
DensityItA :Observ ation
Density :Phenomenon
Densitometry :Observ ationProcedure
2610 kg/T :Measure
2006-11-23 :TM_Instant
Leederv ille, WA :Location
+procedure+observedProperty
+result+time
+featureOfInterest
+material
+featureOfInterest
+observedProperty +procedure
+result
+density
+time
+sampl ingLocation
MineralDistribution :CV_Cov erage
RockSample-A :Specimen
2610 kg/T :Measure
Leederv ille, WA :Location
+material
+density
+sampl ingLocation
Observations, features and coverages
Feature summary
Property-valueevidence
Multiple observations one feature, different properties:feature summary evidence
A property-valuemay be a coverage
Same property onmultiple samplesis a another kindof coverage
Multiple observations different features, one property:coverage evidence
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Development and validation
O&M conceptual model and XML encoding
Developed in the context of
XMML Geochemistry/Assay data
OGC Sensor Web Enablement – environmental and remote sensing
Subsequently applied in
Water resources/water quality (WQDP, AWDIP, WRON)
Oceans & Atmospheres (UK CLRC, UK Met Office)
Natural resources (NRML)
Taxonomic data (TDWG)
Geology field data (GeoSciML)
I could have put dozens of logos down here
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Status
OGC Best Practice paper, r4 – 2006
OGC RFC ends 2007-03-08 (tomorrow!)
OGC Adopted Specification – mid/late 2007?
ISO specification – 2008-9?
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Conclusions
Different viewpoints of same information for different purposes
Summary vs. analysis
Some values are determined by observation
Sometimes the description of the estimation process is necessary
Transformation between views important
Management of observation evidence can be integrated
(Bryan Lawrence issues)
For rich data processing, rich data models are needed
Explicit or implicit
Data models (types, features) are important constraints on service specification
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www.csiro.au
Thank You
CSIRO Exploration and Mining
Name Simon Cox
Title Research Scientist
Phone +61 8 6436 8639
Email [email protected]
Web www.seegrid.csiro.au
Contact CSIRO
Phone 1300 363 400
+61 3 9545 2176
Email [email protected]
Web www.csiro.au
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Features, Coverages & Observations (1)
Observations and Features
An observation provides evidence for estimation of a property value for the feature-of-interest
Features and Coverages (1)
The value of a property that varies on a feature defines a coverage whose domain is the feature
Observations and Coverages (1)
An observation of a property sampled at different times/positions on a feature-of-interest estimates a discrete coverage whose domain is the feature-of-interest
feature-of-interest is one big feature – property value varies within it
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Features, Coverages & Observations (2)
Observations and Features
An observation provides evidence for estimation of a property value for the feature-of-interest
Features and Coverages (2)
The values of the same property from a set of features constitutes a discrete coverage over a domain defined by the set of features
Observations and Coverages (2)
A set of observations of the same property on different features provides an estimate of the range-values of a discrete coverage whose domain is defined by the set of features-of-interest
feature-of-interest is lots of little features – property value constant on each one
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premises:
O&M is the high-level information model
SOS is the primary information-access interface
SOS can serve:
an Observation (Feature)
getObservation == “getFeature” (WFS/Obs) operation
a feature of interest (Feature)
getFeatureOfInterest == getFeature (WFS) operation
or Observation/result (often a time-series == discrete Coverage)
getResult == “getCoverage” (WCS) operation
or Sensor == Observation/procedure (SensorML document)
describeSensor == “getFeature” (WFS) or “getRecord” (CSW) operation
Sensor service
optional – probably required for dynamic sensor use-cases
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SOS vs WFS, WCS, CS/W?
