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Autonomous Urban Agents:
a Santa Fe Approach to City Knowledge

                  Stephen Guerin
     Redfish Group / Santa Fe Complex

                    Fabio Carrera
                            WPI / MIT
WPI.EDU


FORMAURBIS.COM


REDFISH.COM



SIMTABLE.COM



SFCOMPLEX.ORG
Steve
Fabio
S+F
Pipedreams
Steve
Fabio
S+F
Pipedreams
Complexity theory
Autonomous agent modeling
Ambient computing
Complexity theory
Autonomous agent modeling
Ambient computing
Flocking and swarming




Josh Thorp, stigmergic.net
Ants and Pheromones
Complexity theory
Autonomous agent modeling
Ambient computing
Complexity theory
Autonomous agent modeling
Ambient computing
Zozobra crowd dynamics
Pedestrian evacuations
Crowd Egress from Pittsburgh’s PNC Park
DC Metro Subway
Empirical Traffic Flows for Calibration


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                                        Left Turn               Thru 1                   Thru 2    Thru 3           0                   0   0            0      0   0
Cova, T.J., and Church, R.L. (1997) Modelling community evacuation vulnerability using GIS. International
     Journal of Geographical Information Science, 11(8): 763-784
Cova, T.J., and Johnson, J.P. (2002) Microsimulation of neighborhood evacuations in the urban-wildland
     interface. Environment and Planning A, 34(12): 2211-2229
Cova, T.J., and Johnson, J.P. (2003) A network flow model for lane-based evacuation routing.
     Transportation Research Part A: Policy and Practice, 37(7): 579-604
Cova, T.J. (2005) Public safety in the urban-wildland interface: Should fire-prone communities have a
     maximum occupancy? Natural Hazards Review, 6(3): 99-108
Cova, T.J., Dennison, P.E., Kim, T.H., and Moritz, M.A. (2005) Setting wildfire evacuation trigger-points using
     fire spread modeling and GIS. Transactions in GIS, 9(4): 603-617
Complexity theory
Autonomous agent modeling
Ambient computing
Complexity theory
Autonomous agent modeling
Ambient computing
Run applet and download code at http://www.sandtable.org
Reality Mining




MIT Reality Mining with Nathan Eagle
Steve
Fabio
S+F
Pipedreams
Steve
Fabio
S+F
Pipedreams
1961-1977

½ Ma    1977-1978

        1978 -1988

½ Ve    1988 - 2008

        1996 - 2004

½ SFe   2007 -
Venice Project Center
Visual perception and preference
City Knowledge
Venice Project Center
Visual perception and preference
City Knowledge
Venice Project Center

  •Founded in 1988
  •500+ alumni
  •125+ projects
  •10+ Awards
  •15+ Major media stories
Worldwide Recognition




         10/2003
National Geographic Video




Channel:                  National Geographic Video
Series Title:             Out There
Program Title:            City under Siege
Aired around the world:   2002-today
National Geographic Video
20th Anniversary of VPC

    • Web page
    • Blog
    • Venipedia (wiki)
    • Alumni Network (ning)
    • Project Repository (Dspace)
Venice Project Center
Visual perception and preference
City Knowledge
Visual perception & preference

  •Original MIT dissertation
  •Why do we like/dislike cities?
  •Structures and Activities
  •How Carl Steinitz changed my life
Venice Project Center
Visual perception and preference
City Knowledge
City Knowledge
Premises of CK

    Municipalities are the locus of change
    Cities = Structures + Activities
    Reality = Backlog + Future Change
    Space Is the Glue
    Middle-out = Top-down + Bottom-up
    Government only has 6 tools for
     implementation and data collection
Premises of CK

  Municipalities are the locus of change
   Cities = Structures + Activities
      Like politics, “all change is local”
   Reality = is filtered/allowed by municipalities
      Change Backlog + Future Change
   Space Is the Glue
      with CK:
  Middle-out = Top-down information strategies
      City departments implement + Bottom-up
  Government onlyfarmed-in at a fine grain
      Urban information is has 6 tools for
      Documentation becomes Information
     implementation and data collection
      Intra- and Inter-departmental sharing is commonplace
      Regional patterns (SDI) emerge upon municipal
       foundations
Premises of CK

