This document discusses resilience and vulnerability in smart urban systems from two perspectives: spatial economics and transport. It provides background on definitions of smart cities and outlines research questions around whether smart cities can evolve in complex and resilient ways. Key points covered include different definitions of resilience from engineering and ecological perspectives, the use of complex network and dynamic models to study resilience, and different interpretations of resilience in spatial economic studies.

1. Resilience
in Spatial and Urban Systems
Aura Reggiani (University of Bologna, Italy)
INTERNATIONAL ABC WORKSHOP ON
“SMART PEOPLE IN SMART CITIES”
Banská Bystrica (Slovakia), 28-30 August 2016
Overview and Reflections on:
Resilience & Vulnerability in (Smart)Urban
Systems
Two Perspectives: Spatial Economics and Transport

2. Smart Cities and Resilience
Reflections on:
 Complex evolution of smart cities
(multidimensional network perspective)
 Positive and negative network externalities
 Role of resilience/vulnerability vs accessibility
Two main perspectives: Spatial and Transport
Economics

3. Background (1)
 Smart city: no universally accepted definition
(Albino et al., 2015 in JUT): “The concept of smart
city is far from being limited to the application of
technologies to cities”
 “We believe a city to be smart when investments in
human and social capital and traditional(transport)
and modern(ICT)communication infrastructure fuel
sustainable economic growth and a high
quality of life, with a wise management of natural
resources, through a participated governance”
(Caragliu, Del Bo and Nijkamp, 2011)

4. Background (2)
 The conceptualization of smart cities varies from city to city
and from country to country, depending on the level of
development, willingness to change and resources and
aspirations of the city residents:
 e.g: Different connotations in India than in Europe, but also
different connotations in Europe! (Nijkamp, 2016)
 Different concepts -> different measures-> different no of
indicators (60, 18, 9, 6, etc).
 The spatial level of smart city: medium-size (m-s) (pop.
between 100,000-500,000 inhabitants) -> after megacities
 “These m-s cities which have to cope with the larger
metropolitan areas, appear to be less equipped in terms of
critical mass, resources and organizational capacity” (TU-Wien)

5. Smart Cities: Research Questions
 Can smart cities be the core of the economy (after
megacities)?
 Evolution of smart cities? Strong urbanization
trends? Unexpected – even chaotic – shocks?
 Sustainability? (three main perspectives:
economics/environment/social equity)
 Are smart cities complex networks?
 Complex (non-linear) evolution: are smart cities
also resilient?
Role of Connectivity and Accessibility?

6. Smart Cities: RoadMap
 Where are we?
State of the art: how to measure a smart city?
multidimensional analyses/interdisciplinary
approaches and links with players/actors
Volume: ‘Measuring the Unmeasurable’ (Nijkamp et
al., 1987)
 Where are the main problems?
- The understanding of the complex evolution of
smart cities: ‘ability to transform’ -> the role of
resilience and accessibility
 What are the most promising perspectives?
Methodological/empirical/policy reflections: novel
directions

7. Smart City:
Unifying Multidimensional Perspectives
 http://www.smart-cities.eu/
TU-Wien: six main dimensions
 The dynamics of these dimensions: some
can be chaotic or vulnerable in their evolution
-> (un)stable impact on the whole smart
city?
 How to elaborate and test this?
 Synthesis Analysis: two fundamental pillars:
resilience vs accessibility

9. TU-Vienna: The Emerging Smart Cities
Question: are the emerging smart cities also
resilient (able to absorb shocks)?
 Sweden:
UMEAA, JOENKOEPING, ESKILSTUNA (res &acc)
 Germany: ERFURT, GOETTINGEN
KIEL, MAGDEBURG, REGENSBURG, TRIER (res)
 Slovakia: BANSKA BYSTRICA, KOSICE, NITRA
(res)

10. Why Resilience and Vulnerability?
 Growing popularity in research
 Uncertainty due the interconnections between
economic and ecological crises
 Batabyal (1998): “the concept of resilience itself
appears to have been rather resilient”
Other fields:
 David Whythe (philosopher) (2014): “Robust
vulnerability” (A’dam, 26 Sept., 2014)
 Andrew Zolli (2010) (entrepreneur) “Resilience: Why
things bounce back”

11. Resilience and Vulnerability:
Research Questions
1. Definitions of the two terms?
2. Several indicators of resilience and vulnerability co-exist;
are these differences related to specific fields of research in
urban systems? And also: are resilience and vulnerability
complementary or conflicting concepts? (Miller et al, 2010)
3. Is a complex urban network, such the smart city, a
necessary condition for the emergence or presence of
resilience and vulnerability?-> Are smart cities resilient?
4. Can connectivity/accessibility be considered as a useful
complementary framework for better understanding and
interpreting the evolution of the smart cities – and thus their
resilience and/or vulnerability?

