Urban Segregation as a
Complex System
An Agent-Based Simulation
Approach
Flávia da Fonseca
Feitosa
Photo: Fabio
1ª Oficina de Intercâmbio INPE, CEDEPLAR/UFMG e
FEA/USP
2 a 13 de Agosto/2010
An Urban Age
Since 2008, the majority of the world’s population lives in
urban areas
Source: UN-Habitat, 2007
An Urban Age
Is this a problem?
“Cities are not the problem; They are the
solution!”
(Jaime Lerner)
Potential as engines of development
An Urban Age
Inclusive Cities
Promote growth with equity
A place where everyone can benefit from
the opportunities cities offer
Urban Segregation
A barrier to the formation of inclusive cities
Impacts of
Segregation
Obstacles that
contribute to
perpetuate
poverty
Policies to minimize segregation
demand:
A better understanding of the dynamics of
segregation and its causal mechanisms
Complex Nature of
Segregation
Segregation displays many of the hallmarks of
complexity
Complex Nature of
Segregation
The Process Matters!
Require bottom-up simulations
Model

Agent-Based
Agent-Based Models (ABM)
Focus on individual decision-making units (agents),
which interact with each other and their
environment
Natural way to explore the emergence of global
structures
MASUS
Multi-Agent Simulator for Urban Segregation
Scientific tool to explore alternative scenarios of
segregation
Purpose
Improve the understanding about segregation
and its relation with different contextual
mechanisms
Support planning actions by offering insights
about the impact of policy strategies
MASUS Conceptual Model
São José dos Campos,
Brazil
City of São José dos
Campos
São Paulo State
Study
Area
MASUS: Process Schedule
Decision-making sub-model
ALTERNATIVES
• Not Move
• Move within the same neighborhood
• Move to the same type of neighborhood (n alternatives)
• Move to a different type of neighborhood (m alternatives)
Higher probability to choose alternative with
higher utility
Decision-making sub-model
Nesting Structure of the Model
MASUS: Process Schedule
Operational Model
Simulation Experiments
Comparing simulation outputs with
empirical data
Testing theoretical issues
Testing anti-segregation policy strategies
Comparison with Empirical
Data
Initial condition: São José dos Campos in
1991
•
Import GIS layers (households,
environment)
•
Set parameters
Run 9 annual cycles
Compare simulated results with real data
(year 2000)
Comparison with Empirical
Data
Dissimilarity Index (local scale)
Initial State
(1991)
0.54
Simulated Data
(1991-2000)
0.51
0.51
0.31
0.30
0.30
0.15
Real Data
(2000)
0.19
0.19
Comparison with Empirical
Data Isolation Poor Households (local scale)
Initial State
(1991)
0.54
Simulated Data
(1991-2000)
0.51
Real Data
(2000)
0.51
Comparison with Empirical
Data Isolation Affluent Households (local scale)
Initial State
(1991)
0.15
Simulated Data
(1991-2000)
0.19
Real Data
(2000)
0.19
Testing a theory
How does inequality affect
segregation?
Relation between both phenomena has caused
controversy in scientific debates
Experiment
Compare 3 scenarios 1991-2000
Scenario 1: Previous run (baseline)
Scenario 2: Decreasing inequality
Scenario 3: Increasing inequality
Testing a theory
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.)
Testing policy strategies
Poverty Dispersion vs. Wealth
Dispersion
Poverty Dispersion: housing vouchers to poor
families
Experiment
Compare 3 scenarios
Scenario 1
no voucher (baseline)
Scenario 2
200 – 1700 vouchers
Scenario 3
400 – 4200 vouchers
Testing policy strategies
Dissimilarity
2.3 - 3.5 %
5.8 - 10.7%
Isolation Affluent HH
2.3 - 5.7 %
5.8 - 8.3 %
Isolation Poor HH
2.3 - 1.7 %
5.8 - 3.4%
Scenario 1
No voucher (baseline)
Scenario 2
200 - 1700 vouchers
(2.3%)
Scenario 3
400 - 4200 vouchers
Testing policy strategies
Poverty Dispersion vs. Wealth
Dispersion
Poverty Dispersion
Demands high and continous investment to
decrease poverty isolation
Slows down the increase in segregation, but does
not change the trends
Testing policy strategies
Poverty Dispersion vs. Wealth
Dispersion
Wealth Dispersion: Incentives for constructing
residential developments for upper classes in poor
regions of the city
Experiment
Compare 2 scenarios
Scenario 1
(baseline)
Scenario 2
new areas for upper classes
Urban areas in 1991
Undeveloped areas for upper classes
Testing policy strategies
Dissimilarity
Isolation Poor HH
Isolation Affluent HH
Scenario 1
baseline
Scenario 2
new areas for upper
classes
Testing policy strategies
Poverty Dispersion vs. Wealth
Dispersion
Wealth Dispersion
Produces long-term outcomes
More effective at decreasing large-scale
segregation
E.g.
Dissimilarity 2010
local scale (700m):
- 19%
large scale (2000m):
- 36%
Testing policy strategies
Poverty Dispersion vs. Wealth
Dispersion
Wealth Dispersion
Positive changes in the spatial patterns of
segregation
Baseline
2010
Wealth Dispersion
2010
Concluding Remarks
MASUS: Multi-Agent Simulator for Urban
Segregation
Virtual laboratory for testing theories and policy
approaches on segregation
Does not focus on making exact predictions
Exploratory tool, framework for assembling
relevant information
Oriented towards understanding and structuring
debates in participative processes of decision
support