Giovanni Virgilio on "An ANN-based approach as support for decision-making processes regarding design of ecodistricts"

1. An ANN-based approach as support
for decision-making processes
regarding design of ecodistricts
Giovanni Virgilio
Department of Architecture and Spatial Planning,
University of Bologna, Italy

2. AIM OF THE STUDY
• The aim of this study is to methodologically delineate a
process for providing public decision-making support for
analysis and assessment of the performance ratings of eco-
sustainable settlements on all levels.
• The results obtainable from this methodological development
are significant from the training angle, also for designers of
eco-sustainable settlements, enabling learning and the
gathering of knowledge from previous experiences in regard
to urban contexts.
• Hermeneutic analysis of a significant sample of relevant case
studies is required in order to extract all information necessary
for decision-making.

3. AIM OF THE STUDY
• Given the considerable quantity of information and the
extremely complex interactions of the analysed variables,
however, considerable problems may arise which can
objectively impede the operation as a whole, at least in terms
of costs/derivable benefits.
• An artificial neural network was therefore tested. Neural
networks can extract new information from raw data by
constructing computer-aided decision-making models shown
to be efficacious as support for planner decision-making.
Policymakers’ and planners’ assessment capabilities are also
enhanced.

4. THE ECODISTRICTS
• It is not easy to establish with precision the specific characteristics
that connote an eco-sustainable district.
• Within the ambit of urban development processes, an “urban
green catalyst” role may be ascribed to such districts.
• “ […] A urban green catalyst reflects the principles of sustainability,
and is able to stimulate new dynamics that guide urban
transformations: ecological, economic, social, institutional, etc. But
it is the engine of change too, able to stimulate a new culture of
urban planning, based on an integrated approach that combines
reduction of consumes and use of renewable energies, innovation
and local participation, creativity and good governance”
• (Cerreta and Salzano, 2009).

5. THE ECODISTRICTS
• It should now be clear what ecodistricts are. However, it is
less clear how one is to plan for, or assess, the efficacy of the
strategies adopted in creating an eco-compatible district.
• Selecting efficacious design strategies is an increasingly
difficult task since public decision-makers must face up to
highly complex problems with limited resources.
• Lessons can be drawn from consolidated practices already
considered successful. Of course, these experiences cannot be
transposed without considering the specific conditions of the
territorial context within which they developed.

6. THE ECODISTRICTS
• However, by analysing a significant sample of cases we can go some way
toward isolating shared structural conditions which, in all likelihood,
provide the essential premises for successful projects.
• Enormous quantities of non-homogeneous data and items of information
must be processed and rendered comparable (through in-depth study of
the aforesaid cases).
• For analysis, we require expert knowledge to extrapolate quantitative and
qualitative information considered suitable for the purposes of pinpointing
the structural characteristics of the cases analysed.
• Furthermore, various sophisticated analytic instruments are required
which are capable of ‘perceiving’ not only the nature and extent of
interactions within a local territorial context but also the context’s ability
to self-organise. Such instruments are vital, since traditional techniques
are limited in their capacity to pinpoint the “implicit nexuses” subtending
the dense web of interactions that turns territorial systems into complex
systems.

7. METHODOLOGICAL APPROACH
• This study is therefore methodological in nature. Its aim is to demonstrate
that we can extract informaƟon on complexity from complexity itself − via
the proposed method of systemic analysis using Artificial Neural
Networks. The general aim is to pinpoint underlying trends characterising
behaviour patterns, ascribable to actions, reactions and repercussions
within urban systems.
• Observation of territorial dynamics tells us that the territorial components
are related to each other in a variety of manners. They form a lattice of
“implicit nexuses” which we must uncover if we are to grasp the principles
upon which the urban project is based (a lattice that generates efficacious
performance through interaction with the territorial context).
• An artificial neural network (ANN) is used for this purpose. The idea is to
reveal the relations between the design options pertaining to the various
ecodistricts and respective contextual attributes. ANNs can in fact capture
the non-linear behaviour patterns of planning processes in complex urban
systems.