12. 1. Definitions
 Resilience concept is stemming from ecology: clear
definition(s)
↓
 Several applications in urban economics; rare
applications in transport/communication systems
 Vulnerability concept is more ambiguous from the
theoretical viewpoint
↓
 Rare applications in urban economics; several
applications in transport/communication systems
↓
Myriad of interpretations!

13. 2. Resilience and Vulnerability
Indicators
Many different approaches and indicators exist:
 Multidisciplinary nature of these two concepts (economic,
environmental, energy, digital systems connected to
transport)
 Context-specific characteristics, aims, etc. (Carlson et al.,
2012)
 Even though urban resilience and vulnerability are two
states of complex networks – it is difficult to observe and
measure them in unambiguous operational terms

14. 3. Complex Networks, Resilience &
Vulnerability
Complex networks – and thus connectivity – a sine qua non for
the development of resilience and vulnerability in smart cities
 Relevance of topological structures of urban/inter-urban
networks (proximity to large hubs; hubs not only as
attractors, but also as most critical nodes: Barabási,2013;
O’Kelly, 2014)
 Large amount of unknown interconnectivity in and between
networks (connected smart city)
 Outcome of these connectivity patterns can be heavily
negative, whether a disruption occurs
↓
1step: Identification of the type of topological configuration- As
this may suggest a tendency towards a resilient or a
vulnerable urban network (network analysis)

15. 4. Connectivity and Accessibility
Connectivity might be a core element
in the recognition of the evolution of resilience/vulnerability
states, as well as in the consequent policy actions towards
either the assessment and enhancement of resilience, or
the reduction of vulnerability:
 Accessibility more complete indicator (weighting
connectivity by means of socio-economic indicators)
 Accessibility as complementary framework
 (Non) linear relationship between accessibility and resilience
↓
Basic definitions of resilience and vulnerability

16. Resilience: Basic Definitions
 Engineering Resilience: it refers to the properties of the
system near some stable equilibrium. This definition, due to
Pimm (1984), takes the resilience of a system to be a
measure of the speed of its return to equilibrium
 Ecological Resilience: it refers to the perturbation/shock that
can be absorbed before the system is displaced from one
state to another. This definition, due to Holling (1973, 1986,
1992), does not depend on whether a system is at or near
some equilibrium (e.g. chaos systems can be resilient)
(see, among others, Gibson, Ostrom, Ahn, 2000; Reggiani et
al., 2002)
↓
Connectivity not so explicit in the definition of resilience

17. Engineering Resilience vs.
Ecological Resilience (1)
The 2 Faces of
Resilience
(Holling 1996)
Attributes
(Holling
1973)
Focus
(Holling
1996)
Methodological
Nature
(Reggiani et al.
2002)
Measures
Engineering
Resilience
Efficiency,
constancy,
predictabilit
y, single
equilibrium
Efficiency
of function
Strength of the
perturbation
Resistance to
disturbance and speed
of return to equilibrium
(O'Neill et al. 1986; Pimm
1984)
Ecological
Resilience
Persistence,
change,
unpredictab
ility, multiple
locally stable
equilibria
Maintenance
of function
Size of the
attractor or
stability domain
Magnitude of
disturbance that can be
absorbed before the
system changes its
structure to new
equilibria (Walker et al.
1969)

18. Engineering Resilience vs.
Ecological Resilience (2)
 Engineering resilience: more feasible under a physical
and mathematical point of view compared to ecological
resilience
 The assessment of a single equilibrium – when dealing
with simple dynamic systems – can be achieved by means
of differential/difference equations
 Ecological resilience refers to extent of shock that a local
stable domain is able to absorb before it is induced into
some other equilibrium (adaptivity) (for the adaptivity
concept: Levins et al., 1998; Martin, 2012)
 Some elements in: Arthur (1990): multiple states among
competing technologies; in prey-predator models, etc.

