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  • \n
  • \n\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
  • Wolman 1965 - the metabolism of cities\nenergy, materials, water, nutrients\n
  • \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
  • 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
  • \n
  • dominated by water,\ndoes not consider CO2\n\n\n
  • \n
  • early urban metabolism study\n
  • black box; no explanatory mechanisms\n
  • \n
  • \n
  • \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
  • 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
  • \n
  • \n
  • 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
  • \n
  • 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
  • \n
  • greater than x% of trips by car \n\n4% of all trips, Bertaud 2002\n\n\n
  • \n
  • \n
  • 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
  • \n
  • \n
  • Using levels of service density, center defined\n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • ATUS time use survey | Artessa | \n
  • \n
  • \n
  • \n
  • 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
  • \n
  • \n
  • \n
  • \n
  • \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
  • 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
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • Building Outlines ; Road Networks ; Land Uses\n\n\n
  • \n
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  • Maybe include here???\n\n
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Imperial_College_Seminar.key Imperial_College_Seminar.key Presentation Transcript

  • Urban MetabolismUnderstanding Resource Flows in CitiesDavid Quinn - PhD CandidateBuilding TechnologySchool of ArchitectureMassachusetts Institute of TechnologyMarch 16, 2011
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • London Image source: http://earthobservatory.nasa.gov/
  • Urban Metabolism FrameworkDEF:
Urban
Metabolism
is
the
study
of
the
flows
required
to
serve
an
urban
economySociometabolic
regimes(Fischer‐Kowalski
and
Haberl
1997;
Krausmann
et
al
2007;
Krausman
et
al.
2008)Material
Flow
Analysis(Fischer‐Kowalski
and
HuFler
1999;
Daniels
and
Moore
2001;
Fernandez
2007)SpaJally
located
urban
resource
intensity
metrics(Wolman
1965;
Kennedy
(various);
Schulz
2007) Slide
adapted
from
John
E.
Fernandez
  • Global Dynamics Global Resource Use Global Population GrowthData:US CensusUN PopulationProjection
  • Global Dynamics Global Resource Use Global Population Growth Source: Krausmann (2009)Data:US CensusUN PopulationProjection
  • Global Dynamics Global Resource Use Global Population Growth Source: Krausmann (2009)Data: Source:US Census NASA (2007)UN PopulationProjection
  • Global Dynamics Global Resource Use Global Population Growth Source: Krausmann (2009)Data: Source:US Census NASA (2007)UN PopulationProjection Source: Folke (2009)
  • Global Dynamics Global Resource Use Global Population Growth Source: Krausmann (2009)Data: Source:US Census NASA (2007)UN PopulationProjection Source: Folke (2009)
  • Global Dynamics
  • Global Dynamics Data: US Census UN Population Projection
  • Global Dynamics Source: Krausmann (2009)Data: Source:US Census NASA (2007)UN PopulationProjection Source: Folke (2009)
  • Global Dynamics Source: Krausmann (2009)
  • Individual Resource Use Source: Krausmann (2009)
  • Individual Energy UseEnergy [GJ] Source: Luzatti (2009)
  • Global Dynamics Source: Krausmann (2009)Data: Source:US Census NASA (2007)UN PopulationProjection Source: Folke (2009)
  • Wolman 1965Concerned with shortages of water and air/water pollutionComputations for a hypothetical city of one million
  • Wolman 1965
  • R E S E A R C H A N D A N A LY S I SFigure 1 The urban metabolism of Brussels, Belgium in the early 1970s. Source: Duvigneaud andDenaeyer-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
  • that has been given much attention.22 choices The for socioeconomic metabolism, they tend to bemade lie somewhere between a fairly restrictive kept separate because of their sheer amounts anddefinition following the boundaries of the the supposedly low impact of their use (Bringezueconomy (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 EnvimnnnntFigure 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)
  • Data Structure & Methodology
  • Data Structure & MethodologyRepresentation ofBuilt EnvironmentAnalysis of Built Environment Improved Understanding
  • Data Structure & MethodologyRepresentation ofBuilt EnvironmentAnalysis of Built Environment Improved Understanding