8. METHODOLOGICAL APPROACH
To achieve all this, a fully articulated methodological approach is
required.as outlined below. The steps which characterize the
methodological path are the following:
• Selecting case studies;
• Analysis of experts group;
• The choice of the neural network model;
• The test phase: recognition of the identification profiles for
the various ecodistricts;
• Interrogation of the network: identification of ideal-type
profiles

9. SELECTING CASE STUDIES
The case studies analysed were selected on the basis of the following characteristics:
• they must have been developed in medium-sized or large European towns/cities,
to ensure, at least in theory, uniformity of the basic problem areas, as well as
potential homogeneousness of social, political and cultural dynamics;
• the interventions must regard processes of new urban transformation (expansion
or integration), and must be executed on an urban district scale;
• the case studies have been implemented and are therefore economically and
socially consolidated and comparable;
• completed interventions are preferred, involving as many aspects of urban
sustainability as possible;
• a characteristic appearance specifically connoting intervention;
• various typologies of promoters;
• validity of the intervention, and availability of reference material.

10. SELECTING CASE STUDIES
• On the basis of the above requisites, the following case
studies were selected:
- SCHEDA 01: Hammaeby Sjostad (Stockholm, Sweden);
- SCHEDA 02: Bo01 (Malmö, Sweden);
- SCHEDA 03: Viikki project (Helsinki, Finland);
- SCHEDA 04: Greenwich Millenium Village (London, UK)
- SCHEDA 05: Villaggio Olimpico ex MOI (Turin, Italy);
- SCHEDA 06: Kronsberg (Hannover - Germany);
- SCHEDA 07: Vauban (Freiburg - Germany);
- SCHEDA 08: Südstadt (Tübingen- Germany);
- SCHEDA 09: Scharnhauser Park (Ostfildern – Germany);
- SCHEDA 10: Solarcity (Linz, Austria);
- SCHEDA 11: GWL-Terrein (Amsterdam - Netherlands);
- SCHEDA 12: EVA(Culemborg, Netherlands);

11. SELECTING CASE STUDIES
• Structuring of information
• This step provides an articulated classification of the information collected
on various case studies, based on following criteria:

12. -Environmental sustainability: understood as the ability to
4 SPHERES conserve the quality of natural resources and safeguard their
reproducibility;
-Economic and financial sustainability: understood as the
ability to generate incomes and employment as means of
support for the population;
-Social sustainability: understood as the ability to ensure, in
terms of class and gender, fairness in distribution of the
conditions of human wellbeing (safety/security, health,
education);
-Political and institutional sustainability: understood as the
ability to ensure conditions of stability, democracy,
participation, justice.

13. 4 SPHERES Amb.1. Biodiversity
Amb.2. Soil Consumption
Amb.3. Settlement
Amb.4. Bioclimatic
Amb.5. Energy
23 GENERAL THEMES Amb.6. Water
Amb.7. Materials
Amb.8. Waste
Amb.9. Transportation
Amb.10. Health
Eco.1. Governance
Eco.2. Implementation
Eco.3. Management
Eco.4. Settlement Costs
Eco.5. Economic Activities
Soc.1. Population
Soc.2. Quality Living
Soc.3. Management
Soc.4. Services And Public Facilities
Pol.1. Implementation
Pol.2. Participation
Pol.3. Governance
Pol.4. Management

14. Biodiversity: vegetation Health: Noise And Air Pollution
4 SPHERES Biodiversity: Landscape Governance: Public Participation
Soil consumption: pre-existing Governance: Private Participation
Soil consumption: soil / subsoil Implementation: Acquisition Areas
Soil consumption: land use Implementation: Programming
Settlement: settlement location Implementation: Security
Settlement: settlement morphology Manager: Maintenance
Settlement: building typologies Management: Use
23 GENERAL THEMES Bioclimatic: Building orientation Manager: Taxation
Bioclimatic: natural lighting Settlement: Property
Bioclimatic: Hygrothermal comfort Settlement: Social Housing
Bioclimatic: ventilation Economic Activities: Job Offer
Energy: reduction / control Economic Activities: Uses / Destinations
Energy: optimizing resources Economic Activities: Tourism
60 FEATURES Energy: use of renewable resources Population: Composition
Water: outflow Population: Marginality
Water: treatment Population: Aggregation / Inclusion
Materials: construction Population: Social Security
Materials: impact energy Quality of Living: Sense of Space
Waste recovery Quality of Living: The Aesthetic Quality
Waste management system Management: Sociability
Waste treatment Services: Sociability
Transportation: connectivity Services: Destination-scale Neighborhood
Transportation: collective mobility Services Destinations Urban Scale
Transportation: Individual Mobility Implementation: Ad Hoc Initiatives
Transportation: slow mobility Participation: Involvement
Transport: Road safety Governance: Institutional And Policy
Health: Indoor Air Quality Governance: Political Orientation
Management: Institutional And
Communication

15. 4 SPHERES
23 GENERAL THEMES
60 FEATURES
some examples of targets .............
131 TARGETS AMB A.1. - Integration of green space to built environment and all living
spaces.
AMB B.1. - Facilitate and promote action of plant vegetation.
AMB C.1. - Environmental qualification of mobility infrastructures
…………………………………………………………………………………………..

