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.
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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
- 6. 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)
- 9. 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)
- 11. REGIONAL TRAIN LINES, FREIGHT TRAINS
- 12. SITE VISIT
- 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. 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
- 16. 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)
- 17. TRAFFIC
- 19. 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
- 24. PASSENGER FLOW vs ATTRACTIONS
- 25. SOCIAL HOUSING FOOTPRINT vs TIME
- 27. EXAMPLE 2: READING URBAN CONTEXT TRANSIT FOR MIT KENDALLSQ. Project by: Ren Tian, Tuan-Yee Ching, Roy Lin
- 28. TRANPORTATION TOOLS INFO MATRIX
- 29. COMMUTER RAIL RAIL TRANSIT BUS TRANSIT MAJOR RAIL STATIONAIRPORT FERRY METRO NETWORKS
- 30. LOCAL NETWORKS RAIL TRANSIT / 400M (5MIN)RADIUS BUS TRANSIT PROTECTED PATH BIKE LANE ZIP CAR
- 31. EXAMPLE 3: CITY STORY TELLING THE CALIFORNIAREGION Project by: Tomas Folch, Kees Lokman, Rou Lin
- 32. LANDSCAPE, ENERGY, INFRASTRUCTURES, MIGRATION, WAR, AGRICULTURE...
- 33. EXAMPLE 4: PREDICT & SUGGEST SOLUTION SPACE SYNTAX
- 34. Tim Stonor and SPACE SYNTAX
- 35. HOT ZONES
- 36. HOT ZONES
- 41. EXAMPLE 5: DEMOCRATIZE URBAN KNOWLEDGE SOCIALEXPLOER
- 44. 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
- 47. www.honda.co.jp
- 48. 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