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.

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)

7. SUBWAY PASSENGER FLOW

8. INDUSTRIAL LAND

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)

10. 30.1%
INDUSTRY
AUTO REPAIR
OFFICE
COMMERCE
PUBLIC FACILITY
HOUSING
Land Use

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

18. Inbound
Outbound
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

26. FLOODING OVER YEARS

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

45. wearedata.watchdogs.com

46. senseable.mit.edu

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