   Municipalities are the locus of change
  Cities = Structures + Activities
  Reality = Backlog stable and permanent
   Structures are more + Future Change
    Space Is the Glue
   Structural change can be captured as it occurs
   Activities are more dynamic and fickle
  Middle-out = Top-down + Bottom-up
   Activities can be frozen in time tools for (snapshots)
    Government only has 6 and space
     with CK:
   implementation and data collection
      Information about structures is routinely updated
      Activities are “spatialized”
      Activities are periodically frozen
Premises of CK

    Municipalities are the locus of change
    Cities = Structures + Activities
  Reality = Backlog + Future Change
   Spaceis atheof “reality” already out there…
      There Is lot Glue
  Middle-out = Top-down + Bottom-up
      But the amount of information is finite
  Government only can be completely captured
      with CK the backlog has 6 tools for
    implementationrather data collection
      Urban change is and slow so, with CK
        all Structural change is captured at the source
        snapshots of activities are creatively obtained
      with CK, municipal information is “farmed” daily
Premises of CK

    Municipalities are the locus of change
    Cities = Structures + Activities
    Reality = Backlog + Future Change
  Space Is the Glue
   Middle-out = Top-down + Bottom-up
      within CK:
  Government key role in municipal information farming
      Space plays a only has 6 tools for

     implementation andprimary spatial identifiers
      Addresses are no longer
                               data collection
      GIS means Geographic Indexing Systems
      Space indexes our datasets
Premises of CK

    Municipalities are the locus of change
    Cities = Structures + Activities
    Reality = Backlog + Future Change
    Space Is the Glue
  Middle-out = Top-down + Bottom-up
   Government only has 6structured…
      Top-down is rigorous and tools for
     implementation an “imposition”collection
      … but is received as and data and resisted
      Bottom-up is passionate and self-interested…
        … but unstructured, unscalable and unsustainable
      with CK:
        Pure top-down and bottom-up approaches disappear
        Middle-out combines the positive traits of both
Premises of CK
  1. Ownership & Operation
  2. Regulation
  Municipalities are the locus of change
  3. Incentives/Disincentives
  Cities = Structures + Activities
  4. Education & Information
  Reality = Backlog + Future Change
  5. Rights
  Space Is the Glue
  6. Mitigation & Compensation
     Middle-out = Top-down + Bottom-up
     Government only has 6 tools for
      implementation and data collection
      with CK:
       Municipalities consciously & creatively combine the 6
         tools for
         Information Farming
         Policy/Plan Implementation
City Knowledge and Santa Fe


   Presentation in 2007
   Santa Fe Institute WPI Connection
   Nicholas De Monchaux
   Meeting Redfish – Steve Guerin
   The WPI Santa Fe Project Center
Steve
Fabio
S+F
Pipedreams
Steve
Fabio
S+F
Pipedreams
Venice Simtable (EU Mobilis)
Energence
NASA DEW
Ideagroup
Marco Polo Airport
Marco Polo Airport
Interactive Dynamic Master Planning
Steve
Fabio
S+F
Pipedreams
High-order geovisual primitives
Piping downhill
Autonomous Urban Agents
High-order geovisual primitives
Piping downhill
Autonomous Urban Agents
Filaments
Threads
Fabrics
Tree Maps
Geospatial SFI graph
High-order geovisual primitives
Piping downhill
Autonomous Urban Agents
Visuals beyond GIS
Connecting the dots
Do your best and pipe the rest!
High-order geovisual primitives
Piping downhill
Autonomous Urban Agents
Mobile agents
Structure agents
Birth Certificates
A new paradigm?
Fabio
carrera@wpi.edu
venice2point0.blogspot.com
venice2point0.org
www.wpi.edu/~carrera