16. 4 SPHERES
23 GENERAL THEMES
60 FEATURES
some examples of strategies .............
131 TARGETS AMB A.1.1. - Environmental connotation of public space
AMB A.1.2. - Inserting inverdite between built areas
AMB A.1.3. - Creation of green spaces for sport activities
AMB B.1.1. - Protection of biodiversity
AMB B.1.2. - Improve the greening of plants vegetation
AMB B.1.3. - Formation of an urban network of link with the biological
system of green land.
285 STRATEGIES AMB C.1.1. - Environmental connotation of mobility spaces
……………………………………………………………………………….

17. ANALYSIS OF EXPERTS GROUP
4 SPHERES
The phase encoding of the data thus allows us to define the
MATRIX OF URBAN SUSTAINABILITY
23 GENERAL THEMES
60 FEATURES
131 TARGETS
285 STRATEGIES

18. ANALYSIS OF EXPERTS GROUP
• The adopted approach has been focused on an analysis conducted by a group
of experts based on the method of Nominal Group Technique (Delbecq et al.,
1975). More precisely, here it has evaluated the level of implementation of
various strategies, that is been identified by assigning a structured score as
follows:
• 0 = no strategy adopted
• 0.5 = partially adopted strategy
• 1 = full implementation of the strategy.
• The final step is to assign a weight to each "general theme". This value
expresses the significance that theme assumes in the development of the
project. This result is easily achieved for all the specific themes, by adding the
scores attributed to the different implemented strategies and by dividing the
obtained value for the maximum score theoretically achievable. By classifying
the various strategies adopted in the case studies according to the criteria of
interpretation given below, we can establish the evaluation matrix (the matrix
being based on the judgments of analysts or experts).

