Ride the Storm: Navigating Through Unstable Periods / Katerina Rudko (Belka G...
Imperial_College_Seminar.key
1.
2. Urban Metabolism
Understanding Resource Flows in Cities
David Quinn - PhD Candidate
Building Technology
School of Architecture
Massachusetts Institute of Technology
March 16, 2011
3. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
4. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
5.
6. London
Image source: http://earthobservatory.nasa.gov/
8. Global Dynamics
Global Resource Use
Global Population Growth
Data:
US Census
UN Population
Projection
9. Global Dynamics
Global Resource Use
Global Population Growth
Source: Krausmann (2009)
Data:
US Census
UN Population
Projection
10. Global Dynamics
Global Resource Use
Global Population Growth
Source: Krausmann (2009)
Data:
Source:
US Census
NASA (2007)
UN Population
Projection
11. Global Dynamics
Global Resource Use
Global Population Growth
Source: Krausmann (2009)
Data:
Source:
US Census
NASA (2007)
UN Population
Projection
Source:
Folke (2009)
12. Global Dynamics
Global Resource Use
Global Population Growth
Source: Krausmann (2009)
Data:
Source:
US Census
NASA (2007)
UN Population
Projection
Source:
Folke (2009)
23. R E S E A R C H A N D A N A LY S I S
Figure 1 The urban metabolism of Brussels, Belgium in the early 1970s. Source: Duvigneaud and
Denaeyer-De Smet 1977.
Image source: Duvigneaud and Denaeyer-De Smet(1977);
from the early 1970s, although Tokyo in 1970 and ing water table falls with increased extraction,Kennedy (2007)
cited by
24. that has been given much attention.22 choices
The for socioeconomic metabolism, they tend to be
made lie somewhere between a fairly restrictive kept separate because of their sheer amounts and
definition following the boundaries of the the supposedly low impact of their use (Bringezu
economy (all materials that are economicallyval- et al. 1997a; Statistisches Bundesamt 1998;
ued are considered as inputs but not, for example, Stahmer et al. 1997; Hiittler et al. 1996; Schandl
$--
J
IMPORTS
DOMESTIC
mcnoN: ‘Direct MaterialInput“
biomass. fuels,
minerals
Domesnc
hidden STOCKS
Domestic Envimnnnnt
Figure I Relevant material flows, terminology. and system boundaries (national level).
Fischer-Kowolski and Hijttler, Society’sMetabolism. Part I 1 I 17
Image source: Fischer-Kowalski (1999)
26. Data Structure & Methodology
Representation of
Built Environment
Analysis of Built
Environment
Improved
Understanding
27. Data Structure & Methodology
Representation of
Built Environment
Analysis of Built
Environment
Improved
Understanding
28. Data Structure & Methodology
Representation of
Built Environment
Analysis of Built
Environment
Improved
Understanding
29. Data Structure & Methodology
Representation of
UrbMetGML
Built Environment Regional
City
Neighborhood
Analysis of Built
Environment LoD 3
LoD 4
Improved
Understanding
Ref:
CityGML, Kolbe 2005
30. Data Structure & Methodology
Representation of
UrbMetGML
Built Environment Regional
City
Neighborhood
Analysis of Built
Environment LoD 3
LoD 4
Improved
Understanding
‘Script-based’ analysis
Ref:
CityGML, Kolbe 2005
31. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
32. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
33. Data Structure & Methodology
Regional
City
Neighborhood
Data: US Census (2000)
LoD 3
LoD 4
34. Data Structure & Methodology
Regional
City
Neighborhood
Data: US Census (2000)
LoD 3
LoD 4
Ref:
‘Predictable Cities’ image from Bettencourt 2010
35. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
36. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
37. Data Structure & Methodology
Regional
City
Neighborhood
●
LoD 3
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LoD 4
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Following the approaches used by:
Clark 1951; Zipf 1972; Newman 1989
38. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
39. Data Structure & Methodology
Neighborhood
Typology
Regional
City
Material Energy Water Neighborhood
LoD 3
LoD 4
Neighborhood
Performance
40. Data Structure & Methodology
Regional
City
Neighborhood
LoD 3
LoD 4
41. Data Structure & Methodology
Neighborhood
Typology
Regional
City
Material Energy Water Neighborhood
LoD 3
LoD 4
Neighborhood
Performance
42. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
44. Regional Scale: Land-Use Change, Costa Rica
• Multinomial logistic regression used to
Landsat Elevation Climate
categorize land-use Data Data Data
• Data formatted and analyzed using
ArcGIS, Python and R
9 Land Use
Classifications
Research project with Dr. Juan Carlos Vargas-Moreno and Eduardo Perez
Work done by Daniel Wiesmann and David Quinn
45. Method Data Flow
Map Landsat TM ElevaJon, Slope & PrecipitaJon &
Preprocessing (6 bands) Aspect Temperature
MulJnomial Land Cover Map Land Cover Map
logisJc regression/ 1989 2001
Naive Bayesian
Change accounJng Land Cover Change
Map
Emission accounJng
IPCC Methodology CO2 Emissions
49. Method Data Flow
Data PopulaJon growth Available Space Criteria for
PreparaJon projecJons ‘AFracJveness’
Urban Growth Model SimulaJon Scenario Analysis
Policy Development Decision?
53. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
64. US Cities: Population Density Gradient
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Data Source:
US Census
(2000)
65. US Cities: Population Density Gradient
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br
y = Ae
Data Source:
US Census
(2000)
66. US Cities: Population Density Gradient
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A
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br
y = Ae
Data Source:
US Census
(2000)
67. US Cities: Population Density Gradient
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b
A
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br
y = Ae
Data Source:
US Census
(2000)
68. US Cities: Population Density Gradient
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b
A
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●●●●●●●●●●
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br
y = Ae
Data Source:
US Census
(2000)
69. US Cities: Population Density Gradient
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●●●
br
y = Ae
Data Source:
US Census
(2000)
70. US Cities: Population Density Gradient
New York
A = 11,263.3
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b = -0.06
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br
y = Ae
Data Source:
US Census
(2000)
71. US Cities: Population Density Gradient
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br
y = Ae
Data Source:
US Census
(2000)
72. US Cities: Population Density Gradient
Chicago
A = 8,387
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b = -0.06
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br
y = Ae
Data Source:
US Census
(2000)
73. US Cities: Population Density Gradient
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●●●
br
y = Ae
Data Source:
US Census
(2000)
74. US Cities: Population Density Gradient
●
Atlanta
A = 2,200
b = -0.06
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br
y = Ae
Data Source:
US Census
(2000)
122. City Scale: USA
Population density y = Ae br
Road Scaling y = Arb
Transportation distances y = Ae br
123. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
125. Neighborhood Scale: Typologies
London Analysis
What?
To develop a standard way to analyze the resource performance of
neighborhoods
Why?
For improved understanding on relationships between urban
configuration and resource consumption and potentials
To inform urban design through guidelines and scenario testing
144. Urban Form
Characterization
Clusters
Renewable
Resource Statistical
Energy
Consumption Relationships
Potential
Neighborhood
Performance
145. Analyzing the Clusters
Statistically relate clusters with resource performance
measures (consumption of electricity, gas, materials)
1. What is the specific tendency for each cluster
variable?
2. Is the clustering meaningful in terms of energy
consumption? What are the differences?
146. Regression with Spatial Autocorrelation
Formal name: Spatial error model
i.e. regression that corrects for spatial autocorrelation
Y = Xβ + ε = Xβ + λWε + μ
Define neighborhood relation
5 Km radius W
Define weighting scheme
inv. distance squared W
Calculate relational matrix W
Maximum likelihood estimation
using neighborhood matrix Y
147. Neighborhood Matrix
Number of regions: 940
Number of nonzero links: 55014
Percentage nonzero weights: 6%
Average number of links: 59
148. Neighborhood Matrix
Cluster
Number of regions: 940
Number of nonzero links: 55014
1
Percentage nonzero weights: 6%
Average number of links: 59 2
150. Urban Form
Characterization
Clusters
Renewable
Resource Statistical
Energy
Consumption Relationships
Potential
Neighborhood
Performance
151. The city as energy plant
More ef'icient and high potential for renewable energy @ district level
GSHP
152. Top-down estimation of urban RE viability
Energy demands and solar supply potential in a city How close together can ground‐source heat pumps be 'itted?
(source: Barret, 2009) (source: MacKay, 2009)
Analysis of London, UK – REP indexes based on spatial configuration & land use
+ = urban REP
RE viability
153. Top-down estimation of urban RE viability
Energy demands and solar supply potential in a city How close together can ground‐source heat pumps be 'itted?
(source: Barret, 2009) (source: MacKay, 2009)
Analysis of London, UK – REP indexes based on spatial configuration & land use
+ = urban REP
RE viability
154. Top-down estimation of urban RE viability
Energy demands and solar supply potential in a city How close together can ground‐source heat pumps be 'itted?
