ICTlogy

Communications session: Smart Cities (I)
Chairs: Álvaro Nicolás

Open smart cities: ¿whose are the data?
Julián Valero Torrijos, Juan Ramón Robles Albero

Whose are the data gathered by some smart cities initiatives? This question is especially relevant when many public services are managed by private firms. It’s interesting because these are data that are needed to provide the service, and thus private firms do need them. But, on the other hand, these data is generated by the user and thus likely to be ownership of the citizen. How do we solve this?

Our conclusion is that most data should be regulated as usual, protecting the citizen, etc. But. In some cases, especially when it deals about the know how of the private firm and how to improve the provision of the service, in some of these cases maybe data should remain property of the private firm, as it is part of their know how and own protocols and processes.

Smart mobility, data protection and social surveillance
Alessandro Mantelero

We are moving towards a pervasive data ecosystem. Big data and the Internet of Things are having an impact on individual and collective data protection, a need for balancing conflicts of interests, and have to move from a theoretical approach to an empirical approach, as the smart mobility case. We need to address open data and risk assessment, such as the factors that increase the risk of re-identification, and the different levels of access to mobility data. Examples: the London bike-sharing case or the user-centric approach adopted in the Piedmont case.

In the cases above, many data and at many levels is gathered, including personal information and travel information. Data protection is applied by design, both at the collection, storage and access and analysis of the datasets.

Conclusions: proportionality, risk-assessment, empower the citizen.

Urban governance and smart cities. The case of Barcelona
Mariona Tomàs Fornés

Since the end 0f 1980s we are facing a new concept of governance. Global governance is a process of coordination of actors, social groups, institutions to reach certain goals that have been debated and defined collectively. It implies a change in decision-making and policy-making. It includes different geographical scales, new public and private actors, etc. The hypothesis of this work is that the development of the smart city implies a shift towards the pro-growth model.

Goals for the case of the smarty city in Barcelona: based on efficiency, sustainability and a mix of several projects of many kinds put together under the umbrella of ‘smart cities’. Many of these projects already existed and the city council just rephrases them under this common umbrella.

The city council will transform the city into a urban lab so that the city (and the citizen) can be used as a lab by technological firms so that they can test initiatives, devices, etc.

How has the urban governance of Barcelona changed after their involvement in smart city projects? The participation of the private sector in financing urban projects has definitely increased, as has been the scheduling of big international events and culture as a development strategy. Citizen participation still is important, but somehow it seems that the usual spaces of participation have not been integrated with other initiatives and spaces more related to the smart city strategy. On the other hand, there is less strategic planning and less new institutions to lead new projects: private firms do not seem to be interested in strategic planning and new institutions have been replaced by ad hoc created public-private partnerships.

Barcelona is a typical case of conceiving the smart city within the principles of the entrepreneurial city: competitiveness, growth policies, use of public-private partnerships.

Pierre (1999) proposes different models of urban governance:

• Managerial
• Participative.
• Pro-growth.
• Redistributive.

The Politics of Governance, Citizen Participation and the City: is the smart revolution on its way?
Wim Vanobberghen. Researcher, iMinds-SMIT (Vrije Universiteit Brussel).

The smart city should be a citizen platform to bring all the actors involved in the city together.

Top-down vision is important because of policy, regulation, governance and businesses can provide a level playing field an set the rules of the game; and they seek efficiency gains in light of sustainability issues. However, this vision entails issues and questions: on control; on technological centeredness; on the status of citizens, which are seen as consumers and passive innovators, in citizens’ consultation in design only in the last phase; or turning the smart city as an “achievement” in itself.

Bottom-up vision: the smartest cities are the ones that embrace openness, randomness and serendipity — everything that makes a great city (Greg Lindsay, 2011); embrace complexity; attention to local innovation. However, this vision entail issues on scalability, long-term vision and barriers and incentives to entry.

Smart cities as a platform: collaborative, contextual, collective (Breuer, Walravens & Ballon, 2014, Beyond defining the smart city: meeting top-down and bottom-up appraoches in the middle).