WFS/Obs
getFeature, type=Observation
WCS
getCoverage
getCoverage(result)
Sensor Registry
getRecord
SOS
getObservation
getResult
describeSensor
getFeatureOfInterest
WFSgetFeature
SOS interface is effectively a composition of (specialised) WFS+WCS+CS/W operations
e.g. SOS::getResult == “convenience” interface for WCS
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ISO 19101, 19109 General Feature Model
Properties include
attributes
associations between objects
value may be object with identity
operations
Metaclass diagram
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class Fig 03 - CV_Cov erage subclasses
CV_GeometryValuePair{n}
+ geometry: CV_DomainObject+ value: Record
«Abstract»CV_ValueObject
{n}
+ geometry: CV_DomainObject+ interpolationParameters[0..1]: Record
+ interpolate(DirectPosition) : Record
Discrete Cov erages::CV_DiscreteCov erage{n}
+ locate(DirectPosition) : Set<CV_GeometryValuePair>
«Abstract»CV_ContinuousCoverage
{n}
+ interpolationParametersType[0..1]: Record+ interpolationType: CV_InterpolationMethod
+ locate(DirectPosition) : Set<CV_ValueObject>+ locateRegion(GM_Object) : Set<CV_ValueObject>
«CodeList»CV_InterpolationMethod
{n}
+ barycentric: + bicubic: + bi l inear: + biquadratic: + cubic: + l inear: + lostarea: + nearestneighbor: + quadratic:
«Abstract»CV_Coverage
{n}
+ commonPointRule: CV_CommonPointRule+ domainExtent[1..*]: EX_Extent+ rangeType: RecordType
+ evaluate(DirectPosition, Sequence<CharacterString>) : Record+ evaluateInverse(Record) : Set<CV_DomainObject>+ find(DirectPosition, Integer) : Sequence<CV_GeometryValuePair>+ l ist() : Set<CV_GeometryValuePair>+ select(GM_Object, TM_Period) : Set<CV_GeometryValuePair>
geometry implements the association Domain in Figure 2value implements the association Range in Figure 2
+extension
0..*Control
+controlValue
1..*
+col lection 0..*
CoverageFunction
+element 1..*
+col lection 0..*
CoverageFunction
+element 1..*
ISO 19123 Coverage model
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«DataType»CV_GeometryValuePair
+ geometry: CV_DomainObject+ value: Record
CV_Coverage
CV_DiscreteCov erage
«DataType»CV_PointValuePair
+ geometry: GM_Point
CV_DiscretePointCov erage
+element 1..*
+collection 0..*
+collection 0..*
+element 1..*
«DataType»CV_GeometryValuePair
+ geometry: CV_DomainObject+ value: Record
CV_Coverage
CV_DiscreteCov erage
«DataType»CV_PointValuePair
+ geometry: GM_Point
CV_DiscretePointCov erage CV_DiscreteTimeInstantCov erage
«DataType»CV_TimeInstantValuePair
+ geometry: TM_Instant
+element 1..*
+collection 0..*
+collection 0..*
+element 1..* +element 1..*
+collection 0..*
Discrete coverage model
«DataType»CV_GeometryValuePair
+ geometry: CV_DomainObject+ value: Record
CV_Coverage
CV_DiscreteCov erage
+element 1..*
+collection 0..*
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Value estimation process: observation
An Observation is a kind of “Event Feature type”, whose result is a value estimate,
and whose other properties provide metadata concerning the estimation process
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«FeatureType»Observ ation
+ quality: DQ_Element [0..1]+ responsible: CI_ResponsibleParty [0..1]+ result: Any
«FeatureType»Event
+ eventParameter: TypedValue [0..*]+ time: TM_Object
«DataType»TypedValue
+ property: ScopedName+ value: Any
«Union»Procedure
+ procedureType: ProcedureSystem+ procedureUse: ProcedureEvent
AnyIdentifiableObject
«FeatureType»AnyIdentifiableFeature
AnyDefinition
«ObjectType»Phenomenon
+followingEvent 0..*+precedingEvent 0..*
+generatedObservation
0..*
+procedure 1
+observedProperty1{Definition must be of aphenomenon that is a propertyof the featureOfInterest}
+propertyValueProvider
0..*
+featureOfInterest
1
Observation model – Value-capture-centric view
An Observation is an Event whose result is an estimate of the value of some Property of the Feature-of-interest, obtained using a specified Procedure
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“Cross-sections” through collections
Specimen Au (ppm) Cu-a (%) Cu-b (%) As (ppm) Sb (ppm)
ABC-123 1.23 3.45 4.23 0.5 0.34 A Row gives properties of one feature
A Column = variation of a single property across a domain (i.e. set of features)
A Cell describes the value of a single property on a feature, often obtained by observation or measurement
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Feature of interest
may be any feature type from any domain-model …
observations provide values for properties whose values are not asserted
i.e. the application-domain supplies the feature types
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SamplingFeature
Specimen
+ currentLocation: Location [0..1]+ mass: Measure+ material: CV_Coverage
SamplingFeature
Specimen
+ currentLocation: Location [0..1]+ mass: Measure+ material: CV_Coverage
Observation
Measurement
+ result: RelativeMeasure
Observation
Cov erageObserv ation
+ result: CV_DiscreteCoverage
Mass :Phenomenon
Material :Phenomenon
+observedProperty
+propertyValueProvider
+featureOfInterest
+observedProperty
+propertyValueProvider
+featureOfInterest
Observations support property assignment
These must match if the observation is coherent with the feature property
Some properties have interesting types …
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SEEGrid Observations, 2006-11-30 40 of 22
Observations and coverages
If the property value is not constant across the feature-of-interest
varies by location, in time
the corresponding observation result is a coverage
individual samples must be tied to the location within the domain, so result is set of e.g.
time-value
position-value
(stationID-value ?)
Time-series observations are a particularly common use-case