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This chapter aims at theoretically linking entrepreneurship and institutional conditions in the case of “smart cities”. After discussing how agglomeration economies shape entrepreneurship through the main relevant body of literature, the chapter articulates on the concept of “smart city” and presents a newly collected dataset on a “smart city” for the Emilia-Romagna Region in Northern Italy. Elements that drive improvements in the “smartness” of a city are presented in order to derive policy implications and the theoretical linkages between entrepreneurship, particularly “green” entrepreneurship, and a peculiar case of such an institutional setting as a “smart city”. The absence of proper data to test for a bi-directional causal link between entrepreneurship and smart city is discussed as a limitation of the current analysis and as a research line that deserves further investigation.

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application. © Springer International Publishing AG 2017.

In this paper, we propose that a smart approach to city development must seek a continuous and recursive interplay between two levels of codes. On the one hand, computer codes, which rule the development and the functioning of the ubiquitous ICTs infrastructure. On the other hand, normative codes, which govern the practices through which social actors perceive ICTs and decide to exploit them in order to improve their lives. We thus take an exploratory standpoint and investigate to what extent key players in the EU smart cities policy domain are framed according to such a sociotechnical perspective. To this purpose, we first map an online issue network on the topic of smart cities in Europe and then explore the frames that circulate within its core. Our results suggest that, although smart cities are framed sociotechnically, EU key players tend to better converge around technological aspects rather than social ones.

The work done deals with the concept of Volunteered Geographic Information and is based on the use of a mobile mapping collection tool to retrieve geographical data from an urban fieldwork. The research has been carried on during the academic year 2014/2015, involving students from the course of Geography of Networks within the post graduate degrees in ‘Economics’ and ‘Business’ of the University of Trieste (Italy). The intent was testing the potential of crowdsourcing in retrieving data using a bottom up approach, relying on a set of trained and aware ‘urban sensors’ as data collectors. This allowed us to derive first-hand geographical data concerning a particular topic and analyze its spatial distribution by means of Geographical Information Systems and spatial analytical tools. The topic studied was represented by the urban ‘movida’, the analysis of the areas of the city that are more or less active during the days and during the week. This major aim was also coupled with an ancillary one, as the coverage of Wi-Fi hotspots and networks over the urban area of Trieste. It is known that many Italian cities still do not allow a very wide coverage of wireless networks to access the Internet. The city of Trieste, on the border between Italy and Slovenia, is suited with a certain degree of coverage, particularly in main roads and squares, thanks to free Wi-Fi coverage managed by the municipality and an academic network of Eduroam system, quite spread over European and world cities hosting universities and research centers.