Steve
stephen.guerin@redfish.com
www.redfish.com
www.sfcomplex.org
Ucgis Summer 09 Final
Ucgis Summer 09 Final

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Ucgis Summer 09 Final

  • 1. Autonomous Urban Agents: a Santa Fe Approach to City Knowledge Stephen Guerin Redfish Group / Santa Fe Complex Fabio Carrera WPI / MIT
  • 5. Complexity theory Autonomous agent modeling Ambient computing
  • 6. Complexity theory Autonomous agent modeling Ambient computing
  • 7. Flocking and swarming Josh Thorp, stigmergic.net
  • 9. Complexity theory Autonomous agent modeling Ambient computing
  • 10. Complexity theory Autonomous agent modeling Ambient computing
  • 12.
  • 14. Crowd Egress from Pittsburgh’s PNC Park
  • 15.
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  • 18.
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  • 20. Empirical Traffic Flows for Calibration 8000 9000 7000 8000 6000 7000 6000 5000 Traffic Volume Traffic Volume 5000 4000 4000 3000 3000 2000 2000 1000 1000 0 0 00:00 - 01:00 04:00 - 05:00 08:00 - 09:00 12:00 - 13:00 16:00 - 17:00 20:00 - 21:00 9/1/2006 9/5/2006 9/9/2006 9/13/2006 9/17/2006 9/21/2006 Date Time of Day Left Turn Thru 1 Thru 2 Thru 3 0 0 0 0 0 0 Left Turn Thru 1 Thru 2 Thru 3 0 0 0 0 0 0
  • 21. Cova, T.J., and Church, R.L. (1997) Modelling community evacuation vulnerability using GIS. International Journal of Geographical Information Science, 11(8): 763-784 Cova, T.J., and Johnson, J.P. (2002) Microsimulation of neighborhood evacuations in the urban-wildland interface. Environment and Planning A, 34(12): 2211-2229 Cova, T.J., and Johnson, J.P. (2003) A network flow model for lane-based evacuation routing. Transportation Research Part A: Policy and Practice, 37(7): 579-604 Cova, T.J. (2005) Public safety in the urban-wildland interface: Should fire-prone communities have a maximum occupancy? Natural Hazards Review, 6(3): 99-108 Cova, T.J., Dennison, P.E., Kim, T.H., and Moritz, M.A. (2005) Setting wildfire evacuation trigger-points using fire spread modeling and GIS. Transactions in GIS, 9(4): 603-617
  • 22.
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  • 26.
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  • 28.
  • 29. Complexity theory Autonomous agent modeling Ambient computing
  • 30. Complexity theory Autonomous agent modeling Ambient computing
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. Run applet and download code at http://www.sandtable.org
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. Reality Mining MIT Reality Mining with Nathan Eagle
  • 43. 1961-1977 ½ Ma 1977-1978 1978 -1988 ½ Ve 1988 - 2008 1996 - 2004 ½ SFe 2007 -
  • 44. Venice Project Center Visual perception and preference City Knowledge
  • 45. Venice Project Center Visual perception and preference City Knowledge
  • 46. Venice Project Center •Founded in 1988 •500+ alumni •125+ projects •10+ Awards •15+ Major media stories
  • 48. National Geographic Video Channel: National Geographic Video Series Title: Out There Program Title: City under Siege Aired around the world: 2002-today
  • 50. 20th Anniversary of VPC • Web page • Blog • Venipedia (wiki) • Alumni Network (ning) • Project Repository (Dspace)
  • 51. Venice Project Center Visual perception and preference City Knowledge
  • 52. Visual perception & preference •Original MIT dissertation •Why do we like/dislike cities? •Structures and Activities •How Carl Steinitz changed my life
  • 53. Venice Project Center Visual perception and preference City Knowledge
  • 55. Premises of CK  Municipalities are the locus of change  Cities = Structures + Activities  Reality = Backlog + Future Change  Space Is the Glue  Middle-out = Top-down + Bottom-up  Government only has 6 tools for implementation and data collection
