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Smart cities and digital workplace culture in the global European context: Amsterdam, London and Paris

Paper for city culture and society scientific journal

Until recently, knowledge-intensive work activities have predominantly taken place in office buildings as a specialized form of economic infrastructure. New digital technologies together with an economic and organizational transition from closed firms to open platforms has changed the pattern of work within the modern metropolis. The office building is no longer the sole workplace typology and work activity has intensified in other urban locations. The questions then are: “How might smart cities reinterpret workplace culture at the urban scale outside the framework of office buildings typology?” and “Which tools and methodologies can be used to make digital workplace culture visible at the urban scale?” In order to answer these questions, workplaces are observed not as private architectural spaces but as compositions of “subjective urban experiences”. A Twitter data analysis provides evidence of workplace spatial culture within the innovative global cities of Amsterdam, London and Paris, interpreted as behavior settings. This analysis shows that office pattern locations are generally distributed independently to knowledge intensive business services and workplace demand, as expressed through social media analyses. In addition to office buildings, transit hubs, urban amenities and new digital services play a key role in reframing workplace location. Moving beyond generic visions for digital work in outer spaces, big data therefore provides specific insights and incentives for considering workplace design at the urban scale.

This matrix shows the most significant clustering values for each dataset in the three cities.


 A higher number of highly ranked clusters shows a “distributed” or polycentric pattern, while a smaller number of highly ranked clusters defines a more “hierarchical” or monocentric pattern of proximity.


The clusters mapped at their different proximity (d) values only for the case of Amsterdam.


The frequency (count of nearest points) of closest amenities to defined Twitter interaction densities. Frequencies = 1 have been excluded.



Author:

Michelangelo Vallicelli

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© Node Architecture All Rights Reserved