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Urban form and building energy : quantifying relationships using a multi-scale approach Miller, Nicole 2013

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URBAN FORM AND BUILDING ENERGY: QUANTIFYING RELATIONSHIPS USING A MULTI-SCALE APPROACH by NICOLE MILLER B.Arch., University of Kansas, 2004 M.A.S.A., University of British Columbia, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2013  © Nicole Miller, 2013  Abstract Cities and energy are fundamentally connected. Approximately half of the energy consumed in urban areas is used by buildings, resulting in over 35% of the world’s greenhouse gas (GHG) emissions. Urban form (i.e. building morphology and urban structure) plays an important role in building energy; however, few studies have addressed this role in a comprehensive and quantitative way due to the complexities involved in modeling urban systems. This study provides a multiscale examination of the relationships between urban form and building energy, using the Metro Vancouver region of British Columbia, Canada as an example. The thesis applies consistent methods of 3D and energy simulation modeling at three scales, ranging from individual buildings to urban patterns (i.e. neighbourhood-scale). Quantified impacts of urban form on building heating demand, cooling demand and local energy generation potential are presented. In total, 12 building archetypes and 14 urban patterns are modeled, ranging in density from 0.3 to 2.3 FAR (10 to 250 uph). Each pattern varies in building form, building arrangement, street configuration and mix of building activities. The results illustrate that building morphology and urban structure do influence building energy demand and local energy generation potential; however, the complexity and heterogeneity of urban form at larger scales is found to abate net impacts as “tradeoffs” occur between the energy-reducing and energy-increasing effects of urban form characteristics. For example, while the heating and cooling demand of individual buildings can vary significantly (30%70%) with urban horizon angle (an indicator of shading from adjacent buildings), the net impact modeled at the urban pattern scale is far less (as little as 5%). The results suggest that urban form will be one of many tools needed to mitigate current energy consumption and GHG emission levels, and synergies between urban form, building systems and materials, and occupant behaviour should be sought. Findings from this thesis will inform the work of local governments and urban planners with interest in building energy at scales larger than individual buildings.  ii  Preface This dissertation is original and independent work by the author, Nicole Miller. No portion of the work has been previously published.  iii  Table of Contents Abstract����������������������������������������������������������������������������������������������������������������������������������������� ii Preface����������������������������������������������������������������������������������������������������������������������������������������� iii Table of Contents�������������������������������������������������������������������������������������������������������������������������� iv List of Tables������������������������������������������������������������������������������������������������������������������������������� viii List of Figures������������������������������������������������������������������������������������������������������������������������������� xi List of Abbreviations������������������������������������������������������������������������������������������������������������������� xiv Acknowledgements��������������������������������������������������������������������������������������������������������������������� xv CHAPTER 1: INTRODUCTION���������������������������������������������������������������������������������������������������������� 1 1.1 Problem Statement������������������������������������������������������������������������������������������������������������������� 3 1.2 Research Purpose and Significance�������������������������������������������������������������������������������������������4 1.3 Research Questions������������������������������������������������������������������������������������������������������������������� 5 1.4 Research Strategy���������������������������������������������������������������������������������������������������������������������� 6 1.4.1 A three part approach���������������������������������������������������������������������������������������������������������������� 7 1.4.2 Urban form characteristics at three scales�������������������������������������������������������������������������������� 8 1.4.3 Three studies������������������������������������������������������������������������������������������������������������������������������ 9 1.5 Structure of Dissertation���������������������������������������������������������������������������������������������������������11 CHAPTER 2: LITERATURE REVIEW������������������������������������������������������������������������������������������������� 12 2.1 Urban Form and Energy����������������������������������������������������������������������������������������������������������12 2.1.1 Building morphology and heat transfer����������������������������������������������������������������������������������� 15 2.1.2 Building morphology and solar access������������������������������������������������������������������������������������� 16 2.1.3 Urban structure������������������������������������������������������������������������������������������������������������������������ 18 2.2 Urban Form Research Methods����������������������������������������������������������������������������������������������21 2.2.1 Urban patterns������������������������������������������������������������������������������������������������������������������������� 22 2.2.2 Qualitative pattern approaches����������������������������������������������������������������������������������������������� 24 2.2.3 Quantitative pattern approaches��������������������������������������������������������������������������������������������� 25 2.2.4 Patterns in computer modeling applications��������������������������������������������������������������������������� 26 2.3 Building Energy Modeling Methods����������������������������������������������������������������������������������������29 2.3.1 Building-scale energy models��������������������������������������������������������������������������������������������������� 30 2.3.2 Regional and national-scale energy models ��������������������������������������������������������������������������� 31 2.3.3 Neighbourhood-scale energy models�������������������������������������������������������������������������������������� 32 2.4 Conclusion������������������������������������������������������������������������������������������������������������������������������� 34  iv  CHAPTER 3: METHODS����������������������������������������������������������������������������������������������������������������� 35 3.1 Methodological Gaps in Current Research������������������������������������������������������������������������������35 3.2 Research Approach�����������������������������������������������������������������������������������������������������������������38 3.3 Urban Form Characteristics at Three Scales����������������������������������������������������������������������������39 3.3.1 Geographic context������������������������������������������������������������������������������������������������������������������ 42 3.3.2 Building archetype (BA) scale �������������������������������������������������������������������������������������������������� 44 3.3.3 Local shading (LS) scale ����������������������������������������������������������������������������������������������������������� 49 3.3.4 Urban pattern (UP) scale ��������������������������������������������������������������������������������������������������������� 51 3.3.5 Urban form 3D modeling��������������������������������������������������������������������������������������������������������� 55 3.4 Energy Simulation�������������������������������������������������������������������������������������������������������������������56 3.4.1 VE-Pro simulation tool�������������������������������������������������������������������������������������������������������������� 56 3.4.2 VE-Pro outputs�������������������������������������������������������������������������������������������������������������������������� 60 3.5 Analysis of Energy and Urban Form ���������������������������������������������������������������������������������������63 3.6 Limitations of the Research�����������������������������������������������������������������������������������������������������64 CHAPTER 4: EXISTING URBAN FORM CONDITIONS����������������������������������������������������������������������� 67 4.1 Methods���������������������������������������������������������������������������������������������������������������������������������� 68 4.1.1 Selection of building archetypes���������������������������������������������������������������������������������������������� 68 4.1.2 Selection of existing urban patterns����������������������������������������������������������������������������������������� 69 4.1.3 Urban form 3D modeling��������������������������������������������������������������������������������������������������������� 77 4.1.4 Building energy simulation������������������������������������������������������������������������������������������������������� 78 4.2 Results and Discussion �����������������������������������������������������������������������������������������������������������79 4.2.1 Heating demand����������������������������������������������������������������������������������������������������������������������� 79 4.2.2 Cooling demand����������������������������������������������������������������������������������������������������������������������� 88 4.2.3 Solar potential�������������������������������������������������������������������������������������������������������������������������� 91 4.2.4 District energy potential����������������������������������������������������������������������������������������������������������� 94 4.3 Conclusion ������������������������������������������������������������������������������������������������������������������������������ 96 CHAPTER 5: BUILDING ENERGY AND URBAN STRUCTURE������������������������������������������������������������ 99 5.1 Methods Part 1: Parametric Analysis������������������������������������������������������������������������������������100 5.1.1 3D modeling��������������������������������������������������������������������������������������������������������������������������� 100 5.1.2 Energy simulation������������������������������������������������������������������������������������������������������������������� 102 5.1.3 Analysis metrics ��������������������������������������������������������������������������������������������������������������������� 104 5.1.4 Regression analysis����������������������������������������������������������������������������������������������������������������� 104  v  5.2 Methods Part 2: Sensitivity Testing���������������������������������������������������������������������������������������106 5.2.1 Building activity���������������������������������������������������������������������������������������������������������������������� 106 5.2.2 Envelope performance������������������������������������������������������������������������������������������������������������ 107 5.3 Results and Discussion: Parametric Analysis�������������������������������������������������������������������������109 5.3.1 Heating demand��������������������������������������������������������������������������������������������������������������������� 109 5.3.2 Cooling demand��������������������������������������������������������������������������������������������������������������������� 118 5.3.3 Solar potential������������������������������������������������������������������������������������������������������������������������ 126 5.4 Results and Discussion: Sensitivity Testing����������������������������������������������������������������������������132 5.4.1 Building activity���������������������������������������������������������������������������������������������������������������������� 132 5.4.2 Envelope performance������������������������������������������������������������������������������������������������������������ 136 5.5. Conclusions��������������������������������������������������������������������������������������������������������������������������142 CHAPTER 6: URBAN FORM ALTERNATIVES FOR BUILDING ENERGY PERFORMANCE�������������������� 145 6.1 Methods�������������������������������������������������������������������������������������������������������������������������������� 146 6.1.1 Pattern development�������������������������������������������������������������������������������������������������������������� 146 6.1.2 Urban form 3D modeling������������������������������������������������������������������������������������������������������� 156 6.1.3 Building energy simulation����������������������������������������������������������������������������������������������������� 156 6.1.4 Analysis metrics���������������������������������������������������������������������������������������������������������������������� 157 6.2 Results and Discussion����������������������������������������������������������������������������������������������������������157 6.2.1 Heating demand��������������������������������������������������������������������������������������������������������������������� 157 6.2.2 Cooling demand��������������������������������������������������������������������������������������������������������������������� 164 6.2.3 Solar potential������������������������������������������������������������������������������������������������������������������������ 170 6.2.4 District energy potential��������������������������������������������������������������������������������������������������������� 174 6.3 Conclusions��������������������������������������������������������������������������������������������������������������������������� 176 CHAPTER 7: DISCUSSION AND CONCLUSIONS���������������������������������������������������������������������������� 180 7.1 Summary of Results ��������������������������������������������������������������������������������������������������������������182 7.2 Synthesis of Results ��������������������������������������������������������������������������������������������������������������186 7.2.1 Research Question 1: Urban form and energy demand�������������������������������������������������������� 186 7.2.2 Research Question 2: Urban form and local energy generation������������������������������������������� 193 7.2.3 Research Question 3: Sensitivity to other factors������������������������������������������������������������������ 197 7.2.4 Tradeoffs��������������������������������������������������������������������������������������������������������������������������������� 198 7.2.5 Summary of key findings�������������������������������������������������������������������������������������������������������� 200 7.3 Research Contributions and Implications������������������������������������������������������������������������������203 7.3.1 Implications for urban form��������������������������������������������������������������������������������������������������� 204 7.3.2 Implications for local governments in Metro Vancouver������������������������������������������������������� 207  vi  7.4 Research Limitations and Future Research���������������������������������������������������������������������������208 References�������������������������������������������������������������������������������������������������������������������������������� 212 Appendix A: Building Archetypes����������������������������������������������������������������������������������������������� 222 Appendix B: Urban Patterns������������������������������������������������������������������������������������������������������ 234 Appendix C: Modeling Assumptions������������������������������������������������������������������������������������������ 248 Appendix D: Modeling Results��������������������������������������������������������������������������������������������������� 255 Building Archetype Results�������������������������������������������������������������������������������������������������������������� 255 Urban Pattern Results���������������������������������������������������������������������������������������������������������������������� 263 LS Scale Heating Demand Results - UHA Variations����������������������������������������������������������������������� 270 LS Scale Cooling Demand Results - UHA Variations������������������������������������������������������������������������ 289 LS Scale Solar Potential Results - UHA Variations��������������������������������������������������������������������������� 308  vii  List of Tables  Table 2.1: Urban form variables affecting energy at different scales��������������������������������������������12 Table 2.2: Energy of implications of structural variables, Owens, 1986����������������������������������������13 Table 2.3: Factors influencing building energy consumption��������������������������������������������������������13 Table 2.4: Summary of issues involved in the description and analysis of urban form�����������������23 Table 2.5: Typical energy modeling approaches at different scales����������������������������������������������29 Table 2.6: Categories of building energy tools with examples������������������������������������������������������30 Table 3.1: Urban form characteristics considered in selected building energy studies�����������������37 Table 3.2: Urban form characteristics at the building archetype (BA) scale����������������������������������49 Table 3.3: Urban form characteristics at the local shading (LS) scale��������������������������������������������52 Table 3.4: Variables included in pattern scale building energy analysis����������������������������������������55 Table 4.1: Building archetypes and variations�������������������������������������������������������������������������������70 Table 4.2: Preliminary patterns and urban form characteristics (qualitative)�������������������������������71 Table 4.3: Existing urban patterns�������������������������������������������������������������������������������������������������76 Table 4.4: Annual space heating demand by pattern��������������������������������������������������������������������80 Table 4.5: Typical residential unit size and average household size����������������������������������������������81 Table 4.6: Annual space heating demand by building archetype��������������������������������������������������82 Table 4.7: Annual space cooling demand by pattern��������������������������������������������������������������������88 Table 4.8: Annual space cooling demand by building archetype��������������������������������������������������88 Table 4.9: Annual heating and cooling demand by building activity���������������������������������������������90 Table 4.10: Annual heating and cooling demand by glazing ratio (residential activity only)���������90 Table 4.11: Annual urban pattern solar potential (SP)������������������������������������������������������������������92 Table 4.12: Total annual heating demand by pattern for district energy potential�����������������������94 Table 4.13: Proposed district energy systems in BC����������������������������������������������������������������������95 Table 5.1: Building archetypes included in sensitivity testing for building activity���������������������106 Table 5.2: Building archetypes included in sensitivity testing for envelope performance����������107 Table 5.3: Comparison of BC Building Code and Harmony House insulation levels��������������������109 Table 5.4: Annual heating demand linear regression statistics���������������������������������������������������111 Table 5.5: Total average UHA influence on annual heating demand�������������������������������������������112  viii  Table 5.6: UHA influence on annual heating demand, select examples��������������������������������������113 Table 5.7: UHA influence on annual heating demand, sorted by building orientation���������������115 Table 5.8: UHA influence on annual heating demand, sorted by adjacent building height��������116 Table 5.9: Effects of SR coverage on annual heating demand�����������������������������������������������������117 Table 5.10: Annual cooling demand linear regression statistics��������������������������������������������������120 Table 5.11: Total average UHA influence on annual cooling demand�����������������������������������������120 Table 5.12: UHA influence on annual cooling demand, select examples������������������������������������121 Table 5.13: UHA influence on annual cooling demand, sorted by building orientation��������������123 Table 5.14: UHA influence on annual cooling demand, sorted by adjacent building height�������123 Table 5.15: Effects of SR coverage on annual cooling demand, select examples������������������������125 Table 5.16: Annual solar potential linear regression statistics����������������������������������������������������128 Table 5.17: UHA influence on annual solar potential, select examples���������������������������������������129 Table 5.18: UHA influence on annual solar potential, sorted by building orientation����������������130 Table 5.19: Effects of SR coverage on annual solar potential, select examples���������������������������131 Table 5.20: Average UHA influence on annual heating demand, building activity variations�����133 Table 5.21: Average UHA influence on annual cooling demand, building activity variations������134 Table 5.22: Average UHA influence on annual heating demand, building activity examples������134 Table 5.23: Average UHA influence on annual cooling demand, building activity examples������135 Table 5.24: Average UHA influence on annual heating demand, building envelope variations��139 Table 5.25: Average UHA influence on annual cooling demand, building envelope variations��140 Table 5.26: Average UHA influence on annual heating demand, building envelope examples���141 Table 5.27: Average UHA influence on annual cooling demand, building envelope examples���142 Table 6.1: List of building archetypes������������������������������������������������������������������������������������������149 Table 6.2: Calculation assumptions���������������������������������������������������������������������������������������������149 Table 6.3: North-south oriented transit corridor patterns����������������������������������������������������������152 Table 6.4: East-west oriented transit corridor patterns ��������������������������������������������������������������154 Table 6.5: Annual space heating demand by pattern������������������������������������������������������������������158 Table 6.6: Annual space cooling demand by pattern������������������������������������������������������������������164 Table 6.7: Annual solar potential by pattern�������������������������������������������������������������������������������171 Table 6.8: Annual heating demand by pattern for district energy potential�������������������������������174 Table 6.9: Annual heating demand by pattern for district energy potential (corridor only)�������175 Table 6.10: Summary of annual energy performance by pattern�����������������������������������������������176  ix  Table 7.1 Matrix of chapters and research questions�����������������������������������������������������������������181 Table 7.2 Matrix of urban form and building energy measures��������������������������������������������������184 Table 7.3 Relative influences of urban form characteristics on building energy�������������������������187  x  List of Figures  Figure 1.1: Research process���������������������������������������������������������������������������������������������������������10 Figure 2.1: Relationship between height (h) to width (w) ratio, urban horizon angle (u) and sky view factor (Vs), adapted from Robinson, 2006����������������������������������������������������������������������������19 Figure 3.1 Unidirectional and multi-directional analysis of urban structure���������������������������������37 Figure 3.2 Inputs to and outputs of building energy analysis ������������������������������������������������������38 Figure 3.3: Research process���������������������������������������������������������������������������������������������������������40 Figure 3.4: Three scales of urban form analysis����������������������������������������������������������������������������41 Figure 3.5: Metro Vancouver region���������������������������������������������������������������������������������������������42 Figure 3.6: Roof shapes, illustrated example��������������������������������������������������������������������������������46 Figure 3.7: Glazing distribution, illustrated example���������������������������������������������������������������������47 Figure 3.8: Glazing ratio variation, illustrated example�����������������������������������������������������������������48 Figure 4.1: Building archetypes and urban form characteristics���������������������������������������������������69 Figure 4.2: Urban patterns and urban form characteristics����������������������������������������������������������73 Figure 4.3: Urban pattern street configurations����������������������������������������������������������������������������78 Figure 4.4: Annual urban pattern space heating demand per capita (GJ/cap)������������������������������80 Figure 4.5: Annual urban pattern space heating demand per unit of floor area (GJ/m2)�������������82 Figure 4.6: Annual building archetype space heating demand per unit of floor area (GJ/m2)������83 Figure 4.7: Annual space heating demand per unit of floor area (GJ/m2), Provincial data�����������83 Figure 4.8: Constant residential unit, number of shared surfaces������������������������������������������������84 Figure 4.9: Surface to volume ratio and annual space heating demand���������������������������������������85 Figure 4.10: Surface to volume ratios and glazing ratios by building archetype���������������������������85 Figure 4.11: Effects of glazing ratio on annual space heating demand�����������������������������������������86 Figure 4.12: Comparison of annual space heating and cooling demand per m2 floor area for isolated and contextualised building archetypes��������������������������������������������������������������������������87 Figure 4.13: Annual space heating demand by number of shared surfaces and effect of envelope thermal performance��������������������������������������������������������������������������������������������������������������������87 Figure 4.14: Annual urban pattern space cooling per capita (GJ/cap)������������������������������������������89 Figure 4.15: Annual urban pattern space cooling demand per unit of floor area (GJ/m2)������������89  xi  Figure 4.16: British Columbia energy consumption by end use.