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

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
The fundamental question of our time
source: IGBP
How is the Earth’s environment
changing, and what are the
consequences for human
civilization?
Impacts of global environmental change
By 2020 in Africa, agriculture yields could be cut by up to 50%
sources: IPCC and WMO
Global Change
Where are changes taking place?
How much change is happening?
Who is being impacted by the change?
Permanent
Global Earth Observation
System of Systems
Vantage Points
Capabilities
FarSpace
L1/HEO/GEO
TDRSS &
Commercial
Satellites
LEO/MEO
Commercial
Satellites
and Manned
Spacecraft
NearSpace
Aircraft/Balloon
Event Tracking
and Campaigns
Deployable
Airborne
Terrestrial
Forecasts & Predictions
User
Community
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
1975
1992
1986
INPE´s R&D agenda in Geoinformatics:
modelling change
source: USGS
Slides from LANDSAT
Geoinformatics and Change: A Research
Programme
Aral Sea
1973
1987
Understanding how humans use space
2000
Predicting changes resulting from human actions
Modeling the interaction between society and nature
Bolivia
1975
1992
2000
Spatial segregation indexes
Remote sensing image mining
INPE´s strong point: a combination of
problem-driven GI research and engineering
GI software: SPRING and
Land change modelling
GI Engineering: from GIS-20 to GIS-21
Chemistry
Physics
Computer
Science
GI Science
Chemical Eng.
Electrical Eng.
Computer Eng.
GI Engineering
GI Engineering:= “The discipline of systematic construction of
GIS and associated technology, drawing on scientific
principles.”
Scientists and Engineers
Photo 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 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)
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)
Spatial
database
contains
contains
Coverage
Geo-field
Is-a
Geo-object
Cadastral
Is-a
Categorical Numerical
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
mobile devices
augmented reality
GIS-21
Data-centered, mobile-enabled, contribution-based,
field-based modelling
sensor networks
ubiquitous images and maps
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 
domain
:: cv a b 
num
:: cv a b 
values
:: cv a b 
a  Maybe b
[a]
Int
[b]
Geospatial data processing is a collection of types and functions
Functional programming allows rigorous development of GIS
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
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
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.
“Remotely sensed images are ontologically
instruments for capturing landscape dynamics”
GIS-21: Dynamical spatial modelling
with Agents in Cell Spaces
Tiago Garcia de Senna Carneiro, “"Nested-CA: A Foundation for Multiscale Modelling of
Land Use and Land Cover Change”. PhD Thesis, INPE, june 2006
Cell Spaces
Generalized Proximity Matrix – GPM
Hybrid Automata model
Nested scales
TerraME: Based on functional programming
concepts (second-order functions) to develop dynamical models
GIS-21: Dynamical modelling integrated
in a spatio-temporal database
TerraME INTERPRETER
• model syntax semantic checking
• model execution
TerraView
• data acquisition
• data visualization
• data management
• data analysis
LUA interpreter
TerraME framework
data
model
model
TerraME/LUA interface
MODEL DATA
Model
source code
TerraLib
database
data
Eclipse & LUA plugin
• model description
• model highlight syntax
GIS-21: Networks as enablers of human
actions
Bus traffic volume in São Paulo
Innovation network in Silicon Valley
Ana Aguiar, Gilberto Câmara, Ricardo Souza, “Modeling Spatial Relations by
Generalized Proximity Matrices”. GeoInfo 2003
GIE-21: Network-based analysis
Emergent area
Consolidated 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?
S:
T:
I:
V:
Basic spatio-temporal types
set of locations (space)
set of intervals (time)
set of identifiers (objects)
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
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
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)
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
P3
P0
P0
P2
P2
S1
S1
life: I

(T

P0
S1
(S,V))
The function life produces the evolution of a modifiable
object
A life´s trajectory
life : I ⟶(T⟶(S,V))
The life of the object is also a trajectory
Which objects are alive at time T
and 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 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)
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
A model for time-varying geospatial
data....
Temporal
entity
set
is-a
T-field
(coverage set)
is-a
T-object
hist(oi)
(feature)
has-a
snap(t)
(coverage [t])
has-a
has-a
Feature
instance[t]
location
T-fields have snapshots
T-objects have histories
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
f (It+2)
f ( It+n )
F
..