LONDON’S GLOBAL UNIVERSITY
The Resilience of City Systems
Interdependencies – antagonistic and synergistic
– can we measure them?
Jeremy Watson CBE FREng FICE FIET
Professor of Engineering Systems, UCL
Chief Scientist & Engineer, BRE (the Building Research Establishment)
SCIENCE, TECHNOLOGY, ENGINEERING
AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
City resilience considerations
‘The ability of a system, community or society exposed to hazards to resist, absorb,
accommodate to and recover from the effects of a hazard in a timely and efficient
manner, including through the preservation and restoration of its essential basic
structures and functions’ – UNISDR
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Characteristics of Resilient Urban Systems
Acknowledgement: Jo da Silva, Arup
SCIENCE, TECHNOLOGY, ENGINEERING
AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Cities: Systems of systems
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Cities: Systems of systems
Community
Water
People
Energy
Mobility
Logistics
Food
Waste
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Cities: Hierarchies of systems
SCIENCE, TECHNOLOGY, ENGINEERING
6
Acknowledgement: David Birch, Imperial College SynCity project
AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Cities: Interdependent systems
Power &
fuel
Human
services
Waste
Comms
Workforce
Water
Buildings
Transport
Food
A
B
‘B is dependent on A’
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
What are infrastructure interdependencies?
• Infrastructure is a network of networks
• With multiple – and increasing numbers of –
interdependencies, including:
Geospatial co-location
Shared use of equipment/resource/corridor
Reliance on another network’s function
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8
AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Why do interdependencies matter?
• Increased risks; increased impact of
perturbations
• Opportunities for decreasing costs, increasing
value, improving functionality/future flexibility
Cascade risk:
• Inter-system: one form of infrastructure is
able to affect another
• Intra-system: one stage of a given
infrastructure system affects later stages
Physical: Dependency on
something tangible
Digital: electronic or
informational connections
affect others
Single point:
• Do a number of parts of the system depend
on a single asset
Potential
opportunities
Potential
risks
Organisational: linkage
between organisations or
their processes
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AND PUBLIC POLICY (UCL STEaPP)
Acknowledgement: Richard Ploszek, IUK HM Treasury
LONDON’S GLOBAL UNIVERSITY
Exploring Infrastructure interdependences
Railway network
Electricity transmission
network
Substation
linkages to rail
lines
Acknowledgement: Professor Jim Hall, ECI Oxford
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Cities and the Food Supply System
The role of cities in the food system
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Trends and opportunities in city monitoring
• Pervasive CCTV
• Sensing of traffic flows and speeds
• Smart meters
• Environmental monitoring
Accidents, crime,
road obstruction
Fine-grained
energy use,
inferred activities
• Personal digital devices
o
GPS and 4/5G networks
• Social networking
o
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Twitter
Opportunistic
sensing,
emotional/health
trends
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AND PUBLIC POLICY (UCL STEaPP)
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Data integration in Cities
Mapping social data
(eg. Crimes)
Mapping Energy Efficiency of
Buildings
Flood
simulation
Exploration of
multiple agendas in
city development
(transport, housing,
employment etc)
Public
Consultations
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Acknowledgement: Professor T Fernando, University of Salford
AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
‘Big Data’ opportunities
• Fusing disparate data types to create new insights
o Validation, continuity, prediction
• Private-sector mashing services
o Combining proprietary and open data sources for knowledge and
value creation
• Live data plus GIS; city-scale object data
o Building and infrastructure attributes and live information
• Social network feeds
o Can identify health trends (e.g. Norovirus) before reporting by
healthcare providers
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Synergies vs. Antagonistics
Different ‘problem spaces’?
• For synergies it’s more about business models for sharing the
benefits
• Need an ‘Value Aggregating’ umbrella entity – public or private
sector? – Gain sharing
• Generic issue – associated with many socio-economic systems
(e.g. Health vs. Social Care)
• For antagonistics, need to consider effect of failure of one
infrastructure system on another – an asymmetrical relationship
E.g. Running fibre down a rail link
 Failure of fibre will not interrupt rail – maintenance may disrupt, however
 Failure of railway (catastrophic) may interrupt fibre
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Drivers and Trends: CO2
Keeling curve
CO2 rise derived from
Antarctic ice core
measurements and readings
from Mauna Loa, Hawaii.
