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GEOMA LECTURES SERIES. DYNAMIC SIMULATION OF LAND USE CHANGE IN URBAN AREAS THROUGH STOCHASTIC METHODS AND CA MODELING. Cláudia Maria de Almeida.

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GEOMA LECTURES

SERIES

DYNAMIC SIMULATION

OF LAND USE CHANGE IN URBAN AREAS

THROUGH STOCHASTIC METHODS AND CA MODELING

Cláudia Maria de Almeida

Supervisors:Dr. Antonio Miguel Vieira Monteiro and Dr. Gilberto Câmara

12/2002


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DINAMICA DATA MODEL

Static Variables

technical infrastructure; social infrastructure;

real state market;

occupation density; etc.

Initial Land Use

Dynamic Variables

type of land use-neighbouring cells;

dist. to certain land uses.

P ij

P ik

Calculates Dynamic Variables

P jk

Calculates Spatial Transition Probability

Calculates Amount of Transitions

Transition

Iterations

Final Land Use

Source: Adapted from SOARES (1998).


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Forecast

tp + 10

Calibration

Calibration

LAND USE CHANGE MODELLING - TIME SERIES

tp - 20

tp - 10

tp


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MACRO STUDY AREA

Subdivision of Tietê Watershed

Downstream

Lower Midstream

Waterway Catchment Area

Tiete-Parana Waterway

Development Poles

São Paulo Metropolitan Region

Upper Midstream

Source: CESP, 2000

Upstream


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STUDY AREA

City Plan: Bauru - 1979

City Plan: Bauru - 1988


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Aerial Photo

Street Network

Main Roads

Railways

INTRAURBAN STRUCTURES

Bauru - 1979: Regional Development Pole - 305,753 inh., 2000


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INTRAURBAN STRUCTURES

Street Network

Main Roads

Railways

N/S - E/W Services Axes : Bauru - 1979


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INTRAURBAN STRUCTURES

Street Network

Main Roads

Railways

Central Commercial Triangle: Bauru - 1979


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INTRAURBAN STRUCTURES

Street Network

Main Roads

Railways

Northeastern Industrial Sector: Bauru - 1979


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INTRAURBAN STRUCTURES

Street Network

Main Roads

Railways

Residential Rings: Bauru - 1979


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STUDY AREA

179,823 inh. 232,005 inh.

Residential Use

Industrial Use

Services Use

Mixed Use

Commercial Use

Leis./Recreation

Institutional Use

Non-Urban Use

Initial and Final Land Use Maps: Bauru - 1979 and 1988


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Technical Infrastructure: the underlying or hard framework of a town, consisting of:

INTERVENING VARIABLES

- traffic and transport systems;

- energy supply systems;

- water supply and sewage disposal and treatment systems;

- telecommunications (e.g. radio, TV, internet connections);

- solid waste collection, disposal and treatment;

- cemiteries; etc.


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Social Infrastructure: the soft framework necessary for the functioning of a town, such as:

INTERVENING VARIABLES

- educational system;

- health care system;

- public institutions;

- religious institutions;

- security systems;

- commercial and services activities;

- sports facilities;

- leisure /entertainment and green areas systems;

- social care facilities (kindergartens, youth centres, retirement homes);

- cultural institutions (museums, libraries, cultural centres,etc.); etc.


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Odds

n

O {R M1 M2 M3 ...  Mn} = O {R} .  LSi

i=1

n

logit {R M1 M2 M3 ...  Mn} = logit {R} +  W+i

i=1

Logits

WEIGHTS OF EVIDENCE

Variáveis Endógenas (Lentas):


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EXPLORATORY ANALYSIS

Water Supply

Distances to Railways

Occupation Density

Commerce - Kernel Est.

Social Equipments

Distances to Industries


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Cross-Tabulation Map: 1979x1988

TRANSITION PROBABILITIES

Land Use 79

Land Use 88


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TRANSITION RATES

Transition Matrix:

Non-Urban

Residential

Commercial

Industrial

Institutional

Services

Mixed Zone

Leis./Recr.

