Digital twin city platforms have made significant progress in recent years. However, the existing platforms often have problems “focusing on accuracy of the physical model rather than social cognition” and “focusing on specialized application rather than data system.” The lack of data construction demonstrates that the current digital twin platforms do not pay enough attention to the mapping rates in the physical cities and virtual cities, which makes it difficult to meet the actual needs of refined urban governance. Therefore, it is necessary to define the characteristics of a digital twin platform oriented to urban governance, further explain and parse specific indications in the mapping rate methodology, and build a corresponding refined evaluation mechanism to realize the guidelines for practice. Through this evaluation mechanism, the enhanced attention to the data mapping rate can solve the current problems of data integrity, data timeliness, data consistency, and data interconnection in the construction of the digital twin platform. It can effectively enhance the digital twin city model’s ability to control, strain, and respond to the various needs of the city as a complex system. This study explores the methodology framework of mapping rate, proposing three mapping rate indicators: data resolution, data freshness, and data relevance as the fundamental indicators to evaluate the mapping rate. The three indicators represent the level of data collection, updating, and interconnection, reflecting the development quality of digital twin platforms, and constitute the methodology system of mapping rate. In terms of proposing a quantitative assessment method of the mapping rate, it is necessary to define a data dimensional composition system of the urban digital twin platform. Thus, this study also comprehensively explores the dimensions of urban data based on the practice and needs of urban governance. The urban subjects are divided into four categories: spatial carriers, urban components, social subjects, and urban flows, and aggregated according to three levels: small, medium, and large. Based on the four data categories and the three mapping rate indicators, data resolution, data freshness, and data relevance, a quantifiable evaluation model was built to assess the level of data collection, the data timeliness, and the data network correlation.To evaluate the three specific indicators, this study establishes three corresponding data evaluation models and introduces mathematical algorithms such as Hierarchical Analysis Method (AHP for short), Gaussian Function, Knowledge Graph, and other methods and techniques. Taking the Huamu Sub-district Digital Twin platform in the Pudong New Area in Shanghai as a test bed, each of the three indicators is calculated, and a comprehensive mapping rate is assessed manually. The total Digital Resolution score of the Huamu Sub-district Digital Twin City is 39. 7, the Data Freshness score is 34. 1, and the Data Relevance score is 13. 9 on a 100-point scale, indicating that the platform is still at the initial level of mapping rate. A real-time evaluation platform for the mapping rate could be developed for long-term monitoring and timely feedback. In conclusion, this study achieves a quantitative analysis of the development quality of digital twin city platforms, which was hard to measure accurately before. The data assessment model centered on the mapping rate has been constructed, which is not only applicable to the Huamu Sub-district Digital Twin City but also a universal example in terms of thedata dimensional composition system, the ideal updating cycle of the data, and the data correlation network of multiple co-management, etc. Furthermore, incorporating various techniques, such as fuzzy matching, ensures that the methodology is as free from project-specific bias as possible. Additionally, the data assessment model is designed to be real-time and interactive and aids in the subsequent development of a mapping rate evaluation platform equipped with a self-feedback mechanism. For the context of building a people’s city, this study offers a viable approach to systematic, diverse, and standardized management in smart city construction. The proposal of mapping rate methodology and the construction of an assessment model will expand the development of a digital twin city from the simple reproduction and visualization of physical objects in virtual space to the creation of a multi-dimensional digital governance system centered on data construction. It can help promote the digital twin city platform to respond to the complex needs of urban governance and to facilitate the construction of a “bottom-up” scientific urban governance framework.