James Watt’s steam engine
developments took place in
the 1750s
Around 45% of all present
carbon emissions come
from existing buildings, with
~25% from homes
• Tipping point – 500ppm? Currently 400ppm (Scripps Institution)
Ice caps melt, more sunlight absorbed, trapped CH4 & CO2 released
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Temperature data & modelling
Met Office
Observed temperatures
Simulated temperatures
Summer 2003:
normal by 2040s, cool by 2080s
Stott Nature 2004 – updated to 2007 – HadGEM1
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Inundation – Somerset floods 2014
• 200 homes affected
• 30,000 hectares of
wildlife habitat
• 11,500 hectares of
farmland
• 10 weeks duration
• Low run-off from
River Parrett
• Capital investment in
tidal defences for
long-term protection
• £100m needed for
the next decade
2014 German Aerospace Center (DLR), 2014 Airbus Defence
and Space / Infoterra GmbH : Environment Agency Geomatics
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Adapting Infrastructure to Climate Change
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Acknowledgement: Royal Academy of Engineering
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Network Resilience
Additional cost of resilience
10
9
8
7
6
5
4
3
2
1
0
Cost
Number of Links
Resilience
0
20
40
60
80
100
4,5
4
3,5
3
2,5
2
1,5
1
0,5
0
0
Resilience
20
40
60
80
100
Resilience
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AND PUBLIC POLICY (UCL STEaPP)
Acknowledgement: Richard Ploszek, IUK HM Treasury
LONDON’S GLOBAL UNIVERSITY
Estimating resilience 1
Resilience concerns the maintenance of operational capabilities of
systems and sub-systems, with acceptable levels of degradation
1. Subsystems may be interdependent such that ‘cascade failure’ is
possible
2. Subsystems may be redundant, such that the failure of one is
supported by the continuing operation of another
• An estimate of resilience can be derived from a network analysis of
these properties in real systems
Probability calculations apply
• Redundancy costs money
• Synergistic interdependency can save money, but carries
(manageable) risk
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Estimating resilience 2
Redundancy
Pfail = Pf1 x Pf2
So if the probabilities of failure of both
sub-systems is 10%, the probability of
total failure is 1%
Sub-system 1
Sub-system 2
Dependency
Pfail = 1 – (1 - Pf1) x (1 - Pf2)
So if the probabilities of failure of both
sub-systems is 10%, the probability of
total failure is 19%
Sub-system 1
Sub-system 2
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Estimating resilience 3
Risk = [Hazard x Likelihood] x Vulnerability
Primarily a
spatial issue
Reduce by landuse planning,
coastal defences,
drainage, etc.
Exposure
Vulnerability =
Adaptive capacity
Primarily a
systems issue
Resilience = Vulnerability -1
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AND PUBLIC POLICY (UCL STEaPP)
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Cities: Interdependent systems
Power &
fuel
Human
services
Waste
• Series/parallel analytics can be applied
Comms
• Cost/benefit of redundancy can be considered
Workforce
• Reduction of serial dependency options?
Water
Buildings
Transport
Food
A
B
‘B is dependent on A’
with a certain availability
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AND PUBLIC POLICY (UCL STEaPP)
LONDON’S GLOBAL UNIVERSITY
Static and dynamic interdependencies
• City infrastructure elements are interdependent, and can be viewed
statically and dynamically
-
Antagonistic
Synergistic
• Business model challenges
-
Value aggregation
• Dynamics
Optimisation of capacity
- Collaborative streetworks
-
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Behavioural Research challenges
Behaviour is often a dominant effect compared with physical interventions
• Built Environment and product design influences sensitivity to behaviour
• Rebound and contrary behaviours
How to engineer design from objective outcomes?
• Understanding corporate behaviours
• Transition dynamics – adoption curves
• Role of regulation and fashion alongside technology
Need for multi-disciplinary research to guide engineering and policy
• Systems which learn (and maybe question) choices and behaviour
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Design for behavioural outcomes
Buildings
• Key challenges
-
Access control
Surveillance
Sensing exceptions (e.g. fire)
Evacuation from tall buildings
• Technologies
-
-
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Physiological recognition
Action recognition, time-sequences
Building Information Modelling
Agent-based crowd behaviour simulation
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Design for behavioural outcomes
Cities & Districts
• Challenges
-
Encouraging wellbeing
Reducing energy use
Minimising street crime
Creating social inclusion: ethographic/demographic
• Technologies
-
-
District modelling and simulation
Agent-based crowd behaviour simulation
Earth observation
The pattern of street robbery over five years in a London borough set against the
background of a space syntax analysis of the street network in which potential movement
through each street segment is shown by the colouring form red for high through to blue for
low. It is clear that the pattern of robbery relates strongly to the ‘foreground; network of red
and orange streets.
Acknowledgement: Space Syntax
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AND PUBLIC POLICY (UCL STEaPP)
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Foresight – Future of Cities Project
Examples of previous projects
• Public policy is delivered via cities,
which are centres of innovation and
growth
• Foresight project will take a cross
government interdisciplinary
approach, building on existing work.
• Aim to provide a holistic
understanding of the challenges
and opportunities UK cities will face
in the future
• Seeking input in order to shape
project and focus outputs on most
important questions facing policy
makers
Acknowledgement: GO - Science
Flooding &
Coastal Defence
Land Use Futures
Sustainable
Energy & the Built
Environment
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