Source: Virtual Shanghai Project (www.virtualshanghai.net)
Analyze the dynamic trends in urbanism over the course of its history Data Analyzed Number Of Buildings
Percentage of Public/Green Space
200
80
150
60
100
40
50
20
0
0
1849
1883
1917
1941
1984 2013
1849
Average Building Height (m)
The purpose of the design is to project a possible future outcome of the site, considering the beneficial qualities of various time periods, and how these might be adapted to meet the demands of flexible, future urbanisms. So firstly, some important data of the site are mined since the area appeared at various junctures throughout history (years 1849, 1883, 1917, 1941, 1984, and 2013)
1883
1917
1941
1984 2013
1941
1984 2013
Percent New Volume
20000
80
15000
60
10000
40
5000
20
Different Urban Swatch 1849
1883
1917
0
1849
1883
1917
1941
1984 2013
1849
Gross Floor Area
1883
1917
Clustering by Program
0.8
1000 Commercial
1941
1984
0.6
Commercial
800
0.4
OďŹƒce
600
2013
0.2
400 OďŹƒce Residential
0
1849
21
real photos in this page from Google Earth
Residential
200
1883
1917
1941
1984 2013
1849
1883
1917
1941
1984 2013
Apply long data to project future urban growth patterns Historic Trend: Clustering Factor
Extrapolation of Recent Trend
1000
First Variation: Increase
0:1
Second Variation: Decrease 0:1
0:1
Commercial 800
600
400 OďŹƒce Residential
200
1849
1883
1917
1941
2013
0:100
2113
0:100
2013
2113
2013
0:100
2113
Additional Variable: Demand for GFA 0:1
1984 2013
Driving Parameter:
Clustering Factor Hypothesis: We will test three possible future trends in clustering factors, with a constant increse in Demand for GFA. We believe that by projecting future development with a low clustering factor (meaning programs are evenly distributed on the site) the result will be more diverse in terms of architecture typologies. Additionally, by distributing programs evenly on the site, a more activated 24 hour street life should emerge, unlike current conditions where much of the site is empty and neglected during certain times of the day.
2013
0:100
2113
Additional Variable: Percent Open Space 0:1
Commercial
High Clustering Factor: Programs are tightly clustered by city block with little or no intermingling between programs. This is not ideal because it causes areas of the site to become deactivated at certain times of the day.
Office
Residential
Low Clustering Factor: Programs are evenly dispersed throughout the site. Each urban clock contains each program, allowing for maximum 24 hour activation of the site. This is the ideal target of the experiment.
0:100
2013
2113
22
Use hoopsnake for recursive time-based projections of future urbanisms.
Loop Process
starting data
output data
Driving Variable
Year Year Additional Variable
Data Processing Year Additional Variable
Year
23
Geometry Adjustment
Different outcome got from different future trends of driving parameter Extrapolation of Recent Trend
2013
2063
2113
2013
2063
2113
2013
2063
2113
0:1
0:100 2013
2113
First Variation: Increase 0:1
0:100 2013
2113
Second Variation: Decrease 0:1
0:100 2013
2113
24
Simulation series
25
2013
2015
2017
2019
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
2091
2093
2095
2097
2099
2101
2103
2105
2107