Very large amounts of data are generated by the interaction of people with social networks, smartphones, GPS, etc., creating the need for specific technologies (Internet of Things, Big Data Analysis and so on). Such data and tools are key instruments for transforming traditional cities into Smart Cities, as well as for regional development. At the international level, six operational areas are recognised for development: smart economy, people, governance, mobility, living, environment. A model that takes into account and combines these operational areas can be used to better understand the smartness level of a city, and to monitor its performance for assessing progress. European governments, like those of the whole world, are familiar with the importance of these tools to organise urban and extra-urban services in the light of the needs of citizens, their mobility, the environment; in a nutshell, all the elements that form the operational areas of smart cities. In addition to experimenting with algorithms for collecting and using data automatically provided by the devices that people use in their daily lives, information is also gathered about the needs and opinions of citizens. The latter, unlike any information of "technological" origin, can only be obtained through sample surveys, however extensive, and its use combined with technological data requires some precautions and preliminary operations: for starters, to identify the subjective information that interacts the most with objective data, and therefore useful for their optimal use. In this article, data from the European Social Survey 2017 is used to test a useful methodology. The European Social Survey (ESS) is an academically driven cross-national survey, whose aims are: - to monitor and interpret changing public attitudes and values within Europe and to investigate how they interact with Europe's ever-changing institutions, - to advance and consolidate improved methods of cross-national survey measurement in Europe and beyond, - to develop a series of European social indicators, including attitudinal criteria. In the 2017 edition, Round 8, the survey covered 23 countries (largely EU countries, along with Switzerland, Israel and the Russian Federation) employing the most rigorous methodologies in order to collect several information and opinions (more than 400 variables) from circa 44.400 European citizens. The key issue is to identify, in this stream of information, the variables that are actually important for joint analyses with other data (for example, regional economic indicators), taking into account that methods of statistical inference are not useful with Large and Big Data. Therefore, we chose to test multivariate statistical methods such as Categorical Principal Component Analysis and Neural Network Analysis.

Una "cerca" di dati sulle prospettive della gente. Navigazione nel grande flusso di informazioni provenienti dalle indagini UE

Antonella Nannavecchia;Francesco D. d’Ovidio;
2019

Abstract

Very large amounts of data are generated by the interaction of people with social networks, smartphones, GPS, etc., creating the need for specific technologies (Internet of Things, Big Data Analysis and so on). Such data and tools are key instruments for transforming traditional cities into Smart Cities, as well as for regional development. At the international level, six operational areas are recognised for development: smart economy, people, governance, mobility, living, environment. A model that takes into account and combines these operational areas can be used to better understand the smartness level of a city, and to monitor its performance for assessing progress. European governments, like those of the whole world, are familiar with the importance of these tools to organise urban and extra-urban services in the light of the needs of citizens, their mobility, the environment; in a nutshell, all the elements that form the operational areas of smart cities. In addition to experimenting with algorithms for collecting and using data automatically provided by the devices that people use in their daily lives, information is also gathered about the needs and opinions of citizens. The latter, unlike any information of "technological" origin, can only be obtained through sample surveys, however extensive, and its use combined with technological data requires some precautions and preliminary operations: for starters, to identify the subjective information that interacts the most with objective data, and therefore useful for their optimal use. In this article, data from the European Social Survey 2017 is used to test a useful methodology. The European Social Survey (ESS) is an academically driven cross-national survey, whose aims are: - to monitor and interpret changing public attitudes and values within Europe and to investigate how they interact with Europe's ever-changing institutions, - to advance and consolidate improved methods of cross-national survey measurement in Europe and beyond, - to develop a series of European social indicators, including attitudinal criteria. In the 2017 edition, Round 8, the survey covered 23 countries (largely EU countries, along with Switzerland, Israel and the Russian Federation) employing the most rigorous methodologies in order to collect several information and opinions (more than 400 variables) from circa 44.400 European citizens. The key issue is to identify, in this stream of information, the variables that are actually important for joint analyses with other data (for example, regional economic indicators), taking into account that methods of statistical inference are not useful with Large and Big Data. Therefore, we chose to test multivariate statistical methods such as Categorical Principal Component Analysis and Neural Network Analysis.
978-88-6629-050-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/255542
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