19. Ecological Resilience
Ecological resilience: More revolutionary concept!
(Holling, 1973)
 Computational difficulties may emerge in the presence of multiple
equilibria (more than two steady states), or in the presence of a
complex network (prey-predator systems; chaos systems (May,
1976); accelerator/multiplier by Samuelson’s business cycle, 1939)
 Ecological resilience (and not engineering resilience) can be a
property even of a chaotic regime (Reggiani et al., 2002)
↓
 The equilibrium/stability notions reinforce the concept of
engineering resilience
 The uncertainty and unpredictability of the current network
phenomena call for the investigation of ecological resilience
(more theory is necessary here!)

20. Dynamic Complexity and Models
 Multiple-chains of dynamic logistic-models (e.g. competition
/symbiosis/prey-predator models):
x(t+1) = x(t) (K1 - b x(t) –(+)c y(t)) (income)
(1)
y(t+1) = y(t) (K2 –(+)e x(t) – f y(t)) (inflows)
 If system (2) is expressed in discrete time: unstable,
vulnerable and chaotic/unpredictable trajectories may
emerge, depending on the parameters’ values and initial
conditions, according to the Poincaré-Bendixson Theorem!
 System (1) has frequently been utilized in spatial economic
analysis as an ‘epidemic’ model for describing technological
innovation diffusion, urban growth (e.g., Batty, 2005, Haag, 2005)
↓
Connectivity is ‘hidden’ in the interaction parameters c and e!

21. Dynamic Models: First Remarks
Chaos models worth to be ‘revisited’
 Chaos models can embed both vulnerability and ‘ecological’
resilience elements:
 Strange attractors (limited domain) can absorb extreme
waves of fluctuations
 In chaos models small uncertainties grow exponentially,
but these ‘erratic’ and often ‘disruptive’ patterns can lead
to new equilibria (ecological resilience): relevance of
parameters’ values!
↓
Chaos models can be revisited by means of ‘ecological
resilience’

22. Resilience in Urban/Spatial Systems
 Resilience linked to the evolution of spatial economic
entities, such as smart cities
 Spatial economic is concerned with “the spatial pattern and
interaction of systems of production, distribution or
consumption (or more generally, human activities) in a spatial
context, including the management, planning and forecasting
of spatial development” (Nijkamp and Ratajczak, 2013)
 Relevance of space as action container, as well as the
result of human action (social interactions)
Review of about 40 studies (Modica and Reggiani, 2015):
 Different resilience interpretations
 Different resilience indicators!

