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

Gilberto Câmara, INPE, Brazil

Master Class at ITC, September

2008

First, let´s look at the big picture

LBA tower in Amazonia

source: IGBP

How is the Earth’s environment changing, and what are the consequences for human civilization?

The fundamental question of our time

sources: IPCC and WMO

Impacts of global environmental changeBy 2020 in Africa, agriculture yields could be cut by up to

50%

Global Change

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

Terrestrial

Airborne

Near-Space

LEO/MEO Commercial Satellites and Manned Spacecraft

Far-Space

L1/HEO/GEO TDRSS & CommercialSatellites

Dep

loyab

le

Perm

an

en

t

Forecasts & Predictions

Aircraft/Balloon Event Tracking and Campaigns

User Community

Vantage Points

Capabilities

Global Earth Observation System of

Systems

Earth observation satellites and geosensor webs provide key

information about global change…

…but that information needs to be modelled and extracted

How does INPE´s research in Geoinformatics fits in the big picture?

LBA tower in Amazonia

Geoinformatics enables crucial links between nature and society

Nature: Physical equations Describe processes

Society: Decisions on how to Use Earth´s resources

19861975

1992

INPE´s R&D agenda in Geoinformatics: modelling

change

Slides from LANDSAT

Aral Sea

Bolivia 1975 1992 2000

1973 1987 2000

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

Spatial segregation indexes

Remote sensing image mining

GI software: SPRING and TerraView

Land change modelling

INPE´s strong point: a combination of problem-driven GI research and

engineering

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.”

Scientists and EngineersPhoto 51(Franklin, 1952)

Scientists build in order to study

Engineers study in order to build

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)

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

GIS-20: Map-like User interfacesJackson, 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)

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 Module

GeoDA: Spatial data analysis

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 Is-a

contains contains

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

augmented reality

sensor networks

mobile devices

GIS-21

ubiquitous images and maps

Data-centered, mobile-enabled, contribution-based, field-based modelling

GIS-21: Functional Programming Frank, A. (1999). One Step up the Abstraction Ladder: Combining Algebras – From Functional Pieces to a Whole. COSIT 99S. 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

GIS-21: Mobile ObjectsR.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

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)

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.

GIS-21: Dynamical spatial modellingwith 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

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

Eclipse & LUA plugin• model description• model highlight syntax

TerraView• data acquisition• data visualization• data management• data analysis

TerraLibdatabase

da

ta

Model source code

MODEL DATA

mod

el

• model syntax semantic checking• model execution

TerraME INTERPRETER

LUA interpreter

TerraME framework

TerraME/LUA interface

model d

ata

GIS-21: Networks as enablers of human actionsBus traffic volume in São

PauloInnovation network in Silicon

Valley

Ana Aguiar, Gilberto Câmara, Ricardo Souza, “Modeling Spatial Relations by Generalized Proximity Matrices”. GeoInfo 2003

Consolidated area

GIE-21: Network-based analysis

Emergent area

Modelling beef chains in Amazonia

Modelling change…from practice to theory

Outiline of a theory for change modelling in

geospatial data

What is a geo-sensor?What is a geo-sensor?

measure (s,t) = vs ⋲ S - set of locations in spacet ⋲ T - is the set of times. v ⋲ V - set of values

Basic spatio-temporal typesS: set of locations (space)T: set of intervals (time)I: set of identifiers (objects)V: set of values (attributes)

What is a geo-sensor?What is a geo-sensor?

measure (s,t) = vs ⋲ S - set of locations in spacet ⋲ T - is the set of times. v ⋲ V - set of values

Field (static)field : SVThe function field gives the value of every location of a space

Slides from LANDSATAral Sea

Bolivia

snap (1973)

Time-varying fields are modelled by snapshots

snap : T Fieldsnap : 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)

Sensors: sources of continuous information

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

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

Evolving (modifiable) object

life: I (T (S,V))

The function life produces the evolution of a modifiable object

S1

P0

S1

P0

P2

S1

P0

P2

P3

A life´s trajectory

life : I ⟶(T⟶(S,V))The life of the object is also a trajectory

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

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 modelResolution e.g 10 x 10 km

Hydrology, VegetationSoil Topography (e.g, 1 x 1 km)

Regional land use changeSocio-economic changesAdaptative responses (e.g., 10 x 10 m)

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

A model for time-varying geospatial data....

Temporal entity

T-field (coverage set)

T-objecthist(oi)

(feature)

snap(t)(coverage [t])

Featureinstance[t]

set

has-a

is-ais-a

has-a

location

has-a

T-fields have snapshots T-objects have histories

f ( It+n )

. . FF

f (It) f (It+1) f (It+2)

INPE´s vision for modelling changeCombine GI science and engineering to produce a new generation of dynamical models integrated in a spatio-temporal database