���������������������������������������������������91 Figure 5.1: Relationship between UHA and adjacent building height�����������������������������������������100 Figure 5.2: Adjacent building locations���������������������������������������������������������������������������������������101 Figure 5.3: Number of adjacent buildings and shading range (SR), plan view����������������������������102 Figure 5.4: Comparison of simulation results for four and eight adjacent structures, by UHA and orientation���������������������������������������������������������������������������������������������������������������������������������� 103 Figure 5.5: Shading range and building widths���������������������������������������������������������������������������104 Figure 5.6: Annual heating demand by building vintage�������������������������������������������������������������108 Figure 5.7 Annual heating demand, linear regression����������������������������������������������������������������110 Figure 5.8 Examples of typical building arrangements and UHA������������������������������������������������114 Figure 5.9: Effects of SR coverage on annual heating demand, select examples������������������������116 Figure 5.10 Annual cooling demand, liner regression�����������������������������������������������������������������119 Figure 5.11: Effects of SR coverage on annual cooling demand��������������������������������������������������124 Figure 5.12 Annual solar potential, linear regression�����������������������������������������������������������������127 Figure 5.13: Effects of SR coverage on annual solar potential�����������������������������������������������������130 Figure 5.14: Effects of building activity on annual heating demand, linear regression���������������132 Figure 5.15: Effects of building activity on annual cooling demand, linear regression���������������132 Figure 5.16: Total annual space conditioning demand by building archetype����������������������������136 Figure 5.17: Effects of envelope performance on annual heating demand, linear regression����137 Figure 5.18: Effects of envelope performance on annual cooling demand, linear regression����138 Figure 6.1: Cambie Corridor Plan������������������������������������������������������������������������������������������������147 Figure 6.2: Street configuration��������������������������������������������������������������������������������������������������148 Figure 6.3: Transit corridor patterns and urban form characteristics������������������������������������������155 Figure 6.4 Total annual corridor pattern space heating demand (GJ)�����������������������������������������158 Figure 6.5: Annual corridor pattern space heating demand per capita (GJ/cap)������������������������159 Figure 6.6: Annual corridor pattern space heating demand per unit of floor area (GJ/m2)��������159 Figure 6.7: Spatialized annual heating demand (GJ/m2), north-south oriented patterns�����������162 Figure 6.8: Spatialized annual heating demand (GJ/m2), east-west oriented patterns���������������163 Figure 6.9 Total annual corridor pattern space cooling demand (GJ)������������������������������������������165 Figure 6.10: Annual corridor pattern space cooling demand per capita (GJ/cap)�����������������������165 Figure 6.11: Annual corridor pattern space cooling demand per unit of floor area (GJ/m2)������166 Figure 6.12: Spatialized annual cooling demand (GJ/m2), north-south oriented patterns����������168  xii  Figure 6.13: Spatialized annual cooling demand (GJ/m2), east-west oriented patterns��������������169 Figure 6.14 Spatialized annual solar potential (Asp/Afloor), north-south oriented patterns�����������172 Figure 6.15 Spatialized annual solar potential (Asp/Afloor), east-west oriented patterns��������������173 Figure 6.16: Blocks included for DE feasibility assessment, CNS-Lin pattern example������������������175 Figure 6.17 Tradeoffs in annual heating demand, cooling demand and solar potential at the UP scale�������������������������������������������������������������������������������������������������������������������������������������������� 178 Figure 7.1: Three scales of urban form analysis��������������������������������������������������������������������������181 Figure 7.2 Inputs to and outputs of building energy analysis�����������������������������������������������������182 Figure 7.3: Effects of glazing ratio on annual space heating demand�����������������������������������������188 Figure 7.4: Surface to volume ratio and annual space heating demand�������������������������������������189 Figure 7.5 Impacts of UHA at the LS scale on annual heating and cooling demand (IC%), by building archetype����������������������������������������������������������������������������������������������������������������������191 Figure 7.6 Impacts of UHA at the LS scale on annual heating and cooling demand (IC, MJ/m2), by building archetype����������������������������������������������������������������������������������������������������������������������191 Figure 7.7 Examples of differences in solar potential at the LS scale by roof shape and building orientation���������������������������������������������������������������������������������������������������������������������������������� 194 Figure 7.8 FAR and annual solar potential for modeled urban patterns�������������������������������������196 Figure 7.9 Tradeoffs in annual heating demand, cooling demand and solar potential at the UP scale�������������������������������������������������������������������������������������������������������������������������������������������� 200  xiii  List of Abbreviations  The following list identifies key abbreviations and acronyms used throughout the dissertation. Abbreviations used to identify building archetypes and urban patterns discussed in the dissertation are identified separately in Appendices A and B. 3D  three-dimensional  ASHRAE  American Society of Heating, Refrigerating and Air Conditioning Engineers  BA  building archetype (scale)  BC  British Columbia  BCBC  British Columbia Building Code  BES  building energy simulation  DE  district energy  DEM  digital elevation model  FAR  floor area ratio  GHG  greenhouse gas  GIS  geographic information system  HR  height ratio  IC  influence coeffecient  LS  local shading (scale)  LSRP  Livable Region Strategic Plan  LT method  Lighting and Thermal method  RGS  Regional Growth Strategy  SP  solar potential  SR  shading ratio  S:V  surface to volume ratio  UHA  urban horizon angle  UHI  urban heat island  UP  urban pattern (scale)  UPH  units per hectare  xiv  Acknowledgements I would like to express my gratitude to my supervisory committee: Dr. Stephen Sheppard (UBC Institute for Resources, Environment and Sustainability), Professor Ron Kellett (UBC School of Architecture and Landscape Architecture) and Dr. Jeff Carmichael (Metro Vancouver). Their diverse expertise and insight took my research in directions I would have never imagined at the outset. I am further indebted to Stephen Sheppard for the opportunity to work on a number of exciting projects throughout the course of my doctoral studies and to Ron Kellett for helping me to lay the groundwork for this research over eight years of enjoyable and enlightening learning and collaboration. Thanks to my friends and colleagues at IRES for their support and welcome distractions, my workmates and dear friends Duncan, Ellen and Sara who have cheered me on throughout this process and kept me engaged in the professional side of things, and to my parents for their constant support and advice – it’s so much nicer having you right here in the northwest. Finally, thanks to Courtney for his patience, hard work, and hours of discussion and comparing notes – as I said the last time I wrote one of these, I would not and could not do it without you. This work was made possible with generous funding from the University of British Columbia, the Pacific Institute for Climate Solutions, the Lincoln Institute of Land Policy and the Mitacs Accelerate Internship Program.  xv  CHAPTER 1: INTRODUCTION Cities and energy are fundamentally connected. While urban areas account for roughly 2% of the earth’s surface area, they account for three-quarters of the world’s energy consumption (Salat, 2008). Of the energy consumed in urban areas approximately half is used in the operation of buildings, resulting in over 35% of the world’s greenhouse gas (GHG) emissions (Price, et al., 2006). With this magnitude of global impact, the building sector has been identified as a key component in the mitigation of climate change, in British Columbia and internationally. Energy demand in buildings is affected by a variety of factors. Mitchell (2005) lists eight factors for building energy consumption, including the thermal performance of construction materials, the efficiency of building systems, occupant activities, building morphology and urban geometry. Of these factors, materials (e.g. insulation) and technology (e.g. high-efficiency building systems) at the scale of individual buildings have been a primary focus of research, programs and policy initiatives for building energy demand reductions and building-related GHG emissions mitigation. However, literature reviewed for this project suggests that urban form – both the morphology of individual buildings and the urban structure in which they are located – also plays an important role in building energy. Urban form affects building energy both directly through specific physical attributes (e.g. surface to volume ratio) and indirectly through the facilitation of technology (e.g. solar energy technologies and district energy systems). Ratti et al. suggest that urban form, particularly the relationships between buildings, may account for a factor of two difference in building energy performance (2005). Baker and Steemers (2000) offer two compelling reasons why urban form should be addressed when considering opportunities to reduce building energy consumption. First, urban form factors are less likely to change than technologies and occupants, such that urban form elements are likely to have much longer term positive (or negative) consequences. Second, they suggest that form, technology and occupants do not act independently; rather, good urban form may result in better system performance and occupant behavior. Wener and Carmalt (2006) suggest that form-based design choices such as compact building shapes are less dependent on, or susceptible to, behavioural changes than technologies requiring occupant interaction. The consideration of urban form as a mechanism for reducing building energy demand is particularly relevant to local governments in British Columbia, where the establishment of GHG targets and the development of related policy have increasingly mandated local government action on climate change, primarily through the building and transportation sectors.  1  The Province of British Columbia has recently estimated that 45% of total GHG emissions in the Province are either controlled or influenced by local governments (Province of British Columbia, 2010). However, local government has also been largely limited in its power to act on energy and climate issues. Bulkeley and Kern (2006) discuss the challenges faced by local governments in both the UK and Germany in instituting climate change policies. Numerous authors have noted the same challenge for local governments in British Columbia where the Local Government Act limits municipal authority, making it difficult or impossible for municipalities to regulate issues such as renewable energy or energy efficiency in buildings (Fraser Basin Council, 2007; Energy Efficiency Working Group, 2008; Gore et al., 2009; McDowall, 2008; Union of British Columbia Municipalities, 2007). With limited ability to mandate building energy efficiency, local governments in British Columbia desiring to reduce building energy demand and GHG emissions must instead rely on available planning and land use zoning powers, of which urban form – not building materials or technology – is the primary opportunity. If urban form is a primary opportunity for local governments to reduce building energy demand, new and better ways of understanding urban form and planning decisions in terms of energy and GHG emissions are required. Existing literature identifies that urban form is a strong influence in urban energy consumption and the generation of GHG emissions, potentially determining urban energy performance to a greater degree than small-scale choices (Mehaffy et al., 2009; Ratti et al., 2005). However, describing and quantifying the specific energy and GHG impacts of urban form, particularly at the neighbourhood and larger scales, are challenging due to the many factors and complex interactions involved in these systems. Such challenges have resulted in a limited number of studies addressing urban formenergy relationships in a comprehensive and quantitative way. This thesis provides a multi-scale examination of the relationships between urban form characteristics and building energy performance, using 3D urban form modeling and building energy simulation tools. Building on the work of several important urban form-building energy studies, the following chapters present quantitative findings on the impacts of urban form on building heating and cooling demand and local energy generation potential. Using climatic data and urban form conditions specific to the Metro Vancouver region of British Columbia, Canada, the thesis explores the magnitude of change in building energy demand that could potentially be realised through changes in urban form within the Metro Vancouver climate and urban form context.  2  1.1 Problem Statement Despite increased awareness that “green communities” are an essential component of energy reduction and GHG mitigation (Province of British Columbia, 2008), local governments have not yet bridged substantial gaps between stated energy and GHG reduction goals, planning practice and implementation, including the development of urban forms responsive to building energy issues. While it can be argued that the present inertia of energy and emissions-intensive patterns of development is due in large part to implementation limitations (e.g. economics, community resistance, etc.) there is also a lack of clear, quantifiable and spatial information on specific relationships between urban form choices and energy and GHG consequences. This information is critical to the provision of solid rationale and support for effective energy and emission policies and their implementation. Literature reviewed for this project finds that neighbourhood-scale modeling capturing the effects of urban form characteristics on building energy demand is especially lacking (Barton, 2004), and that work at the neighbourhood scale tends to be limited in scope, issue-specific and methodologically less developed than approaches at either the building or regional scales. At the same time, the neighbourhood scale is critical to the understanding of urban form-energy relationships, as it captures key interactions between buildings that are not typically represented at smaller or larger scales. The problem of modeling energy and urban form is spatial, multi-scale and multi-issue, encompassing factors including not only form, but also technology, climate and behaviour, among others. While building-scale energy simulations have sought to incorporate many of these factors into comprehensive, data-intensive and time consuming models, the trend at the neighbourhood scale has been to reduce complexity through the use of archetypes, averages and assumptions for many building-scale and contextual factors in order to highlight the impacts of urban form and planning decisions. This has sometimes led to the critique that such models are not sufficiently accurate (Robinson, 2006). Urban form-energy models are further challenged to reflect the performance of urban form choices under a variety of future conditions in ways and through methods that are defensible and valid. Most urban form-based work on neighbourhood-scale energy modeling has evaluated only existing conditions, potentially due to difficulties in generating future form-based data at sufficient levels of detail. The availability of approaches able to spatially describe future urban form and energy conditions in ways that are measurable, as well as understandable and relevant to policy makers, is arguably the largest gap in current research.  