23. Table 3. Different interpretations for spatial economic resilience
Author(s) Year Main Field Definition
Kind of
Resilience
Adger 2000 Community
‘the ability of groups or communities to cope with external stresses
and disturbances as a result of social, political and environmental
change’ (p. 347)
Ecological
resilience
Ashby et al. 2008 Local places
‘the extent to which local places and local government are capable
of riding the global economic punches, working within
environmental limits, dealing with external changes, bouncing back
quickly, and having high levels of social inclusion’
Both kinds of
resilience
Bristow 2010 Places
‘Resilience emphasises the importance of healthy, dynamic local
businesses—businesses which are ‘competitive’ and successful—
and yet it does so in a manner which sees virtuous
interrelationships between competition, environment and
distribution’ (p.156)
Ecological
resilience
Bruneau et
al.
2003 Community
‘the ability of social units […]to mitigate hazards, contain the
effects of disasters when they occur, and carry out recovery
activities in ways that minimize social disruption and mitigate the
effects of future earthquakes’ (p. 735)
Engineering
resilience
Coles and
Buckle
2004 Community
‘the total of the individual elements that thorough capacities, skills,
and knowledge are able to participate fully in recovery from
disasters and to cope with wider social, economic and political
communities’ (p. 6)
Engineering
resilience
Davies 2011 Region
‘the capacity of a regional economy to withstand change or to
retain its core functions despite external upheaval’, (p.370)
Both kinds of
resilience
Foster 2007 Region
‘the ability of a region to anticipate, prepare for, respond to and
recover from a disturbance’ (p.14)
Both kinds of
resilience
Hill et al. 2011 Region
‘[regional resilience] is the ability of a regional economy to
maintain or return to a pre-existing state (typically assumed to be
an equilibrium state) in the presence of some type of exogenous
(i.e., externally generated) shock’ (p. 1)
Engineering
resilience
Martin 2012 Region
‘the capacity of a regional economy to reconfigure, that is adapt, its
structure (firms, industries, technologies and institutions) so as to
maintain an acceptable growth path in output, employment and
wealth over time’ (p.10)
Ecological
(adaptive)
resilience
Paton and
Johnston
2001 Community
‘the capability to “bounce back” and to use physical and economic
resources effectively to aid recovery following exposure to hazard
activity’ (p. 158)
Engineering
resilience
Pendall et
al.
2010 City
‘Resilient city would be one that resumed its previous
[economic/population/built form] growth trajectory after a lag’ (p.
73)
Engineering
resilience
Pendall et
al.
2012 Region
‘A resilient region, is one whose governance decisions identify and
anticipate stresses, avoid those that can be avoided, and mitigate
those that cannot, thereby protecting individuals and households
from many harms and helping them recover from others’ (p. 272)
Both kinds of
resilience
Pfefferbaum
et al.
2005 Community
‘the ability of community members to take meaningful, deliberate,
collective action to remedy the effect of a problem, including the
ability to interpret the environment, intervene, and move on’ (p.
349)
Ecological
resilience
Rose and
Liao
2005
Firm and
region
‘inherent ability and adaptive response that enables firms and
regions to avoid maximum potential losses’ (p.76)
Engineering
resilience
Swanstrom 2008 Region
‘a resilient region would be one in which markets and local
political structures continually adapt to changing environmental
conditions and only when these processes fail, often due to
misguided intervention by higher level authorities which stifle their
ability to innovate, is the system forced to alter the big structures’
(p. 10)
Ecological
resilience
Wolfe 2010 Region
‘how a particular economy gets locked into a specific pattern of
growth through a cumulative series of decisions over time. This
perspective is also concerned with how new paths are launched and
regions alter their trajectory of development’ (p.140)
Ecological
resilience

24. Authors, year Sub-division
No. of
vars.
Variables Weighting
Graziano,
2013
Infrastructure
Innovation and technology
Socio-economic
19
Broadband services
Electrical network
Energy networks
Rail infrastructure
Application of designs
Application of models
European application of designs
European application of models
Patents
Bank deposits
Business density
Housing
Liquidity ratio
Loans to firms
Non food consumption/total
consumption
Pensions per capita
Population growth rate
Return on equity
Value added per capita
Factor
analysis
Martin,
2012
Socio-economic 1 Employment -
Resilience
Alliance,
2009
Infrastructure
Natural environment
Socio-economic
10
Water table depth
Water table equilibrium
Biodiversity measure
River condition
Riverine ecosystem condition
Soil acidity
Water infrastructure
Balance among values held
Farm income
Presence of high multiplier economic
sectors
Equal
weight
University at
Buffalo
Regional
Institute,
2011
Community
Socio-economic
12
Civic infrastructure
Home ownership
Without disability
Business environment
Economic diversification
Educational attainment
Health insured
Income equality
Metropolitan stability
Regional affordability
Out of poverty
Voter participation
Equal
weight

25. Spatial Economic Resilience: Summary
(Review Paper by Modica and Reggiani, 2015)
 Recessionary, industry and disaster shocks
 Both engineering and ecological/adaptivity resilience of a
region/community/urban area
 Multeplicity of applications in USA, UK and EU: mostly
at regional level, despite a few exceptions…;-)
 Role of the scale of analysis: local/urban vs region
 Different socio-economic indicators (mobility
factors ara rarely present)
 Different methods and measures (econometric models,
regression analyses, performance indices)
Rare connectivity considerations ->

26. Spatial Economic Vulnerability (1)
No clear definition (origins from political ecology)
 Vulnerability: more negative connotation, as the overall
reduction of a system’s performance as a consequence of
dynamic factors stressing the system
↓
“It is an oversimplification to treat resilience as the
converse of vulnerability” (Seeliger and Turok, 2013)
↓
 Vulnerability is more about the susceptibility of the
urban system or any of its constituents to harmful external
pressures;
 Resilience concerns more the response of the urban
system: “its elasticity or capacity to rebound after a shock,
indicated by the degree of flexibility, persistence of key functions,
or ability to transform (Seeliger and Turok, 2013)

