1. Describing change in the real world: from observations to events Gilberto Camara Karine Reis Ferreira Antonio Miguel Monteiro INPE – National Institute for Space Research AGILE Conference 2012, Avignon (France)

2. Useful References AU Frank, “One step up the abstraction ladder: combining algebras – from functional pieces to a whole”, COSIT 1999 RH Guting et al., “A foundation for representing and querying moving objects”, ACM Transactions on Database Systems, 2000 M Worboys, “Event-oriented approaches to geographic phenomena”, IJGIS, 2003 A Galton & R Mizoguchi, “The Water Falls but the Waterfall does not Fall: New Perspectives on Objects, Processes and Events”, Applied Ontology, 2009. W Kuhn, “A Functional Ontology of Observation and Measurement”, GeoS 2009.

3. Terrestrial Airborne Near- Space LEO/MEO Commercial Satellites and Manned Spacecraft Far- Space L1/HEO/GEO TDRSS & Commercial Satellites Deployable Permanent Forecasts & Predictions Aircraft/Balloon Event Tracking and Campaigns User Community Vantage Points Capabilities Welcome to the Age of Data-intensive GIScience!

4. Data-intensive GIS = principles and applications of geoinformatics for handling very large data sets

5. Which data is out there? How to organize big spatial data? How to get the data I need? Challenges for data-intensive GIScience How to model big data? How to access and use big data?

6. Data-intensive GIS is not “more maps” Spatio-temporal data that captures change We need new theories and methods

8. Objects and events The coast of Japan is an object The 2011 Tohoku tsunami was an event

9. Processes and events Flying is a process - Virgin flight VX 112 (LAX-IAD) on 26 Apr 2012 is an event

10. When did the Aral Sea shrank to 10% of its original size? Aral Sea (an object) – disaster (an event)

11. objects exist, events occur Mount Etna is an object Etna’s 2002 eruption was an event

12. A view on processes and events ObjectsEvents MatterProcesses SpaceTime Count Mass water or lake? football or game? (Worboys & Galton)

13. A pragmatic view on objects and events ObjectsEvents MatterProcesses SpaceTime Observable Abstract water or lake? football or game?

14. Object (GPS buoy) + event (tsunami)

15. Data types for moving objects (Guting) mpoint: instant → point mregion: instant → region Frank, Kuhn, Guting – algebras are better than 1 st order logic for modelling geo-things

16. Data types for moving objects (Guting) flight (id: string, from: string, to: string, route: mpoint) weather (id: string, kind: string, area: mregion)

17. Detecting flood (gauges in Netherlands) Source: Llaves and Renschler, AGILE 2012

18. Event processing architecture Source: ENVISION project (http://www.envision-project.eu/)

19. source: USGS Events are categories (Frank, Galton) identity : id · a = a composition : ∀ a, ∀ b, ∃ c, c = a.b associativity : a · (b · c) = (a · b) · c

20. How can we design an algebra for spatiotemporal data that represents change?

21. Observations allow us to sense external reality

22. An observation is a measure of a value in a location in space and a position in time

23. Building blocks: Basic Types type BASE = {Int, Real, String, Boolean} operations: // lots of them…

24. Building blocks: Geometry (OGC) type GEOM = {Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon} operations: equals, touches, disjoint, crosses, within, overlaps, contains, intersects: GEOM x GEOM → Bool

25. Building blocks: Time (ISO 19108) type TIME = {Instant, Period} operations: equals, before, after, begins, ends, during, contains, overlaps, meets, overlappedBy, metBy, begunBy, endedBy: TIME x TIME → Boolean

26. Observation data type type Obs [T: TIME, G: GEOMETRY, B: BASE] operations: new: T x G x B → Obs value: Obs → B geom: Obs → G time: Obs → T

27. From observations to events

28. Why do we need interpolators? How long do you take from Frankfurt to Beaune?

29. Why do we need interpolators? We cannot sample every location at every moment – we need to estimate in space-time

30. Sensors: water monitoring Brazilian Cerrado Wells observation 50 points 50 semimonthly time series (11 Oct 2003 – 06 March2007) Rodrigo Manzione, Gilberto Câmara, Martin Knotters

31. MAY JUNEJULY AUGUSTSEPTEMBER Estimates of water table depth for an area in Brazilian Cerrado Manzione, Câmara, Knotters

32. Three types of interpolators IntValueInTime [T: TIME, B: BASIC] estimate: {Obs} x T → B IntSpaceInTime [T: TIME, G: GEOM] estimate: {Obs} x T → G IntInSpaceTime [T: TIME, G: GEOM, B: BASIC] estimate: {Obs} x (T,G) → B

33. type STgen [T: TIME, G: GEOM, B: BASE] operations: getObs: ST → {Obs} begins, ends: ST → T boundary: ST → G after, before: ST x T → ST during: ST x Period → ST What do ST types have in common?

