This document summarizes a presentation about using mathematical models to analyze smart cities. It discusses: 1) Puglia Smart Lab, a new living lab in Italy that brings together citizens, government, and businesses to create smart city services based on community needs. 2) Models of city growth and indicators that can measure aspects of smart cities. However, existing models from biology do not apply well to cities, which see superlinear rather than sublinear scaling effects from increased size. 3) A proposed "G indicator" and "G*" metric that aims to quantify overall city outputs and maximize conditions for residents by shifting G* forward through citizen participation, innovative planning, and balancing density, mobility and connectivity.

1. Smart Cities in
Mathematical models for city analysis:
one indicator!!!
an approach applied to Smart Cities
+ 14
Valentino Moretto
valentino.moretto@dhitech.it
Puglia Smart Lab

2. Puglia Smart Lab
PSLab is a newborn LL, a space of innovation in which
citizens, Government and enterprises collaborate together to
co-create services derived from concrete needs in our
territory.
PSLab is one of the first outcomes of Puglia@Service, a
research project funded by the Italian Ministry of University
and Research. The aim of P@S is providing a 15-young
professionists group with multidisciplinary skills necessary for
smart territory strategic development.
Puglia@Service project is supervised by Dhitech Scarl, a
High Tech District (established since 2005 in Apulia).
PSLab has officially joined ENoLL, European Network of Living
Labs, as an adherent member, since August 2013 at "The 7th
wave of ENoLL members”.

3. CITIES EVOLUTION
•In the USA urbanization was minimal until 200 years ago; today it’s above 82%
•China will be building 300 new cities in 20 years
•One million people a week will be moving in cities until 2050
18 La Habana
7 Houston
11 Marseille
5 Santo
Domingo
12 Athene
3 Naples
10 Igmir
4 Sapporo
8 Istanbul
6 Bayrut
13 Shangai
17 Ningbo
16 Fuzhou
19 Port au
Prince
15 Tel
Aviv
14 Benghazi
2 Baranquilla
9 Jakarta
20 Algeri
1 Alexandria
Source: www.zonu.com/detail-en/2009-09-18-7103/Growth-in-Megacities-1950-2015.html
Source: www.befan.it/entro-il-2050-136-citta-a-rischio-scomparsa-ce-anche-napoli/

4. A JUNGLE OF INDICATORS
Use of non- car transport
Green transport
promotion
Green technology
Green Action Plan
PM10 Emission
GDP/GHG Emission
NOx Emission
Deficit/GDP
CO2 Emission
Density
GDP
ROI- Return on Investment
Water Consumption
Waste Water
Treatment
Energy Intensity
Nitrogen
Dioxide
Total Fertility Rate
Total Population
Congestion Reduction
Policies
Public Participation in
green policy
Green Transport
Urban Growth Rate
ROS- Return on Sales
ROE- Return on Equity
Source: www.internazionale.it/la-formula-che-spiega-le-citta/

5. IN BIOLOGY…
“…bigger species require less
energy per Kg when compared to
smaller species.
As an example, an elephant
needs 1000 times more energy
than a guinea-pig despite being
10000 times bigger…”
…AND, INSTEAD, WHAT HAPPENS IN CITIES?
http://www.internazionale.it/la-formula-che-spiega-le-citta/

6. WEST - BETTENCOURT THEORY
Every living being gets slower as its size increases. This is called sublinear
behaviour. However cities are different: when their size increases, every
social aspect speeds up. There is no equivalent model in nature!
Source: intersci.ss.uci.edu/wiki/index.php/Realistic_modeling_of_complex_interactive_systems

7. 15% RULE – CITY SUPERLINEAR SCALING
There are two different trends due to city size increase
•Superlinear - raising social interactions means more innovation and wealth, but
higher crime rates as well (β = 1.15).
•Sublinear
- less investment per capita infrastructure required such as
streets, cables, sewage, etc. (β = 0.85).
Source: /intersci.ss.uci.edu/wiki/index.php/Realistic_modeling_of_complex_interactive_systems

8. WEST-BETTENCOUR MID STEPS
Non linear behaviour in urban
MIXING networks
transport
POPULATION
Decentralized networks for people
INCREMENTAL
connectivity
NETWORK GROWTH
City Behaviours
The bigger the city, the more
BOUNDED
mental/physical effort but
HUMAN EFFORT
social interactions as well
Socio-economic outputs are
SOCIO-ECONOMIC
proportional to local social
OUTPUT
interactions

9. G INDICATOR
G is the output that represents the entire set of urban aspects as well as the
condition for existence and enhancement of cities.
G = Y (Social interaction outcome) – W (dissipation)
Gmin: City does not exist
Gmax : City collapses
G*: City output maximization
Source: www.theatlanticcities.com/neighborhoods/2013/06/scientific-proof-cities-are-nothing-else-nature/5977/

10. G* INDICATOR
It is necessary to shift G* forward, in order to improve city users’ life
conditions, in spite of city dimension increasing. This would result in
better socio-economic outcomes and more social interactions.
How?
Citizenship proactive participation
Innovative urban planning
Balancement
between
density,
mobility
and
social
connectivity
Source: G. West, "Is a quantitative predictive,science of cities conceiveable? What can we learn from physics & biology?
Santa Fe Institute, Said Business School, Oxford University. February,2012

11. OUR GOAL
An innovative analytic assessment way to exploit cities’
full potential
A tool to provide support to Government for a better
understanding of cities
A new methodology for long term strategical planning of
cities

12. …SO, WHAT’S OUR REAL GOAL?
…Involve you, stimulate your
partecipation to Puglia Smart
Lab, experiment together this
new methodology.
Next year, in Smart City Exhibition 2014, we would
like to present outstanding.
Do you wanna team up with us?

13. 15!!!
Thank you!
www.pugliasmartlab.it
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