3  Comparative studies conclusively illustrate that building and neighbourhood-scale urban form choices impact building energy consumption, but a lack of consistent approaches and typically small sample sizes make further conclusions on particular urban form characteristics and energy demand difficult. In addition, available literature has tended to separate morphological factors from land and building uses, despite the emphasis by Baker and Steemers (2000) on the important differences between residential and commercial building energy patterns and performance. Research in the academic realm has largely dealt with abstract tests of morphological conditions (e.g. Cheng et al., 2006) or detailed morphological analysis of very small subsets of urban form typologies (e.g. Salat, 2009; Ratti et al., 2005). A large number of government and applied research projects rely on the use of residential unit type (e.g. single family detached, apartment) as a placeholder for urban form characteristics, likely due to the greater availability of residential unit data and the difficulties of modeling complex urban geometry (Ratti et al., 2005). As a result, building morphology and urban structure are not easily characterised in these studies. Numerous authors note that further research on urban form-building energy relationships, including multivariate analysis (Mitchell, 2005), improved analytical approaches (Steemers, 2003), and simplified energy tools able to evaluate form at early stages of design (Baker and Steemers, 2000) are needed. This dissertation suggests that to support local governments in achieving energy and GHG emission reduction targets, the role of urban form in building energy demand must be addressed in greater detail, as urban form will be a primary local government lever in situations where the power to regulate building systems and materials is limited, such as in British Columbia. More research is needed to explore specific relationships between energy and urban form at a variety of scales and within specific regional and climatic contexts, including tradeoffs between and sensitivity to a variety of urban form characteristics, rather than more general relationships.  1.2 Research Purpose and Significance The purpose of this research is to provide an in-depth examination of the relationships between urban form characteristics and building energy performance to better inform urban planning and development decisions, using the Metro Vancouver area of British Columbia, Canada as an example. Extending from the literature described in Chapter 2, the research centres on the use of 3D and building energy simulation modeling tools to quantify building energy impacts of and interactions between urban form characteristics at the building and neighbourhood scales, concentrating on building morphology and urban structure.  4  Findings from this research have the potential to inform the work of local governments, urban planners and designers, energy planners and others with interest in reducing building energy demand at scales larger than individual buildings. While the findings presented in the following chapters are specific to the Metro Vancouver climate and context, the methodological approach to the project, which pairs an archetype-based method of describing urban form with commercially available 3D modeling and building energy simulation tools to compare urban form alternatives at multiple scales, is applicable in many contexts. This research uniquely uses consistent methods of 3D and energy simulation modeling across building energy studies at multiple scales, allowing for a greater understanding of both the parts and the whole of the urban form-energy system. Lessons learned at smaller scales of inquiry assist in providing explanations for results that would be otherwise challenging to understand at larger scales. Importantly, the multi-scale methods used in this research provide a foundation for discussing the unexpectedly small effects of urban form on building energy at the neighbourhood scale found in some studies (e.g. Ratti et al., 2005).  1.3 Research Questions The following three research questions will structure the work. The research approach developed to answer these questions is presented in the remainder of this chapter and in Chapter 3. 1)	 How do urban form characteristics affect building energy demand in Metro Vancouver? a.	 What are the quantitative differences in building energy demand among existing urban form conditions? b.	 What changes in building energy demand might be achieved through the manipulation of urban form for future development? Question 1 can be considered the primary focus of the research. To answer this question, the impacts of key urban form characteristics on building energy demand are examined through 3D and building energy simulation modeling at three scales. While research and policy often discuss generalised benefits of urban form characteristics such as building shape, glazing, density and mixed uses for building energy performance, detailed studies isolating and quantifying the effects of these and other urban form characteristics occur less frequently. Specifically, the spatial arrangement (i.e. urban structure) of density and land use is often neglected. Based on a review of existing literature (Chapter 2), the hypothesis of this dissertation for Research Question 1 is that targeted changes in urban form characteristics will affect heating and cooling  5  demand in individual buildings. However, at larger scales, it is anticipated that the complexity of urban form conditions will create energy-related “tradeoffs” that limit the overall potential to reduce building energy demand. 2)	 How do urban form characteristics affect capacity for local energy supply in Metro Vancouver? To more fully address the potential impacts of urban form on building energy, the effects of urban form characteristics on the potential for building energy supply should also be considered. At the building and neighbourhood scales of urban form considered in this research, localised energy supply options that may be impacted by changes in urban form are of particular interest. Within the scope of this project, solar energy potential and district energy potential (Section 3.4) are modeled as key local energy supply options impacted by urban form choices. As in the hypothesis for Question 1, it is expected that targeted changes in urban form characteristics will affect the potential for local energy generation, and that energy-related “tradeoffs” between urban form characteristics will limit local energy generation potential as the complexity of urban form increases. For example, tradeoffs between density, building spacing and solar access have been noted by a number of studies (Baker and Steemers, 2000; Cheng et al., 2006; Ratti et al. 2005; Compagnon, 2004; Carneiro et al., 2008). 3)	 How do other factors affecting building energy, such as building activity (e.g. residential, commercial) and building envelope performance, impact the effects of urban form characteristics on building energy demand? The purpose of Question 3 is to further contextualize the impacts of urban form on building energy demand through targeted, energy simulation-based sensitivity testing. This question is intended to be secondary to Questions 1 and 2 and will focus on a subset of the selected urban form conditions for comparison purposes.  1.4 Research Strategy The intent of this research is to provide a more comprehensive understanding of the relationships between urban form and building energy to better inform urban planning and development decisions. Towards that end, the project examines several identified urban form characteristics: building shape; glazing; orientation; and urban structure (i.e. building spacing and arrangement) (see Chapter 3 for rationale). To achieve this, the thesis addresses urban form and building energy  6  using a three part approach (Section 3.1), applied to three scales of urban form (Section 3.2) and completed over three separate but related studies (Chapters 4, 5 and 6).  1.4.1 A three part approach To fully understand the relationships between energy and urban form, one must consider the means by which to describe and measure urban form, the means by which to describe and measure energy and the means by which to discern relationships between the two. A general framework for this approach is described by Steinitz (1990) who presents six, iterative levels of inquiry, applicable to a number of design and planning problems. Adapting Steinitz’s approach, the methodology used for this project is comprised of three key parts: 1)	 To understand and describe existing urban conditions and possible changes, urban form characteristics at three scales (Section 3.2) were selected and modeled using 3D modeling software, providing the quantitative and spatial urban form data for the project. 2)	 To evaluate the building energy performance of existing urban conditions and possible changes, each 3D urban form model was imported to and analysed in building energy simulation software (Section 3.3), providing energy performance data. 3)	 Using the urban form data as independent variables and the energy performance data as dependent variables, sensitivity, regression and other analyses were completed to compare urban form characteristics and to draw conclusions regarding the relationships between urban form decisions and building energy (Section 3.4). Urban form 3D modeling Urban form conditions at three scales were modeled three-dimensionally using SketchUp (sketchup., a 3D building modeling software widely used for architecture, urban design and other applications. SketchUp was chosen for this research based on its ability to quickly model 3D building forms, and the availability of software plug-ins that allow building geometry created in SketchUp to be directly imported to building energy simulation programs. Further information on the 3D modelling approached used for this research can be found in Section 3.2.5. Building energy simulation Energy simulations were carried out at three scales using VE-Pro, described in detail in Section 3.3. Standard assumptions were used as inputs into the energy simulations for all building characteristics not related to urban form, such as the thermal properties of building envelopes and lighting and appliance energy loads.  7 Data analysis Using the urban form measures described in Section 3.2 as independent variables and the VEPro outputs (i.e. energy performance data) described in Section 3.3 as dependent variables, relationships between urban form and building energy were able to be described and quantified at three scales. Each study (Chapters 4 through 6) considers a different scale or combination of scales of urban form; consequently, each study also applies different approaches to the analysis of results. For example, in Chapters 4 and 6 only a small number of urban form conditions (i.e. 12 building archetypes and six urban patterns) were modeled. For this small number of conditions, additional statistical analysis (e.g. regression analysis) was not completed; rather, comparisons were drawn and trends identified across modeled building archetypes and urban patterns individually. In Chapter 5, linear regression was used to establish urban form-energy trends over a much larger data set, resulting from the completion of a parametric analysis. R-squared values were calculated for each relationship to quantify relationship strength. Influence coefficients (IC) were calculated for each urban form measure and associated energy outcome to “quantify the influence of one variable on another” (Spitler et al. 1989). Chapter 5 also includes additional sensitivity analysis, testing how regression results changed as building activity and building envelope performance changed.  1.4.2 Urban form characteristics at three scales This project applies modeling and analysis methods at three scales, building from a number of research precedents described in Chapter 3: individual building archetype (BA); building archetype with local shading (LS); and urban pattern (UP). Examining urban form and building energy at multiple scales enables a more comprehensive analysis of urban form effects on building energy. Each of the three scales has distinct benefits and limitations for building energy analysis. The BA scale enables the impacts of building morphology (i.e. building shape, glazing and orientation) on building energy to be isolated and quantified. However, in application, buildings are rarely isolated from the impacts of adjacent structures, such that consideration of the BA scale alone does not provide sufficient information to assess relationships between urban form and building energy. In total, 12 building archetypes were developed for the project, with many of the building archetypes carried forward for use at the LS and UP scales. The building archetypes, detailed modeling methods and study findings at the BA scale are described in Chapter 4. The LS scale refers to an individual building archetype together with the structures directly adjacent to each building archetype facade. This scale was used for a series of parametric analysis studies, maximising the number of urban form variations considered. In total, 1,656 model runs  8  were completed at the LS scale, including initial simulations as well as a variety of sensitivity tests for variations in building activity and building envelope performance. However, the LS scale still does not capture the full complexity of urban form conditions at larger scales. Specific methods and findings from the parametric analysis studies are presented in Chapter 5. Findings from the parametric analysis were used to inform choices regarding the development of the urban patterns modeled in Chapter 6. Urban patterns (i.e. UP scale), for the purposes of this project, can be defined as spatially explicit representations of urban form, assembled from building archetypes and representative street configurations, and capturing key attributes of complex urban structure over a larger land area. Unlike the BA and LS scales, the UP scale enables the study of complex urban structures representative of real-world urban development, including building arrangements within street patterns, varying densities of development and varying building shapes and orientations. However, at the UP scale, building morphology, urban structure and relationships to building energy become more difficult to describe and quantify, as many different conditions may be occurring within the same urban pattern. Further, the urban pattern (UP) scale models necessary to capture complexity involve substantial processing times for energy simulation, limiting the number of urban patterns that can be realistically considered within the scope of a project. The UP scale is included in two chapters (Chapters 4 and 6). In Chapter 4, urban patterns are used to describe and analyse existing urban form conditions within the Metro Vancouver region. In Chapter 6, the UP scale is used to apply key findings from the research to evaluate infill development options along a transit corridor. Specific rationale, methods and findings from the UP scale analyses are presented in Chapters 4 and 6.  