34. Time Series Continuous variation of a property value over time (water table depth sensors)

35. Time Series Type TimeSeries [T: TIME, B: BASE] uses ST operations: new: {Obs [T,S,B]} x IntValueInTime [T,B] → TimeSeries value: TimeSeries x T → B

36. Moving objects MOVING OBJECTS Objects whose position and extent change continuously

37. Moving objects individual entity that varies its location (and its extent) over time

38. Moving Object data type type MovingObject [T: TIME, G: GEOM] uses ST operations: new: {Obs [T,G,B]} x IntSpaceInTime [T,G] → MovingObject value: MovingObject x T → G

39. Moving Object data type distance: MovingObject x MovingObject → TimeSeries distance (mo 1, mo 2 ) { ObsSet oset for t = mo 1.begin(); t <= mo 1.end(); t.next() Point p 1 = mo 1.value (t) Point p 2 = mo 2.value (t) o 1 = new Obs (t, dist (p 1, p 2 )) oset.add (o 1 ) ts = new TimeSeries (oset) return ts }

40. How many walruses reached Baffin island?

41. source: USGS Coverage: T → G → B Multi-temporal collection of values in space. Two-dimensional grids whose values change Samples from fixed or moving geosensors.

42. source: USGS type Coverage [T: TIME, G 1 : GEOM, G 2 : GEOM, B: BASE] uses ST operations: new: {Obs [T, G 1, B} x IntInSpaceTime[T, G 1, B] x G 2 → Coverage value: Coverage x G 1 x T → B

43. Functions on coverages getWaterArea (Coverage cov, Time t) area = 0 forall g inside cov.boundary() if cov.value (g,t) == "water” area = area + g return area }

44. From a coverage to a time series

45. timeSeries: Coverage x S → TimeSeries timeSeries (c 1, loc) ObsSet oset for t = c 1.begin(); t <= c 1.end(); t.next() Real v = c 1.value (loc, t) o 1 = new Obs (t, loc, val) oset.add ( o 1 ) ts = new TimeSeries ( oset ) return ts }

46. When did the large flood occur in Angra? When precipitation was > 10mm/hour for 5 hours Coverage set (hourly precipitation grid)  Event (precipitation > 10 mm/hour for 5 hrs)

47. The event data type An event is an individual episode with a beginning and end, which define its character as a whole. An event does not exist by itself. Its occurrence is defined as a particular condition of one spatiotemporal type.

48. The event data type Type Event [T 1 : TIME, T 2 : TIME] uses ST operations: new: {ST x (T 1, T 2 ) → Event compose: Event x Event → Event intersect: Event x Event → Event

49. Exploração intensiva Floresta Perda >90% do dossel Corte raso Perda >50% do dossel time Event 1 Event composition Forest loss > 20% Floresta Loss > 90% Clear cut Loss > 50% Event 2 Event 3 Event 4

50. When did the large flood occur in Angra?

51. Coverage prec = getData (weather forecast) flood = new Event() from t0 = prec.begin(); t0 <= prec.end(); t.next() if getRain (prec, t0, t0 + 24) > 100 strong = new Event (prec, t0, t0 + 24) flood.compose (strong)

52. When did the Aral Sea shrank to 10% of its original size? getWaterArea (Coverage cov, Time t) area = 0 forall g inside cov.boundary() if cov.value (g,t) == "water” area = area + g return area }

53. When did the Aral Sea shrank to 10% of its original size? aralSea = new Coverage (images) findDisaster (aralSea) { t0 = aralSea.begin() areaOrig = getWaterArea (aralSea,t0) for t = aralSea.begin(); t <= aralSea.end(); t.next() if getWaterArea (aralSea,t) < 0.1* areaOrig disaster = new Event (aralSea, t, t.aralSea.end()) break return disaster }

54. From observations to events

55. TerraLib: spatio-temporal database as a basis for innovation Visualization (TerraView) Spatio-temporal Database (TerraLib) Modelling (TerraME) Data Mining(GeoDMA) Statistics (aRT)

56. GIS technology for big data

57. Algebras for spatio-temporal data are a powerful way of representing change