  • Data Structure & MethodologyRepresentation ofBuilt EnvironmentAnalysis of Built Environment Improved Understanding
  • Data Structure & Methodology Representation of UrbMetGML Built Environment Regional City Neighborhood Analysis of Built Environment LoD 3 LoD 4 Improved UnderstandingRef:CityGML, Kolbe 2005
  • Data Structure & Methodology Representation of UrbMetGML Built Environment Regional City Neighborhood Analysis of Built Environment LoD 3 LoD 4 Improved Understanding ‘Script-based’ analysisRef:CityGML, Kolbe 2005
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology      Regional   City      Neighborhood Data: US Census (2000) LoD 3 LoD 4
  • Data Structure & Methodology      Regional   City      Neighborhood Data: US Census (2000) LoD 3 LoD 4Ref:‘Predictable Cities’ image from Bettencourt 2010
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology Regional City Neighborhood ● LoD 3   ●   LoD 4 ● ● ● ●● ●●●●●●● ●● ● ●●● ●●● ●● ● ●●●●●●● ●●●●● ● ● ●● ● ● ● ●●●Following the approaches used by:Clark 1951; Zipf 1972; Newman 1989
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology Neighborhood Typology Regional CityMaterial Energy Water Neighborhood LoD 3 LoD 4 Neighborhood Performance
  • Data Structure & Methodology Regional City Neighborhood LoD 3 LoD 4
  • Data Structure & Methodology Neighborhood Typology Regional CityMaterial Energy Water Neighborhood LoD 3 LoD 4 Neighborhood Performance
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • Regional Scale: Land-Use Change, Costa Rica
  • 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
  • Method Data
FlowMap
 Landsat
TM ElevaJon,
Slope
&
 PrecipitaJon
&
Preprocessing (6
bands) Aspect TemperatureMulJnomial
 Land
Cover
Map Land
Cover
MaplogisJc
regression/ 1989 2001Naive
BayesianChange
accounJng Land
Cover
Change
 MapEmission
accounJngIPCC
Methodology CO2
Emissions
  • Regional Land-Use Change - Costa Rica Region Urban Area 1989
  • Regional Land-Use Change - Costa Rica Region Urban Area 2001
  • Regional Scale: Urban Growth Modeling
  • Method Data
FlowData
 PopulaJon
growth
 Available
Space Criteria
for
PreparaJon projecJons ‘AFracJveness’Urban
Growth
Model SimulaJon Scenario
AnalysisPolicy
Development Decision?
  • Regional Scale: Urban Growth Modeling 45 km Residential Land Use Orlando
  • Regional Scale: Urban Growth Modeling 45 km Residential Land Use Orlando
  • Regional Scale: Urban Growth Modeling Residential Land Use
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • City Scale: USA
  • City Scale: USAPopulation density
  • City Scale: USAPopulation density Road Scaling
  • City Scale: USA Population density Road ScalingTransportation distances
  • Atlanta
  • Atlanta Road Network
  • Atlanta: Population Density
  • Atlanta: Population Density ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●●
  • US Cities: Population Density Gradient ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● A ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● b A ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● b A ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient New York A = 11,263.3 ● b = -0.06 ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient Chicago A = 8,387 ● b = -0.06 ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • US Cities: Population Density Gradient ● Atlanta A = 2,200 b = -0.06 ● ● ● ● ●●● ●●●● ●●●●●●●●●● ●●●●● ●●●●●●●●● ●●●●●●●●●● ●●● bry = Ae Data Source: US Census (2000)
  • Road Density Road CBD Radius
  • Road Density (all) ● ● ● ● ● ●● ●●●●●●● ●●●●●● ●●●●●● ●●●●●●●●●●● ●●●●● ●●●● ●● b y = Ar
  • Road Density (all) ● ● ● ● ● ●● ●●●●●●● ●●●●●● ●●●●●● ●●●●●●●●●●● ●●●●● ●●●● ●● b y = Ar
  • b Local Road Density: y = Ar Legend
  • b Local Road Density: y = Ar Legend
  • b Local Road Density: y = Ar Legend
  • City Typologies ?
  • City Typologies ?
  • City Typologies ?
  • Image source: UNEP (2008)
  • Distance to service CBD Radius RadiusDistance to Services
  • Atlanta: Service Density
  • Distance to service CBD Radius RadiusDistance to Services
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBDDistance to serviceRadius
  • CBD Distance to service Radiusy = Ae br
  • Households
  • Distance Travelled per week
  • Total Distance Travelled in City
  • t‐value:









30.23989P‐value:










0.000Adj‐R‐Square:
0.9345
  • Distance to Services
  • Distance to Services
  • Distance to Services 10 km
  • Distance to Services 10 km 130 km Radius: 60 km
  • ● ●● ● ●● ●● ●● ●● ● ●● ●●●●●●●●● ●●● ●● ●●● ●● ●●● ●●●● ● ●●●● ●●● ●●
  • ● ●● ● ●● ●● ●● ●● ● ●● ●●●●●●●●● ●●● ●● ●●● ●● ●●● ●●●● ● ●●●● ●● ● ●● bry = Ae
  • t‐value:










