Multi-Agent Simulator for Urban Multi-Agent Simulator for Urban Segregation (MASUS)Segregation (MASUS)A Tool to Explore Alternatives for Promoting Inclusive A Tool to Explore Alternatives for Promoting Inclusive CitiesCities

Flávia F. Feitosa, Quang Bao Le, Paul L.G. Vlek

Center for Development Research (ZEF)University of Bonn

3rd ICA Workshop on Geospatial Analysis and Modeling, University of Gävle, August 6-7, 2009

A Global A Global ““Urban Age Urban Age ””

Since 2008, the majority of the world’s population lives in urban areas

Source: UN-Habitat, 2007

A Global A Global ““Urban Age Urban Age ””

Since 2008, the majority of the world’s population lives in urban areas

Inclusive cities Promote growth with equity A place where everyone can benefit from the

opportunities cities offer

“Cities are not the problem; they are the solution” J.Lerner

Need to fulfill their potential as engines of development

Urban segregationUrban segregation

A barrier to the formation of inclusive cities

Impacts of SegregationImpacts of Segregation

Obstacles that contribute to the

reproduction of poverty

Policies to counteract segregation demand:

A better understanding of the dynamics of segregation and its causal

mechanisms

Causes of SegregationCauses of Segregation

Personal preferences

Labor market

Land and real estate markets

State policies and investments

But…

How to understand the influence of these mechanisms on segregation

dynamics?

The Complex Nature of SegregationThe Complex Nature of Segregation

Segregation displays many of the hallmarks of complexity

MASUSMASUS

Purpose Provide a scientific tool for exploring the

impact of different mechanisms on segregation dynamics

“Virtual Laboratory”

Multi-Agent Simulator for Urban Segregation

MASUS Conceptual ModelMASUS Conceptual Model

URBAN-POPULATION ModuleURBAN-POPULATION Module

Micro-Level: Household Agent

(a) Agent profile Age, income, education, size,

tenure status, presence of kids, location

(b) Household Transition Sub-Model (H-TRANSITION)

(c) Decision-Making Sub-Model (DECISION) Bounded-rational approach nested logit functions

URBAN-POPULATION ModuleURBAN-POPULATION Module

Macro-Level: Population

(a) Socio-Demographic State Size, income inequality level, and

other socio-demographic statistics (non-spatial)

(b) Population Transition Sub-Model (P-TRANSITION)

(c) Segregation State Product of the spatial location of all households Depicted by spatial measures of segregation

URBAN-LANDSCAPE ModuleURBAN-LANDSCAPE Module

Landscape PatchMinimal portion of the environment100X100m

(a)Landscape Patch State Land use, infrastructure, land value, number of dwellings, distance to roads, distance from CBD, slope, type of settlement, zoning variables.

(b) Urban Sprawl Sub-Model (U-SPRAWL)

(c) Dwelling Offers Sub-Model (D-OFFER)

(d) Land Value Sub-Model (L-VALUE)

(e) Infrastructure Sub-Model (INFRA)

EXPERIMENTAL-FACTOR ModuleEXPERIMENTAL-FACTOR Module

Specification templates to test theories and policies:

Change global variables that affect the socio-demographic composition of the population

Change parameters that drive behavior of agents

Change structure of DECISION sub-model Change the state of urban landscape

Process SchedulingProcess Scheduling

Decision-Making Sub-ModelDecision-Making Sub-Model

Decision-Making Sub-ModelDecision-Making Sub-Model

Nesting Structure of the Model

Decision-Making Sub-ModelDecision-Making Sub-Model

Process SchedulingProcess Scheduling

Urban Population Sub-ModelsUrban Population Sub-Models

Household Transition Sub-Model (H-TRANSITION)

Rule-based functions representing some natural dynamics of the agent profile (e.g., aging)

Population Transition Sub-Model (P-TRANSITION)

Keeps the socio-demographic state of the population according to levels provided by the modeler.

Process SchedulingProcess Scheduling

Urban Landscape Sub-ModelsUrban Landscape Sub-Models

Urban Sprawl Sub-Model (U-SPRAWL)

Transition phase: how many patches become urban?

