MARETTO, R. V.; ASSIS, T. O.; GAVLAK, A. A. Simulating Urban Growth and Residential Segregation through Agent-Based Modeling. In: BRAZILIAN WORKSHOP ON SOCIAL SIMULATION, 2., , São Bernardo do Campo. Proceedings... Los Alamitos: IEEE, 2010. p. 52 - 57. DVD. ISBN 978-0-7695-4471-7, 978-1-4577-0895-4. doi: <10.1109 />.

1. Simulating Urban Growth and Residential Segregation through Agent-
Based Modeling
Raian Vargas Maretto¹
Talita Oliveira Assis²
André Augusto Gavlak¹
¹ {gavlak, raian}@dpi.inpe.br
²{talitaoliveiraassis}@gmail.com

2. Residential segregation

3. Objective
• The purpose of this work is to replicate three classical models of urban
segregation through the simulation of the individual behavior of agents in
a city.
KohlBurgess
Hoyt
Comprehend how Agent Based Modeling can be useful to replicate classical urban growth and
urban segregation and models

4. Kohl
City Center
Low-income residential areas
Medium-income residential areas
High-income residential areas
Thompson, J.K.J. (1983) “Variations in industrial structure in pre-industrial Languedoc”, in
Berg, Maxine, Hudson, Pat, and Sonenscher, Michael, Manufacture in town and country
before the factory, Cambridge: Cambridge University Press, 61-91.19th century
Kohl generalized the way social groups were
distributed inside the pre-industrial cities of
continental Europe

5. 1920’s
Burgess
City Center
Low-income residential areas
Medium-income residential areas
High-income residential areas

6. Hoyt
City Center
Low-income residential areas
Medium-income residential areas
High-income residential areas
Calgary, Canada - 1969
Smith, P.J. (1962) "Calgary: A study in urban pattern",
Economic Geography, 38(4), pp.315-329
According to the American economist Hoyt
(1939), spatial segregation did not used to
assume a circle pattern around the center, but
spatial sectors originating from there.

7. Methodology – The environment99cells
99 cells
Dwelling unit
Can contein an agent – Household
Low income – 50%
High income – 25%
Medium income – 20%
Financial agent (commerce, industry and services) – 5 %
City starts from the central cell – SEED
One agent inserted at each iteraction – Random class

8. Methodology – Hierarchy
Financial Agents
High Income Agents
Medium Income Agents
Low Income Agents

9. Methodology – Model Dynamics
Attempts to allocate
the agent in cell
Choose neighbor
randomly
Expels agent and
takes place in cell
Insert new
agent
Takes place
in cell
Does new
agent have
priority?
Empty
cell?
N N
Y
Y
Expelledagent

10. Methodology – Hoyt Model
High-Income Agents
Low-Income Agents
Medium-Income Agents

11. Source: (CARNEIRO, 2006)
TerraME: Terra Modeling Environment
A development plataform for spatially-explicit dynamical
modeling

12. TerraME: Concepts Components
Spatial structure: Nested CA
1:32:00 Mens. 11.
1:32:10 Mens. 32.
1:38:07 Mens. 23.
1:42:00 Mens.44.
. . .return value
true
1. Get first pair
2. Execute the ACTION
3. Timer =EVENT
4. timeToHappen += period
Temporal structure
Newly implanted
Deforesting
Slowing down
latency
> 6 years
Iddle
Year of
creation
Deforestation =
100%
Agents Modeling Generalised Spatial relations
Source: [Carneiro, 2006]

13. TerraME: Architecture
Source: (CARNEIRO, 2006)

14. Simulation results – Kohl

15. Simulation results - Burgess

16. Simulation results – Hoyt

17. Concluding Remarks
Next step  use of real data
Agent‐based model was proved to be a suitable tool to
reproduce urban growth and urban Residential Segregation
models.
Methodology makes possible to replicate other models of this kind
of phenomena.
TerraME has shown to be an powerful tool to implement these
models

18. Thank You!
Questions?