19. ANALYSIS OF EXPERTS GROUP
THE EVALUATION MATRIX
ECODISTRICTS(*)
THEMES
q.1 q.2 q.3 q.4 q.5 q.6 q.7 q.8 q.9 q.10 q.11 q.12
AMB.1. Biodiversity 0,67 0,50 0,72 0,89 0,33 0,56 0,89 0,44 0,94 0,56 0,50 0,56
AMB.2. Soil Consumption 0,56 0,72 0,59 0,75 0,34 0,53 0,78 0,41 0,78 0,53 0,75 0,56
AMB.3. Seetlement System 0,72 0,78 0,38 0,53 0,59 0,41 0,59 0,63 0,56 0,50 0,72 0,41
AMB.4. Bioclimatic 0,75 0,54 0,50 0,71 0,29 0,64 0,64 0,29 0,79 0,57 0,46 0,36
AMB.5. Energy 0,78 0,72 0,58 0,83 0,56 0,69 0,61 0,50 0,81 0,67 0,56 0,53
AMB.6. Water 0,77 0,62 0,58 0,73 0,27 0,62 0,77 0,42 0,73 0,50 0,58 0,65
AMB.7. Building Materials 0,50 0,67 0,67 0,72 0,56 0,44 0,78 0,50 0,78 0,67 0,67 0,61
AMB.8. Waste 0,68 0,45 0,50 0,55 0,27 0,59 0,86 0,32 0,59 0,55 0,55 0,55
AMB.9. Transportation 0,68 0,78 0,63 0,78 0,35 0,63 0,95 0,65 0,68 0,65 0,75 0,63
AMB.10. Health 0,55 0,58 0,48 0,63 0,30 0,50 0,60 0,50 0,58 0,60 0,63 0,50
ECO.1. Governance 0,69 0,75 0,50 0,50 0,31 0,56 0,88 0,81 0,56 0,69 0,56 0,56
ECO.2. Implementation 0,56 0,67 0,61 0,44 0,50 0,78 0,83 0,78 0,50 0,72 0,67 0,50
ECO.3. Management 0,50 0,52 0,62 0,52 0,17 0,69 0,86 0,55 0,57 0,45 0,69 0,57
ECO.4. Seetlement System 0,20 0,45 0,55 0,35 0,30 0,80 0,90 0,70 0,50 0,50 0,60 0,50
ECO.5. Economic Activities 0,70 0,87 0,33 0,53 0,27 0,83 0,77 0,77 0,87 0,77 0,50 0,43
SOC.1. Population 0,34 0,53 0,63 0,50 0,22 0,81 0,81 0,53 0,91 0,78 0,78 0,56
SOC.2. Living Quality 0,63 0,50 0,58 0,71 0,58 0,79 0,71 0,54 0,79 0,71 0,42 0,46
SOC.3. Management 0,25 0,50 0,83 0,33 0,08 0,83 1,00 0,67 0,67 0,83 0,83 0,67
SOC.4. Services 0,69 0,69 0,47 0,78 0,53 0,78 0,72 0,50 0,72 0,56 0,50 0,47
POL.1. Implementation 0,50 1,00 0,50 1,00 0,50 1,00 1,00 0,50 1,00 1,00 1,00 0,50
POL.2. Partecipation 0,08 0,42 0,67 0,46 0,04 0,54 0,96 0,79 0,46 0,75 0,75 0,46
POL.3. Governance 0,50 0,50 0,81 1,00 0,50 0,56 1,00 1,00 1,00 0,88 0,69 0,44
POL.4. Management 0,75 0,50 0,58 0,83 0,33 0,58 0,92 0,83 0,83 0,50 0,50 0,58
(*The following codes are used to indicate the different ECO DISTRICTS: q.1 = Hammaeby Sjostad; q.2 = Bo01; q.3 = Viikki project;
q.4 = GMV; q.5 = Villaggio Olimpico ex MOI; q.6 = Kronsberg; q.7 = Vauban; q.8 = Südstadt; q.9 = Scharnhauser Park; q.10 =
Solarcity; q.11 = GWL-Terrein; q.12 = EVA.

20. ANALYSIS OF EXPERTS GROUP
• Interpretation of the information produced is an arduous task, and the
sheer volume of this information very greatly complicates decision-making
tasks. Above all, considering the various strategies adopted, an account of
the (positive and negative) interactions noted cannot be fully provided.
• It was therefore decided to employ an instrument capable of efficaciously
and rapidly performing this function. When compared to those obtained
exclusively through the assessments of experts, the error margins are
generally encouraging.
• The opportunities provided by Artificial Neural Networks are therefore of
considerable interest.

21. THE CHOICE OF THE NEURAL NETWORK MODEL
• There are different types of networks, for the purposes of our study, we
have chose a neural network algorithm that exploits the internal
recirculation. Recirculation Neural Network (RNN) were introduced by
Geoffrey Hinton and James McClelland (Hinton and McClelland,1988).
• This type of networks belongs to the family of autoassociative neural
networks, their main feature is take input and output the same vector
more specifically, in a RNN, data is processed in one direction and learning
takes place using only local knowledge. The weights matrix of a RNN
consists of maximum gradient connections between the Input and the
Output layer.
• Also, each element in both the hidden and visible layers are connected to
a bias element. These connections have variable weights which learn in
the same manner as the other variable weights in the network.

22. THE CHOICE OF THE NEURAL NETWORK MODEL
• Therefore, if there are N Input Nodes in a RNN, the weights matrix Wij will be made up of N2
connections. The scheme of used RNN is shown in the following scheme:
Massimo Buscema and the
Semeion Group have
developed a technique
called the Re-entry (see
Buscema, 1994,1999), that is
repurposed in this study. The
algorithm, on which this
technique is founded, is very
simple, but at the same time
effective, as it allows to
create a cycle in which the
output that is generated by
the network is re-entered
into the network as new
input. The process ends
when the output generated
is not subject to additional
mutations.

23. THE CHOICE OF THE NEURAL NETWORK MODEL
• The training phase
We can, at this stage, proceed with the RNN training phase by inserting
the following vector as real input:
where GThi indicates the value attributed in the evaluation matrix to the
h-th general theme relative to the i-th district.
The term aij= 0 if j ≠ i and aij= 1 if j = i;
the term in question therefore assumes the value of 1 only for the i-th
district, thus enabling identification of the said term.

