Understanding Urban Segregation Through Agent-Based Simulation

1. Urban Segregation as a Complex System An Agent-Based Simulation Approach Flávia da Fonseca Feitosa 1ª Oficina de Intercâmbio INPE, CEDEPLAR/UFMG e FEA/USP 2 a 13 de Agosto/2010

2. An Urban Age Since 2008, the majority of the world’s population lives in urban areas Source: UN-Habitat, 2007

3. An Urban Age Is this a problem? “Cities are not the problem; They are the solution!” (Jaime Lerner) Potential as engines of development

4. An Urban Age Inclusive Cities • Promote growth with equity • A place where everyone can benefit from the opportunities cities offer

5. Urban Segregation A barrier to the formation of inclusive cities

6. 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

7. Complex Nature of Segregation Segregation displays many of the hallmarks of complexity

8. Complex Nature of Segregation The Process Matters! Require bottom-up simulations  Agent-Based Model 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

9. 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

10. MASUS Conceptual Model

11. São José dos Campos, Brazil City of São José dos Campos Study Area São Paulo State

12. MASUS: Process Schedule

13. 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

14. Decision-making sub-model Nesting Structure of the Model

15. MASUS: Process Schedule

16. Operational Model

17. Simulation Experiments Comparing simulation outputs with empirical data • Testing theoretical issues Testing anti-segregation policy strategies

18. 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)

19. 0.51 0.30 0.19 Comparison with Empirical Data Dissimilarity Index (local scale) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.54 0.51 0.31 0.30 0.15 0.19

20. 0.51 0.51 Comparison with Empirical Data Isolation Poor Households (local scale) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.54

21. 0.19 0.19 Comparison with Empirical Data Isolation Affluent Households (local scale) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.15

22. 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

23. Testing a theory Proportion Poor HH Inequality (Gini) Proportion Affluent HH Isolation Affluent HH Isolation Poor HH Dissimilarity Scenario 1 (Original) Scenario 2 (Low-Ineq.) Scenario 3 (High-Ineq.)

24. Testing policy strategies Experiment • Compare 3 scenarios Scenario 1 no voucher (baseline) Scenario 2 200 – 1700 vouchers Scenario 3 400 – 4200 vouchers Poverty Dispersion vs. Wealth Dispersion Poverty Dispersion: housing vouchers to poor families

25. Testing policy strategies Isolation Poor HH Dissimilarity 2.3 - 1.7 % 5.8 - 3.4% 2.3 - 3.5 % 5.8 - 10.7% • Scenario 1 • No voucher (baseline) • Scenario 2 • 200 - 1700 vouchers (2.3%) • Scenario 3 • 400 - 4200 vouchers (5.8%) Isolation Affluent HH 2.3 - 5.7 % 5.8 - 8.3 %

26. Testing policy strategies Poverty Dispersion Demands high and continous investment to decrease poverty isolation Poverty Dispersion vs. Wealth Dispersion Slows down the increase in segregation, but does not change the trends

27. Urban areas in 1991 Undeveloped areas for upper classes Testing policy strategies Experiment • Compare 2 scenarios Scenario 1 (baseline) Scenario 2 new areas for upper classes Poverty Dispersion vs. Wealth Dispersion • Wealth Dispersion: Incentives for constructing residential developments for upper classes in poor regions of the city

28. Testing policy strategies Isolation Poor HH Dissimilarity Isolation Affluent HH • Scenario 1 • baseline • Scenario 2 • new areas for upper classes

29. Testing policy strategies Wealth Dispersion Produces long-term outcomes Poverty Dispersion vs. Wealth Dispersion • More effective at decreasing large-scale segregation • E.g. Dissimilarity 2010 • local scale (700m): - 19% • large scale (2000m): - 36%

30. Testing policy strategies • Wealth Dispersion • Positive changes in the spatial patterns of segregation Poverty Dispersion vs. Wealth Dispersion Baseline 2010 Wealth Dispersion 2010

31. 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