Trends with benefits parametric urban design

Page 1

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


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