The publication authored by three academics is filled with tips, tricks and templates for the implementation of smart cities projects. It is the result of ongoing research being carried out by the Institute of Technology Management and the Centre for Energy Innovation, Governance and Investment at the University of St Gallen.
The intended readers are mayors, councillors, administrators and managers who work on the ground in towns and cities, as well as corporate decisions makers and citizens who want to have a better understanding of the future of urban living environments.
It is split into four parts, The first explaining what a smart city is and how it works. The second offers up the Smart Cities Management Model which can be applied by practitioners. Part three offers examples of what smart city success looks like and part four lays out the questionnaire, worksheets and matrices to help practitioners actually apply the Smart City Management Model.
My understanding of smart cities is that they are places in which the buildings, the infrastructure, the fixtures and fittings as well as the residents are all connected by sensors and beacons, the data from which is sent up to the cloud and back down again to improve functionality and usability. To repurpose the well-known phrase, data is the oil that lubricates the machinery of a city making it smarter, in my view at least.
So it was a bit disappointing to see scant reference to data in the index of the book. Data governance is listed, as is GDPR, Internet of Things and artificial intelligence. However, throughout the book a lot more reference was made to the importance of data in smart cities projects than is listed in the index.
A shaded box on ‘digital shadows’ informs the reader that “Modern data analytics through algorithms, generally referred to as artificial intelligence (AI) identify patterns and autonomously improve systems without requiring human intervention (machine learning).” Unfortunately, the definition given of a digital shadow was vague and unclear.
Data is again mentioned when discussing micro-jobs - which seemed to be another way of saying the gig economy - and the collection information about how citizens use of transportation systems.
The book is peppered with examples and case studies of success stories or ‘lighthouse cities,’ some of which would be of particular interest to data professionals that they may not have known about and might want to find out more.
One is the administration of the Estonian digital identification and citizen number. This chip card can serve as an ID card, driver’s license, insurance card, store of medical information, library card and store loyalty card. Furthermore, the Estonian government has a ‘data embassy’ based in a high-security data centre in Luxembourg which serves as a backup for all the national data.
Much more detail is dedicated to Toronto which is partnering to Alphabet’s Sidewalk Labs to develop its waterfront as a smart city. The authors made clear that due to public scrutiny of the project and major stakeholders stepping down, much of the information presented is publicly available. The writers stated that other smart cities should pay attention to what is happening in Toronto because the openness with which data usage frameworks are being discussed by the companies involved is atypical.
Chapter five is the one that concentrates most attention on data and analytics. It discusses concepts such as edge computing, Moore’s law, surveillance by authorities and ‘algorithmic governance.’ It ends with a thought-provoking list of questions to consider when thinking about cities in the 21st century.
Instead of viewing Smart Cities: Introducing Digital Innovation to Cities as a data practitioner’s introduction to smart cities, I see this publication more as a smart cities professional’s manual with some important references to data.