How can GIScience contribute to land change modelling?

Gilberto CâmaraDirector, National Institute for Space Research, Brazil

GIScience 2006, Munster, Germany

Motivation

Let’s start from a real problem….

Building a road in the Amazon rain forest

Área de estudo – ALAP BR 319 e entorno

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

Portos

new road

Source: Carlos Nobre (INPE)

Can we avoid that this….

Fire...

Source: Carlos Nobre (INPE)

….becomes this?

Amazonia Deforestation rate 1977-2004

Annual deforestation rate

0

5000

10000

15000

20000

25000

30000

35000

77/8

8 *

88/8

9

89/9

0

90/9

1

91/9

2

92/9

4 **

94/9

5

95/9

6

96/9

7

97/9

8

98/9

9

99/0

0

00/0

1

1/fe

v

02/0

3(*)

03/0

4(*)

Period

km

2 /ye

ar

?

BASELINE SCENARIO – Hot spots of change (1997 a 2020)

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

0.0 – 0.10.1 – 0.20.2 – 0.30.3 – 0.40.4 – 0.50.5 – 0.60.6 – 0.70.7 – 0.80.8 – 0.90.9 – 1.0

% mudança 1997 a 2020:

GOVERNANCE SCENARIO – Differences from baseline scenario

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

0.0 -0.50Less:0.0 0.10More:

Differences:Protection areas

Sustainable areas

“Give us some new problems” (Dimitrios Papadias, SSTD 2005)

“Give us some new problems”

What about saving the planet?

The fundamental question

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

Source: NASA, IGBP

GIScience and change

We need a vision for extending GIScience to have a research agenda for modeling change

The Greek vision of spatial dataEuclid (x + y)2 = x2 + 2xy + y2

The Greek vision of spatial dataEuclid

Egenhofer

(x + y)2 = x2 + 2xy + y2

spatial topology

The Greek vision of spatial dataAristotle categories -

The Greek vision of spatial dataAristotle

Smith

categories -

SPAN ontologies

A challenge to GIScience

Time has come to move from Greece to the Renaissance!

The Renaissance Vision

“No human inquiry can be called true science unless it proceeds through mathematical demonstrations” (Leonardo da Vinci)

“Mathematical principles are the alphabet in which God wrote the world” (Galileo)

The Renaissance vision for space

Rules and laws that enable:

Understanding how humans use space;

Predicting changes resulting from human actions;

Modeling the interaction between humans and the environment.

The Renaissance visionKepler

The Renaissance visionKepler

Frank

The Renaissance visionGalileo

The Renaissance visionGalileo

Batty

Challenge: How do people use space?

Loggers

Competition for Space

Soybeans

Small-scale Farming Ranchers

Source: Dan Nepstad (Woods Hole)

Statistics: Humans as clouds

Establishes statistical relationship with variables that are related to the phenomena under study

Basic hypothesis: stationary processes Exemples: CLUE Model (University of

Wageningen)

y=a0 + a1x1 + a2x2 + ... +aixi +E

Statistics: Humans as cloudsMODEL 7: R² = .86

Variables Description stb p-level

PORC3_ARPercentage of large farms, in terms of area 0,27 0,00

LOG_DENS Population density (log 10) 0,38 0,00

PRECIPIT Avarege precipitation -0,32 0,00

LOG_NR1Percentage of small farms, in terms of number (log 10) 0,29 0,00

DIST_EST Distance to roads -0,10 0,00

LOG2_FER Percentage of medium fertility soil (log 10) -0,06 0,01

PORC1_UC Percantage of Indigenous land -0,06 0,01

Statistical analysis of deforestation

The trouble with statistics

Extrapolation of current measured trends

How do we know if tommorow will be like today?

How do we incorporate feedbacks?

Agents and CA: Humans as ants

Old Settlements(more than 20 years)

Recent Settlements(less than 4

years)

Farms

Settlements 10 to 20 anos

Source: Escada, 2003

Identify different actors and try to model their actions

Agent model using Cellular Automata

1985

1997 1997Large farm environments:

2500 m resolution

Continuous variable:% deforested

Two alternative neighborhood relations:•connection through roads• farm limits proximity

Small farms environments:

500 m resolution

Categorical variable: deforested or forest

One neighborhood relation: •connection through roads

The trouble with agents

Many agent models focus on proximate causes directly linked to land use changes (in the case of deforestation, soil type, distance

to roads, for instance)

What about the underlying driving forces? Remote in space and time Operate at higher hierarchical levels Macro-economic changes and policy changes

Underlying Factorsdriving proximate causes

Causative interlinkages atproximate/underlying levels

Internal drivers

*If less than 5%of cases,not depicted here.

source:Geist &Lambin

5% 10% 50%

% of the cases

What Drives Tropical Deforestation?

Humans are not clouds nor ants!

“Third culture” Modelling of physical phenomena Understanding of human dimensions

How to model human actions? What makes people do certain things? Why do people compete or cooperate? What are the causative factors of human

actions?

Some promising approaches

Hybrid automata

Flexible neighbourhoods

Nested cellular automata

Game theory

Hybrid Automata

Formalism developed by Tom Henzinger (UC Berkeley)

Combines discrete transition graphs with continous dynamical systems

Infinite-state transition system

Control Mode A

Flow Condition

Control Mode B

Flow Condition

Jump condition

Event

Flexible neighbourhoods

Consolidated area Emergent area

Nested Cellular Automata

UU

U

Environments can be nested

Space can be modelled in different resolutions

Multiscale modelling

Game theory and mobility

Two players get in a strive can choose shoot or not shoot their firearms.