1.4.3 Three studies To support a multi-scale analysis of urban form-energy relationships, the research has been undertaken through three related studies, each captured in a separate chapter (Chapters 4, 5 and 6). Each study examines one or more scales of urban form, and lessons from each study are fed into subsequent parts of the research (Figure 1.1). Consistent methods of 3D and energy simulation modeling are applied across all three scales of urban form; however, as each study considers a different scale or combination of scales of urban form, each study applies different approaches to the analysis of results. In Chapter 4, both the building archetype (BA) and urban pattern (UP) scales are examined for existing conditions in the Metro Vancouver region. This component of the research was undertaken as an initial testing and exploration of existing regional conditions as well as the modeling methods  9  URBAN FORM STUDIES  ENERGY SIMULATIONS  Urban Pattern  Local Shading  Building Archetype  (UP)  (LS)  (BA)  Chapter 4: Existing urban form conditions  2 12 building archetypes 1  3  6 existing urban patterns  Chapter 5: Building energy and urban structure 4 Parametric studies  Chapter 6: Urban form alternatives for building energy performance 5  4 8 new pattern variations  6  LEVELS OF INQUIRY 1  Representation of existing conditions  2  Understanding of existing system  3  Evaluation of existing system  4  Development of alternatives  5  Evaluation of alternatives  6  Recommendations for change  Energy simulation completed in IES VE-Pro  Figure 1.1: Research process  10  developed for the project, including both urban form (3D) modeling and building energy simulation (BES). The results from Chapter 4 represent early and, at times, incomplete findings and should be considered as an initial step in identifying patterns and potential relationships to be considered further in subsequent chapters. In Chapter 5, regression analysis is used to establish urban form-energy trends resulting from parametric analysis completed at the local shading (LS) scale. This study was used to maximise the number of urban form variations that could be considered within the timeframe of the project by examining strategic, incremental urban form variations at an intermediate scale (larger than individual buildings, but smaller than urban patterns) which reduced simulation times. Chapter 5 focuses on the impacts of building spacing and arrangement (as measured by urban horizon angle) on building heating demand, building cooling demand and solar potential. Findings from this chapter were used to inform choices regarding the urban patterns modeled in Chapter 6. Chapter 6 applies lessons from the previous chapters in the development and modeling of eight urban patterns representing infill development options along a transit corridor. The eight urban patterns are evaluated for energy performance, from which conclusions are drawn regarding the potential of urban form to affect building energy performance within the limitations of a specific development context, bounded by constraints (e.g. zoning regulations) on development density, building forms, street configurations and building activities.  1.5 Structure of Dissertation The dissertation is divided into seven chapters. Following the Introduction (Chapter 1), Chapter 2 presents a summary of relevant literature covering three themes: the relationship between urban form and building energy; urban form research methods; and building energy modeling methods. Chapter 3 describes the methodology employed for the project, including the approaches to urban form modeling and building energy simulation. Chapter 4 identifies existing urban conditions within the Metro Vancouver region, as defined by six patterns of urban development and 12 building archetypes, and discusses results from energy simulations conducted early in the project. Chapter 5 builds on the urban form and energy literature by presenting the quantitative results for LS scale parametric analysis of urban form characteristics. Chapter 6 applies the lessons learned in the parametric analysis to neighbourhood-scale urban form choices, presenting eight urban pattern variations representing infill development options along a transit corridor. Energy simulation results for each pattern variation are provided. The dissertation concludes with Chapter 7, which summarizes the key findings from the research and extends lessons learned to recommendations for urban planning and development.  11  CHAPTER 2: LITERATURE REVIEW This chapter presents a summary of the literature reviewed in preparation for this research. Section 2.1 examines literature on the current understanding of relationships between urban form and building energy, identifying that these relationships centre on heat transfer and solar access, which are affected by both building morphology and urban structure. Section 2.2 reviews varying approaches to the study of urban form generally, focusing on “pattern” based qualitative and quantitative methods. Section 2.3 reviews building energy modeling methods, ranging from the simulation of individual buildings to regional and national scale data aggregation methods. Of particular note is a lack of robust building energy analysis methods appropriate for neighbourhoodscale studies.  2.1 Urban Form and Energy The relationships between urban form and energy have been explored from a variety of perspectives over several decades. Urban form variables affecting energy use, from the building to regional scales, have been identified by Owens, 1986 (Table 2.1). Owens further attempts to quantify the magnitude of potential impacts on energy consumption related to these variables (Table 2.2). The energy demand in buildings is affected by its own set of factors. Mitchell (2005) lists eight factors for building energy consumption, including occupant activities, urban geometry and thermal properties of construction materials. These characteristics are echoed by others (Table 2.3). Mitchell, as well as Salat (2009) extends energy-related factors to greenhouse gas (GHG) generation  Table 2.1: Urban form variables affecting energy at different scales, adapted from Owens, 1986 Structural Variable Settlement pattern Communication network between settlements Size of settlement Shape of settlement Communication network within settlement Density Interspersion of land uses Degree of centralisation of facilities Layout Orientation Siting Design  Scale  Region Settlement Orientation  Building  12  Table 2.2: Energy of implications of structural variables, Owens, 1986 Structural Variable  Mechanism  Energy Implications  Shape  travel requirements  variation of up to about 20%  Interspersion of activities  travel requirements (trip length)  variation of up to 130%  Combination of structural variables  (shape, size, land use mix, etc.)  travel requirements (trip length and  frequency)  variation of up to 150%  Density/built form  surface area to volume ratio affects  200% variation between different  energy requirements for space heating building forms  Density/clustering of trip ends  facilitates running of public transport  system  energy savings of up to 20%  Density/mixing of land uses  facillitates introduction of energy‐ efficient CHP/DH systems  efficiency of primary energy use  improved by up to 100%  Density/siting/orientation and  landscaping  maximises potential to use "free"  ambient energy sources  can reduce conventional energy  consumption by at least 20%  Table 2.3: Factors influencing building energy consumption Mitchell, 2005  Salat, 2009  Ratti et al., 2005  Urban geometry Building morphology Thermal performance of materials Efficiency of internal systems Occupant activity and behaviour Fuel price Opportunity to share infrastructure Internal and external temperature  Urban form  Urban geometry Building design  Building performance Equipment and system efficiency Inhabitant behaviour Type of energy  Systems efficiency Occupant behaviour  by including determinants of energy source. Consistent within the literature is the notion that urban form – both the morphology of individual buildings and the urban structure in which they are located – contributes in part to building energy consumption. While building energy is affected by much more than urban form, Baker and Steemers (2000) offer two compelling reasons why urban form should be considered and addressed. First, urban form factors are less likely to change than technologies and occupants, so design elements are likely to have much longer term positive (or negative) consequences. Second, the authors suggest that form, technology and occupants do not act independently. Rather, good design may result in better system performance and occupant behaviour. Wener and Carmalt (2006) have also noted that design choices, such as passive design, are less dependent on, or susceptible to, behavioural changes.  13  Studies considering the overall impacts of urban form on building energy present several key findings, although the magnitude of impacts differs between sources. Baker and Steemers (2000) find that building design (i.e. building morphology and envelope characteristics) accounts for a factor of 2.5 variation in energy consumption, while systems efficiency and occupant behaviour each account for a factor of 2 variation. These effects cumulatively result in a factor of 10 difference in energy performance among buildings, but actual energy consumption in buildings can range by a factor of 20, even among buildings with comparatively similar functions (Ratti et al., 2005). Ratti et al. postulate that urban structure, particularly the relationships between buildings, may account for the additional factor of 2 differences in performance (2005). Their study, however, is only able to account for a 9% difference in energy consumption, despite changes in building and neighbourhood characteristics. Other studies (e.g. Johnston et al., 2005) assert that the impact of urban form characteristics is relatively small in comparison to thermal and building system performance, particularly for larger, aggregate studies. A survey of the literature suggests that, at whatever magnitude of influence, urban form plays a role in building energy consumption within two predominant spheres of urban form characteristics: building morphology and urban structure, and in two key ways: heat transfer and solar access. Building morphology refers to the size and shape of a building, including plan, elevation and sectional characteristics. Building morphology can also include facade design characteristics such as the amount and distribution of glazing and building orientation (Mitchell, 2005). Common measures of building morphology include surface to volume ratio (S:V), plan depth, building height, passive area and glazing ratios (Steadman et al., 2009; Batty et al., 2008; Baker and Steemers, 2000). Several of these measures are further described in the remainder of Section 2.1. Urban structure refers to the arrangement and spacing of (i.e. relationships between) urban elements including buildings, streets and open spaces (Mitchell, 2005; Steemers, 2003; Ratti et al., 2005; Cheng et al., 2006). Common measures of urban structure include density measures such as floor-area ratios (FAR) and land coverage, building spacing, urban horizon angles (UHA), sky view factor and others. Several of these measures are further described in the remainder of Section 2.1. Building morphology and urban structure and their effects on heat transfer and solar access will shape the discussion for the remainder of this section. The building morphology and urban structure characteristics and measures used for this research are described in detail in Chapter 3.  14  2.1.1 Building morphology and heat transfer Among the morphological characteristics of buildings that have been shown to influence building energy consumption, compactness (Mitchell, 2005) and building density are primary factors. A variety of researchers note that more compact, higher density buildings typically consume less energy on a per capita or per area basis (e.g. March, 1972; Steemers, 2003; Miller and Cavens, 2008; Salat, 2009; Cobalt Engineering, 2009). At the building scale, compactness has the predominant effect of reducing heat transfer, as more compact building shapes enclose more building volume with less surface area through which heat can escape. March (1972) mathematically describes compactness using surface to volume ratios (S:V), a measure later echoed by Owens (1986) and others (e.g. Ratti et al., 2005; Salat, 2009; Steemers, 2003; Knowles, 1974). Studies attempting to quantify the impact of building compactness on energy consumption vary significantly in both approaches and findings. For surface to volume ratios, Owens (1986) suggests that building energy can vary by as much as 200%, while Ratti et al. find an inverse relationship between surface to volume ratios and energy consumption in the comparison of non-domestic buildings in three European neighbourhoods. Across the literature on building density and building energy, a variety of representations of density have been utilised. The building density-energy relationship has been described by relating energy consumption to density measures such as units per hectare (e.g. Canmet Energy, 2009), floor area ratio (e.g. Steemers, 2003) and residential unit type (e.g. single family detached, attached and apartments) (Owens, 1986; Miller and Cavens, 2008). Used in reference to a single building, density measures are often used to imply a relationship to compactness (although this is not always the case), and also reflect the effects of shared walls and floors and smaller unit sizes on energy demand. In a study examining FAR for commercial buildings, Steemers (2003) finds potentials for a 50% reduction in heating demand at increased building densities. In the same paper, Steemers also finds that doubling building density, measured by FAR, can increase total energy demand per square meter by 25% in office buildings. Studies of building density by residential unit type have been somewhat more consistent. In their 2008 report, Ewing and Rong conclude that households living in low-density, single family homes consume 54% more energy for space heating and 26% more for cooling than a similar household living in an attached, multi-family dwelling. This is consistent with Canadian findings based on data from the BC Hydro Conservation Potential Review (Miller and Cavens, 2008) and Natural Resources Canada (2009) which show that attached residential units such as rowhouses use approximately half the heating energy of the average single family house, while apartment units use between 65%  15  and 75% less heating energy on a per unit basis. The difference in performance is due both to the thermal efficiencies of shared walls and floors between units and also to the smaller average unit sizes for attached and apartment dwellings. Comparative