27. Spatial Economic Vulnerability (2)
 Vulnerability – analogously to resilience – depends on
factors such as nature of the system, and type of
shock, which vary for different spatial and socio-
economic contexts
 Developmental factors including poverty, health status,
economic inequality, and types of governance may
constitute vulnerability (Brooks et al., 2005)
 These factors are also included in various resilience
indicators…
↓
Links and differences between resilience and
vulnerability in urban economics appear to be
ambiguous

28. Resilience in Spatial Economics:
Follow-up
 As anticipated, the majority of the applications in spatial
economics do not take into account dynamics and
connectivity
 But (smart) cities are connected (virtually, physically, intra-,
inter-)!
↓
Zipf’s Law Coeff., Rank-size Rule and Gibrat’s law (based on
population) are linked to connectivity structures (Reggiani
and Nijkamp, EPB, 2015)
↓
New theoretical steps: More Efforts on the Role of the
Parameters’ Values, also by means of dynamic models
What about Transport (Communication)
Resilience/Vulnerability in urban systems?

29. Transport Resilience & Vulnerability
 Transport & communication’s evolution has strong
feedback effects on spatial economic
developments (positive and negative externalities)
 Our modern society strongly depends on large scale
infrastructure networks: “Recent disasters have vividly
demonstrated the importance and vulnerability of our
transportation and critical infrastructure systems -
local disturbance has led to the global failure or
interruption of systems” (Nagurney, 2011)
 Relevance of the identification of the potential ‘risk
areas’ in a early stage
 Relevance not only of shock entities, but also of
propagation of shocks -> Vulnerability!

30. Transport Resilience:
Interpretations
SURVEY OF 33 ARTICLES (Reggiani et al., TRA, 2015):
↓
Adoption of similar concepts :
Robustness (Engineering resilience):
 “The system will retain its systems structure (function)
intact (remain unchanged or nearly unchanged) when
exposed to perturbations” (Holmgren, 2007)
 A network is “robust if the network performance stays close
to the original level” (Nagurney and Qiang, 2012)
Reliability (Ecological resilience):
 Operability of the network under strenuous conditions
 Ability to continue to function after shocks (Husdal, 2005)
 Demand side: user’s behavioural response (Van Exel and
Rietveld, 2001)

31. Transport Resilience:
Applications/Simulations
 Rare empirical applications of resilience in transport:
 Change in modal split after terrorist attack on the London
subway and bus bombing in 2005 (Cox et al., 2011)
 Use of network equilibrium /traffic assignment models
 Several simulations of network robustness/reliability:
 Hub reliability of telecommunication networks in the USA
(Kim and O’Kelly, 2009)
 Robustness of the Dutch road network (Knoop et al., 2012)
and Snelder et al., 2012 )
 Reliability of the Dutch railway system (Vromans et al.,
2006)
 Network robustness and performance models, in the context
of financial (merger and acquisitions) and logistic networks
(Nagurney and Qiang, 2012)

32. Transport Vulnerability:
Interpretations
 From network reliability to the impact of variability in the factors
that affect the urban system (Clark and Watling, 2005)
 Vulnerability of reliability: vulnerability of connectivity/capacity
reliability (Watling and Balijepalli, 2012)
 Reliability focuses on transport network performance, in terms
of probability; vulnerability focuses on network weaknesses or
failure, irrespective of the probability of failure (Taylor, 2008)
 “Vulnerability is primarily a pre-disaster condition; resilience is
the outcome of a post-disaster response” (Rose, 2009)
Vulnerability as attention to potential weak points (susceptibility to
shocks)

33. Transport Vulnerability:
Applications/Simulations (1)
 More empirical works in transport vulnerability than
in transport resilience
 Vulnerability studies concern mainly road infrastructure
networks, given the extensive road coverage (Berdica, 20012;
Jenelius et al., 2006 – Swedish School by Lars-Goran Mattsson)
 Network vulnerability measured as: reduction in road
network serviceability, function of recovery time (Cats &
Jenelius, 2014; Jenelius et al., 2006; Jenelius & Mattsson, 2012)
 Network vulnerability as ‘reduced accessibility’ (Berdica
2002; Kondo et al., 2012; Taylor et al., 2006)

