Nommon Smart Cities CSS BigData

Smart Cities, Complex Systems,

Big Data and other stuff

Ricardo Herranz

Co-founder and CEO

CIBALL, 30 April 2013

about us

technology company based in Madrid

founded in 2012

predictive modelling and decision support

“Prediction is difficult, especially about the future”

Niels Bohr

our mission

assist both private and public organisations in creating

sustainable value,

by helping them make better-informed decisions in

complex and uncertain environments

we provide decision support tools and consulting services

for the design, optimisation, and management of

socio-technical systems


from policy making to business management, decision-makers deal with socio-technical systems

“Everything should be made as simple as possible, but not simpler”

Albert Einstein

modelling socio-technical systems

the modelling approach must be suitable to reproduce the salient features of the system:

• heterogeneity,

• emergent behaviour,

• self-organisation,

• uncertainty…

traditional, reductionist approaches are not well adapted to model complexity

complex systems science: a new paradigm in socio-technical systems modelling and management

complex systems science

overview and applications

many interacting units

autonomous, decentralised decisions

different spatial and temporal scales

emergence

collective phenomena not reducible to unit behaviour

complex adaptive systems

adaptability: adapt to each other and to the

environment uncertainty

from reductionism…

…to emergence

“We say nothing essential about the cathedral when we speak of its stones”

Antoine de Saint-Exupéry

and of course CITIES

(we’ll talk about it later)


macroscopic (emergent) behaviour

microscopic processes

graph theory

ABM

stochastic processes

multiscale modelling

non-linear dynamics

game theory

from micro to macro

solid state physics

elementary particle physics

the elementary particles of science B obey the rules of science A

yet B is not just “applied A”: new concepts and laws are needed

complexity science is interdisciplinary in nature

mollecular biology

chemistry

cellular biology

mollecular biology

sociology

psychology

macroeconomics

microeconomics

science B: macroscopic (emergent) behaviour

science A: microscopic processes

…

…

…

modelling complex


tools and techniques

agent-based modelling

data science

game theory stochastic modelling

network theory modelling of complex

socio-technical systems (STS)

tools & techniques

ABM

• behaviour defined at individual level, global behaviour emerges from agents’ actions and interactions (bottom-up)

• agent features:

– autonomy: acts autonomously without exogenous interventions

– social behaviour: interaction with other agents

– re-activeness: responds to external influences from its environment

– pro-activeness: acts with initiative and goal-orientation

ABM in STS modelling

• include heterogeneity and individualism

• explicit space and local interactions

• bottom‐up analysis: capture emergent phenomena without aprioristic assumptions

• bounded rationality

• direct correspondence with real world entities

• participatory processes in modelling and validation

game theory

• conflict and cooperation between (rational) decision-makers

• players make decisions choosing from a strategy space

• the resulting outcome (payoff) depends on other players’ decisions

• different types of games

– cooperative / non-cooperative

– symmetric / asymmetric

– stochastic games

– perfect / imperfect information (e.g. delayed or partial information)

– games with non-rational players (evolutionary game theory)

game theory in STS modelling

descriptive use:

model how agents’ actually make decisions

(vs prescriptive or normative use:

determine which decisions should be taken)

network theory

a graph is an ordered pair G = (V, E) comprising

– a set V of N vertices (or nodes)

– a set E of K edges (or links, or lines)

undirected graph directed graph weighted (undirected) graph

network theory

adjacency matrix

structural properties

degree distribution

characteristic path length

betweenness

assortativity

clustering

motifs

…

network theory in STS modelling

• understand the networks underlying complex systems

• network topology influences the dynamics of the processes on top of it

– example: shortcuts (small world property) speeds up communication

stochastic processes

probability distributions for the potential outcomes of a problem

random processes:

– discrete: Markov, Bernouilli…

– continuous: Poisson, Wiener (Brownian motion)…

Monte Carlo simulation

stochastic processes in STS modelling

• socio-technical systems are rife with uncertainty

• stochastic nature of many system elements, including agents' decision-making rules

• distributions can be sampled, inferred, or modelled from the set of actual data available

• a comprehensive treatment of uncertainty is a requisite for:

– development of plans that are robust (graceful degradation) and have the greatest likelihood of success

