Table 3 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

Data governance

Urban growth and data growth

Cost and large-scale deployment

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

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

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

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