From GIS-20 to GIS-21: The New Generation

1. From GIS-20 to GIS-21: The New Generation Gilberto Câmara, INPE, Brazil Master Class at ITC, September 2008

2. First, let´s look at the big picture LBA tower in Amazonia

3. The fundamental question of our time How is the Earth’s environment changing, and what are the consequences for human civilization? source: IGBP

4. Impacts of global environmental change By 2020 in Africa, agriculture yields could be cut by up to 50% sources: IPCC and WMO

5. Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change?

6. Global Earth Observation System of Systems Capabilities Vantage Points L1/HEO/GEO TDRSS & Commercial Satellites Far-Space Permanent LEO/MEO Commercial Satellites and Manned Spacecraft Near-Space Aircraft/Balloon Event Tracking and Campaigns Airborne Deployable Terrestrial User Community Forecasts & Predictions

7. Earth observation satellites and geosensor webs provide key information about global change… …but that information needs to be modelled and extracted

8. How does INPE´s research in Geoinformatics fits in the big picture? LBA tower in Amazonia

9. Geoinformatics enables crucial links between nature and society Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources

10. 1975 1986 INPE´s R&D agenda in Geoinformatics: modelling change 1992

11. source: USGS Slides from LANDSAT Geoinformatics and Change: A Research Programme Understanding how humans use space Predicting changes resulting from human actions Modeling the interaction between society and nature Aral Sea 1973 1987 2000 Bolivia 1975 1992 2000

12. Spatialsegregation indexes Remotesensingimagemining INPE´s strong point: a combination of problem-driven GI research and engineering GI software: SPRING andTerraView Landchangemodelling

13. GI Engineering: from GIS-20 to GIS-21 Chemistry Chemical Eng. Physics Electrical Eng. Computer Computer Eng. Science GI Science GI Engineering GI Engineering:= “The discipline of systematic construction of GIS and associated technology, drawing on scientific principles.”

14. Scientists and Engineers Photo 51(Franklin, 1952) Scientists build in order to study Engineersstudy in order to build

15. What set of concepts drove GIS-20? Map-based (cartographicaluser interfaces) Toblerianspaces (regionalized data analysis) Object-orientedmodelling andspatialreasoning Spatial databases (vectorsandimages)

16. GIS-20: Topological Spatial Reasoning Egenhofer, M. and R. Franzosa (1991). "Point-Set Topological Spatial Relations." IJGIS 5(2): 161-174 OGC´s 9-intersection dimension-extended Open source implementations (GEOS) used in TerraLib

17. GIS-20: Map-like User interfaces Jackson, J. (1990) “Visualization of metaphors for interaction with GIS”. M.S. thesis, University of Maine. G. Câmara, R.Souza, A.Monteiro, J.Paiva, J.Garrido, “HandlingComplexity in GIS Interface Design”. I BrazilianSymposium in Geoinformatics, GeoInfo1999. Geographer´s desktop (1992) TerraView (2005)

18. GIS -20: Region-based spatial analysis MF Goodchild, “A spatial analytical perspective on GIS”. IJGIS, 1987 L Anselin, I Syabri, Y Kho, “GeoDa: AnIntroduction to Spatial Data Analysis”, GeographicalAnalysis, 2006. R Bivand, E Pebesma, V Gómez-Rubio, “AppliedSpatial Data Analysiswith R”. Springger-Verlag, 2008. SPRING´s Geostatistics Module GeoDA: Spatial data analysis

19. Coverage Geo-field GIS-20: Object-orientedmodelling G.Câmara, R.Souza, U.Freitas, J.Garrido, F. Ii. “SPRING: Integrating Remote Sensing and GIS with Object-Oriented Data Modelling. Computers and Graphics, vol.15(6):13-22, 1996. SPRING´s object-oriented data model (1995) ARCGIS´s object-centred data model (2002) Spatial database contains contains Geo-object Cadastral Is-a Is-a Numerical Categorical

20. GIS-20: Image and geospatial databases R.H. Güting, “An Introduction to Spatial Database Systems”. VLDB Journal, 1994. L Vinhas, RCM Souza, G Câmara, “Image Data Handling in Spatial Databases”. Brazilian Symposium in Geoinformatics, GeoInfo2003. G. Câmara, L. Vinhas, et al.. “TerraLib: An open-source GIS library for large-scale environmental and socio-economic applications”. In: B. Hall, M. Leahy (eds.), “Open Source Approaches to Spatial Data Handling”. Berlin, Springer, 2008. • TerraAmazon- A LargeEnvironmental Database Developedon TerraLib andPostgreSQL

21. mobiledevices augmented reality GIS-21 Data-centered, mobile-enabled, contribution-based, field-basedmodelling sensor networks ubiquitousimagesandmaps