If none of them shoots, nothing happens. If only one shoots, the other player runs

away, and then the winner receives $1. If both decide to shoot, each group pays

$10 due to medical cares.

B shoots B does not shoot

A shoots (-10,-10) (+1,-1)

A does not shoot (-1,+1) (0,0)

Game theory and mobility

A - ((10%;; $200; 0)B - ((50%;; $200; 0)C - ((100%;; $200;; 0))

Three strategies

Game theory and mobility

What happens when players can move?

If a player loses too much, he might move to an adjacent cell

Mobility breaks the Nash equilibrium!

The big challenge: a theory of scale

Scale

Scale is a generic concept that includes the spatial, temporal, or analytical dimensions used to measure any phenomenon.

Extent refers to the magnitude of measurement.

Resolution refers to the granularity used in the measures.

(Gibson et al. 2000)

Multi-scale approach

The trouble with current theories of scale

Conservation of “energy”: national demand is allocated at local level

No feedbacks are possible: people are guided from the above

The search for a new theory of scale

Non-conservative: feedbacks are possible Linking climate change and land change Future of cities and landscape integrate to

the earth system

Atmospheric Physics/Dynamics

Tropospheric Chemistry

Global Moisture

Ocean Dynamics

MarineBiogeochemistry

Terrestrial Ecosystems

Terrestrial Energy/Moisture

Climate Change

Pollutants

CO2

CO2

Soil

Land Use

Physical Climate System

Biogeochemical Cycles

Human Activities

(from Earth System Science: An Overview, NASA, 1988)

Earth as a system

Global Land Project•What are the drivers and

dynamics of variability and change in terrestrial human-environment systems?

•How is the provision of environmental goods and services affected by changes in terrestrial human-environment systems?

•What are the characteristics and dynamics of vulnerability in terrestrial human-environment systems?

The Renaissance visionNewton Principia

The Renaissance vision

Your picture here

Newton

????

Principia

Multiscale theory of space

Why is it so hard to model change?

source: John Barrow(after David Ruelle)

Complexity of the phenomenon

Un

cert

ain

ty o

n b

asic

eq

uati

on

s

Solar System DynamicsMeteorology

ChemicalReactions

HydrologicalModels

ParticlePhysics

Quantum Gravity

Living Systems

GlobalChange

Social and EconomicSystems

Towards a research agenda

Moving GIScience from Greece to the Renaissance….

GIScience – Formal and mathematical tools for dealing with space

GIScience tools are crucial for supporting earth system science

We have a lot of challenges ahead of us!!

References

Max Egenhofer Egenhofer, M., Franzosa, R.: Point-Set Topological

Spatial Relations. International Journal of Geographical Information Systems, 5 (1991) 161-174.

Egenhofer, M., Franzosa, R.: On the Equivalence of Topological Relations. International Journal of Geographical Information Systems, 9 (1995) 133-152.

Egenhofer, M., Mark, D.: Naive Geography. In: Frank, A., Kuhn, W.(ed.): Spatial Information Theory—A Theoretical Basis for GIS, International Conference COSIT '95, Semmering, Austria. Springer-Verlag, Berlin (1995) 1-15.

References

Barry Smith Smith, B., Mark, D.: Ontology and Geographic

Kinds. In: Puecker, T., Chrisman, N. (ed.): International Symposium on Spatial Data Handling. Vancouver, Canada (1998) 308-320.

Smith, B., Varzi, A.: Fiat and Bona Fide Boundaries. Philosophy and Phenomenological Research, 60 (2000).

Grenon, P., Smith, B.: SNAP and SPAN: Towards Dynamic Spatial Ontology. Spatial Cognition & Computation, 4 (2003) 69-104.

References

Andrew Frank Frank, A.: One Step up the Abstraction Ladder:

Combining Algebras - From Functional Pieces to a Whole. In: Freksa, C., Mark, D. (ed.): COSIT 1990- LNCS 1661. Springer-Verlag (1999) 95-108.

Frank, A.: Higher order functions necessary for spatial theory development. In: Auto-Carto 13 Vol. 5. ACSM/ASPRS, Seattle, WA (1997) 11-22.

Frank, A.: Ontology for Spatio-temporal Databases. In: Koubarakis, M., Sellis, T.(ed.): Spatio-Temporal Databases: The Chorochronos Approach. Springer, Berlin (2003) 9-78.

References

Mike Batty Batty, M. Cities and Complexity:

Understanding Cities Through Cellular Automata, Agent-Based Models, and Fractals. The MIT Press, Cambridge, MA, 2005.

Batty, M.; Torrens, P. M. “Modelling and Prediction in a Complex World”. Futures, 37 (7), 745-766, 2005.

Batty, M. Xie, Y. Possible Urban Automata. Environment and Planning B, 24, 175-192, 1996.

References

INPE’s recent work (see www.dpi.inpe.br/gilberto) Almeida, C.M., Monteiro, A.M.V., Camara, G., Soares-

Filho, B.S., Cerqueira, G.C., Pennachin, C.L., Batty, M.: “Empiricism and Stochastics in Cellular Automaton Modeling of Urban Land Use Dynamics” Computers, Environment and Urban Systems, 27 (2003) 481-509.

Ana Paula Dutra de Aguiar, “Modeling Land Use Change in the Brazilian Amazon: Exploring Intra-Regional Heterogeneity”. PhD in Remote Sensing, INPE, 2006.

Tiago Garcia de Senna Carneiro, “"Nested-CA: A Foundation for Multiscale Modelling of Land Use and Land Cover Change”. PhD in Computer Science, INPE, 2006.