Smarter Cities’ Attractiveness. Testing New Criteria or Facets: “Data Scientists” and “Data Platforms”

The promotion of cities has usually been done by means of “attractiveness’ indicators.” “The New Economic Geography” and the book of Richard Florida about “The Rise of the Creative Class” have attempted to justify the choice of indicators of new forms of attractiveness. New dashboards containing indicators of the context (infrastructures and other factors) and, since Florida works (2002, 2005), local creative and innovative power indexes are added to the ancient territorial profiles. The competition between town centers and large and medium size cities is now illustrated with methods of ranking surveyed by OECD (OECD 2006). The emergence of digital societies is now analyzed as a structural change (Merryl, 2016). Some cities could now be beneficiary of the presence of big data actors or big data software and services (Townsend, 2013). From Los Angeles to a lot of so-called “smarter cities”( http://www.smart-cities.eu /) in Europe and in the world, the sharing and processing of public and private data is supposed to be more and more a key-factor of potential development, so of attractiveness (Batty, 2013, USC, 2014). So we will ask whether the traditional attractiveness’ ranking for cities should take account of the context of smarter cities embedded in a digital society. A lot of publications wants to deal with answers to this question (Stock Journal of the American Society for Information Science and Technology, 62(5), 963–986, 2011, Anthopoulos and Tougountzolou, 2011, Yigitcanlar ITU Journal of the Faculty of Architecture, 8(1), 53–67, 2011, Cohen, 2013). Taking account of the emergence of the digital society and of the digital divide between cities, this article propose to complement the approach of new cities’ attractiveness by adding two following supplementary dimensions of interest in the ranking and outranking of cities. These new dimensions are the location in some cities of a human potential constituted by data scientists (new criterion 1) and the existence of software capacities huge sharing and processing of big data by means of interoperable data platforms (new criterion 2). The article analyzes the relevance of these new criteria and concludes by a call for new empirical testing of the correlation between these criteria and the local productivity and welfare.