Non-Urban

0,91713

0,06975

0

0,00953

0

0,00358

0

0

Residential

0

0,93798

0

0

0

0,05975

0,00226

0

Commercial

0

0

1,00000

0

0

0

0

0

Industrial

0

0

0

1,00000

0

0

0

0

0

Institutional

0

0

0

1,00000

0

0

0

Services

0

0

0

0

0

1,00000

0

0

Mixed Zone

0

0

0

0

0

0

1,00000

0

Leis./Recr.

0

0

0

0

0

0

0

1,00000


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non-urban_non-urban non-urban_residential residential_residential commercial_commercial non-urban_industrial industrial_industrial institutional_institutional non-urban_services residential_services services_services residential_mixed zone mixed zone_mixed zone leisure/recr._leisure/recr.

TRANSITION PROBABILITIES

Cross-Tabulation Map - Land Use Change in Bauru: 1979 x 1988


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previously urbanised areas remain. non-urb.areas/change to other uses non-urban_residential

TRANSITION PROBABILITIES

Table “Edit” and Map of Transition Non-Urban - Residential:


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TRANSITION PROBABILITIES

Partial Cross-Tabulation (“Ermatt”) and Numerical Output Table:


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TRANSITION PROBABILITIES

Input Data

Calculation of W+


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Natural logarithm of the ratio between the probability of occurring the event R and the probability of not occurring R, in face of the previous occurrence of evidences M1-Mn.

TRANSITION PROBABILITIES

Weights of Evidence:

n

logit {R M1 M2 M3 ...  Mn} = logit {R} +  W+i

i=1


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1 +

1 +

Similarity with the logistic function, where the sum of the products

between coefficients and variables is replaced by the sum of W+.

TRANSITION PROBABILITIES

Weights of Evidence (Dinamica - CSR/UFMG):

n

  W +x,y

i=1

Px,y{(n,r)/(S1…Sn)} = e

n

  W +x,y

i=1

1 + e

W+ = loge P {S/R}

P {S/R}

S = Water Supply (Evidence)

R = Non-Urb._Resid. (Event)


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MAP OF TRANSITION PROBABILITIES

Map of Probabilities

Map of Transition 79-88: nu_ind


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MAP OF TRANSITION PROBABILITIES

Map of Probabilities

Map of Transition 79-88: nu_serv


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MAP OF TRANSITION PROBABILITIES

Map of Probabilities

Map of Transition 79-88: nu_res


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MAP OF TRANSITION PROBABILITIES

Map of Probabilities

Map of Transition 79-88: res_serv


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MAP OF TRANSITION PROBABILITIES

Map of Probabilities

Map of Transition 79-88: res_mix


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MODEL CALIBRATION

Empirical procedure: visual comparative analysis


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water

mh_dens

soc_hous

com_kern

dist_ind

dist_res

per_res

dist_inst

exist_rds

serv_axes

plan_rds

per_rds

MODEL CALIBRATION

Final Sets of Evidence Maps for Each Type of Land Use Change:

NU_RES

NU_IND

NU_SERV

RES_SERV

RES_MIX


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Distances to Periph. Residential Settlements

Distances to Ranges of Commercial Clusters

Distances to Periph. Institutional Equipments

Distances to Main Roads

Distances to Peripheral Roads

EVIDENCES MAPS: NU_RES


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proximity to commercial activities clusters

accessibility to such areas

previous existence of residential settlements in the surroundings

EVIDENCES MAPS: NU_RES

POSSIBILITY OF EXTENDING BASIC INFRASTRUCTURE

NEED OF COMMUTING TO WORK IN CENTRAL AREAS

WITHIN REASONABLE DISTANCES


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Distances to the Services Axes

Distances to Industrial Use

EVIDENCES MAPS: NU_IND


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nearness to previously existent industrial use

availability of road access

EVIDENCES MAPS: NU_IND


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Distances to Residential Use

Distances to the Services Axes

Distances to Ranges of Commercial Clusters

EVIDENCES MAPS: NU_SERV


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proximity to clusters of commercial activities

closeness to areas of residential use

strategic location in relation to the N-S/ E-W services axes

EVIDENCES MAPS: NU_SERV

SUPPLIERS MARKET

CONSUMERS MARKET

ACCESSIBILITY TO MARKETS


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Distances to the Services Axes

Water Supply

EVIDENCES MAPS: RES_SERV


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takes place in nearly consolidated urban areas

strategic location in relation to the N-S/ E-W services axes

absence of water supply (ti)

