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 1

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

“Cities are not the problem; They are the solution!” 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

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

Obstacles that contribute to perpetuate poverty 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

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

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

Multi-Agent Simulator for Urban Segregation MASUS Multi-Agent Simulator for Urban Segregation Scientific tool to explore alternative scenarios of segregation Support planning actions by offering insights about the impact of policy strategies Purpose Improve the understanding about segregation and its relation with different contextual mechanisms

MASUS Conceptual Model

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

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

Nesting Structure of the Model 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) Simulated Data (1991-2000) Real Data (2000) 0.51 0.30 0.19 0.54 0.51 0.31 0.30 0.15 0.19

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

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

Testing a theory How does inequality affect segregation? Experiment 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 And here are the graphs: In blue is the baseline scenario (inequality increases a bit, but it is almost constant), the green represents the scenario where inequality decreases; and in the red, inequality increases. These graphs show the proportion of poor households and affluent households, because they influence the results of the isolation indices. The dissimilarity graph shows the the index really follow the inequality trends The same happens with the isolation of poor households, but this graph is not so revealing, because, as I told before, the isolation index tends to follow to proportion of the groups… so, if there are more poor families, the isolation of poor families is higher But what is really revealing is the graph with the isolation of affluent families, because it challenges the natural trend of the index. We see that in the low-inequality scenario, we have a higher proportion of affluent families, but we have a LOWER isolation of this group. Which is quite impressive, something I have never seen, and reinforces the idea that YES, inequality does promote segregation. Cities with lower inequality are more likely to be less segregated. Scenario 1 (Original) Scenario 2 (Low-Ineq.) Scenario 3 (High-Ineq.)

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

Testing policy strategies Dissimilarity Isolation Poor HH 2.3 - 3.5 % 5.8 - 10.7% 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 (5.8%) Isolation Affluent HH 2.3 - 5.7 % 5.8 - 8.3 %

Poverty Dispersion vs. Wealth Dispersion 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 So, evaluating the strategy of poverty dispersion, it is possible to say that : (1) In a city like SJC, It demands massive and continous investment to decrease poverty isolation, to promote substantial change in the isolation of poverty. (2) I continued the experiments, for the years 2000-2010, keeping the investment constant. And it was possible to see that this continued investment slows down the increase in segregation, but does not change the segregation trends. Global indices continue to increase and the maps of segregation show the same patterns in comparison with the baseline scenario. (3) Although this approach has been aplied, often sucessfully, in cities of developed countries, it does not seem realistic for cities in the developing world, where poor households represent a large share of the population.

Poverty Dispersion vs. Wealth Dispersion Testing policy strategies Experiment Compare 2 scenarios Scenario 1 (baseline) Scenario 2 new areas for upper classes Urban areas in 1991 Undeveloped 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

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

Poverty Dispersion vs. Wealth Dispersion 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%

Poverty Dispersion vs. Wealth Dispersion 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

Concluding Remarks For the next version of MASUS Decision-making Dynamic representation of household‘s reasoning Computational cost Improve MASUS usability and effectiveness Feedbacks from potential users Additional experiments

Dimensions of Segregation Exposure/Isolation: Chance of having members from different groups living side-by-side Evenness/Clustering: Balance of the population groups distribution

TRADITIONAL PATTERN ‘CENTER-PERIPHERY’ Segregation Patterns TRADITIONAL PATTERN ‘CENTER-PERIPHERY’ RECENT TRENDS: NEW PATTERN OF SEGREGATION WEALTHY AXIS EXPANDING INTO ONE DIRECTION SLUMS (FAVELAS) GATED NEIGHBORHOODS WEALTHY CENTER POOR PERIPHERY Up to the 1980’s “Center-Periphery pattern” Macrosegregation Nowadays Not so simple Sectorial: wealthy axis expanding into a single direction At smaller scales Slums (favelas) Gated communities

MASUS Methodological Steps

Decision-making sub-model

NMNL: Affluent Households Level Choice Variable Coef. Std. err. 1st Move Age of the household head -0.040*** 0.011 Renter 2.542*** 0.425 Renter * household income -9.4(10-5) -7.5(10-5) 2nd Move within the same neigh. Constant -2.532 *** 0.693 Move to the same type of neighborhood -2.464 *** 0.855 Type A neighborhood 0.477 0.661 Type B neighborhood 0.062 0.495 Kids * Type A -0.368 0.636 Move to another type of neighborhood -3.457 *** 1.053 -0.256 0.732 1.760 *** 0.709 1.49 ** 0.784 3rd Generic variables Land price/ income -0.084 0.053 Real estate offers 1.4(10-3) *** 5.1(10-4) Distance from orig. neighborhood -4.9(10-5) ** 2.5(10-5) Distance to CBD 2.3(10-5) 2.9(10-5) Prop. of high-income families 0.960 ** 0.503