###### Abstract  
Geographic data are the foundation of geographic model construction, and any stage of their acquisition, processing, and analysis may have an impact on the efficiency and quality of geographic modeling and simulation. With the advent of the era of big data, a large number of data resources are generated in the field of geographic information. However, due to the heterogeneity of geographic data and the security of data usage, massive geographic data resources are difficult to fully explore and utilize, resulting in the formation of data islands. This paper proposes a service-oriented geographic data-sharing and computing framework, which provides users with a complete set of geographic data access and application processes (such as data acquisition, processing, configuration, etc. ), so as to reduce the difficulty of using data and improve the efficiency of data sharing. The framework mainly consists of three core components: (1) the \"Data service container\" can publish data resources as data services to provide a consistent data access interface; (2) the \"Workspace\" provides a series of methods and tools for users to develop data-computing solutions; and (3) the \"Data-computing engine\" is responsible for performing computing tasks such as data processing and configuration. Finally, a case of runoff simulation using the SWAT model is designed, in which the whole process of data sharing, acquisition, calculation, and application is realized, so as to verify the validity of the proposed framework. geographic data service; geographic data sharing; geographic data computing; geographic modeling and simulation +
Footnote †: journal: Applied sciences  
fields of geography in recent years, and the era of geographical big data is quietly coming [5; 6; 7; 8]. Multiple scientific data centers in the geoscience field have been established, such as the National Earth System Science Data Center [9], the ChinaGEOSS Data Sharing Network [10], the National Meteorological Science Data Center [11], and the National Marine Data Center [12]. Such large-scale data resources provide a solid data foundation for geographical research. At present, geographical data sharing is mainly divided into three modes: (1) \"Apply-download\" mode, in which users need to fill in an online application form (such as the identity of the applicant, the purpose of the applied data, etc. ), and download the data after it is approved. For example, all of the data centers mentioned above fall into this data-sharing mode. (2) \"Open download\" mode, in which users follow the agreement of the data provider and are free to download and use the data. For example, Geospatial Data Cloud [13] and HydroShare [14]. (3) \"Limited-access\" mode, in which, for some classified data, the data user needs to use it under supervision. For example, the cell phone signaling data [15; 16]. For a long time, data sharing has almost always been carried out in these three ways. However, with the deepening of the comprehensive study of geography, the use of interdisciplinary data makes the disadvantages of traditional data sharing gradually appear. First, when data providers share data, they cannot guarantee that their data resources will not be tampered with, misused, and abused, that is, they lose control over the data [17; 18; 19]. Especially cross-disciplinary researchers, for whom it is easy to misuse the data, and then lead to wrong research results, which will affect the reputation of the data provider [20; 21; 22]. Second, when users obtain the data files, they need to spend a lot of time reading the relevant metadata documentation, trying to understand the organization of the data, and then extract the desired data from these data files [23; 24]. For interdisciplinary researchers, it may be difficult to understand these metadata documents, making it difficult for them to use the data even if they have access to it [25].