34. Transport Vulnerability:
Applications/Simulations (2)
 Applications/Simulations on real case studies:
 Montpellier’s road network (Appert and Chapelon,2013)
 Stockholm public transport sytems (Cats and Jenelius, 2014)
 Road network of Northern Sweden (Jenelius et al., 2006)
 Swedish Road network (Jenelius and Mattsson, 2012
 Delft and Rotterdam road network (Knoop et al., 2012)
 Ohio interstate highway (Maticziw and Murray, 2007)
 Kobe urban area road network (Nagae et al., 2012)
 Supply chain (Qiang and Nagurney, 2012)
 Rural locations in south East Australia (Taylor and Susilawati,
2012
 Chinese railyway network (Ip and Wang, 2011)
 Swedish commuting network (Osth and Reggiani, 2014)
 Emilia Romagna-network (Rupi et al., 2014)

35. Transport Vulnerability:
First Remarks
Transport vulnerability: richer analysis than transport
resilience
 Different interpretations (decrease of network performance, etc.)
 Different approaches (generalized travel costs, optimization
models, risk analysis, weighted multi-criteria decision approach,
network weakeness indicators, etc.)
 Analogously to resilience in economics, applications are rather
recent
Relevance of ‘Propagation of shocks’ in a network

36. First Concluding Remarks
Vulnerability: richer analysis in transport than in
spatial economics!
Resilience: richer analysis in spatial economics than
in transport!
↓
More studies on the links between these two concepts and
fields are necessary
Again: Measuring the Unmeasurable...
Multi-disciplinary approaches, for example..:
Chaos models linked to network analysis and contagion
models

37. Theoretical and Empirical Perspectives:
 Theoretically: Chaos models/properties might be
revisited in a positive perspective, by means of ecological
resilience
- Small changes -> higher effects which are not
necessarily negative (as we considered in the past)
- The new equilibria - eventhough arising on the
distruction of the previous ones – can create new
opportunities (‘Old concept’ from Socrates: Chaos as the
Divinity…)
- New theoretical efforts
 Empirically: (Dynamics of) Resilience vs Accessibility in
smart cities

38. Accessibility
 Accessibility more complete than connectivity (economic weight)!
Accessibility: Σj Dj f(cij) (1)
f(cij) = impedance/deterrence (cost) function, which embeds the
aggregate behaviour (by means of the cost-sensitivity parameters) and
the connectivity structure ; Dj is the economic weight (e.g. workplaces)
 Accessibility can identify the potential “risk areas” (least accessible)
(Berdica, 2002; Jenelius and Mattsson, 2012; Taylor et al, 2006)
 Accessibility might be an instrument for enhancing resilience
↓
Application to Municipalities in Sweden
(Osth, Reggiani and Galiazzo, 2015, CEUS)

39. Measuring Resilience vs Accessibility
in Urban Areas in Sweden
(Osth, Reggiani, Galiazzo, 2015)
RCI (Resilience Capacity Index) (Kathryn A.
Foster; Cowell, 2013): http://brr.berkeley.edu/rci/
 12 Socio-economic (not mobility) indicators
 Three components (at municipality level in Sweden)
• Economic Capacity (4 indicators)
• income equality (income distr. Gini), economic diversity
(deviation from national industrial mix), affordability (housing
market – related to income in SE), and business environment
(ranking of local business climate)
• Socio-demographic capacity (4 indicators)
• Educational attainment (% 25+ with bachelor’s degree),
’Without disability’ (share of pop without need of care), ’out of
poverty’ (% pop above the poverty-line) and health insured
(sick leave in Sweden)
• Community connectivity capacity (4 indicators)
• Civic infrastructure (share of ‘NGO’ workers), metropolitan
stability (Stability of pop), Homeownership (residing in owned
home), and Voter participation (share voting)

40. Measuring Accessibility in Sweden
 Accessibility as potential of opportunity for
interaction:
Ai = Σj Dj f (γ, dij) (Hansen, 1959)
Accessibility at location i = the sum of surrounding
opportunities/workplaces j, under influence of
cost/time/distance for reaching j
 Use of a power-decay in a doubly-constrained spatial
interaction model, which has proven to be good for the
analysis of accessibility in the 290 Swedish
municipalities (Osth, Reggiani and Galiazzo, 2015)
 All statistics are computed at a municipality level

41. Resilience vs Accessibility:
The Relationships

42. Clustering of Resilience and
Accessibility in Sweden (2)

43. Concluding Remarks (1)
 Experiments in Sweden show that socio-economic
resilience and accessibility are linked:
 Resilient smart centres are the most accessible
 Suburb and commuting municipalities often have poor
resilience ranks – but high accessibility (!)
 Probably accessibility will enhance resilience of these
locations in the future
 Dynamics: analyses in the coming years/different countries
such as Slovakia (how is accessibility)?

