1. Urban Planning by Simulation of Population Growth Cirano Iochpe Flavio Rech Wagner Marcia Aparecida da Silva Almeida Guillermo Nudelmann Hess André Dias Bastos GEOINFO 2004 6th Brazilian Symposium on Geoinformatics

2. Outline Related work Introduction The System The beginning Functions and functionalities The architecture Tools and technologies The covering map Modelling

3. Outline Related work Introduction The System Next Steps Last considerations The beginning Functions and functionalities The architecture Tools and technologies The covering map Modelling

4. 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

5. 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

6. 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

7. 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

8. 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

9. The InterSIG Simulation Subsystem - The Beginning Visualizing influence zone of a public resource

10. 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

11. 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

12. The System Architecture

13. 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

14. The covering map algorithm SchoolDarnCanalStreamLakeRiverDeclivity Generate buffer zone Generate hidrology layer Generate appropriated geographical zones Generate influence zones Sectors Covering Map

15. The covering map algorithm

16. 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

17. Finding a Model Main approaches in dynamic modeling of urban growth Cellular automata Heuristic methods Neural networks

18. Finding a Model Spatial Dynamic Modeling Simulation of urban land use changes Claudia Almeidas (INPE) phD tesis Empirical probabilistc methods

19. 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

20. 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)

21. 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)

22. 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

23. 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