– comprehensive assessment of risks and opportunities

data science and Big Data

• buzzwords that encompass a set of tools and techniques to capture, integrate, manage, analyse and visualise large data sets in order to extract non-trivial, relevant information

• data science includes a variety of techniques from statistical analysis and artificial intelligence

• Big Data:

– from structured to unstructured data

– high volume, high velocity, high variety

data science in STS modelling

• predictive (non-explicative) models

• discover patterns that can suggest new theoretical models, e.g., agents’ behavioural rules (yet, it is worth reminding that correlation is different from causation)

• model calibration and validation

• exploration of the parameter space

some example applications

computational sociology

cultural drift

opinion formation

conflict resolution

segregation

…

organisational intelligence: consumer behaviour

computational economics

epidemiology: disease spreading

source: http://www.gleamviz.org/

traffic modelling

and of course CITIES

(we’ll talk about it in a minute, we’re almost there…)

what we do

“The future cannot be predicted, but futures can be invented”

Dennis Gabor

shaping the future

• focus on prediction

• data mining - analyse the past to extrapolate the future

• lack of explanatory power

business analytics perspective

• focus on fundamental mechanisms

• explanatory models

• predictive power not yet fully exploited in practical applications

complexity science perspective

• integrative approach, exploit the synergies between data science of complex systems science

• understand - model - predict - assess - explain

Nommon perspective

“Don't get involved in partial problems, but always take flight to where there is a free

view over the whole single great problem”

Ludwig Wittgenstein

our vision

• comprehensive decision-making frameworks

• multidimensional nature of the problems holistic approach

• analytical and simulation models

• complex systems science

• statistical analysis, data mining, operations research

quantitative models

• multidisciplinary team: engineering, physics, mathematics, economics…

• open partnership with universities, research centers, industry, public bodies

interdisciplinarity and

multidisciplinarity

• intense R&D activity

• transfer knowledge from academia to policy and business, and ultimately to society

knowledge transfer

databases

solutions

findings: patterns, trends…

model building & calibration

results

validation

findings: patterns, trends…

data mining

data mining

scenarios (strategies, policies…)

KPIs (optimisation, trade-offs, sensitivity analysis…)

models: virtual laboratories

agents’ decisions & interactions

information flow

physical layer

smart cities urban mobility

energy systems

markets

air transport

business intelligence

strategic planning

policy studies

sustainability analysis

demand management

optimisation

products and services

simulation & decision support tools

consulting services

so how does all this relates to

CITIES?

(at last)

After having attended a number of events about smart cities, you will have already

noticed that 9 out of 10 presentations begin with some impressive figures about world urbanisation process and/or a quote from

Jane Jacobs…

…so I’ll skip this part

smart cities, urban modelling, Big Data… (and some other stuff)

urban challenges

urban modelling - a bit of history

smart cities - opportunities for urban planning

our projects: EUNOIA, INSIGHT

conclusions

urban challenges

in the short term, cities are facing the challenge of overcoming the current financial and economic crisis

but cities are also facing other structural and long term challenges

• globalisation: how to combine competitiveness in the global economy with geographical diversity

• environmental sustainability: energy scarcity, emissions (climate change, local air quality), soil sealing

• demographic, social and behavioural changes (migration, aging…)

• new forms of spatial organisation

• social polarisation and segregation

three fundamental, coupled problems

understanding

prediction

governance

the many components of the natural, social, economic, cultural and political urban ecosystems are strongly interwoven, giving rise to complex dynamics which are often difficult to grasp

the limited understanding of urban dynamics makes it difficult to anticipate the impact and unintended consequences of public action

highly distributed, multi-level decision processes and profound impact on a wide variety of stakeholders, often with conflicting and/or contradictory objectives

urban modelling

urban models are mathematical representations of the ‘real world’ —typically implemented through computational simulation tools— that describe, explain, and forecast the behaviour of and interactions between different elements of the urban system

urban models

urban models

• understanding of urban dynamics explanatory role

• virtual experimentation: prediction of the impact of new infrastructures, technologies, or policies

predictive role

• collaborative policy assessment narrative and

deliberative role

a bit of history

• urban planning:

– up to 1950: blueprint planning

– 1950s-1960s: synoptic planning (systems viewpoint, relating objectives to resources and constraints, heavy reliance on quantitative analysis)

