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- 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
- 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 ● 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
- 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
- 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
- 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
- www.urbmet.org
- www.urbmet.org (currently showing sample data)
- 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

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