Flávia F. Feitosa, Quang Bao Le, Paul L.G. Vlek Center for Development Research (ZEF)

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

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

3. A Global “Urban Age ” Since 2008, the majority of the world’s population lives in urban areas • “Cities are not the problem; they are the solution” J.Lerner • Need to fulfill their potential as engines of development • Inclusive cities • Promote growth with equity • A place where everyone can benefit from the opportunities cities offer

4. Urban segregation A barrier to the formation of inclusive cities

5. Policies to counteract segregation demand: A better understanding of the dynamics of segregation and its causal mechanisms Impacts of Segregation Obstacles that contribute to the reproduction of poverty

6. Causes 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?

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

8. MASUS Multi-Agent Simulator for Urban Segregation Purpose • Provide a scientific tool for exploring the impact of different mechanisms on segregation dynamics • “Virtual Laboratory”

9. MASUS Conceptual Model

10. URBAN-POPULATION Module • Micro-Level: • Household Agent • 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

11. URBAN-POPULATION Module • Macro-Level: Population • 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

12. URBAN-LANDSCAPE Module • Landscape Patch • Minimal portion of the environment • 100X100m • 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)

13. EXPERIMENTAL-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

14. Process Scheduling

15. Decision-Making Sub-Model

16. Decision-Making Sub-Model Nesting Structure of the Model

17. Decision-Making Sub-Model

18. Process Scheduling

19. Urban 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.

20. Process Scheduling

21. Urban 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

22. Urban 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)

23. Urban 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

24. Operational MASUS Model São José dos Campos, Brazil City of São José dos Campos Study Area São Paulo State

25. Operational MASUS Model

26. Simulation Experiments • Comparing simulation outputs with empirical data • Testing theoretical issues on segregation • Testing an anti-segregation policy

27. Experiment (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

28. Experiment (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)

29. 0.51 0.30 0.19 Experiment (1): Validation Dissimilarity Index (bw = 700m) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.54 0.51 0.31 0.30 0.15 0.19

30. 0.51 0.51 Experiment (1): Validation Isolation Poor Households (bw = 700m) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.54

31. 0.19 0.19 Experiment (1): Validation Isolation Affluent Households (bw = 700m) Real Data (2000) Initial State (1991) Simulated Data (1991-2000) 0.15

32. Experiment (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

33. Experiment (2): Inequality 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.)

34. Experiment (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

35. Experiment (3): Poverty Dispersion Dissimilarity Isolation Poor HH Scenario 1 No voucher (baseline) Scenario 2 200 - 1700 vouchers Scenario 3 400 - 4200 vouchers Isolation Affluent HH

36. Concluding 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

37. Concluding 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

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