simulating urban growth and residential segregation through agent-based modeling
DESCRIPTION
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: .TRANSCRIPT
Simulating Urban Growth and Residential Segregation through Agent-
Based Modeling
Raian Vargas Maretto¹Talita Oliveira Assis²
André Augusto Gavlak¹
¹ {gavlak, raian}@dpi.inpe.br²{talitaoliveiraassis}@gmail.com
Residential segregation
Objective• The purpose of this work is to replicate three classical models of urban
segregation through the simulation of the individual behavior of agents ina city.
KohlBurgessHoyt
Comprehend how Agent Based Modeling can be useful to replicate classical urban growth and urban segregation and models
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”, inBerg, Maxine, Hudson, Pat, and Sonenscher, Michael, Manufacture in town and countrybefore the factory, Cambridge: Cambridge University Press, 61-91.19th century
Kohl generalized the way social groups weredistributed inside the pre-industrial cities ofcontinental Europe
1920’s
Burgess
City Center
Low-income residential areas
Medium-income residential areas
High-income residential areas
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 toassume a circle pattern around the center, butspatial sectors originating from there.
Methodology – The environment9
9 c
ells
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
Methodology – Hierarchy
Financial Agents
High Income Agents
Medium Income Agents
Low Income Agents
Methodology – Model Dynamics
Attempts to allocate the agent in cell
Choose neighborrandomly
Expels agent and takes place in cell
Insert newagent
Takes placein cell
Does newagent have
priority?
Emptycell?
N N
Y
Y
Expelle
d agen
t
Methodology – Hoyt ModelHigh-Income Agents
Low-Income AgentsMedium-Income Agents
Source: (CARNEIRO, 2006)
TerraME: Terra Modeling EnvironmentA development plataform for spatially-explicit dynamical modeling
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]
TerraME: Architecture
Source: (CARNEIRO, 2006)
Simulation results – Kohl
Simulation results - Burgess
Simulation results – Hoyt
Concluding Remarks
Next step use of real data
Agent‐based model was proved to be a suitable tool toreproduce urban growth and urban Residential Segregationmodels.
Methodology makes possible to replicate other models of this kindof phenomena.
TerraME has shown to be an powerful tool to implement thesemodels
Thank You!
Questions?