studies relating density and urban structure to building energy at the neighbourhood scale provide correlations between increased development density and reduced neighbourhood energy demand, but at varying degrees of intensity. Comparisons between studies are difficult, as the ranges of development density considered in each study differ substantially. Salat (2009) finds that for three neighbourhoods ranging in density from 13-32 units per hectare, energy consumption varied by 44% per square meter; however, the case studies also included numerous other factors, including a wide range of neighbourhood ages and building construction types that also affect results. Comparisons of neighbourhoods completed as part of Canmet’s Urban Archetypes (2009) project show that energy use can vary by 33% per capita, with a strong relationship between increased development density and lower energy consumption. While increasing compactness and building density generally reduce building heat loss, increasing these characteristics does not guarantee overall building energy reductions. The relationships between compactness, building density and building energy are complicated by differences between residential and commercial/institutional uses. Residential building energy is dominated by heating demand. Increasing building density from detached housing to apartments can reduce heating energy demand; however, such density increases typically require increased building depth, increased building height or reduced building spacing that limit access to passive heating and daylighting (see Section 2.1.2). In certain contexts, low-density residential design with high surface to volume ratios maximised for passive solar heating may provide greater opportunities than density for reduced energy demand (Steemers, 2003). Conversely, commercial and institutional buildings are dominated by lighting and cooling energy demands. For these buildings, shallow floor plans (1215 meters) are the most critical characteristic. Models examining increased density in commercial office buildings have shown potential overall increases in energy consumption from increased cooling demands, due to the high levels of internal heat gains present in these buildings (Baker and Steemers, 2000; Mitchell, 2005; Steemers, 2003). Steemers concludes that the arguments for and against density for building design are “finely balanced” and dependent on local contexts.  2.1.2 Building morphology and solar access Density and compactness alone do not account for all building morphology related variation in energy demand. As suggested in the previous sections, an overemphasis on minimising building surface area to reduce heat loss neglects other energy issues and may not reduce total energy  16  consumption, as the availability of daylight and solar energy (i.e. solar access) also have significant impacts on residential and commercial building energy use (Baker and Steemers, 2000; Ratti et al., 2005). Arguments for and against density, balancing the need to reduce heat loss with the need for solar access, are complex (Baker and Steemers, 2000; Steemers, 2003) and additionally affected by building use, building design, available infrastructure, local contexts and specific design intent (for example, passive versus active solar energy strategies). Tradeoffs between density and solar access for residential buildings are relatively balanced, while increasing the density of office spaces tends to have negative energy consequences. At the same time, these conclusions become less certain as the complexity of surrounding urban forms increases (Steemers, 2003). In the study by Ratti et al. (2005), more compact buildings (by surface to volume ratio) were associated with higher energy consumption; greater access to light and air, measured through the quantification of passive building area, was found to be a better predictor of reduced energy consumption for non-domestic buildings. The study notes, however, that such findings are likely context specific. Steemers (2003) explains many of the potential tradeoffs between density and solar access, suggesting there may be thresholds beyond which density does not improve energy performance. In terms of building morphology, energy reduction strategies such as passive heating, daylighting and natural ventilation as well as renewable energy strategies such as solar hot water rely heavily on increased surface area, in opposition to the decreased surface area required for reduced heat loss. Increasing the amount of building surface area increases the proportion of “passive” building floor area (Baker and Steemers, 2000), or the area with the ability to utilize daylight, sunlight and natural ventilation. The proportion of building area in passive zones indicates the potential of buildings to use passive energy strategies (Baker and Steemers, 2000; Ratti et al., 2005). Passive areas are typically considered to be within six meters of the building perimeter, although this dimension varies with floor to floor height, and maximised passive areas require shallow building depths (approximately 10-12 meters). To compensate for greater heat loss potential due to increased surface areas, improved insulation standards are typically recommended for passively designed buildings (Steemers, 2003). The concept of passive areas is today widely referenced at both the building and neighbourhood scales as an essential characteristic in relating urban form to energy performance (e.g. Steemers, 2003; Ratti et al., 2005; Steadman, 2000; Salat, 2009), although less frequently applied at the neighbourhood scale for research applications. A limited number of studies comparing amounts of  17  passive area at the neighbourhood scale suggest energy impacts ranging between 10% (Ratti et al., 2005) and 40% (Salat, 2009) per square meter of building area. Beyond passive building area, building morphology also affects solar access through facade glazing and building orientation. Optimised glazing ratios have the potential to reduce energy consumption in a building’s passive zones by an additional 6-7% beyond standard building practices (Ratti et al., 2005). However, increased glazing also incurs greater heat losses (Baker and Steemers, 2000); compensatory strategies such as increased glazing insulation standards and thermal massing for heat storage must be considered in parallel. Data from a study of mid- and high- rise residential buildings conducted for the City of Vancouver show that energy consumption can vary by 33% per square meter, with a relationship between lower glazing ratios and lower energy use (RDH Building Engineering Ltd., 2009). Optimal glazing strategies will balance solar gains and heat losses (Baker and Steemers, 2000), reduce glazing on higher portions of building facades and vary in response to building orientation (Steemers, 2003; Compass Engineering, 2009). Building orientation, or the direction in which the main building facade faces, also has significant impacts on passive strategies and is determined by street or block configuration as well as building design. A comparison of orientations in the four cardinal directions in the UK suggests that daylight availability can vary by as much as 20% (Baker and Steemers, 2000). For space heating, glazing oriented to the east and south provide the most useful solar gains, in comparison to western orientations that provide solar gains late in the day when temperatures are at a maximum and buildings are already heated (Baker and Steemers, 2000). Steemers (2003) finds that glazing with southern orientations can reduce heating energy demand an additional 11% over western orientations. Building orientation will have less of an impact in urban areas, where a higher number of obstructions reduce differences in the amount of solar and light access (Steemers, 2003). Paired with the considerations for reducing heat loss above, the optimum building morphology for energy efficient buildings would ideally increase compactness while ensuring limited building depths, strategic glazing patterns and appropriate orientation to provide maximum opportunities for passive design and renewable energy strategies (Steemers, 2003).  2.1.3 Urban structure Beyond building morphology, urban structure characteristics establishing relationships between buildings (e.g. spacing, arrangement) also play a role in determining energy consumption (Mitchell, 2005; Owens, 1986). These are particularly relevant in considering solar access (Ratti et al., 2005), for which a small subset of neighbourhood-scale energy literature exists. At the neighbourhood  18  scale, solar access is influenced primarily by building spacing and arrangement, which are measured in several ways including urban horizon angles and the obstruction sky view (Ratti et al., 2005). The urban horizon angle (UHA) is the average angle of elevation of surrounding buildings from the centre of a given facade. Affected by the height of and distance between structures, the UHA accounts for building shading and significantly affects solar access, particularly in winter when the sun is lower in the sky (Baker and Steemers, 2000). High UHAs have the potential to shift heating and cooling energy demands in buildings by reducing solar gain through glazing while heat loss remains constant (Steemers, 2003; Baker and Steemers, 2000). Lighting energy demands are also affected by this factor (Steemers, 2003). Tables prepared by Steemers (2003) illustrate that the UHA can affect heating energy demand by as much as 30%, cooling demand by 20% and lighting demand by 150%, with greatest impacts occurring for south facing facades. For Steemers’ work, UHAs less than 15 degrees are considered to have a negligible effect on energy demand. For non-residential buildings, increasing the UHA by 10 degrees can result in a 10% increase in energy consumption, with greater impacts occurring for passive solar designs (Steemers, 2003). The calculation of the UHA can be relatively straightforward when dealing with uniform spacing and building heights (i.e. tan(UHA)=h/w). However, the calculation is complicated by more complex urban geometry. To account for variations and building heights and spacing in an actual urban context, Ratti et al. (2005) apply an algorithm using a weighted average of the UHA perpendicular to the facade and 6 UHAs within the range of 67.5 degrees to either side, at 22.5 degree intervals.  h  Vs u w  Figure 2.1: Relationship between height (h) to width (w) ratio, urban horizon angle (u) and sky view factor (Vs), adapted from Robinson, 2006  19  A review of the literature also illustrates that beyond UHA, two other main approaches are used to quantify obstructions adjacent to a given building facade: height to width ratios (ShashusaBar and Hoffman, 2003) and sky view factors (Cheng et al., 2006; Compagnon, 2004). Height to width ratios are simply the ratio of the height of an obstruction above a given facade reference point and the distance between the obstruction and facade (Robinson, 2006). Sky view factor is a measure of openness for a given surface, represented as a value between 0 and 1, where 1 means an unobstructed view of the sky and 0 means a completely obstructed view (Cheng et al., 2006). While these approaches and UHA each use different methods of calculation, they are all related quantities, as illustrated in Figure 2.1. Each measure, in essence, represents the same qualities of urban morphology: the distances between buildings and their respective heights, resulting in the obstruction of solar radiation and daylight. Cheng et al. (2006) illustrate an application utilising sky view factors, finding that the sky view factor is highly affected by FAR and site coverage (both measures of density) as well as building arrangement within the site (e.g. regularized versus random layouts). Changes in the sky view factor (between 0.06 and 0.50) resulted in variations in available daylight as great as 66%. The obstruction sky view, as defined by Ratti et al. (2005), is essentially the same as UHA, but calculated for adjacent, obstructing facades. Solar energy falling on a facade comes from two distinct sources: direct radiation from the sky and indirect radiation reflected by other surfaces, including obstructions. The obstruction sky view accounts for the amount of radiation reflected onto a given facade (Ratti et al., 2005). Steemers (2003) notes that highly compact urban structures may negatively affect the feasibility of passive heating or building integrated renewable energy production due to shading and other solar access limitations. More dispersed development has greater potential for solar energy, daylighting and natural ventilation; however, the counter-productive effects of increased density can be avoided if UHAs (which limit access to sunlight and daylight) are kept under 30 degrees – typical for densities of approximately 2.5 FAR or 200 units per hectare (Baker and Steemers, 2000). Building morphology and urban structure can also work together to play a significant role in enhancing solar access on an urban scale. Knowles reviews how particular building shapes, such as stepped building facades and appropriately angled roofs can virtually eliminate issues of overshadowing on adjacent parcels (2003). Cheng et al. (2006) find that rearranging building layouts in a theoretical neighbourhood in more random configurations (i.e. not aligned in a grid), as well as randomly varying building heights, substantially improve solar access without reducing the amount of building floor area. This implies that relatively high development densities can be achieved without negatively impacting energy performance.  