44. Regional GDP per capita in the EU-2011
SLOVAKIA 12 800
 Bratislavský kraj 31 500
 Západné Slovensko 12 200
 Stredné Slovensko 10 000
 Východné Slovensko 8 700
SWEDEN 40 800
 Stockholm 56 200
 ……
 Norra Mellansverige 34 400
(Italy: Lombardia -> 33 900)

45. Concluding Remarks (2)
Socio-economic resilience – in conjunction with
accessibility analyses – might provide ‘stability results’
on (smart) cities evolution
 Policy implications
 Some locations are worse of than others:
• Low socio-economic resilience is less of a problem in
locations with high accessibility
• Low socio-economic resilience and low accessibility can
be a lethal combination
• High resilience and low accessibility might be
problematic in the future
 Preventive action should be targeting urban areas
with low socio-economic resilience and low
accessibility

46. Conclusions: From Resilience to
Vulnerability to Reality....
(Smart) Cities as Evolutionary Accessible Networks
Two joint (Synthetic) pillars in the smart cities evolution:
resilience and accessibility
 Different indicators at different spatial levels -> need for more
reflections on the measurement of resilience/vulnerability:
multidimensional analysis
 For more operability, more theoretical efforts on the formalization
of these concepts are also necessary:
- e.g. Entropy vs Zipf/Gibrat’s law vs. Dynamic/Chaos models,
in the light of resilience
- Role of parameters’ values/behavioural patterns
 Multi-disciplinary approaches: Spatial/Urban Economics vs
Transport Economics vs Network & Social Sciences...

47. Special Issues
Different perspectives on Resilience & Vulnerability:
 Caschili, Reggiani, Medda (2015), Special Issue on “Resilience and
Vulnerability in Spatial Economic Networks”, Networks and
Spatial Economics.
 Caschili, Medda, Reggiani (2015), Special Issue on “Resilience and
Vulnerability in Transport Networks”, Transport Research A.↓
Unifying framework necessary, also jointly with
accessibility:
Reggiani, Thill, Martin (2016) Special issue on “Resilience,
Vulnerability and Accessibility”, Transportation

48. Thank you,
for your ‘resilient’ attention!
Questions and comments
are welcome

49. Complexity
“Complexity has turned out to be very difficult to define”
(‘From Complexity to Perplexity’: Heylighen, 1996)
 31 Definitions of complexity and associated concepts
 From Latin: Complexus means ‘entwined’, ‘twisted together’
 Oxford Dictionary: ‘Complex’ if it is ‘made of (usually several)
closely connected parts’
 The term ‘complexity’ embeds both the assemblage of
different units in a system and their intertwined
dynamics
↓
In other words, the term ‘complexity’ is strictly related
to the concept of networks

50. Spatial Economic Networks
 Net-works: ‘operations via nets’: NECTAR (1990)
 Spatial (economic) networks: ordered connectivity structure
for spatial communication and transportation which is characterized
by the existence of main nodes which act as receivers or senders
(push and pull centres), and which are connected by means of
corridors and edges (Nijkamp and Reggiani, 1998)
↓
The relevance of the dynamic function of the (spatial) networks
via organized linkage patterns is embedded in this
definition
 Spatial networks are networks for which the nodes are located in a
space equipped with a metric (Barthélemy, 2010)

51. Complexity and Spatial Networks
Complexity of Space-Time Phenomena
“Large number of parts that interact in a nonsimple way” (Simon,1962)
“The primary idea of complexity concerns the mapping of a system’s
non-intuitive behaviour, particularly the evolutionary patterns of
connections among interacting components of a system whose long-
run behaviour is hard to predict” (Casti, 1979)
Static vs. Dynamic Complexity
 Static Complexity: network configuration, where the components are put
together in an interrelated and intricate way (high dimension of the network,
high no. of hierarchical subsystems, type of the connectivity patterns etc.)
 Dynamic Complexity: dynamic (random) network behaviour governed by
non-linearities in the interacting components (computational complexity and
the evolutionary complexity; for the latter measure: chaos and evolutionary
models)