EVIDENCES MAPS: RES_SERV

CLOSE TO SUPPLIERS AND CONSUMERS MARKETS

ACCESSIBILITY

IMMEDIATE FRINGE OF CONSOLIDATED URBAN AREAS


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HJFGH

Occurrence of Medium-High Density

Distances to Peripheral Roads

Distances to Planned Roads

Existence of Social Housing

EVIDENCES MAPS: RES_MIX


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mixed zones play the role of urban sub-centers

existence of a greater occupational gathering

nearness to planned or peripheral roads

EVIDENCES MAPS: RES_MIX

STRENGTHENING OF FORMER SECONDARY COMMERCIAL CENTRES

IMPLIES ECONOMIC SUSTAINABILITY

ACCESSIBILITY IN FARTHER AREAS


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economic theories of urban growth and change, where there is a continuous search for optimal location, able to assure:

competitive real state prices,

good accessibility conditions,

rationalization of transportation costs

or a strategic location in relation to suppliers and

consumers markets.

EVIDENCES MAPS: CONCLUSIONS

Land use transitions show to comply with


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Average Size

of Patches

Varianceof

Patches

Proportion of

“Expander”

Proportion of

“Patcher”

Number of

Iterations

Transitions

NU_IND

NU_SERV

RES_SERV

RES_MIX

nu_res

1100

500

0,65

0,35

5

nu_ind

320

1

1,00

0

5

nu_serv

25

2

0,50

0,50

5

res_serv

25

2

0,10

0,90

5

res_mix

35

2

0

1,00

5

MODEL CALIBRATION

Dinamica - Final Parameters:


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transitions from “i" to “j “,

only in the adjacent vicinities of

cells with state “j “.

TRANSITION ALGORITHMS

“Expander”:


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transitions from “i" to “j “,

only in the adjacent vicinities of

cells with a state other than “j “.

TRANSITION ALGORITHMS

“Patcher”:


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S 1

SIMULATIONS OUTPUTS

Reality - Bauru Land Use in 1988


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S 2

S 3

SIMULATIONS OUTPUTS

Reality - Bauru in 1988


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the services corridors, the industrial area and the mixed use zone were well modelled in all of the three simulations;

non-urban areas to residential use: the most challenging category for modelling, due to the fact that:

COMMENTS ON SIMULATIONS

- boundaries of detached residential settlements are highly unstable factors (merging/split of plots);

- exact areas for their occurrence is imprecise (landlords’ and entrepreneur’s decisions);

- 65% of this type of change is carried out through the expander, in which, after a random selection of a seed cell, its neighbouring cells start to undergo transitions regardless of their transition probability values.


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MODEL VALIDATION

Multiple Resolution Procedure or “Goodness of Fit” (Constanza, 1989)

SCENE 1

SCENE 2

REAL

SIMULATION

1 x 1 WINDOW

F = 1

2 x 2 WINDOW

F = 1 - 4/8 = 0.50

3 x 3 WINDOW

F = 1 - 6/18 = 0.66


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GOODNESS OF FIT

Multiple Resolution

Procedure:


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Simulations

Goodness of Fit (F)

F = 0.902937

S 1

S 2

F = 0.896092

S 3

F = 0.901134

MODEL VALIDATION

Results for Windows 3 x 3, 5 x 5 and 9 x 9:


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NEW TRENDS - CELL SPACE MODELS

regularity

irregularity

Structure

Space

uniformity

nonuniformity

Properties

stationarity

nonstationarity

Neighborhood

universality

nonuniversality

Transition function

regularity

nonregularity

Time

closed

open

System closure

Source: Couclelis, 1997


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incorporation of a 2-D and 3-D CS module in SPRING and TerraLib;

flexible multiscale and multipurpose device;

with both deterministic and stochastic transition algorithms.

irregular neighborhoods, multiattributes, and continuous states and time;

FUTURE WORK

Division for Image Processing of the Brazilian National Institute for Space Research (DPI-INPE)


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GEOMA LECTURES

SERIES

DYNAMIC SIMULATION

OF LAND USE CHANGE IN URBAN AREAS

THROUGH STOCHASTIC METHODS AND CA MODELING

Cláudia Maria de Almeida

Supervisors:Dr. Antonio Miguel Vieira Monteiro and Dr. Gilberto Câmara

12/2002


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