Markov chain: global transition probabilities

Allocation phase: where? Binary logistic regression: probability of a non-

urban patch becoming urban Variables: urban patches and population density in

the neighborhood (radius 700m), dist CBD, dist roads, slope, zoning

Urban Landscape Sub-ModelsUrban Landscape Sub-Models

Dwelling Offers Sub-Model (D-OFFER)

Transition phase: updates the total number of dwellings

Occupied dwellings (pop) + housing stock

Allocation phase: where? Linear regression model 1: estimates the patches’

loss of dwellings (expansion of non-residential use) Linear regression model 2: estimates the patches’

gain of dwellings (new developments)

Urban Landscape Sub-ModelsUrban Landscape Sub-Models

Land value sub-model (L-VALUE)Hedonic Price Model: Linear regression

functions to estimate patches’ land value

Infrastructure sub-model (INFRA)Linear regression model to estimate patches’

infrastructure quality

Operational MASUS ModelOperational MASUS Model

São Paulo StateStudy Area

City of São José dos Campos

SSãão José dos Campos, o José dos Campos, BrazilBrazil

Operational MASUS ModelOperational MASUS Model

Simulation ExperimentsSimulation Experiments

(1)Comparing simulation outputs with empirical data

(2)Testing theoretical issues on segregation

(3)Testing an anti-segregation policy

Experiment (1): ValidationExperiment (1): Validation

Is the simulation model an accurate representation of the target-system?

Initial condition - São José dos Campos in 1991

Import GIS Layers Households (Agents): Census 1991, microdata

Environment (Landscape patches) Urban Use, Zoning, Infrastructure, Distance CBD,

Distance Roads, Land Value, Dwelling Offers, Neighborhood Type, Slope.

Set Variables and Parameters

Experiment (1): ValidationExperiment (1): Validation

Is the simulation model an accurate representation of the target-system?

Run 9 annual cycles

Compare simulated results with real data (year 2000)

Experiment (1): ValidationExperiment (1): ValidationDissimilarity Index (bw = 700m)

Initial State (1991)

Simulated Data

(1991-2000)

Real Data (2000)

0.54

0.31

0.15

0.51

0.30

0.19

0.51

0.30

0.19

Experiment (1): ValidationExperiment (1): ValidationIsolation Poor Households (bw = 700m)

0.54 0.51

Initial State (1991)

Real Data (2000)

0.51

Simulated Data

(1991-2000)

Experiment (1): ValidationExperiment (1): ValidationIsolation Affluent Households (bw = 700m)

Initial State (1991)

Simulated Data

(1991-2000)

Real Data (2000)

0.15 0.19 0.19

Experiment (2): InequalityExperiment (2): Inequality

How does inequality affect segregation?

Relation between both phenomena has caused controversy in scientific debates

Experiment Compare 3 scenarios

Scenario 1: Previous run

Scenario 2: Decreasing inequality

Scenario 3: Increasing inequality

Experiment (2): InequalityExperiment (2): Inequality

Inequality (Gini)

Proportion Poor HH

Proportion Affluent HH

Dissimilarity Isolation Poor HH

Isolation Affluent HH

Scenario 1 (Original) Scenario 2 (Low-Ineq.) Scenario 3 (High-Ineq.)

Experiment (3): Poverty DispersionExperiment (3): Poverty Dispersion

What is the impact of a social-mix policy based

on the distribution of housing vouchers?

Experiment Compare 3 scenarios

Scenario 1 No voucher (baseline)

Scenario 2 200 - 1700 vouchers

Scenario 3 400 - 4200 vouchers

Scenarios

Experiment (3): Poverty DispersionExperiment (3): Poverty Dispersion

Scenario 1 No voucher (baseline)

Scenario 2 200 - 1700 vouchers

Scenario 3 400 - 4200 vouchers

Dissimilarity

Isolation Poor HH

Isolation Affluent HH

Concluding RemarksConcluding Remarks

MASUS: A Multi-Agent Simulator for Urban Segregation Explore the impact of different causal mechanisms

on the emergence of segregation patterns Virtual laboratory that contributes to scientific and

policy debates on segregation

Three different types of experiment Validation: comparison with real data Theoretical question: inequality vs. segregation Policy approach: poverty dispersion

Concluding RemarksConcluding Remarks

Suggestions for additional experiments Dispersion of wealthy families Regularization of clandestine settlements Promotion of equal access to infrastructure

Improve MASUS usability and effectiveness Participatory modeling approach Feedbacks from potential users

Multi-Agent Simulator for Urban Multi-Agent Simulator for Urban Segregation (MASUS)Segregation (MASUS)A Tool to Explore Alternatives for Promoting Inclusive A Tool to Explore Alternatives for Promoting Inclusive CitiesCities

Flávia F. Feitosa, Quang Bao Le, Paul L.G. Vlek

Center for Development Research (ZEF)University of Bonn

3rd ICA Workshop on Geospatial Analysis and Modeling, University of Gävle, August 6-7, 2009