Spatially Optimised Sustainable Urban Development Daniel Caparros-Midwood Doctor of ...

303 Pages·2016·12.69 MB·English

Spatially Optimised Sustainable Urban
Development

a thesis submitted by
Daniel Caparros-Midwood
for the degree of
Doctor of Philosophy

Newcastle University
School of Civil Engineering and Geosciences

May 2016

Abstract

Tackling urbanisation and climate change requires more sustainable and resilient cities, which
in turn will require planners to develop a portfolio of measures to manage climate risks such as
flooding, meet energy and greenhouse gas reduction targets, and prioritise development on
brownfield sites to preserve greenspace. However, the policies, strategies and measures put in
place to meet such objectives can frequently conflict with each other or deliver unintended
consequences, hampering long-term sustainability. For example, the densification of cities in
order to reduce transport energy use can increase urban heat island effects and surface water
flooding from extreme rainfall events. In order to make coherent decisions in the presence of
such complex multi-dimensional spatial conflicts, urban planners require sophisticated
planning tools to identify and manage potential trade-offs between the spatial strategies
necessary to deliver sustainability.

To achieve this aim, this research has developed a multi-objective spatial optimisation
framework for the spatial planning of new residential development within cities. The
implemented framework develops spatial strategies of required new residential development
that minimize conflicts between multiple sustainability objectives as a result of planning policy
and climate change related hazards. Five key sustainability objectives have been investigated,
namely; (i) minimizing risk from heat waves, (ii) minimizing the risk from flood events, (iii)
minimizing travel costs in order to reduce transport emissions, (iv) minimizing urban sprawl
and (v) preventing development on existing greenspace.

A review identified two optimisation algorithms as suitable for this task. Simulated Annealing
(SA) is a traditional optimisation algorithm that uses a probabilistic approach to seek out a
global optima by iteratively assessing a wide range of spatial configurations against the
objectives under consideration. Gradual ‘cooling’, or reducing the probability of jumping to a
different region of the objective space, helps the SA to converge on globally optimal spatial
patterns. Genetic Algorithms (GA) evolve successive generations of solutions, by both
recombining attributes and randomly mutating previous generations of solutions, to search for
and converge towards superior spatial strategies. The framework works towards, and outputs,
a series of Pareto-optimal spatial plans that outperform all other plans in at least one objective.
This approach allows for a range of best trade-off plans for planners to choose from.

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Both SA and GA were evaluated for an initial case study in Middlesbrough, in the North East
of England, and were able to identify strategies which significantly improve upon the local
authority’s development plan. For example, the GA approach is able to identify a spatial
strategy that reduces the travel to work distance between new development and the central
business district by 77.5% whilst nullifying the flood risk to the new development. A
comparison of the two optimisation approaches for the Middlesbrough case study revealed that
the GA is the more effective approach. The GA is more able to escape local optima and on
average outperforms the SA by 56% in in the Pareto fronts discovered whilst discovering double
the number of multi-objective Pareto-optimal spatial plans.

On the basis of the initial Middlesbrough case study the GA approach was applied to the
significantly larger, and more computationally complex, problem of optimising spatial
development plans for London in the UK – a total area of 1,572km2. The framework identified
optimal strategies in less than 400 generations. The analysis showed, for example, strategies
that provide the lowest heat risk (compared to the feasible spatial plans found) can be achieved
whilst also using 85% brownfield land to locate new development. The framework was further
extended to investigate the impact of different development and density regulations. This
enabled the identification of optimised strategies, albeit at lower building density, that
completely prevent any increase in urban sprawl whilst also improving the heat risk objective
by 60% against a business as usual development strategy. Conversely by restricting
development to brownfield the ability of the spatial plan to optimise future heat risk is reduced
by 55.6% against the business as usual development strategy.

The results of both case studies demonstrate the potential of spatial optimisation to provide
planners with optimal spatial plans in the presence of conflicting sustainability objectives. The
resulting diagnostic information provides an analytical appreciation of the sensitivity between
conflicts and therefore the overall robustness of a plan to uncertainty. With the inclusion of
further objectives, and qualitative information unsuitable for this type of analysis, spatial
optimization can constitute a powerful decision support tool to help planners to identify spatial
development strategies that satisfy multiple sustainability objectives and provide an evidence
base for better decision making.

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Acknowledgments

I would like to thank my supervisors Stuart Barr and Richard Dawson for their support and
guidance during my PhD. Stuart for his help with the methodological aspects of the thesis and
Richard for allowing me to go to a number of international conferences. Acknowledgement is
also given to the UK’s Engineering and Physical Sciences Research Council for funding the
research and making it possible for me to present research at several conferences. Lastly I’d
like to thank my parents who have been incredibly supportive throughout.

