A global dataset on tools, frameworks, and indicator sets for smart city assessment.

Area of Science:

• Urban Planning and Smart Cities
• Data Science and Analytics
• Public Administration

Background:

• Numerous frameworks exist for assessing city smartness, leading to a fragmented landscape.
• A systematic analysis of these assessment tools is needed to understand their typology, content, and structure.
• Existing research often focuses on specific tools rather than providing a comparative overview.

Purpose of the Study:

• To present a curated dataset of 34 selected smart city assessment schemes.
• To analyze the typology, content, and structure of these schemes.
• To provide a foundation for understanding and comparing different approaches to smart city evaluation.

Main Methods:

• Content analysis of official documents (manuals, working papers, scorecards) for 34 assessment schemes.
• Systematic data extraction covering scheme development, implementation, scope, indicators, and assessment methods.
• Categorization and typological analysis of the collected data.

Main Results:

• The dataset details the geographic and thematic scope of each scheme.
• It outlines indicator selection approaches, common themes, and specific indicators used in smart city assessments.
• Information on assessment methods, implementation status, and city rankings is provided.

Conclusions:

• This dataset offers a structured and comprehensive resource for researchers and practitioners in the smart city domain.
• It facilitates comparative analysis of different smart city assessment tools and indicator sets.
• The findings contribute to a better understanding of the landscape of urban smartness evaluation.