## 3 Results
### Local Spatial Autocorrelation for ESD Distribution
In a scene of complex analysis and batch processing scenarios, GeoHPC computing nodes were deployed in real time under the integrated scheduling mechanism, and each node performed spatial analysis and computation concurrently. According to the functions and characteristics of spatial analysis, the computing task scheduling tool automatically adapts and calls the required data, analysis models and computing clusters and automatically divides computing tasks into multiple sub-computing tasks, which are allocated to each computing node for parallel computing. The model repository is used to edit, modify and manage various spatial analysis models. Taking the ESD spatial analysis in Section 3.4 of this paper as an example, the Computing Center split the intersect analysis computation of land use data and grid data of various scales into multiple computing subtasks to form a computing subtask queue. Each GeoHPC computing node performed parallel spatial stacking analysis according to the computing subtask queue. Finally, the results of each subtask were aggregated to the GeoHPC management node to form the ESEV grid dataset. Figure 10: The macro-architecture of the NRIDB. The Service Center includes the services administration catalog and server cluster. The server cluster adopts microservice technology and applies the Docker container as the service publishing node to publish various data and spatial analysis computing functions as Web services with standardized interfaces. The server cluster provides the control functions of starting, stopping and updating each service. The services administration catalog is a unified export for Web services. After the data resources in the database and the spatial analysis models in the model repository were published as services and registered in the service administration catalog, they supported various applications of natural resource management and comprehensive decision-making and collaborative services in GHM. The services monitor can check the running status of the services, check whether the service starts normally and dynamically adjust the load balancing and elastic scaling of the server cluster according to the concurrent access pressure of the services. Different types of data and spatial analysis computing functions were published as different types of Web services. Vector data were published as Web Feature Service (WFS) or Spatial Query Service, or they were made into a thematic map tile and then published as Web Vector Map Service. Image data were published as Web Map Tile Service (WMTS). Spatial analysis models in the model repository were published as Web Processing Service (WPS). The Visual Web Front-End provides functions such as online visual browsing, query and measurement of natural resources, geospatial data and other source data, and it realizes the integrated display of 2D and 3D data such as vectors, grids, terrain and 3D models. The land use data shown in NRIDB as Figure 11. Combined with text annotations, tables and other forms, the Visual Web Front-End achieved an all-round and intuitive visual display. Through the adaptation of Visual Web Front-End, the visual results can be seamlessly displayed on the Web, large screens and OGC-compatible mobile apps. Through application promotion, the NRIDB's data and computing resources will be more effective and useful in the form of Web services. We shared the standardized data services of the Data Center and the spatial analysis computing services of the Computing Center with the Territorial Spatial Planning One Map (TSPOM) and the Land Management Decision Support System. These governmental systems support the management of natural resources in Guangdong Province, such as land resource management, cultivated land protection, territorial spatial planning and control and utilization, and decision support  
Figure 6.0 The land use in the study area is shown in the NRIDB. for natural resource development and utilization.