Transcript Urban Planning by Simulation of Population Growth

Urban Planning by Simulation of Population Growth
GEOINFO 2004
6th Brazilian Symposium on Geoinformatics
Cirano Iochpe
Flavio Rech Wagner
Marcia Aparecida da Silva Almeida
Guillermo Nudelmann Hess
André Dias Bastos
Outline
• Related work
• Introduction
• The System
•The beginning
•Functions and functionalities
•The architecture
•Tools and technologies
•The covering map
•Modelling
Outline
• Related work
• Introduction
• The System
•The beginning
•Functions and functionalities
•The architecture
•Tools and technologies
•The covering map
•Modelling
• Next Steps
• Last considerations
Introduction
• InterSIG Project
• Main goal: to integrate a set of algorithms,
techniques, tools, data models, and protocols
into an Internet based system that supports
both access and manipulation of geographic
data
Introduction
• Simulation Subsystem of Geographic
Scenarios:
• Fase 3 of the InterSIG Project
• Main Goal: to offer a web based simulation system
that can be remotely used by municipalities to
support urban planning activities
• Focused on urban growth
• Partners
• Data availability
Related Work
• A number of systems has been proposed to
address urban growth simulation
• Most of them are not available on the Internet
• Most of them are dependent on specific GIS
platforms and data formats
• UrbanSim
• Uplan
The InterSIG Simulation Subsystem - The Beginning
• Partnership: Porto Alegre City Hall
• Project: “Planning the Future of the Lomba do
Pinheiro District”
• Availability of geographic data
• Hidrology, declivity, population,
infrastructure
• Public resources – schools,
public health centers,
kinder gardens, squares
The InterSIG Simulation Subsystem - The Beginning
• Partnership: Porto Alegre City Hall
• Project: “Planning the Future of the Lomba do
Pinheiro District”
• Needs
• Visualize covering or influence area of a public resource
• Simulate inclusion of new resources
• Simulate increasing the population and its consequence to the
covering area of public resources
The InterSIG Simulation Subsystem - The Beginning
• Visualizing influence zone of a public resource
The InterSIG Simulation Subsystem - The Beginning
• Porto Alegre City Hall
• Rules about public resources
• Declivity < 25%
• Each type of public resource has a specific range of
influence given no geographic obstacles are provided
• Each instance of public resource has a maximum
number of citizens which it can serve at any time
The System
• Functions and functionalities
•
•
•
•
•
Accessible through the Web
Upload of geographic scenarios by the user
Upload of simulation rule sets by the user
Generation of covering maps
Simulation of the evolution of influence areas during
a time interval
The System Architecture
Geotools
API
Metadata
DBMS
Programa
Files
(Java)
Files
(shp, gml)
Rules
files
(xml)
Geographic scenario
manager
(wrapper)
Rules manager
(Wrapper)
Cellular
automata
Area (map)
Neighbourhood (map)
State
Transition Rules
Simulador
Engine
Covering
Simulation
Covering
map
Interface
WEB
XML
rules
GML / SHP
Geographic data
usuário
Tools and Technologies
• Tools and technologies
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•
•
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JSP/Servlets to interface
GeoTools to handle geographic information
GML, XML and Shapefiles to exchange data
An owner simulator kernel in Java
SVG to visualize maps
The covering map algorithm
Darn
Canal
Stream
Lake
River
Declivity
School
Generate hidrology layer
Generate appropriated geographical zones
Generate buffer zone
Sectors
Generate influence zones
Covering Map
The covering map algorithm
Modeling – The Major Difficulty
• Find a urban growth population model
• Just rules are not enough to build the system
• Population is distributed following a
growth/distribution model
• Each urban area can have a different model
• How to obtain a generic (basic) model?
• Dynamic modeling
Finding a Model
• Main approaches in dynamic modeling of
urban growth
• Cellular automata
• Heuristic methods
• Neural networks
Finding a Model
• Spatial Dynamic Modeling
• Simulation of urban
land use changes
• Claudia Almeida’s
(INPE) phD tesis
• Empirical probabilistc
methods
Finding a Model
• Spatial Dynamic Modeling
• Simulation of urban land use changes
• Bayes theorem
• How about Bayesian Networks?
• At first glance, it can be used to obtain a model
directly from a database
Finding a Model
• Bayesian networks
P(Xa)
P(Xb|Xa)
P(Xd|Xa)
P(Xc|Xb,Xd)
• Qualitative aspect
• Variables and their relationships (nodes and edges)
• Quantitive aspect
• Intensity degree of relationship between variables
(probabilities)
Finding a Model
• Bayesian networks
Smooker?
(S)
Visit Asia?
(A)
Tuberculosis?
(T)
Lung cancer?
(C)
Bronchisis?
(B)
(T) or (C)?
(O)
X-ray+
(X)
Dispnesis
(D)
Finding a Model – Next steps
• Steps to build a Bayesian Network
• To obtain variables
• To obtain a causal relationship between
variables
• From the database
• To obtain condicional probabilities tables
• Through bayesian learning methods
Conclusions
• To build a generic tool in terms of modeling
is a quite dificult task
• To have the possibility of developing a new
technique evolving dynamic modeling is
quite interesting
• Next step is to continue investigating
Bayesian Networks as the possible solution
of our problem