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

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Presentation on theme: "From GIS-20 to GIS-21: The New Generation Gilberto Câmara, INPE, Brazil Master Class at ITC, September 2008."— Presentation transcript:

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

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

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

6 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 Global Earth Observation System of Systems

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 19861975 1992 INPE´s R&D agenda in Geoinformatics: modelling change

11 Slides from LANDSAT Aral Sea Bolivia 1975 19922000 197319872000 source: USGS Geoinformatics and Change: A Research Programme Understanding how humans use space Predicting changes resulting from human actions Modeling the interaction between society and nature

12 Spatial segregation indexesRemote sensing image mining GI software: SPRING and TerraView Land change modelling INPE´s strong point: a combination of problem-driven GI research and engineering

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 Engineers study in order to build

15 What set of concepts drove GIS-20? Map-based (cartographical user interfaces) Toblerian spaces (regionalized data analysis) Object-oriented modelling and spatial reasoning Spatial databases (vectors and images)

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, “Handling Complexity in GIS Interface Design”. I Brazilian Symposium in Geoinformatics, GeoInfo 1999. 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: An Introduction to Spatial Data Analysis”, Geographical Analysis, 2006. R Bivand, E Pebesma, V Gómez-Rubio, “Applied Spatial Data Analysis with R”. Springger-Verlag, 2008. SPRING´s Geostatistics ModuleGeoDA: Spatial data analysis

19 GIS-20: Object-oriented modelling 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) Geo-object Cadastral Coverage Spatial database Categorical Geo-field Numerical Is-a contains

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, GeoInfo 2003. 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 Large Environmental Database Developed on TerraLib and PostgreSQL

21 augmented reality sensor networks mobile devices GIS-21 ubiquitous images and maps Data-centered, mobile-enabled, contribution-based, field-based modelling

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: Integration of Functional Programming and Spatial 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 “Remotely sensed images are ontologically instruments for capturing landscape dynamics” M. Silva, G.Câmara, M.I. Escada, R.C.M. Souza, “Remote Sensing Image Mining: Detecting Agents of Land Use Change in Tropical Forest Areas”. International Journal of Remote Sensing, vol 29 (16): 4803 – 4822, 2008.

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

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

28 GIS-21: Networks as enablers of human actions Bus traffic volume in São PauloInnovation network in Silicon Valley Ana Aguiar, Gilberto Câmara, Ricardo Souza, “Modeling Spatial Relations by Generalized Proximity Matrices”. GeoInfo 2003

29 Consolidated area GIE-21: Network-based analysis Emergent area Modelling beef chains 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? measure (s,t) = v s ⋲ S - set of locations in space t ⋲ T - is the set of times. v ⋲ V - set of values Basic spatio-temporal types S: set of locations (space) T: set of intervals (time) I: set of identifiers (objects) V: set of values (attributes)

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

33 Slides from LANDSAT Aral Sea Bolivia snap (1973) 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. snap (1987)snap (2000) 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 life : I  (T  (S,V)) the life of an object in space-time exist: T  (I  (S,V)) objects alive in a time T

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

43 f ( I t+n ). FF f (I t )f (I t+1 )f (I t+2 ) 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

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