24. THE CHOICE OF THE NEURAL NETWORK MODEL
• The data-input matrix
• Slightly more than 14,000 ANN training cycles (epochs) took place. The error value noted is
approximately 0,4% .
• Following the training phase, the network should be capable of reconstructing its own
matrix of weights (which can be attributed to the various strategies applied to the analysed
districts) and of providing useful indications if appropriately interrogated.
•

25. THE CHOICE OF THE NEURAL NETWORK MODEL
• The test phase: recognition of the identification profiles for
the various ecodistricts
• At this stage, verification of the level of “self-awareness” reached by the
neural network becomes important. In other words, we must verify whether
the network is capable of reconstructing the profile of the 12 districts.
• Hence, the aim is to test the capacity of the network to recognise a given
district, to see what scores the network assigns to the set of eco-oriented
strategies, and then to assess the deviation between the data of the original
evaluation matrix (produced by expert group) and the data of the evaluation
matrix produced by the network.
• The Index of compliance obtained as total value of the ratio for the value
proposed by the neural network (NN) and the real value (R) is meanly equal to
0.95. A lower mean deviation of 5% is noted between the profile proposed by
the neural network and the real profile of the 12 ecodistricts. All in all, for the
scopes of this study, this deviation may be considered satisfactory

26. EVALUATION MATRIX PRODUCED BY THE RNN
ECODISTRICTS
THEMES
q.1 q.2 q.3 q.4 q.5 q.6 q.7 q.8 q.9 q.10 q.11 q.12
AMB.1. Biodiversity 0,65 0,46 0,71 0,89 0,26 0,50 0,92 0,41 0,96 0,52 0,45 0,52
AMB.2. Soil Consumption 0,54 0,75 0,58 0,76 0,29 0,49 0,80 0,37 0,79 0,49 0,77 0,54
AMB.3. Seetlement System 0,74 0,81 0,36 0,51 0,58 0,36 0,60 0,63 0,56 0,46 0,76 0,38
AMB.4. Bioclimatic 0,78 0,54 0,49 0,73 0,22 0,67 0,67 0,24 0,81 0,58 0,44 0,30
AMB.5. Energy 0,78 0,74 0,56 0,85 0,51 0,72 0,60 0,48 0,81 0,67 0,54 0,48
AMB.6. Water 0,80 0,62 0,56 0,75 0,20 0,61 0,80 0,38 0,72 0,46 0,57 0,66
AMB.7. Building Materials 0,46 0,69 0,66 0,71 0,54 0,37 0,80 0,47 0,79 0,66 0,68 0,61
AMB.8. Waste 0,72 0,41 0,45 0,52 0,22 0,60 0,90 0,26 0,55 0,54 0,53 0,58
AMB.9. Transportation 0,67 0,78 0,59 0,79 0,27 0,59 0,97 0,60 0,62 0,60 0,74 0,60
AMB.10. Health 0,56 0,58 0,45 0,65 0,25 0,49 0,61 0,48 0,57 0,59 0,65 0,49
ECO.1. Governance 0,71 0,77 0,46 0,49 0,25 0,52 0,91 0,83 0,54 0,66 0,52 0,55
ECO.2. Implementation 0,55 0,67 0,62 0,42 0,49 0,79 0,86 0,78 0,47 0,74 0,65 0,47
ECO.3. Management 0,52 0,52 0,62 0,52 0,12 0,70 0,88 0,52 0,55 0,39 0,70 0,57
ECO.4. Seetlement System 0,16 0,44 0,53 0,33 0,29 0,82 0,93 0,70 0,49 0,47 0,57 0,49
ECO.5. Economic Activities 0,70 0,90 0,27 0,50 0,20 0,85 0,78 0,80 0,90 0,77 0,44 0,38
SOC.1. Population 0,30 0,51 0,59 0,45 0,18 0,81 0,81 0,50 0,94 0,79 0,80 0,53
SOC.2. Living Quality 0,62 0,47 0,54 0,72 0,56 0,83 0,72 0,52 0,83 0,74 0,36 0,42
SOC.3. Management 0,21 0,47 0,82 0,26 0,05 0,81 1,00 0,55 0,61 0,84 0,81 0,64
SOC.4. Services 0,69 0,70 0,41 0,80 0,50 0,82 0,74 0,49 0,71 0,56 0,46 0,42
POL.1. Implementation 0,28 1,00 0,18 1,00 0,32 1,00 1,00 0,31 1,00 1,00 1,00 0,23
POL.2. Partecipation 0,06 0,43 0,69 0,48 0,02 0,47 0,97 0,79 0,40 0,76 0,74 0,42
POL.3. Governance 0,36 0,34 0,70 1,00 0,35 0,41 1,00 1,00 1,00 0,80 0,61 0,29
POL.4. Management 0,76 0,44 0,53 0,85 0,25 0,56 0,94 0,86 0,85 0,42 0,46 0,57

