Urban Planning by Simulation of Population Growth

Cirano IochpeFlavio Rech WagnerMarcia Aparecida da Silva AlmeidaGuillermo Nudelmann HessAndré Dias Bastos

GEOINFO 20046th Brazilian Symposium on Geoinformatics

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

• Next Steps

• Last considerations

•The beginning •Functions and functionalities•The architecture•Tools and technologies•The covering map•Modelling

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

GML / SHPGeographic data

XMLrules

usuáriousuário

Geographic scenariomanager

(wrapper)

GeotoolsAPI

SimulationCovering

map

SimuladorEngine

Covering

InterfaceWEB

Rules manager(Wrapper)

Cellularautomata

Area (map)Neighbourhood (map)StateTransition Rules

Files(shp, gml)

ProgramaFiles(Java)

Rulesfiles(xml)

MetadataDBMS

Tools and Technologies

• Tools and technologies• 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

SchoolDarn Canal Stream Lake River Declivity

Generate buffer zone

Generate hidrology layer

Generate appropriated geographical zones

Generate influence zones

Sectors

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

• Qualitative aspect• Variables and their relationships (nodes and edges)

• Quantitive aspect• Intensity degree of relationship between variables

(probabilities)

P(Xa) P(Xd|Xa)

P(Xb|Xa) P(Xc|Xb,Xd)

Finding a Model

• Bayesian networks

Visit Asia?(A)

Tuberculosis?(T)

Lung cancer?(C)

(T) or (C)?(O)

Bronchisis?(B)

Smooker?(S)

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