30.240P‐value:











0.000Adj‐R‐Square:

0.935
  • t‐value:










30.240 P‐value:











0.000 Adj‐R‐Square:

0.935Walking/Biking
  • Public Transit t‐value:










30.240 P‐value:











0.000 Adj‐R‐Square:

0.935Walking/Biking
  • Private Auto Public Transit t‐value:










30.240 P‐value:











0.000 Adj‐R‐Square:

0.935Walking/Biking
  • City Scale: USA y = Arb
  • City Scale: USAPopulation density y = Ae br y = Arb
  • City Scale: USAPopulation density y = Ae br Road Scaling y = Arb
  • City Scale: USA Population density y = Ae br Road Scaling y = ArbTransportation distances y = Ae br
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • Neighborhood Scale: Typologies ! (
  • Neighborhood Scale: Typologies London AnalysisWhat?To develop a standard way to analyze the resource performance ofneighborhoodsWhy?For improved understanding on relationships between urbanconfiguration and resource consumption and potentialsTo inform urban design through guidelines and scenario testing
  • Population Densities
  • Energy Consumption
  • Road Densities
  • Road Densities
  • AreaType
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • Provisional Results from Case Study Analysis of London, UK Buildings Roads Visual Check 0.08 0.08 KM KMwall / floor / roof areas Road Area
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • Provisional Results from Case Study Analysis of London, UK 0 20 40 60 0e+00 2e+05 4e+05 20000physical characteristics 0.47 *** *** 0.46 *** 0.58 *** 10000 popDen 0.27of neighborhood 0 *** *** *** 60 40 Detach_Perc 0.16 0.41 0.55 20 0 *** *** 5kmeans clustering 4 Plot_Ratio 0.35 3 0.20 2algorithm 1 0 0e+00 2e+05 4e+05 GreenSp_Per_HH *** 0.34calculations aggregated 4000to census unit of ~7000 2000 SurfaceAreapeople (MLSOA) 0 0 10000 20000 0 1 2 3 4 5 0 2000 4000
  • Provisional Results from Case Study Analysis of London, UK Clusters Site Coverage ● Population Density ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Cluster ● 1 ● 210 KM 3
  • Provisional Results from Case Study Analysis of London, UKFraction Green Space ● Enclosed Surface ● Percent Detached ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
  • Provisional Results from Case Study Analysis of London, UKCluster 1
  • Provisional Results from Case Study Analysis of London, UKCluster 2
  • Provisional Results from Case Study Analysis of London, UKCluster 3
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • Analyzing the ClustersStatistically relate clusters with resource performancemeasures (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?
  • Regression with Spatial AutocorrelationFormal name: Spatial error modeli.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
  • Neighborhood MatrixNumber of regions: 940Number of nonzero links: 55014Percentage nonzero weights: 6%Average number of links: 59
  • Neighborhood Matrix ClusterNumber of regions: 940Number of nonzero links: 55014 1Percentage nonzero weights: 6%Average number of links: 59 2
  • Comparing Cluster Means kwh/cap ~ cluster1 + cluster2 radius: 5 Km, weights: pop 10231 kWh 8407 kWh7935 kWh EstimateC3 --> C1 2295 kWh ***C3 --> C2 471 kWh ***Lambda: 0.84532 LR test value: 292.51 p-value: < 2.22e-16Signif. codes: 0 *** 0.001 ** 0.01 * 0.05
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • The city as energy plantMore
eficient
and
high
potential
for
renewable
energy
@
district
level GSHP
  • 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
  • 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
  • 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
  • + + + - - hhFaçade Solar Roof Solar Dom Shallow GSHP
  • Urban Form Characterization ClustersRenewable Resource Statistical Energy Consumption Relationships Potential Neighborhood Performance
  • www.urbmet.org
  • www.urbmet.org (currently showing sample data)
  • Outline1 intro to urban metabolism & global dynamics2 regional scale analysis: Costa Rica & Florida 3 city scale analysis: US cities 4 neighbourhood analysis: London 5 conclusions & future work
  • Conclusions and Outlook1 Integration of spatial components and urban modelling with urbanmetabolism is becoming easier to do2 Standardized analysis is an useful approach for estimating gross resourceperformances3 More work is needed using bottom-up analysis for validation in this work
  • Conclusions and Outlook1 Integration of spatial components and urban modelling with urbanmetabolism is becoming easier to do2 Standardized analysis is an useful approach for estimating gross resourceperformances3 More work is needed using bottom-up analysis for validation in this work questions or comments? djq@mit.edu