Table 2 Computational, analytical, technical, and logistic challenges.

Design science and engineering constraints

Data processing and analysis

Data management in dynamic and volatile environments

Data sources and characteristics

Database integration across urban domains

Data sharing between city stakeholders

Data uncertainty and incompleteness

Data accuracy and veracity (quality)

Data protection and technical integration

Fault tolerance and scalability

Data governance

Urban growth and data growth

Cost and large-scale deployment

Evolving urban intelligence functions and related simulation models and optimization and prediction methods as part of exploring the notion of smart cities as innovation labs

Building and maintaining data-driven city operations centres or citywide instrumented system

Relating the urban infrastructure to its operational functioning and planning through control, automation, management, optimization, enhancement, and prediction

Creating technologies that ensure fairness, equity, inclusion, and participation

Balancing the efficiency of solutions and the quality of life against environmental and equity considerations

Privacy and security