The living lab definition: a real-life test and experimentation environment; where users and produces co-create, test and validate innovations; in a trusted, open ecosystem that enables service and business innovation. Characteristics of living labs: exploration/idea, experimentation/prototype, evaluation/minimum viable product.

From ICT to urban living labs: adaptation of living lab to smart city service delivery; focus more on public value than economic value as in traditional ICT-living labs; the user here confronted directly as citizens with his environment (“sense of place”); the city as an enabler: vision, allocate resources, strategic leaderhip, promote networking (Juujärvi & Pesso, 2013).

Governance and citizen participation:

• ‘Participatory turn’ in media technologies: online, collaborative platforms on the Internet (e.g. social networking sites); blurring of ‘production and consumption’ practices.
• Since recent years, open government practices in (smart) cities: creating thematic portals with open government data; facilitating citizens in the production of (local) information and services; supported by institutional-provided toolkits.

Research on how two local city administrations (Ghent, Athens) can facilitate and optimize citizen involvement in the co-production of city services (tourism, transportation).

To what extent is this citizen involvement a revolution? ICTs in the city present great opportunities:

• Active citizenship.
• Creative communities.
• The city as a process of collective production and co-design.

Discussion

Ismael Peña-López: you presented a view where smart cities and new social movements are complementary one to another, instead of the usual confrontational approach where new social movements frontally oppose to the concept of smart city. Why do you think it is so? Wim Vanobberghen: there surely is an ideological opposition from social movements to what they see it is a mostly technological and business fostered initiative, instead of seeing it as a piece related to community building and technology appropriation. Of course, the bias towards financial sustainability that city councils have is also opposed to a more communitarian point of view led by social movements.

Connected New Urbanism. The future of the city is about people
Daniele Quercia, Computer Scientist and Urban Computing Researcher

The debate around smart cities is usually led by technology — and the industry — and not by the citizen.

Smart cities can (or should…) be understood as the study of the dynamics of online networked individuals and use it for the improvement of cities in the future.

Kevin Lynch stated that one’s degree of well-being is highly conditioned by the layout of the city in which one lives, the layout of streets, etc. And it is quite much about visibility: the ease with which each part of a city can be recognized and organized in a coherent pattern.

Stanley Milgram studied what parts of the city were visible and what where not. And he found out, again, that visibility had to do with one’s degree of well-being.

This visibility, or better put, the recognizability was measured through an experiment: Urban Opticon. And we can aggregate peoples visions, how people recognize what places, and put them in the map. That map — actually a cartogram — shows how some places in one city are highly relevant for people’s lives, while other are just “invisible” to most people’s eyes.

What data says is that the more a recognizable a place is, the more correlated its well-being level. There is a high and positive correlation between recognizability and well-being. This is important for policy-making as it may be a good idea to put up initiatives that increase recognizability in order to contribute to the improvement of well-being.

Smells, odours, colours, etc. also help humans in mapping their environment. After analyzing how people tagged colours, or smells on social networking sites, a dictionary of urban smells was created. It was found that there is a high correlation between how some terms were tagged and the reality of the landscape at that given place (pollution, nature, etc.) and, thus, one can draft a map of the city and its assets after what people say in social media.

With social media we can contribute to map the city and, most especially, how people see and live the city.

Discussion

Xavier Campos: can’t these methodologies be used for urban planning? Daniele Quercia: usually not, most architects or urban planners do not use these methodologies. Only after the realization that these methodologies can help in designing projects that will make happier citizens, then maybe architects are more positive about using them.

Clara Marsan: how do you assess the relevance, the significance and representativeness of the data you get from social media? Daniele Quercia: during experiments, some personal data were also asked for, so that biases according to profiles can be corrected. We found out that our data is representative by age, gender and race, but not by professional experience. On the other hand, you can also have filtering techniques to improve the words (the language dictionary) used, to correct biases, etc.