(source: Barret, 2009) (source: MacKay, 2009)
Analysis of London, UK – REP indexes based on spatial configuration & land use
+ = urban REP
RE viability
155. + + +
- -
hh
Façade Solar Roof Solar Dom Shallow GSHP
156. Urban Form
Characterization
Clusters
Renewable
Resource Statistical
Energy
Consumption Relationships
Potential
Neighborhood
Performance
161. Outline
1 intro to urban metabolism & global dynamics
2 regional scale analysis: Costa Rica & Florida
3 city scale analysis: US cities
4 neighbourhood analysis: London
5 conclusions & future work
162. Conclusions and Outlook
1 Integration of spatial components and urban modelling with urban
metabolism is becoming easier to do
2 Standardized analysis is an useful approach for estimating gross resource
performances
3 More work is needed using bottom-up analysis for validation in this work
163. Conclusions and Outlook
1 Integration of spatial components and urban modelling with urban
metabolism is becoming easier to do
2 Standardized analysis is an useful approach for estimating gross resource
performances
3 More work is needed using bottom-up analysis for validation in this work
questions or comments?
djq@mit.edu
Editor's Notes
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Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
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Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
global material usage for the last ~100 years\n\nFigure from Krausmann et al. (2009):\n“Growth in global materials use, GDP and population during the 20th century”\n
per capita material usage\n\nChange in energy use is not really shown here, as the energy content of fuels has changed dramatically\n
per capita material usage\n\n20% increase since 1970s\n
Population growth growing exponentially - doubling time of 40 years urban areas (Sterman 2000) with the majority of it in industrializing countries. \nRef: http://data.giss.nasa.gov/gistemp/graphs/Fig.A2.txt\n
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dominated by water,\ndoes not consider CO2\n\n\n
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early urban metabolism study\n
black box; no explanatory mechanisms\n
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Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Spatial and semantic detail\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
Costa Rica has a goal of being Carbon Neutral by 2020. This project is one attempt to analyze the carbon dynamics of the main city, and to understand what the effect of urbanization will be on the carbon dynamics of the country\n
multinomial logistic regression\n\nRemote sensing data from Costa Rica converted to Land-Use map\nLand-Use related to carbon dynamics\nSD model and dynamic visualization for policy makers\n
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working on completing an R-package that does this analysis and returns a carbon accounting value...\n
insert map of area and orlando\n30 year time span; illustrates an approach to urban growth modeling that is relevant to estimating energy and material use in cities\n
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This is an example of one area of urban growth, from the overall southern Florida region.\n\nI developed the specific implementation of the preferential allocation algorithm using a Python script for ArcGIS.\n\nThis is allocation algorithm is being used as part of an ongoing research project.\n
I will focus on one small part of this area, and show the urban projected urban development pattern of this area.\nAnimation of growth patterns from 2009 - 2040 using attractiveness as a criteria for where urban growth is allocated.\n\n
Base on attractiveness measures (Distance to schools, public services, facilities) \nthis illustrates where growth will occur, depending on what land is available, population growth, and zoning.\n\nThis enables policy exploration of the area, and could be use to influence what the material and energy use will \n\nbringing me to the final part of my presentation.\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
US cities\n
US cities\n
US cities\n
Illustration of interesting patterns\n
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greater than x% of trips by car \n\n4% of all trips, Bertaud 2002\n\n\n
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Using levels of service density, center \n\n\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
min: -0.01, \nmax:-0.5\n
CLARK 1951\n
CLARK 1951\n
CLARK 1951\n
CLARK 1951\n
CLARK 1951\n
CLARK 1951\n
Exponentially decreasing\n
Use CBD as center point for measurement\n
all roads\n
local roads\n
relevant for heat island calculations\n
relevant for heat island calculations\n
local roads\n
local roads\n
local roads\n
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Using levels of service density, center defined\n
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ATUS time use survey | Artessa | \n
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ATUS time use survey | Artessa | \n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
can be converted into joules | multiplied by number of people there\nlower bound of energy use | cities examined were primarily car \nverify using MAPC data which has distance traveled by owner by location\n\ncaveats - people maximize utility, rather than minimizing distance\n\n\n
the story that I want to tell is that based on the pop gradient; cities will have very different transportation patterns that they can move towards based on path dependence.\n\n\n
ATUS time use survey | Artessa | \n
ATUS time use survey | Artessa | \n
ATUS time use survey | Artessa | \n
US cities\n
US cities\n
US cities\n
US cities\n
US cities\n
US cities\n
US cities\n
US cities\n
Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
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Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
5 measures used\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
Building Outlines ; Road Networks ; Land Uses\n\n\n
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Building Outlines ; Road Networks ; Land Uses\n\n\n
