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Ucgis Summer 09 Final
 

Ucgis Summer 09 Final

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    Ucgis Summer 09 Final Ucgis Summer 09 Final Presentation Transcript

    • 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 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
    • 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