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

1. Vahid Moosavi
Modeling in Coexistence with Data
Toward a Generic Notion of Space

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. 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. 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. Rational Modeling
and
Modern Analysis
based on the
Idealized dictionaries
ObservedModel
Fourier Decomposition

6. Urban Modeling and Idealization
Space Syntax
Agent Based Modeling
System Dynamics

7. This approach of modeling reaches to a theoretical
limit in complex systems...

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. What if instead
of given
dictionaries and
ideals, we learn
them in a
Coexistence
with
Urban Data
An Alternative
Approach

10. We need a conceptual inversion in the role of
data in the modeling process
Concrete(Universals(
Unstructured*Data* Modeling*Process* Final*Model*

11. Modeling in Co-existence with Data
Beyond the notion of error and the ideal form

12. Second part: Toward a generic
notion of space

13. Given Dictionary of Forms

14. Learned Dictionary of Forms

15. Self Organizing Map (SOM)
(As a Generic Space, where closer items are more similar to each other)

16. CAAD 2010
Benjamin Dillenburger

17. The interplay between two different spaces

18. The interplay between two different spaces
Ludger Hovestadt, Vahid Moosavi, Mathias Standfest 2012

19. First Application
Urban Air Pollution Modeling

20. Urban Air Pollution Modeling
Vahid Moosavi, Gideon Aschwanden, Erik Velasco 2014

21. Urban Air Pollution Modeling
Vahid Moosavi, Gideon Aschwanden, Erik Velasco 2014

22. Urban Information
+
few measurements
Urban Air Pollution Modeling
Learning a dictionary
A new space based on similarity of urban parameters

23. Urban Air Pollution Modeling
Learning a dictionary Estimating the air pollution level with different confidence
levels with only 3% direct measurement

24. Urban Air Pollution Modeling
Locating the monitoring stations

25. Second Application
Modeling the Dynamics of Real Estate Market

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. 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. Modeling the Dynamics of Real Estate Market
The topology of this dynamic
It seems there should
be some patterns

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. From 2002
To 2015
But not just one, several
puzzles at once

31. Modeling the Dynamics of Real Estate Market
SOM$
Learning a temporal dictionary

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. 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. Modeling the Dynamics of Real Estate Market
Two notions of space

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. Modeling the Dynamics of Real Estate Market

37. Two notions of space
Geo Space
SOM Space

38. Modeling the Dynamics of Real Estate Market
Spatial Distribution of Error

39. Third Application
Contextual Mapping
Or how to be able to create personalized images of the space?

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. Age + Health

42. Method: Learn a one dimensional dictionary and take its elements as an ordered
set of numbers.

43. Age + Health

44. Levels of Education + Health

45. Distance travel to work

46. Crime Categories

47. Contextual Mapping
Racial Mixture in New York City?

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. 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.