Urban design & data

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Aug 3, 2013 Taipei @ Code for Tomorrow's Data Weekend.
Introducing "Urban Design" and why it's related to Urban Data. How does data utilization helps designer's decision making, and why designers and data engineers/scientists should collaborate.

Published in: Design, Technology, Business

Urban design & data

  1. URBAN DESIGN & DATA Roy Lin Aug.3 2013 Code for Tomorrow
  2. EXAMPLE 1: DATA FOR URBAN PROJECT DESIGN MEGASTORAGE, QUEENS Project by: Zhou Wu, Roy Lin
  3. JUSTUFIED ISSUE INSIGHTS! Observe Data Analyse Data Colect Data DomainKnowledge Consultants,Experts,Authorities Info Overlap Future Forcast Supporting Data Client Field Study Site Visit Interview . . . . . . CONCEPT PROPOSALOPINION STATEMENT DESIGN (Subjective) (Objective) Design Process and Data. by Roy Lin, 2013
  4. JUSTUFIED ISSUE INSIGHTS! Observe Data Analyse Data Colect Data DomainKnowledge Consultants,Experts,Authorities Info Overlap Future Forcast Supporting Data Client Field Study Site Visit Interview . . . . . . CONCEPT PROPOSALOPINION STATEMENT DESIGN (Subjective) (Objective)
  5. SUBWAY PASSENGER FLOW
  6. INDUSTRIAL LAND
  7. JUSTUFIED ISSUE INSIGHTS! Observe Data Analyse Data Colect Data DomainKnowledge Consultants,Experts,Authorities Info Overlap Future Forcast Supporting Data Client Field Study Site Visit Interview . . . . . . CONCEPT PROPOSALOPINION STATEMENT DESIGN (Subjective) (Objective)
  8. 30.1% INDUSTRY AUTO REPAIR OFFICE COMMERCE PUBLIC FACILITY HOUSING Land Use
  9. REGIONAL TRAIN LINES, FREIGHT TRAINS
  10. SITE VISIT
  11. JUSTUFIED ISSUE INSIGHTS! Observe Data Analyse Data Colect Data DomainKnowledge Consultants,Experts,Authorities Info Overlap Future Forcast Supporting Data Client Field Study Site Visit Interview . . . . . . CONCEPT PROPOSALOPINION STATEMENT DESIGN (Subjective) (Objective)
  12. Industry-Others Bronx Queens Brook lyn JerseyCity Manhattan Housing Industry-Storage OthersPublic Com m ercial New York Region Population Queens Land Use Portions NY POPULATION DISTRIBUTION, QUEENS LANDUSE
  13. JUSTUFIED ISSUE INSIGHTS! Observe Data Analyse Data Colect Data DomainKnowledge Consultants,Experts,Authorities Info Overlap Future Forcast Supporting Data Client Field Study Site Visit Interview . . . . . . CONCEPT PROPOSALOPINION STATEMENT DESIGN (Subjective) (Objective)
  14. TRAFFIC
  15. Inbound Outbound TRAFFIC
  16. Area = 597122 m² Total warehouse space=3277280 m³ Average floor height = 5.5 Floors 1415.4696 1415.4696 Area = 283308 m² Total warehouse space=3277280 m³ Average floor height = 11.6 Floors --Inbound --Outbound --roads Area = 396979 m² Total warehouse space=3277280 m³ Average floor height = 8.3 Floors less=46620 m³ A=34475m² F=5 A=63678m² F=6 A=67524m² F=7 A=59980m² F=8 A=56238m² F=9 A=57009m² F=10 A=49411m² F=11 A=8663m² F=12 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 A=34475m²F=5 Area = 396979 m² Total warehouse space=3277280 m³ Average floor height = 8.3 Floors Area = 378749 m² Total warehouse space=3277280 m³ Average floor height = 8.6 Floors
  17. PASSENGER FLOW vs ATTRACTIONS
  18. SOCIAL HOUSING FOOTPRINT vs TIME
  19. FLOODING OVER YEARS
  20. EXAMPLE 2: READING URBAN CONTEXT TRANSIT FOR MIT KENDALLSQ. Project by: Ren Tian, Tuan-Yee Ching, Roy Lin
  21. TRANPORTATION TOOLS INFO MATRIX
  22. COMMUTER RAIL RAIL TRANSIT BUS TRANSIT MAJOR RAIL STATIONAIRPORT FERRY METRO NETWORKS
  23. LOCAL NETWORKS RAIL TRANSIT / 400M (5MIN)RADIUS BUS TRANSIT PROTECTED PATH BIKE LANE ZIP CAR
  24. EXAMPLE 3: CITY STORY TELLING THE CALIFORNIAREGION Project by: Tomas Folch, Kees Lokman, Rou Lin
  25. LANDSCAPE, ENERGY, INFRASTRUCTURES, MIGRATION, WAR, AGRICULTURE...
  26. EXAMPLE 4: PREDICT & SUGGEST SOLUTION SPACE SYNTAX
  27. Tim Stonor and SPACE SYNTAX
  28. HOT ZONES
  29. HOT ZONES
  30. EXAMPLE 5: DEMOCRATIZE URBAN KNOWLEDGE SOCIALEXPLOER
  31. URBAN CONTEXT TRADITIONAL URBAN DATA Public Sectors, GIS, Zoning, Land, Infrastructure, Transportation, Demographic, ...a lot of To-Be- Opened Data! Ashit load of everything Private Sectors Data, User-Gnerated Data, Instant and Dynamic Data, ...More-To-Come Big Data! NEW URBAN DATA Real Innovation! Urban Context, Data, and Innovation. by Roy Lin, 2013
  32. wearedata.watchdogs.com
  33. senseable.mit.edu
  34. www.honda.co.jp
  35. URBAN CONTEXT TRADITIONAL URBAN DATA Public Sectors, GIS, Zoning, Land, Infrastructure, Transportation, Demographic, ...a lot of To-Be- Opened Data! Ashit load of everything Private Sectors Data, User-Gnerated Data, Instant and Dynamic Data, ...More-To-Come Big Data! NEW URBAN DATA Real Innovation! Urban Context, Data, and Innovation. by Roy Lin, 2013

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