## 1 Introduction  
In the context of contemporary urbanization, cities are confronted with numerous challenges, including traffic congestion, environmental pollution, and the uneven distribution of resources. To address these complex issues more effectively, many cities have adopted City Information Modeling (CIM) as a foundation for digital urban management. CIMintegrates advanced technologies such as Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to enable intelligent urban governance. Despite its growing adoption, the definition of CIM remains inconsistent, and the existing research has predominantly focused on technical frameworks or specific urban applications. However, a systematic exploration of CIM's data-driven characteristics is still lacking, limiting its potential to enhance urban intelligence and decision making [1; 2].  
This integrative literature review examines how data types, sources, quality, governance mechanisms, and interoperability influence the evolution of CIM from a data-driven perspective. Unlike systematic literature reviews (SLRs), this paper adopts an integrative review approach to synthesize insights across disciplines and sources without rigid inclusion criteria [3]. CIM, when viewed through the lens of data, can be defined as an information management system that leverages multi-source urban data for comprehensive collection, integration, processing, and analysis to support efficient and intelligent urban planning and decision making. By investigating the data lifecycle within CIM--from data acquisition and processing to analysis and decision support--this survey identifies three key challenges: data accuracy, privacy protection, and seamless data integration. To address these issues, a data-driven CIM framework is proposed, integrating big data analytics and artificial intelligence (AI) technologies to improve the data governance capabilities and support the intelligent development of cities [4; 5]. The main contributions of this survey are as follows:  
* The conceptualization of data-driven CIM is proposed, emphasizing the core role of data (type, source, quality, governance) in the development and implementation of CIM.
* The unique contribution of a data perspective of CIM is distinguished from other perspectives that focus on technology, governance, or specific urban application areas.
* A comprehensive analysis of the data lifecycle in CIM is conducted, covering data collection, integration, processing, analysis, and decision support, revealing the role of data flow and intelligent decision making in each stage.
* Future research directions are proposed, including adaptive urban data infrastructure, ethical frameworks for urban data governance, and data-driven urban intelligent decision-making systems.  
By focusing on the data perspective, this study distinguishes itself from technology-centric or application-specific CIM surveys. The primary aim of this paper is to review and summarize the development and construction of city information modeling from a data perspective. Therefore, the data perspective distinguishes this survey from other surveys on CIM. Section 2 discusses the background information and the definition of CIM. In Section 3, the development of CIM is reviewed. In Section 4, a data-driven construction framework for CIM is proposed. Finally, Section 5 summarizes the current status and future development of CIM.