Microfounding Urban Big Data Infrastructure Through Multiplex Networks

A distinguishing feature of complex adaptive systems is that they can be viewed and described within a multileveled framework, rather than through a narrowly quantitative perspective. This is particularly relevant in cases where competing approaches, hypotheses, and data are concerned with emergent properties and human behaviour. When considered within this framework, the complexity and ubiquitous nature of today’s urban areas, along with the data science and information-based strategies that drive them, has now reached the point where an integrated analysis of urban big data infrastructure (UBDI) can be outlined. Nevertheless, although there is more data and information than ever, it would be appropriate to refine and validate further assumptions, conjectures, and models that we have relied on. The primary objective of this work is not to advance the science, but rather to gain insight into the dimensions that have emerged from the state-of-the-art literature on big data infrastructure in urban contexts. This includes an understanding of the methodological challenges that arise in this nascent field. A second, and more original, objective is to propose a pre-analytical microfoundation of UBDI. This is achieved by representing an adjacency tensor of multiplex networks. Based on the above, UBDI is conceptualised as a novel hybrid organisational form, designed through the adoption of a unified and holistic perspective that considers all the critical dimensions contributing to the development of urban big data infrastructure within contemporary cities. The main focus is on the role of individuals in the urban ecosystem, seen as a complex adaptive research framework.