– contemporary era: participatory planning, aiming at integrating a plurality of interests and an active public engagement (transactive planning, advocacy planning, bargaining, communicative planning…)

• urban models:

– 1950s: four-stage transport model

– 1960s: CGE models (based on Alonso's bid-choice land use model), spatial interaction (Lowry-type) models, first LUTI models (aggregated, static)

– 1970s-1980s: aggregated, dynamic models (system dynamics), activity-based transport models

– Current trends: disaggregated, dynamic models (CA, ABM…)

cities as complex adaptive systems

• from the image of a city as a ‘mechanistic system’ to that of a ‘living, self-organising system’ that evolves from the bottom up

• urban planning is moving from a centralised, top-down approach to a decentralised, bottom-up perspective

• the role of policy makers and urban planners is that of nurturing positive emergent phenomena and minimising negative emergent properties

current trends

• disaggregation and bottom-up approaches (activity-based and agent-based models)

• coexistence of a variety of models: cellular automata, ad hoc agent-based models of particular sectors such as housing markets or retail choice…

• urban simulation models to be refashioned to deal with new forms of transport and spatial interaction

the smart city

the smart city

• the ‘smart city’ emerged during the last decade as a fusion of ideas about how ICT might improve the functioning of cities:

– efficiency

– competitiveness

– sustainable development

– high quality of life

• initially a very technocentric concept, critical voices soon arose asking for a critical use of ICT and a citizen-centric approach

the smart city: opportunities

• maturity of the smart city concept:

– ICT

– investment in human, social, and environmental capital

• opportunities for improved urban planning:

– big data

– new models and decision support tools

– policy interfaces and participatory governance

big urban data

• open data

• automatic collection of vast amounts of spatio-temporal data

• longitudinal data (versus traditional cross-sectional data)

theoretical advances

• better urban theories

• better predictive models

improved interfaces, visual analytics…

• advanced models more accessible to policy makers

• new ways of citizens’ engagement

our research agenda

our research agenda

• how data from multiple distributed sources can be exploited to understand location, activity and mobility patterns in cities

spatio-temporal data analysis

• improved theoretical models

• integration into state-of-the-art agent-based simulation tools

enhanced urban simulation models

• integration between visualisation and analytical functionalities

information visualisation and visual analytics

• integration across policy areas

• integration of urban simulation into (collaborative) policy making processes

policy and governance

EUNOIA

EUNOIA

• project funded under FP7 ICT Call 8

• focused on urban mobility

• example research questions:

– use of non-conventional data sources (Internet social networks, mobile phone call logs, credit card data) to analyse mobility patterns

– interactions between social networks and travel behaviour

– integration of improved travel behaviour models into MATSim

• case studies: Barcelona, London, Zurich

MATSim 73

Source: http://matsim.org/

http://matsim.org/

coordinator

partners

supporting institutions

EUNOIA consortium

INSIGHT

INSIGHT

• proposal submitted to FP7 ICT Call 10

• focused on location models: housing, retail, public services

• example research questions:

– positive/negative synergies between social and economic activities

– impact of the financial crisis

– coupling between short term and long term dynamics

– sustainability indicators based on land use mix/service availability

– integration of improved location models into UrbanSim

• case studies: Barcelona, London, Rotterdam, Madrid

coordinator

partners

INSIGHT consortium

to conclude…

many open research questions

fundamental research questions: a general theory of cities?

model scalability, multiscale aspects, granularity

model calibration and validation

new forms of governance

…

smart cities bring exciting opportunities

availability of an unprecedented amount of data at different temporal and spatial scales

new forms of data analysis and visualisation

new forms of participatory governance

new technologies for citizens’ engagement

…

yet, some precautions…


data is not (always) a substitute for theory

prediction and prescription before explanation can be risky

(“For most applications we don’t need Big Data, but the Big Picture”)


data analysis isn't just about

fancy visualisation


models and data are not a substitute for politics

(“It’s not me, it’s the data”)


models and data are useless if they are not

integrated into governance process

“We have usually thought of city planning as a means whereby the planner’s creative activity could build a system that would satisfy the needs of a populace. Perhaps we should think of city planning as a valuable creative activity in which many members of a community can have the opportunity of participating if we have wits to organize the process that way”

Herbert A. Simon

www.nommon.es

[email protected]

@_nommon

Nommon Smart Cities CSS BigData

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