Towards Interoperability of Data Platforms for Smart Cities

• Published in: Handbook of Smart Cities pp. 1237 - 1257
• Main Authors: Buchinger, Matthias, Kuhn, Peter, Balta, Dian
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• ISBN: 9783030696979; 3030696979
• DOI: 10.1007/978-3-030-69698-6_70

The rapidly advancing, worldwide urbanization confronts citizen and city administrations with new and growing challenges. A main instrument to meet these challenges is digitalization and the massive usage of “city” data. This data-driven approach is referred to as smart city and is increasingly discussed and implemented in theory and practice. Unfortunately, most smart city projects concentrate on silo use cases and solutions with a limited amount of data sources and applications. To overcome the boundaries between projects and data sources, interoperability is an important capability of the digital infrastructure of a smart city. While interoperability increases the benefits of smart cities, it is mostly overlooked in practice and not addressed comprehensively in the theory. For that reason, in this chapter a conceptualization of interoperability approaches in smart cities is presented. To this end, based on the results of a comprehensive literature analysis and multiple interviews with experts from practices, a taxonomy of these approaches was developedTaxonomydevelopement. Following that taxonomy, five main factors can be considered impactful to interoperability in smart cities, and three approaches to establishing interoperability each can be distinguished. Furthermore, advantages and disadvantages of the different approaches are discussed. The taxonomy is applied to two exemplary case studies, and conclusion is drawn upon five main takeaways. The presented comprehensive conceptualization of existing knowledge regarding interoperability of smart city data infrastructure and the given recommendations for action can help decision-makers and implementers in practices to systemize relevant questions and choose the most promising approaches for their use case.