  • 56. Premises of CK  Municipalities are the locus of change   Cities = Structures + Activities Like politics, “all change is local”   Reality = is filtered/allowed by municipalities Change Backlog + Future Change   Space Is the Glue with CK:  Middle-out = Top-down information strategies  City departments implement + Bottom-up  Government onlyfarmed-in at a fine grain  Urban information is has 6 tools for  Documentation becomes Information implementation and data collection  Intra- and Inter-departmental sharing is commonplace  Regional patterns (SDI) emerge upon municipal foundations
  • 57. Premises of CK  Municipalities are the locus of change  Cities = Structures + Activities  Reality = Backlog stable and permanent  Structures are more + Future Change Space Is the Glue   Structural change can be captured as it occurs  Activities are more dynamic and fickle  Middle-out = Top-down + Bottom-up   Activities can be frozen in time tools for (snapshots) Government only has 6 and space with CK:  implementation and data collection  Information about structures is routinely updated  Activities are “spatialized”  Activities are periodically frozen
  • 58. Premises of CK  Municipalities are the locus of change  Cities = Structures + Activities  Reality = Backlog + Future Change   Spaceis atheof “reality” already out there… There Is lot Glue  Middle-out = Top-down + Bottom-up  But the amount of information is finite  Government only can be completely captured  with CK the backlog has 6 tools for  implementationrather data collection Urban change is and slow so, with CK  all Structural change is captured at the source  snapshots of activities are creatively obtained  with CK, municipal information is “farmed” daily
  • 59. Premises of CK  Municipalities are the locus of change  Cities = Structures + Activities  Reality = Backlog + Future Change  Space Is the Glue   Middle-out = Top-down + Bottom-up within CK:  Government key role in municipal information farming  Space plays a only has 6 tools for implementation andprimary spatial identifiers  Addresses are no longer data collection  GIS means Geographic Indexing Systems  Space indexes our datasets
  • 60. Premises of CK  Municipalities are the locus of change  Cities = Structures + Activities  Reality = Backlog + Future Change  Space Is the Glue  Middle-out = Top-down + Bottom-up   Government only has 6structured… Top-down is rigorous and tools for implementation an “imposition”collection … but is received as and data and resisted  Bottom-up is passionate and self-interested… … but unstructured, unscalable and unsustainable  with CK:  Pure top-down and bottom-up approaches disappear  Middle-out combines the positive traits of both
  • 61. Premises of CK 1. Ownership & Operation 2. Regulation  Municipalities are the locus of change 3. Incentives/Disincentives  Cities = Structures + Activities 4. Education & Information  Reality = Backlog + Future Change 5. Rights  Space Is the Glue 6. Mitigation & Compensation  Middle-out = Top-down + Bottom-up  Government only has 6 tools for implementation and data collection  with CK:  Municipalities consciously & creatively combine the 6 tools for  Information Farming  Policy/Plan Implementation
  • 62. City Knowledge and Santa Fe  Presentation in 2007  Santa Fe Institute WPI Connection  Nicholas De Monchaux  Meeting Redfish – Steve Guerin  The WPI Santa Fe Project Center
  • 65. Venice Simtable (EU Mobilis) Energence NASA DEW Ideagroup Marco Polo Airport
  • 66.
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  • 69.
  • 70.
  • 71.
  • 72.
  • 73.
  • 74. Marco Polo Airport Interactive Dynamic Master Planning
  • 76. High-order geovisual primitives Piping downhill Autonomous Urban Agents
  • 77. High-order geovisual primitives Piping downhill Autonomous Urban Agents
  • 79. High-order geovisual primitives Piping downhill Autonomous Urban Agents
  • 80. Visuals beyond GIS Connecting the dots Do your best and pipe the rest!
  • 81. High-order geovisual primitives Piping downhill Autonomous Urban Agents
  • 82. Mobile agents Structure agents Birth Certificates A new paradigm?