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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 & DATARoy Lin
Aug.3 2013 Code for Tomorrow
EXAMPLE 1:DATA FOR URBAN PROJECT DESIGN
MEGA STORAGE, QUEENS
Project by: Zhou Wu, Roy Lin
JUSTUFIEDISSUE
INSIGHTS!
Observe Data Analyse Data
Colect Data
Domain KnowledgeConsultants,Experts,Authorities
Info Overlap Future ForcastSupporting Data
ClientField Study
Site Visit
Interview...
...
CONCEPT PROPOSALOPINION
STATEMENT DESIGN
(Subjective) (Objective)
Design Process and Data. by Roy Lin, 2013
JUSTUFIEDISSUE
INSIGHTS!
Observe Data Analyse Data
Colect Data
Domain KnowledgeConsultants,Experts,Authorities
Info Overlap Future ForcastSupporting Data
ClientField Study
Site Visit
Interview...
...
CONCEPT PROPOSALOPINION
STATEMENT DESIGN
(Subjective) (Objective)
SUBWAY PASSENGER FLOW
INDUSTRIAL LAND
JUSTUFIEDISSUE
INSIGHTS!
Observe Data Analyse Data
Colect Data
Domain KnowledgeConsultants,Experts,Authorities
Info Overlap Future ForcastSupporting Data
ClientField Study
Site Visit
Interview...
...
CONCEPT PROPOSALOPINION
STATEMENT DESIGN
(Subjective) (Objective)
30.1%14.5%
5.5%
12.3%37.6%
INDUSTRY
AUTO REPAIR
OFFICECOMMERCE
PUBLIC FACILITY
HOUSING
OTHER 14.5% OPEN SPACE 0.11%
30.1%14.5%
5.5%
12.3%37.6%
INDUSTRY
AUTO REPAIR
OFFICECOMMERCE
PUBLIC FACILITY
HOUSING
OTHER 14.5% OPEN SPACE 0.11%
Land Use
REGIONAL TRAIN LINES, FREIGHT TRAINS
SITE VISIT
JUSTUFIEDISSUE
INSIGHTS!
Observe Data Analyse Data
Colect Data
Domain KnowledgeConsultants,Experts,Authorities
Info Overlap Future ForcastSupporting Data
ClientField Study
Site Visit
Interview...
...
CONCEPT PROPOSALOPINION
STATEMENT DESIGN
(Subjective) (Objective)
Industry - Others
Bronx Queens
Brook
lyn
J
erse
y C
ity
M
anha
ttan
Hous
ing
Industry - Storage Others Public Commer
cial
New York RegionPopulation
QueensLand Use Portions
NY POPULATION DISTRIBUTION, QUEENS LANDUSE
JUSTUFIEDISSUE
INSIGHTS!
Observe Data Analyse Data
Colect Data
Domain KnowledgeConsultants,Experts,Authorities
Info Overlap Future ForcastSupporting Data
ClientField Study
Site Visit
Interview...
...
CONCEPT PROPOSALOPINION
STATEMENT DESIGN
(Subjective) (Objective)
TRAFFIC
InboundOutbound
TRAFFIC
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 =
3447
5 m
² F
= 5
A =
6367
8 m
² F
= 6
A =
6752
4 m
² F
= 7
A =
5998
0 m
² F
= 8
A =
5623
8 m
² F
= 9
A =
5700
9 m
² F
= 10
A =
4941
1 m
² F
=11
A =
8663
m²
F=1
2
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 5
A =
3447
5 m
² F
= 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
InboundOutbound
PASSENGER FLOW vs ATTRACTIONS
SOCIAL HOUSING FOOTPRINT vs TIME
FLOODING OVER YEARS
EXAMPLE 2:READING URBAN CONTEXT
TRANSIT FOR MIT KENDALL SQ.
Project by: Ren Tian, Tuan-Yee Ching, Roy Lin
TRANPORTATION TOOLS INFO MATRIX
COMMUTER RAIL RAIL TRANSIT BUS TRANSIT
MAJOR RAIL STATIONAIRPORT FERRY
METRO NETWORKS
LOCAL NETWORKS
RAIL TRANSIT /400M (5MIN) RADIUS BUS TRANSIT
PROTECTED PATH BIKE LANE ZIP CAR
EXAMPLE 3:CITY STORY TELLING
THE CALIFORNIA REGION
Project by: Tomas Folch, Kees Lokman, Rou Lin
LANDSCAPE, ENERGY, INFRASTRUCTURES, MIGRATION, WAR, AGRICULTURE...
EXAMPLE 4:PREDICT & SUGGEST SOLUTION
SPACE SYNTAX
Tim Stonor andSPACE SYNTAX
HOT ZONES
HOT ZONES
EXAMPLE 5:DEMOCRATIZE URBAN KNOWLEDGE
SOCIAL EXPLOER
URBAN CONTEXT
TRADITIONAL URBAN DATAPublic Sectors, GIS, Zoning, Land, Infrastructure, Transportation, Demographic, ...a lot of To-Be-Opened Data!
A shit 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
wearedata.watchdogs.com
senseable.mit.edu
www.honda.co.jp
URBAN CONTEXT
TRADITIONAL URBAN DATAPublic Sectors, GIS, Zoning, Land, Infrastructure, Transportation, Demographic, ...a lot of To-Be-Opened Data!
A shit 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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