20  Further, vertical and horizontal surfaces (i.e. walls and roofs) face different considerations for solar access. Roofs – and horizontal surfaces in particular – are less affected by surrounding urban structure than vertical surfaces (Compagnon, 2004). Higher elevation surfaces of any orientation are also less subject to solar obstruction (Carneiro et al., 2008). The relative importance of horizontal and vertical surfaces for solar access may depend on design intent. For example, high site coverage development, which typically limits vertical solar access, is advantageous for photovoltaics due to the high amount of horizontal roof surface provided (Carneiro et al., 2008; Cheng et al., 2006). Compagnon (2004) finds that total solar irradiation in a study neighbourhood (including vertical surfaces) can be three times that of irradiation on roofs alone per square meter of floor area, emphasising that vertical surfaces (i.e. building facades) are an important factor in solar energy collection. Urban structure also relates to building energy through the urban heat island (UHI) effect. As discussed in Ewing and Rong (2008), the UHI effect has several causes, including the proliferation of built surfaces which retain the sun’s radiation, the displacement of vegetation and related cooling and shading effects and the concentration of heat producing activities, such as vehicle travel. Urban structure characteristics associated with this effect include the density of development and spacing of buildings, which can concentrate the amount of heat-absorbing surfaces as well as heatgenerating activities contributing to the UHI. The UHI effect can increase temperatures in typical urban areas by 1-3° C, with larger, more compact cities experiencing greater effects. As expected, this effect serves, on average, to increase the need for cooling in the summer and decrease the need for heating in the winter; however, Ewing and Rong find that for most locations in the United States, the UHI effect provides a net reduction in energy consumption due to savings in heat energy and conclude that everywhere the overall positive effects of compact development far outweigh any negative effects of heat islands. More compact development further minimises the energy required for infrastructure and related maintenance and creates opportunities for district energy and heat sharing technologies that require concentrated energy demand (Steemers, 2003). Compact development and localised energy systems can also reduce energy transmission and distribution losses. For example, these losses account for roughly 7% of generated electricity in the United States (Ewing and Rong, 2008).  2.2 Urban Form Research Methods The attempt to describe and define urban form has been ongoing for centuries, across many disciplines, without arriving at a conclusive approach (Jabareen, 2006; Levy, 1999; Marshall, 2005a). Cities are alternatively thought of as constructed spaces, arrangements of parts, products of linked  21  decisions, patterns of activities or relationships, social or economic constructs, ecosystems or flows of information, among other conceptions (e.g. Kropf, 1993; Lynch, 1981; Marshall, 2005c; Jenks and Dempsey, 2007; Levy, 1999; Marshall, 2009; Anderson et al., 1996). Because cities influence and interact with so many natural and human phenomena, the bounding of urban form for examination or description is difficult (Lynch, 1981) and influenced by values, intentions and interests that are sometimes unexamined or unrecognized (Lynch, 1981; Levy, 1999). Additionally, the large numbers of actors and elements inherent to urban systems demand research approaches capable of simplifying and organising vast amounts of data for particular purposes.  2.2.1 Urban patterns Despite great diversity in the characteristics of cities, most urban forms have some basic features in common (Kostof, 1991; Marshall, 2009; Lynch, 1981; Anderson et al., 1996). The study of urban patterns builds on these commonalities to create abstract types that allow for generalisation across multiple, specific cases (Marshall, 2005c). In the most general terms, a pattern is any kind of recurring structure or feature (Marshall, 2005c). As the understanding of the parts is a prerequisite for understanding the whole (Lynch, 1981), an urban pattern is most often defined as a relatively small number of parts or elements (e.g. streets, parcels, buildings and open spaces) which are linked by a consistent, repeated set of systematic relationships (Habraken, 2000; Jabareen, 2006; Marshall, 2009; Miller and Cavens, 2008). These relationships tend to describe qualities such as connectivity and orientation, rather than metric data such as actual distances and areas (Marshall, 2005c) and aim to extract useful and meaningful information for a given purpose. Relationships imply similarity and continuity, rather than standardisation (Habraken, 2000). The understanding and measurement of the urban environment is necessarily spatial. Urban form has been defined by many as consisting of spatial patterns or attributes (Jabareen, 2006; Lynch, 1981; Anderson et al., 1996; Mindali et al., 2004; Marshall, 2005c). These spatial patterns are defined primarily by the arrangement of permanent objects – land forms, streets, buildings and infrastructure (Jabareen, 2006; Lynch, 1981; Anderson et al., 1996; Miller and Cavens, 2008) within which urban functions (transportation, exchange, interactions) occur (Lynch, 1981). Spatial models, therefore, depend on the location of urban elements and activities, “such that if locations change, results change” (Maguire et al., 2005). Numerous disciplines use spatial models to study various aspects of urban forms and systems: landscape ecology and resource conservation, economics (such as urban growth and change), demographics (including census data), transportation planning, community energy planning and others (Albeverio et al., 2007; Clifton et al., 2008).  22  Table 2.4: Summary of issues involved in the description and analysis of urban form Classification     The classification of urban form involves making distinctions based on specific attributes.  However, in reality, urban form is a morphological continuum (Marshall, 2005a) without  distinct or obvious boundaries.  Many urban conditions are found to be irregular or hybrids  of multiple patterns, unable to fit neatly into generic categories. Attempting to classify all  forms under these conditions runs the risk of ending up with an “arbitrary list” (Marshall,  2005a). Many researchers have found is that there is no set of clear, absolute pattern types  (e.g. Marshall, 2005c; Kostof and Castillo, 1992). Rather, classification depends on the  intended application, and must seek to balance between too few broad categories and too  many narrow categories for the intended purpose (Marshall, 2005c).   Analysis     The methods used to study urban form vary widely across several disciplines including  geography, urban planning, history, landscape planning, ecology and others. Marshall  (2005c) notes that there is no single, consistent method of specifying urban patterns across  the disciplines interested in urban form. Patterns may be differentiated based on one or  many characteristics (Lynch, 1981; Marshall, 2005a). They may be assessed using  qualitative methods (e.g. Marshall, 2005c), quantitative methods (e.g. Pont and Haupt,  2005), or a combination of approaches (Marshall, 2005a).    Scale and  resolution     The reviews of urban patterns presented by Marshall (2005a) and Lynch (1981) illustrate  the range of scales at which urban form has been examined. Lynch provides examples of  patterns at the city scale, representing overall shapes and configurations of entire urban  areas (e.g. linear or satellite cities), as well as finer‐grained patterns representing detailed  urban characteristics such as housing types (e.g. tower in the green) and classes of open  space (e.g. playgrounds and playfields). Marshall (2005c) notes that as interest in  sustainability research increases, the resolution of sustainability indicators is not being  matched by an equally high‐resolution understanding of urban form.    Representation     Due to high levels of variation in scale, classification and methods of analysis, urban form is  rarely represented consistently. Beyond the language used to discuss form and patterns,  patterns are also represented graphically and quantitatively in a variety of ways. Lynch  (1981) notes that 2D maps and diagrams are frequently used to represent urban patterns  but lack essential 3D information. The need for enhanced 3D representation of urban form  has been noted by many (Sheppard, 2005; Appleton and Lovett, 2003; Batty et al., 2000).  Further, Lynch notes the challenge of representing temporal aspects of urban form,  including evolution over time, as well as the challenge of representing patterns with  appropriate graphic detail to highlight significant characteristics (Lynch, 1981).    Application     The study of urban form can be applied in a variety of ways (Marshall, 2005c). In many  applications, patterns are used to interpret and understand existing or potential future  urban forms. Patterns may also be used to assist in the generation and description of  alternative design options (e.g. Criterion Planners, 2008) and associated policy. One  example of policy developed based on patterns is form‐based codes, such as SmartCode  (Duany Plater‐Zyberk and Company, 2008). Recognizing that urban form is evolutionary,  Anderson (1996) argues that the usefulness of archetypes may be limited by their static  nature; however, Marshall (2005c) notes that static representations of form are important  to decision makers, who experience urban form as static while making day to day  decisions. Importantly, patterns applied in the development of planning options and policy  should be recognisable to policy makers and related to local conditions (Marshall, 2005c).       23  Approaches to the description and analysis of urban form through the use of patterns have been methodologically diverse, encompassing a range of both qualitative and quantitative classification systems and varied forms of analysis and representation (Table 2.4). The following sections review some of the primary precedents and directions in pattern-based approaches to urban analysis, including more recent applications of computer-based modeling.  2.2.2 Qualitative pattern approaches The use of morphology, typology and urban pattern classification through history has largely been a qualitative endeavour. Scholars have carefully observed and studied urban conditions and described, categorised and interpreted urban elements and relationships in a variety of ways. Qualitative pattern approaches, as defined for this paper, are any methods of defining urban patterns based on non-quantified qualities or characteristics, such as classifications by pattern shape (e.g. linear, nodal), by the presence of particular urban elements (e.g. services, cul-de-sacs) or by organisational properties (clustered, open plan). Some qualitative approaches also attempt to address less tangible aspects of urban form, such as use, meaning or social and historical contexts. Four examples of qualitative approaches are presented in this section. Kostof, in his last two books, approaches urban history through the examination of urban patterns and urban form elements. For Kostof, there are two types of urban forms – organic forms that develop over the passage of time without design and planned forms, such as grids, which are intentionally created (Kostof, 1991). He notes that these forms are not exclusive, often existing side by side. Moreover, there are parts of each in the other: irregularities of building shape and placement often occur along planned, regularised streets, and ordered patterns often arise from spontaneous development. Kostof also examines urban patterns in elevation, noting the function of urban skylines as a calculated and cumulative process, creating urban “signatures” (Kostof, 1991). All settlement patterns are comprised of four common elements: a changing edge, internal divisions, public places and streets (Kostof and Castillo, 1992), and all elements are intertwined with social and historical context. Kostof’s work focuses more on defining these principles and elements across history than on identifying particular urban patterns; he argues that “cities are too particular a phenomenon, specific to moments in time and to vicissitudes of site and culture, to be pinned down by absolute taxonomies” (Kostof and Castillo, 1992). Lynch (1960) similarly notes that urban form is difficult to define in terms of concrete patterns (see also Lynch, 1981). He hypothesizes that the moving elements of a city, and people in particular, are as important a consideration as the static, physical elements. His classifications of urban form are consequently based on how urban form is perceived by inhabitants (Lynch, 1960). Through studies  24  conducted in three major cities, Lynch concludes that an individual’s understanding of urban form, or “environmental image,” is based not only on the spatial pattern of urban elements, but also on the ability to identify and associate meaning with particular elements (Lynch, 1960). He then identifies key elements present in individuals’ environmental images: paths; edges; districts; nodes; and landmarks. While these classifications are not “morphological” (Hall, 1997), they are closely tied to urban form and physical patterns. In another qualitative approach to the classification of patterns, Marshall (2005c) considers a combination of morphological and other characteristics in defining a set of five patterns of local urban structure, for which seven criteria – each with two to three attributes – were evaluated. Selected criteria include land use, the presence of services and the relationship to transportation spines, among others. What the research finds is that the five local structures can be differentiated with only two criteria: the relationship of the local centre to the neighbourhood and the location of the neighbourhood along the transportation spine. However, the five local patterns of urban structure presented by Marshall are distinctly suburban in nature, highly generalized in characteristics (for example, the description of land use is limited to single-use or mixed), and targeted towards discussions on new development. Steadman (2000) uses a qualitative classification of building morphology as a tool for subsequent energy analysis in the UK. For the project, built forms are described parametrically, rather than dimensionally, enabling a single dimensionless form to stand for a large number of real examples. For example, a simple building shape would be a “shed,” with a rectangular plan and a doublepitched roof with gables, regardless of specific dimensions. The forms are then classified according to two main characteristics identified as important for energy analysis: the “texture” of the internal plan (e.g. open or cellular) and whether spaces are artificially or naturally lit.  2.2.3 Quantitative pattern approaches Less frequently, studies of urban form and patterns have taken a quantitative approach. Pont and Haupt (2005) note that “a quantitative analysis of the built form has not been applied thoroughly.” However, there is a growing recognition that quantitative descriptions and analysis of built form can serve to expand the power and usefulness of typically qualitative approaches (Pont and Haupt, 2005; Marshall, 2005a; Lynch, 1981). Additionally, the increasing power of digital information and analysis tools is providing new opportunities for detailed quantitative studies of urban form. Several approaches to pattern classification using digital tools are described in Section 2.2.4.  