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Modeling in Coexistence with Data: Toward a Generic Notion of Space

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Presented at Future Cities Lab, ETH- Singapore Centre.
Transition workshop 20150408

Published in: Technology
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Modeling in Coexistence with Data: Toward a Generic Notion of Space

  1. 1. Vahid Moosavi Modeling in Coexistence with Data Toward a Generic Notion of Space
  2. 2. First Part Modeling in Coexistence with Data, (the conceptual story line of this talk) Second Part Toward a Generic Notion of Space ( three applications around the notion of space)
  3. 3. The Classical Modeling Process and the Role of Data (A set theoretical notion of a model) Abstract(Universals( Data$ Modeling$Process$ Final$Model$ (Ideals )
  4. 4. Abstract Universals and Rational Modeling Here,“Circle-ness” as an Ideal form is assumed. Then, each observation is a “circle”. Or each observation is a “circle” with specific “radius”.
  5. 5. Rational Modeling and Modern Analysis based on the Idealized dictionaries ObservedModel Fourier Decomposition
  6. 6. Urban Modeling and Idealization Space Syntax Agent Based Modeling System Dynamics
  7. 7. This approach of modeling reaches to a theoretical limit in complex systems...
  8. 8. The Secondary role of the data in the classical modeling and simulation: •First we establish a dictionary and a grammar of the real phenomena based on a set of ideal conditions. •Then we tune the model with the Abstract(Universals( Data$ Modeling$Process$ Final$Model$
  9. 9. What if instead of given dictionaries and ideals, we learn them in a Coexistence with Urban Data An Alternative Approach
  10. 10. We need a conceptual inversion in the role of data in the modeling process Concrete(Universals( Unstructured*Data* Modeling*Process* Final*Model*
  11. 11. Modeling in Co-existence with Data Beyond the notion of error and the ideal form
  12. 12. Second part: Toward a generic notion of space
  13. 13. Given Dictionary of Forms
  14. 14. Learned Dictionary of Forms
  15. 15. Self Organizing Map (SOM) (As a Generic Space, where closer items are more similar to each other)
  16. 16. CAAD 2010 Benjamin Dillenburger
  17. 17. The interplay between two different spaces
  18. 18. The interplay between two different spaces Ludger Hovestadt, Vahid Moosavi, Mathias Standfest 2012
  19. 19. First Application Urban Air Pollution Modeling
  20. 20. Urban Air Pollution Modeling Vahid Moosavi, Gideon Aschwanden, Erik Velasco 2014
  21. 21. Urban Air Pollution Modeling Vahid Moosavi, Gideon Aschwanden, Erik Velasco 2014
  22. 22. Urban Information + few measurements Urban Air Pollution Modeling Learning a dictionary A new space based on similarity of urban parameters
  23. 23. Urban Air Pollution Modeling Learning a dictionary Estimating the air pollution level with different confidence levels with only 3% direct measurement
  24. 24. Urban Air Pollution Modeling Locating the monitoring stations
  25. 25. Second Application Modeling the Dynamics of Real Estate Market
  26. 26. Modeling the Dynamics of Real Estate Market First  Law  of  Geography  (FLG) Waldo Tobler :"Everything is related to everything else, but near things are more related than distant things” Basics  of  Spa7al  Modeling Q: But near in which space and based on which similarity and relatedness? Idea: Is there a way to reach to a generic notion of space which is based on any similarity of interest?
  27. 27. Modeling the Dynamics of Real Estate Market Monthly Median Price per sq.ft of sold houses of each Zip code in the state of Florida
  28. 28. Modeling the Dynamics of Real Estate Market The topology of this dynamic It seems there should be some patterns
  29. 29. Modeling the Dynamics of Real Estate Market But not necessarily a spatial pattern How about the FLG? It is like a jigsaw puzzle
  30. 30. From 2002 To 2015 But not just one, several puzzles at once
  31. 31. Modeling the Dynamics of Real Estate Market SOM$ Learning a temporal dictionary
  32. 32. Modeling the Dynamics of Real Estate Market From a spatial image of the market with random pixels To another image of market that follows FLG? SOM$
  33. 33. Modeling the Dynamics of Real Estate Market An Image of the market Median price of a all the zip codes in Florida from 2002-2015
  34. 34. Modeling the Dynamics of Real Estate Market Two notions of space
  35. 35. Modeling the Dynamics of Real Estate Market Predicting 6 months ahead price Using the same prediction method Only different neighbors Most of the times, we get better results with looking to the new space!
  36. 36. Modeling the Dynamics of Real Estate Market
  37. 37. Two notions of space Geo Space SOM Space
  38. 38. Modeling the Dynamics of Real Estate Market Spatial Distribution of Error
  39. 39. Third Application Contextual Mapping Or how to be able to create personalized images of the space?
  40. 40. Cities are complex and multi-faceted How to look at a city with huge amount of digital data? Is it possible that instead of a top-down map, we provide a simple mapping process? “Toward personalized images of space”
  41. 41. Age + Health
  42. 42. Method: Learn a one dimensional dictionary and take its elements as an ordered set of numbers.
  43. 43. Age + Health
  44. 44. Levels of Education + Health
  45. 45. Distance travel to work
  46. 46. Crime Categories
  47. 47. Contextual Mapping Racial Mixture in New York City?
  48. 48. Contextual Mapping Redistricting and the will of the people How polarized is Colorado in different elections? Is there any bottom up approach to define the electoral borders?
  49. 49. thank YOU •Abstraction dis-solves the current problem and transcends to a new world with new kind of challenges. •Generalization opens up or keeps the discussions in the same world that the problem exists.

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