Computationally Unifying Urban Masterplanning
Author(s)
Birch, David Alan
Type
Thesis
Abstract
Urban masterplanning is the process of creating a coherent design for developing a
campus, suburb, city or region.
Unfortunately these design and analysis teams face challenges which prevent rapid
quantitative analysis of design iterations; precluding potential design improvement.
These include limited automation, poor integration of modelling disciplines and, in
particular, very limited scope for design space exploration.
This thesis investigates these challenges and their solutions. A computational frame-
work HierSynth is presented to help computationally unify the design and analysis sides
of the urban masterplanning community.
The key contribution of this thesis is HierSynths data model. This presents a
reconceptualization of the workflow graph by composing it with tree based design-decompositions commonly found in architectural interoperability formats. This is
achieved through a hierarchy of design queries, templates and analyses which when
executed form a design hierarchy annotated with evaluated analyses. This enables
detailed multi-scale analysis directly on design elements whilst supporting scenario
generation and design space exploration capabilities and techniques to explore design
improvements.
The HierSynth framework is evaluated by application to a major commercial masterplanning project with Arup North America and is used to explore the most effective techniques for generating design insight. HierSynth enabled an order-of-magnitude
more analysis iterations and previously infeasible design space exploration to answer
design questions.
During this collaboration an unexpected challenge was identified in maintaining and
debugging complex, highly interrelated analysis models implemented as spreadsheets.
A toolkit to address this is developed and applied to several generations of complex
multi-disciplinary sustainability models.
In summary this thesis presents evidence of the need for, implementation of, and
practical benefits from, computationally unifying urban masterplanning design and
analysis. The key contribution is a compositional data model supporting this unification. Finally avenues for further work are explored to further aid this community
including data provenance and supporting smart cities.
campus, suburb, city or region.
Unfortunately these design and analysis teams face challenges which prevent rapid
quantitative analysis of design iterations; precluding potential design improvement.
These include limited automation, poor integration of modelling disciplines and, in
particular, very limited scope for design space exploration.
This thesis investigates these challenges and their solutions. A computational frame-
work HierSynth is presented to help computationally unify the design and analysis sides
of the urban masterplanning community.
The key contribution of this thesis is HierSynths data model. This presents a
reconceptualization of the workflow graph by composing it with tree based design-decompositions commonly found in architectural interoperability formats. This is
achieved through a hierarchy of design queries, templates and analyses which when
executed form a design hierarchy annotated with evaluated analyses. This enables
detailed multi-scale analysis directly on design elements whilst supporting scenario
generation and design space exploration capabilities and techniques to explore design
improvements.
The HierSynth framework is evaluated by application to a major commercial masterplanning project with Arup North America and is used to explore the most effective techniques for generating design insight. HierSynth enabled an order-of-magnitude
more analysis iterations and previously infeasible design space exploration to answer
design questions.
During this collaboration an unexpected challenge was identified in maintaining and
debugging complex, highly interrelated analysis models implemented as spreadsheets.
A toolkit to address this is developed and applied to several generations of complex
multi-disciplinary sustainability models.
In summary this thesis presents evidence of the need for, implementation of, and
practical benefits from, computationally unifying urban masterplanning design and
analysis. The key contribution is a compositional data model supporting this unification. Finally avenues for further work are explored to further aid this community
including data provenance and supporting smart cities.
Date Issued
2013-01
Date Awarded
2013-07
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Field, Anthony
Kelly, Paul
Publisher Department
Computing
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)