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)UrbanSystems
Two Perspectives: Spatial Economics and Transport
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 TransportEconomics
Background (1)
Smart city: no universally accepted definition(Albino et al., 2015 in JUT): “The concept of smartcity is far from being limited to the application oftechnologies to cities”
“We believe a city to be smart when investments in human and social capital and traditional(transport) and modern(ICT)communication infrastructure fuelsustainable economic growth and a high quality of life, with a wise management of naturalresources, through a participated governance” (Caragliu, Del Bo and Nijkamp, 2011)
Background (2)
The conceptualization of smart cities varies from city to city and from country to country, depending on the level ofdevelopment, willingness to change and resources and aspirations of the city residents:
e.g: Different connotations in India than in Europe, but alsodifferent connotations in Europe! (Nijkamp, 2016)
Different concepts -> different measures-> different no ofindicators (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 largermetropolitan areas, appear to be less equipped in terms ofcritical mass, resources and organizational capacity” (TU-Wien)
Smart Cities: Research Questions
Can smart cities be the core of the economy (aftermegacities)?
Evolution of smart cities? Strong urbanizationtrends? Unexpected – even chaotic – shocks?
Sustainability? (three main perspectives: economics/environment/social equity)
Are smart cities complex networks?
Complex (non-linear) evolution: are smart citiesalso resilient?
Role of Connectivity and Accessibility?
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
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 smartcity?
How to elaborate and test this?
Synthesis Analysis: two fundamental pillars:
resilience vs accessibility
TU-Vienna: The Emerging Smart Cities
Question: are the emerging smart cities alsoresilient (able to absorb shocks)?
Sweden:
UMEAA, JOENKOEPING, ESKILSTUNA (res &acc)
Germany: ERFURT, GOETTINGEN
KIEL, MAGDEBURG, REGENSBURG, TRIER (res)
Slovakia: BANSKA BYSTRICA, KOSICE, NITRA (res)
Why Resilience and Vulnerability?
Growing popularity in research
Uncertainty due the interconnections betweeneconomic and ecological crises
Batabyal (1998): “the concept of resilience itselfappears to have been rather resilient”
Other fields:
David Whythe (philosopher) (2014): “Robustvulnerability” (A’dam, 26 Sept., 2014)
Andrew Zolli (2010) (entrepreneur) “Resilience: Whythings bounce back”
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?
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!
2. Resilience and VulnerabilityIndicators
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
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)
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 (weightingconnectivity by means of socio-economic indicators)
Accessibility as complementary framework
(Non) linear relationship between accessibility and resilience
↓
Basic definitions of resilience and vulnerability
Resilience: Basic Definitions
Engineering Resilience: it refers to the properties of the system near some stable equilibrium. This definition, due toPimm (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
Engineering Resilience vs. Ecological Resilience (1)
The 2 Faces of Resilience(Holling 1996)
Attributes (Holling1973)
Focus (Holling1996)
MethodologicalNature (Reggiani et al. 2002)
Measures
Engineering Resilience
Efficiency, constancy, predictability, single equilibrium
Efficiency of function
Strength of the perturbation
Resistance to disturbance and speed of return to equilibrium (O'Neill et al. 1986; Pimm1984)
Ecological Resilience
Persistence, change, unpredictability, multiple locally stable equilibria
Maintenance of function
Size of the attractor orstability domain
Magnitude of disturbance that can be absorbed before the system changes its structure to new equilibria (Walker et al. 1969)
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 adaptivityconcept: Levins et al., 1998; Martin, 2012)
Some elements in: Arthur (1990): multiple states among competing technologies; in prey-predator models, etc.
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!)
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!
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 leadto new equilibria (ecological resilience): relevance ofparameters’ values!
↓
Chaos models can be revisited by means of ‘ecologicalresilience’
Resilience in Urban/Spatial Systems
Resilience linked to the evolution of spatial economicentities, 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!
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
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
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: mostlyat regional level, despite a few exceptions…;-)
Role of the scale of analysis: local/urban vs region
Different socio-economic indicators (mobilityfactors ara rarely present)
Different methods and measures (econometric models, regression analyses, performance indices)
Rare connectivity considerations ->
Spatial Economic Vulnerability?
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)
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
Resilience in Spatial Economics: Follow-up
As anticipated, the majority of the applications in spatialeconomics 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?
Transport Resilience & Vulnerability
Transport & communication’s evolution has strong feedback effects on spatial economicdevelopments (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!
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)
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)
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/capacityreliability (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 toshocks)
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)
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)
Transport Vulnerability: First Remarks
Transport vulnerability: richer analysis than transport resilience
Different interpretations (decrease of network performance, etc.)
Different approaches (generalized travel costs, optimizationmodels, risk analysis, weighted multi-criteria decision approach,network weakeness indicators, etc.)
Analogously to resilience in economics, applications are ratherrecent
Relevance of ‘Propagation of shocks’ in a network
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
Theoretical and Empirical Perspectives:
Theoretically: Chaos models/properties might berevisited in a positive perspective, by means of ecologicalresilience
- Small changes -> higher effects which are notnecessarily negative (as we considered in the past)
- The new equilibria - eventhough arising on the distruction of the previous ones – can create newopportunities (‘Old concept’ from Socrates: Chaos as the Divinity…)
- New theoretical efforts
Empirically: (Dynamics of) Resilience vs Accessibility in smart cities
Accessibility
Accessibility more complete than connectivity (economic weight)!
Accessibility: Σj Dj f(cij) (1)
f(cij) = impedance/deterrence (cost) function, which embeds theaggregate behaviour (by means of the cost-sensitivity parameters) andthe 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)
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)
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 surroundingopportunities/workplaces j, under influence ofcost/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 Swedishmunicipalities (Osth, Reggiani and Galiazzo, 2015)
All statistics are computed at a municipality level
Resilience vs Accessibility: The Relationships
Clustering of Resilience and Accessibility in Sweden (2)
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)?
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)
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
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 formalizationof 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...
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 withaccessibility:
Reggiani, Thill, Martin (2016) Special issue on “Resilience, Vulnerability and Accessibility”, Transportation
Thank you,
for your ‘resilient’ attention!
Questions and commentsare welcome
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
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)
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)