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Publications based on this Research

Journal Articles

Caparros-Midwood, D., Barr, S., and Dawson, R. (2015). Optimised spatial planning to meet
long term urban sustainability objectives. Computers, Environment and Urban Systems, pp.
154–164. doi:10.1016/j.compenvurbsys.2015.08.003

Conference Proceedings

Caparros-Midwood, D., Barr, S. & Dawson, R. (2015). Evolutionary Computing for Multi-
Objective Spatial Optimisation- Extended Abstract. In Proceedings of 23rd GIS Research UK
Conference, Leeds, UK.

Caparros-Midwood, D., Barr, S. & Dawson, R. (2014) Optimised Spatial Planning against
Sustainability Objectives- Extended Abstract and Response paper presentation at the
Urbanization and Global Environment Change, Taipei, Taiwan.

Caparros-Midwood, D., Dawson, R. & Barr, S. (2014). Optimization of Urban Spatial
Development against Multiple Sustainability Objectives and Climate Risks. In Proceedings of
2nd International Conference on Vulnerability and Risk Analysis and Management, Liverpool,
UK.

Caparros-Midwood, D., Barr, S., & Dawson, R. (2013). Urban Spatial Planning of Multiple
Sustainability Objectives using Simulated Annealing Spatial Optimisation. In Proceedings of
International Conference 2013 on Spatial Planning and Sustainable Development, Beijing,
China.

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Table of Contents

Abstract i
Acknowledgments iii
Publications based on this Research v
Table of Contents vi
List of Figures xii
List of Tables xvii
List of Abbreviations and Notation xix
Glossary of Notation xx
Introduction 1
Urbanism and Climate Risks 1
Conflicting Sustainability Initiatives 3
Aims, Objectives and Thesis Outline 4
Sustainable Cities and Development 7
Introduction 7
Definition of Sustainable Development 9
Models of Sustainable Urban Spatial Form 14
2.3.1 Introduction 14
2.3.2 Urban Intensification: The Compact City model 15
2.3.3 Dispersed Cities 19
2.3.4 Decentralised Centralisation: Transit Orientated Development 21
2.3.5 Garden Cities 24
2.3.6 Smart and Resilient Cities 26
2.3.7 Multiple Pathways 26
Barriers to Sustainable Urban Form 27
Spatial Planning to Achieve Sustainable Cities 31
2.5.1 Introduction 31
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2.5.2 Environmental Sustainability Assessments 33
2.5.3 Integrated Sustainability Assessments 34
2.5.4 UK Sustainability Appraisals 36
2.5.5 Consideration of Conflicts between Sustainability Objectives 42
Decision Support 43
Conclusion 44
Multi-objective Spatial Optimisation for Urban Planning 47
Introduction 47
Definition of an Optimisation Problem 48
3.2.1 Decision Variables 48
3.2.2 Objective Functions 49
3.2.3 Constraints 51
Multi-objective Optimisation 52
Spatial Optimisation Approaches 56
3.4.1 Introduction 56
3.4.2 Linear Programming 56
3.4.3 Meta-Heuristics 58
3.4.4 Simulated Annealing 58
3.4.5 Tabu Search 60
3.4.6 Population Based Searches 61
3.4.7 Genetic Algorithms 62
3.4.8 Particle Swarm 67
Evaluation Simulation Tools 68
3.5.1 Introduction 68
3.5.2 Water Distribution Networks 69
3.5.3 Transit/ Transport Networks 70
3.5.4 Resource and Land Use Allocation 70
3.5.5 Urban Simulation 71
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3.5.6 Sustainable Spatial Optimisation 77
3.5.7 Evolutionary Urban Planning 79
Discussion 80
Conclusions 83
Methodological Framework 85
Introduction 85
Problem Formulation 85
4.2.1 Design Considerations 85
4.2.2 Planning Problem Representation 87
4.2.3 Selection of Objectives 88
4.2.4 Data Characterisation of Objectives 91
4.2.5 Objective Formulation 92
4.2.6 Pareto-optimality 94
Spatial Optimisation Software 96
4.3.1 Choice of Development Environment 96
4.3.2 Overall Spatial Optimisation Framework Design 97
Simulated Annealing Approach 99
4.4.1 Simulated Annealing Operators 99
4.4.2 Simulated Annealing Implementation 103
Genetic Algorithm Approach 104
4.5.1 Genetic Algorithm Operators 104
4.5.2 Software Implementation 110
Summary 111
Medium Sized Urban Case Study- Middlesbrough 113
Introduction 113
Middlesbrough Case Study 113
5.2.1 Problem Formulation 114
Middlesbrough Datasets 116
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