27. THE CHOICE OF THE NEURAL NETWORK MODEL
• Interrogation of the network: identification of ideal-type
profiles
• At this stage we are in a position to interrogate the network in order to obtain indications
relative to the performance ratings of individual districts. In other words, the aim of this
phase is that of assessing which of the analysed cases may constitute a model (and therefore
an ideal-type profile) with reference to the strategies adopted in order to deal with the four
spheres of sustainability.
• Hence, the first type of interrogation aims to pinpoint 4 ideal-type district profiles, a
summary description of which may be provided as follows:
• Environmental Profile: this profile is characterised by a focus on the sphere of environmental
sustainability, and, therefore, the strategies which are adopted aim, primarily, at ensuring
environmental quality of the settlement and reduction of consumption of natural resources;
• Social Profile: for this profile the strategies receiving most attention are those that aim at
attaining conditions for the settlement such as are capable of fostering social cohesion and
inclusion;
• Economic Profile: this profile is characterised by the mainly economic nature of the strategies
adopted, which aim to produce wealth and jobs for the inhabitants;
• Political Profile: here, the focus is on forms of direct democracy and on the manners of
participation through which direct democracy is implemented in collective decision-making
processes.

28. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• In the cases analysed, of course, these characteristics coexist (with varying intensities). Our
investigation aims to identify which characteristic emerges significantly, to such an extent
that it univocally connotes the case analysed.
• Operationally speaking, it is a question of constructing an input vector relative to each district
which, in regard to the sphere analysed, shall present maximum intensity relative to the
themes characterising it.
ENVIRONMENTAL PROFILE
amb.1 1,00 amb.6 1,00 eco.1 0,00 soc.1 0,00 pol.2 0,00
INDICATORS:
amb.2 1,00 amb.7 1,00 eco.2 0,00 soc.2 0,00 pol.3 0,00
ACTIVED
amb.3 1,00 amb.8 1,00 eco.3 0,00 soc.3 0,00 pol.4 0,00
amb.4 1,00 amb.9 1,00 eco.4 0,00 soc.4 0,00
input
amb.5 1,00 amb.10 1,00 eco.5 0,00 pol.1 0,00
The network result is the following (case of Hammarby Sjöstad)
ENVIRONMENTAL PROFILE
amb.1 0,74 amb.6 0,75 eco.1 0,45 soc.1 0,37 pol.2 0,10
STRATEGIES
amb.2 0,73 amb.7 0,68 eco.2 0,38 soc.2 0,52 pol.3 0,23
IDENTIFIED
amb.3 0,65 amb.8 0,65 eco.3 0,48 soc.3 0,28 pol.4 0,53
amb.4 0,68 amb.9 0,69 eco.4 0,18 soc.4 0,61
output
amb.5 0,75 amb.10 0,54 eco.5 0,34 pol.1 0,88

29. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• The network identifies Hammarby Sjöstad as the district, among those
analysed, with the most marked pertaining characteristics .
• However, the network also indicates the importance assumed, for the
project’s success, both by community involvement during the
implementation phase and by the array of services at hand.
• This latter aspect reveals the network’s ability to pinpoint the implicit
nexuses which come about among the various strategies, underscoring an
ability to fully grasp the complexity of the system and provide valid
support during the decision-making phase.

30. Interrogation of the network: identification of ideal-type profiles
ENVIRONMENTAL PROFILE
HAMMARBY SJÖSTAD (STOCKHOLM, SWEDEN)

31. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• Likewise, the other ideal-type profiles were analysed. While, for all
interrogations, the network is capable of detecting the presence of the
analysed characteristic, in the case of the social and political profiles the
intensity of the characteristic is not high.
• This is probably due to the scarcity of the information gathered during the
reconnoitring phase relative to these two aspects. Hence the input data
were found to be fairly homogeneous. In any case, this fact underscores
even more clearly the potentials provided by the network during
exploratory investigation, in that the network manages (at least in part) to
highlight districts in which certain characteristics are most marked.

