1. www.csiro.au Managing different views of data Simon Cox CSIRO Exploration and Mining 29 November 2006

2. 2 of 24 Observations, Features and Coverages Outline OGC/ISO meta-models for information objects  Features and coverages Property estimation events  Observations Transforming viewpoints

3. 3 of 24 Observations, Features and Coverages Conceptual object model: features Digital objects correspond with identifiable, typed, objects 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 ……

4. 4 of 24 Observations, Features and Coverages ISO 19101, 19109 General Feature Model Properties include  attributes  associations between objects  value may be object with identity  operations Metaclass diagram

5. 5 of 24 Observations, Features and Coverages 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)  …

6. 6 of 24 Observations, Features and Coverages Water resources feature type catalogue Aquifer Storage Stream Well Entitlement Observation …

7. 7 of 24 Observations, Features and Coverages Meteorology feature type catalogue Front Jetstream Tropical cyclone Lightning strike Pressure field Rainfall distribution … Bottom two are a different kind of feature

8. 8 of 24 Observations, Features and Coverages Spatial function: coverage (x 1,y 1 ) (x 2,y 2 ) 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

9. 9 of 24 Observations, Features and Coverages ISO 19123 Coverage model

10. 10 of 24 Observations, Features and Coverages Discrete coverage model

11. 11 of 24 Observations, Features and Coverages 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

12. 12 of 24 Observations, Features and Coverages “Cross-sections” through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC-1231.233.454.230.50.34 A Row gives properties of one feature A Column = variation of a single property across a domain (i.e. set of locations)

13. 13 of 24 Observations, Features and Coverages 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

14. 14 of 24 Observations, Features and Coverages 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

15. 15 of 24 Observations, Features and Coverages 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

16. 16 of 24 Observations, Features and Coverages “Cross-sections” through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC-1231.233.454.230.50.34 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

17. 17 of 24 Observations, Features and Coverages 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

18. 18 of 24 Observations, Features and Coverages Observations support property assignment These must match if the observation is coherent with the feature property Some properties have interesting types …

19. 19 of 24 Observations, Features and Coverages 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

20. 20 of 24 Observations, Features and Coverages 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

21. 21 of 24 Observations, Features and Coverages Observations, features and coverages Feature summary Property-value evidence Multiple observations one feature, different properties: feature summary evidence A property-value may be a coverage Same property on multiple samples is a another kind of coverage Multiple observations different features, one property: coverage evidence

22. 22 of 24 Observations, Features and Coverages 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

23. 23 of 24 Observations, Features and Coverages 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

24. 24 of 24 Observations, Features and Coverages Conclusions Feature and coverage viewpoints used for different purposes  Summary vs. analysis Some values are determined by observation  Sometimes the description of the estimation process is necessary Transformation between feature and coverage views depends on the “feature-type” Management of observation evidence depends on feature-of- interest-type  One big feature, with internal variation, vs  Aggregation of many small features

25. www.csiro.au Thank You CSIRO Exploration and Mining NameSimon Cox TitleResearch Scientist Phone+61 8 6436 8639 EmailSimon.Cox@csiro.au Webwww.seegrid.csiro.au Contact CSIRO Phone1300 363 400 +61 3 9545 2176 Emailenquiries@csiro.au Webwww.csiro.au

26. 26 of 24 Observations, Features and Coverages 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

27. 27 of 24 Observations, Features and Coverages SOS vs WFS, WCS, CS/W? WFS/ Obs getFeature, type=Observation WCS getCoverage getCoverage (result) Sensor Registry getRecord SOS getObservation getResult describeSensor getFeatureOfInterest WFS getFeature SOS interface is effectively a composition of (specialised) WFS+WCS+CS/W operations e.g. SOS::getResult == “convenience” interface for WCS

28. 28 of 24 Observations, Features and Coverages Some feature types only exist to support observations

29. 29 of 24 Observations, Features and Coverages Observation model Generic Observation has dynamically typed result

30. 30 of 24 Observations, Features and Coverages Observation specializations Override result type

31. 31 of 24 Observations, Features and Coverages Observation specializations Override result type Primary use-case for “CommonObservation” matches “CoverageObservation”  N.B. CommonObservation is an implementation

32. 32 of 24 Observations, Features and Coverages Observations and Features An estimated value is determined through observation i.e. by application of an observation procedure

33. 33 of 24 Observations, Features and Coverages Invariant property values: cross-sections through collections SpecimenAu (ppm) Cu-a (%)Cu-b (%)As (ppm)Sb (ppm) ABC-1231.233.454.230.50.34 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

34. 34 of 24 Observations, Features and Coverages Variable property values Each property value is either  constant on the feature instance  e.g. name, identifier  non-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

35. 35 of 24 Observations, Features and Coverages Observations support property assignment