25  In his approach to street pattern classification, Marshall (2005a) argues that “capturing the character of... patterns implies the ability to handle heterogeneity” and suggests that heterogeneity requires the use of quantitatively defined properties. To classify street patterns, he proposes three quantifications: T and X ratios; cell and cul-de-sac ratios (all defined in the book); and the plotting of these numbers to create a 2-dimensional space. By graphing these numbers, Marshall creates a spectrum of street patterns shown in relation to one another, allowing for greater specificity of characteristics. In further work published the same year, Marshall (2005b) uses the quantification of street patterns according to continuity, connectivity and depth measurements to determine the relative probability that a particular pattern will occur. Pont and Haupt (2005; 2007) have likewise attempted to link spatial patterns to quantitative data. They note that the typical use of density as an identifying concept of built form and urban pattern is problematic. An analysis of three case studies illustrates that neither of the two commonly used quantitative metrics of density (units per hectare and FAR), adequately identify similar urban forms or differentiate between unlike forms. Pont and Haupt redefine density using four quantitative metrics: FAR; ground space index (i.e. building coverage); open space ratio (ratio of open land to building floor area); and layers (average number of building storeys). By graphic these metrics simultaneously, a more consistent classification of urban forms is achieved. For classification of density at the neighbourhood and larger scales, a fifth metric, network density (length of street network per hectare), is also considered to differentiate varying block scales (2005). The authors further suggest that the urban patterns defined through the quantification of density can be used as a framework to evaluate pattern performance on a variety of issues, such as amount of open space, building shadowing, parking and energy consumption, although specific methods have not yet been developed. Researchers at the University of British Columbia (Miller and Cavens, 2008) have used the statistical analysis of existing urban form (land use, building type and population) to discern between urban patterns. In recent work, a city was divided into zones representing similar patterns of development. For each zone, land use data was compared and zones with statistically similar land uses clustered to generate “average” patterns. Work currently in progress is using further quantitative analysis to automate the assignment of previously generated patterns to regional scale areas.  2.2.4 Patterns in computer modeling applications One of the emerging approaches in both academic and applied urban research is the use of urban patterns to simplify modeling data while capturing key urban form and related properties. The concept of classifying and describing urban form through the definition of patterns has  26  been discussed in previous sections, and has a long history prior to incorporation in modeling applications. As discussed, most urban forms have some basic features in common, and the study of urban patterns builds on these commonalities to create abstract types that allow for generalisation across multiple, specific cases. The incorporation of patterns in modeling has had, and is having, several important implications in urban form research and urban planning. Some studies are using pattern-based concepts to automate the classification of urban areas or building stock (e.g. Steiniger et al., 2008). Others are using patterns to approximate existing urban conditions and to model future scenarios at multiple scales. Depending on the amount and type of data incorporated in the patterns, analyses of sustainability indicators, including energy and GHG emissions can then be completed (e.g. Criterion Planners, 2005). Patterns are also being included as information and engagement tools in public planning processes, allowing stakeholders to participate in the creation of patterns or translate patterns into key policy decisions. Researchers at the University of British Columbia are conducting research on how patterns may be used for new approaches to transportation modeling. Urban pattern modeling approaches vary greatly depending on the particular scale of the object of study (e.g. urban structure, land use, street pattern, or building type).The variety of possibilities for classifying urban patterns has led to great inconsistency in their use (Marshall, 2005a; Marshall, 2005c), which is also evident in the variety of methods used in classification efforts. This section focuses on the description of several modeling methods used in classification and pattern-making processes. A first set of methods used in the modeling of patterns involves classification through cluster analysis, where multi-criteria datasets are analysed to find groups possessing similar characteristics (Singleton, 2007; Jones et al., 2001). Two examples illustrate how cluster analysis is used in different ways and at different scales to create classifications suited to particular purposes in urban research. In the first example, Jones et al. (2001) use a clustering model to classify houses in the UK according to estimated energy performance and energy retrofit potential. Houses were classified as one of five age groups and one of 20 built form types, resulting in a total of 100 possible classifications. The built form types were defined according to four selected characteristics believed to have the greatest impact on residential energy performance: heated ground floor area; façade; window to wall ratio; and exposed end area. Each of the 100 house types was modeled using the Energy and Environmental Protection (EEP) model to determine GHG emissions, energy ratings and annual energy costs, a process that would have been prohibitive to carry out for each individual building.  27  In a second example, Steiniger et al. (2008) attempted to automate the definition of patterns of urban structure in Switzerland and the UK according to five classifications: industrial and commercial areas; inner city; dense urban areas; disperse suburban areas; and rural areas. For this process, a geographic information system was used to describe urban structures according to five morphological characteristics: built-up area density; building size; building shape; squareness of building walls; and building orientation. These data were analysed using a series of algorithms in order to assign areas to a final structural type. What the project found, however, is that morphological characteristics do not consistently classify urban structures, as the characteristics were often not distinct between urban structure types. Further, the method relies on homogeneity of characteristics within a given urban pattern, and could not account for the more gradual transitions between patterns, such as those occurring between rural and suburban areas. Marshall (2005a) applies similar analysis techniques to the classification of street pattern types according to a more finely delineated spectrum. He suggests that “capturing the character of real street patterns implies the ability to handle heterogeneity” and that heterogeneity requires the use of quantitatively defined properties. To classify street patterns, he proposes three quantifications: T and X ratios; cell and cul-de-sac ratios (defined in his book); and the plotting of these numbers to create a 2-dimensional space. By graphing these numbers, Marshall creates a spectrum of street patterns shown in relation to one another, allowing for greater specificity as well as clustering of characteristics. Pont and Haupt (2005; 2007) similarly graph urban patterns according to five quantifications of urban density: FAR; ground space index (i.e. building coverage); open space ratio (ratio of open land to building floor area); layers (average number of building storeys); and network density (length of network per hectare). While the above urban form modeling methods all deal with pattern classification and modeling based on quantified urban form attributes, another approach models patterns based on the assembly of smaller scale classifications or types (such as parcels, buildings or land uses) to create larger scale patterns of development. This approach to urban pattern modeling typically uses spatial and quantitative municipal data, such as land use and residential unit counts, rather than morphological data, to determine existing patterns according to density and/or land use mix. Because these approaches are more closely aligned with how planning decisions are made (e.g. parcels, streets and open spaces grouped by zoning designations, development plans, etc.) many of these modeling methods have evolved in applied research in planning and urban design (e.g., Miller and Cavens, 2008), or in the private sector (see for example Criterion Planners, 2005; Fregonese Associates et al., 2009). When applied to real planning processes, “future” patterns are often collaboratively developed by experts and stakeholders for the purpose of plan or scenario creation.  28  2.3 Building Energy Modeling Methods The modeling of building energy consumption occurs at several scales, each with varying methods and tools. Work to date on modeling the building energy implications of urban form appears to be relatively well developed at the building scale, where energy simulation models have been refined for decades (Crawley et al., 2005), and also at the regional scale, where aggregate estimations of energy are able to provide sufficient information for high-level policy questions (Johnson et al., 2005). The neighbourhood-scale impacts of urban form on energy, particularly the cumulative effects of relationships between multiple buildings (e.g. building massing, heights, arrangements and spacing) are less frequently considered and have less developed methods available for modeling. However, increasingly sophisticated energy simulation tools, including solar radiation simulation tools such as Radiance (Cheng et al., 2006) and building energy meta-tools such as VE-Pro (see Chapter 3) now have the capacity to model the performance and interactions between multiple buildings. Platforms for spatially explicit data storage and analysis, such as GIS, CityGML and others likewise offer opportunities for enhanced neighbourhood-scale energy analysis. At the same time, approaches such as the lighting and thermal (LT) method (Baker and Steemers, 2000) and energy-specific design indicators (Kellett, 2009) offer means by which to compare simulated energy performance results according to quantified urban form variables. A majority of factors affecting building energy consumption relate to building design (i.e. building morphology and envelope characteristics) and building systems (Mitchell, 2005; Ratti et al., 2005; Baker and Steemers, 2000). Depending on the scale of the model and the number of buildings to be considered, these factors may be assessed for individual buildings (e.g. building energy simulations), or broad assumptions may be applied based on averages and related analysis to simplify complexity (e.g. Environmental Change Institute, 2005; Miller and Cavens, 2008). For the purposes of this paper, models are categorized into three scales – individual building energy models, neighbourhood energy models and regional/national energy models (Table 2.5).  Table 2.5: Typical energy modeling approaches at different scales Building Scale  Neighbourhood Scale  Regional Scale  Building Morphology  Individually modeled  Assumed  Building Envelope Building Systems  Individually modeled Individually modeled  Individually modeled OR  Assumed Assumed Assumed  Assumed Assumed  29  Table 2.6: Categories of building energy tools with examples, adapted from Jacobs and Henderson, 2002 Tool Type  Functions  Practitioner Design Tools  Automation of common tasks associated  Solar‐2 (shading); GS2000 (geothermal  with typical design process sizing); EnergyGuage (home energy rating)  Whole Building Energy Analysis  Tools  Detailed prediction of annual energy use  EnergyPlus; DOE‐2 (calculation engines);   and operating costs eQUEST; Energy‐10 (hourly simulation  tools) RETScreen (renewable energy); Athena  Simplified analysis of economic and  Impact Estimator (life‐cycle impacts); BLCC  environmental impacts of selected  (life‐cycle costing) technologies Radiance (solar/daylighting); THERM (heat  Technically accurate, detailed   and moisture transfer); Flovent (fluid  simulations of building or system  dynamics) performance   Energy and Environmental  Screening Tools Specialised Analysis Tools  Examples  Beyond the individual building scale, for which numerous software packages are available, the assessment of multiple buildings at the neighbourhood and larger scales generally takes one of two approaches (Mitchell, 2005): 1)	 Building activity descriptions (e.g. residential, office, industrial), multiplied by energy coefficients 2)	 Geometric descriptions (e.g. LT method, Section 2.3.3) Based on current levels of data availability at various scales, Mitchell (2005) finds that the activitybased method is most appropriate at the municipal, regional and national scales, while geometric methods are most appropriate at the neighborhood scale. However, in application, crossover between methods and scales does occur. This section will focus on neighbourhood-scale building energy modeling, addressing both approaches described by Mitchell. The section begins with a brief discussion of the other two scales for context.  2.3.1 Building-scale energy models Modeling building energy consumption at the scale of individual buildings has been a field of study for over 50 years (Crawley et al., 2005). Hundreds of models have been developed over this time, designed for different purposes, audiences and levels of complexity (Crawley et al., 2005; Jacobs and Henderson, 2002). The spectrum of available tools ranges from highly technical, engineering tools aimed at simulating particular aspects of energy performance (e.g. detailed HVAC performance) to simplified tools for building designers that assist with common design questions (e.g. equipment 30  sizing). Between these extremes are modeling tools that combine detailed simulations with userfriendly interfaces to support a wider range of users (Jacobs and Henderson, 2002). Jacobs and Henderson (2002), along with Crawley et al. (2005) provide highly detailed reviews of available tools, calculation engines and their respective features (Table 2.6). A majority of building energy simulation tools require detailed data on building construction and systems design to determine energy performance. Common inputs to such models include: building geometry; construction materials; heating and cooling systems; occupancy schedules; and weather data. Some tools also take into account additional external site factors, including building orientation, site terrain, ground contact conditions and external shad