32. INTERROGATION OF THE NETWORK: IDENTIFICATION OF IDEAL-TYPE
PROFILES
ECONOMIC PROFILE
SÜDSTADT (TÜBINGEN- GERMANY)

33. INTERROGATION OF THE NETWORK: IDENTIFICATION OF IDEAL-TYPE
PROFILES
SOCIAL PROFILE
KRONSBERG (HANNOVER - GERMANY)

34. INTERROGATION OF THE NETWORK: IDENTIFICATION OF IDEAL-TYPE
PROFILES
POLITICAL PROFILE
SÜDSTADT (TÜBINGEN- GERMANY)
GREENWICH MILLENIUM VILLAGE (LONDON, UK)

35. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• The next step consists in an analysis of the performance ratings of various
districts in terms of the mix of strategies adopted. Here, too, the
responses obtained on interrogation of the network are of particular
interest.
• As above, here too, from the operational viewpoint, no major problems
arise, because it will be sufficient to activate with maximum intensity the
themes that shall constitute the analysed combination of strategies. This
can be readily achieved by assigning a value of 1 to the theme.
• With the first theme that shall be illustrated, the idea was to carry out
analysis in order to understand which mix will be capable of
simultaneously ensuring encouraging performance ratings in the fields of
environmental and economic options.

36. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• The network identifies the winning strategies:
– strengthening the transportation network and the coexistence of various
forms of mobility;
– the presence of public-private partnerships capable of activating efficacious
governance processes;
– accessibility of the settlement;
– development and safeguarding of water as a resource;
– development of economic activities, with particular attention paid to activities
with low environmental impact;
– activation of processes of implementation and political management.
• Hammarby is the district which most satisfactorily brings together the
aspects investigated in this profile. Hammarby not only confirms its
environmental vocation; it is noted, also, that this vocation is even
enhanced by the presence of strategies aiming at promotion of economic
aspects

37. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
ENVIRONMENTAL-ECONOMIC PROFILE
HAMMARBY SJÖSTAD (STOCKHOLM, SWEDEN)

38. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• The last two profiles presented are socio-economic and political -
economic.
• In regard to the socio-economic profile, the network identifies as winning
strategies implementation and development of economic activities
together with collective forms of management. Thus we see a
strengthening of the position of Kronsberg, which becomes the case study
most capable of representing this profile.

39. INTERROGATION OF THE NETWORK: IDENTIFICATION
OF IDEAL-TYPE PROFILES
• To conclude, the political and economic
profile is the final profile recognised by
the network.
• This profile is characterised by special
attention paid to development of
strategies that foresee implementation
of forms of political and economic
governance; the participation of citizens;
diversification of typologies of home
ownership and, in particular, the
presence of social housing; integration
and integrated development of various
forms of transportation and mobility;
and forms of community management.
The Südstadt district practically fully
embodies the characteristics identified
by the network, thereby confirming a
satisfactory level of integration of the
strategies adopted with reference to the
problem areas analysed.

40. CONCLUSIONS
• The methodological approach which has been developed
has enabled a full appreciation of the structural complexity
of the case studies analysed, while enabling identification
of the particular characteristics which render exemplary a
number of these cases.
• The analytic instrument adopted has been found to be
extremely powerful and efficacious in its ability to identify
the “implicit nexuses” coming about among the various
territorial components, and which are capable of
conditioning the success of the various implemented
strategies. Hence, from this point of view, the neural
network is capable of pinpointing not only synergic
dynamics but also potentially antagonistic dynamics,
thereby most surely facilitating decision-making processes.

41. CONCLUSIONS
• However, we should also note the manners in which the neural network’s
assessment process is implemented. Since this process is based on a
“black box model”, an account of the associative processes cannot be
exhaustively provided. A potential danger, in this regard, may lie, for
example, in inappropriate levelling of the proposals produced – as a
consequence of homogenisation of information. This situation may come
about where information in regard to the contexts of decision-making –
where the choices are made – is insufficient. In other words, the phase of
collection of information becomes vital.

42. • Thanks for your attention!