22. GIS-21: Functional Programming Frank, A. (1999). One Step up the Abstraction Ladder: Combining Algebras – From Functional Pieces to a Whole. COSIT 99 S. Costa, G. Camara, D. Palomo, “TerraHS: IntegrationofFunctional Programming andSpatial Databases for GIS Application Development”, GeoInfo 2006. class Coverage cv where evaluate :: cv a b  a  Maybe b domain :: cv a b  [a] num :: cv a b  Int values :: cv a b  [b] Geospatial data processing is a collection of types and functions Functional programming allows rigorous development of GIS

23. GIS-21: Mobile Objects R.H. Güting and M. Schneider, “Moving Objects Databases.” Morgan Kaufmann Publishers, 2005. R.H. Güting, M.H. Böhlen, et al., “A Foundation for Representing and Querying Moving Objects”. ACM Transactions on Database Systems, 2000. source: Barry Smith

24. GIS-21: Spatio-temporal semantics P Grenon, B Smith, “SNAP and SPAN: Towards Dynamic Spatial Ontology”. Spatial Cognition and Computation, 2004. A Galton, “Fields and Objects in Space, Time, and Space-time”. Spatial Cognition and Computation, 2004. Different types of ST-objects (source: JP Cheylan)

25. GIS-21: Information Extraction from Images M. Silva, G.Câmara, M.I. Escada, R.C.M. Souza, “RemoteSensingImageMining: DetectingAgentsofLand Use Change in Tropical Forest Areas”. International Journal of Remote Sensing, vol 29 (16): 4803 – 4822, 2008. “Remotely sensed images are ontologically instruments for capturing landscape dynamics”

26. Cell Spaces GeneralizedProximityMatrix – GPM Hybrid Automata model Nested scales GIS-21: Dynamical spatial modellingwith Agents in Cell Spaces Tiago Garcia de SennaCarneiro, “"Nested-CA: A Foundation for MultiscaleModelling of Land Use and Land Cover Change”. PhD Thesis, INPE, june 2006 TerraME: Based on functional programming concepts (second-order functions) to develop dynamical models

27. GIS-21: Dynamical modelling integrated in a spatio-temporal database

28. GIS-21: Networks as enablersofhumanactions Bus traffic volume in São Paulo Innovation network in SiliconValley Ana Aguiar, Gilberto Câmara, Ricardo Souza, “ModelingSpatialRelationsbyGeneralizedProximityMatrices”. GeoInfo2003

29. GIE-21: Network-based analysis Emergentarea Consolidatedarea Modellingbeefchains in Amazonia

30. Modelling change…from practice to theory Outiline of a theory for change modelling in geospatial data

31. What is a geo-sensor? What is a geo-sensor? Basic spatio-temporal types S: set of locations (space) T: set of intervals (time) I: set of identifiers (objects) V: set of values (attributes) measure (s,t) = v s ⋲ S - set of locations in space t ⋲ T - is the set of times. v ⋲ V - set of values

32. What is a geo-sensor? What is a geo-sensor? Field (static) field : SV The function field gives the value of every location of a space measure (s,t) = v s ⋲ S - set of locations in space t ⋲ T - is the set of times. v ⋲ V - set of values

33. snap (1973) snap (1987) snap (2000) Aral Sea Slides from LANDSAT Time-varying fields are modelled by snapshots snap : T  Field snap : T  (S  V) The function snap produces a field with the state of the space at each time. Bolivia snap (1975) snap (1992) snap (2000)

34. Sensors: sources of continuous information

35. Sensors: water monitoring in Brazilian Cerrado • Wells observation • 50 points • 50 semimonthly time series • (11/10/03 – 06/03/2007) Rodrigo Manzione, Gilberto Câmara, Martin Knotters

36. Fixed sensors: time series (histories) Well 30 Well 40 Well 56 Well 57 hist: S (T  V) each sensor (fixed location) produces a time series

37. Evolving (modifiable) object life: I  (T  (S,V)) The function life produces the evolution of a modifiable object

38. A life´s trajectory life : I ⟶(T⟶(S,V)) The life of the object is also a trajectory

39. Which objects are alive at time T and where are they? exist : T ⟶ (I⟶(S,V))

40. Models: From Global to Local Athmosphere, ocean, chemistry climate model (resolution 200 x 200 km) Atmosphere only climate model (resolution 50 x 50 km) Regional climate model Resolution e.g 10 x 10 km Hydrology, Vegetation Soil Topography (e.g, 1 x 1 km) Regional land use change Socio-economic changes Adaptative responses (e.g., 10 x 10 m)

41. Models: From Global to Local snap: T  (S  V) evolution of a landscape hist: S  (T  V) History of a location exist: T (I  (S,V)) objects alive in a time T life : I  (T  (S,V)) the life of an object in space-time

42. A model for time-varying geospatial data.... set Temporal entity is-a is-a T-field (coverage set) T-object hist(oi) (feature) has-a snap(t) (coverage [t]) has-a Feature instance[t] has-a location T-fields have snapshots T-objects have histories

43. INPE´s vision for modelling change Combine GI science and engineering to produce a new generation of dynamical models integrated in a spatio-temporal database f (It) f (It+1) f (It+2) f ( It+n ) F F . .