## 3 Results
### Local Spatial Autocorrelation for ESD Distribution
Grids with a significance level of 0.01 were concentrated in the east, southeast and northwest, with a few in the middle, while grids with significance levels of 0.05 and 0.10 were sporadically distributed throughout the study area. In terms of spatial distribution, both the high-high-type area and the low-low-type area showed a strong positive spatial correlation, the spatial similarity of ESD between these grids and their surrounding grids was strong, and their difference in spatial distribution was not prominent. To explore the impact of the urbanization process on the spatial differentiation of ecosystem services, this study selected a 1 km \(\times\) 1 km grid to resample the land use in 3 study years, which contained 1224 complete grids. The scatter diagram of ESD and the proportion of built-up area in each grid are shown in Figure 9. As urbanization progressed, the ecosystem service value generally showed a downward trend, which indicates that the urbanization process had a significant negative effect on ecosystem services: the higher the urbanization, the lower the ESD in the study area. Figure 8: The local spatial autocorrelation analysis significance level of ESD.3.8. Impact of the Urbanization Process on Ecosystem Services. Figure 7: The local spatial autocorrelation analysis cluster graph of ESD. The NRIDB is mainly composed of the Data Center, Computing Center, Service Center and Visual Web Front-End. It is a server-side + web-side GIS designed for the management and service of natural resource big data. The overall architecture is shown in Figure 10.  
The Data Center includes a relational database + spatial data engine, nonrelational database (NoSQL), file storage + distributed file storage system, and data toolset, as well as a grouped and a classified data resource catalog based on the data and technology standard specification system. The data resource catalog serves as a unified entry and exit for natural resource and geospatial data. The data toolset includes ETL tools, data pre-processing tools, data quality inspection tools and data importing tools. The relational database + spatial data engine is the core of the Data Center. After all data results underwent pre-processing, quality inspection and other processes, they were imported into each database, the corresponding metadata were generated, and the metadata were imported into the metadata set of the relational database. The Data Center registered the metadata information in the data resource catalog to realize data registration. The data resource catalog established the connection between the metadata and each dataset in the database through the metadata and the unified data access interface to realize integration and unified management. Based on the hybrid storage framework of the Data Center, the NRIDB realized the centralized management and distributed storage of geospatial and natural resource data. Tabular and vector data were imported and stored in a relational database (PostgreSQL) through a spatial data engine (SDE or PostGIS), raster data such as satellite images were imported and stored in a nonrelational database (MongoDB) after tiling, and data files such as documents were imported and stored in file storage spaces such as HDFS. By integrating the database access interface, file access interface and data service mode into a unified data access interface, various data resources can be shared and accessed to build a Data Center that is consistent and transparent for users. Figure 9: Impact of proportion of built-up area on ESD.4. The Application Practice in the NRIDB. The Computing Center includes a computing management toolset, GeoHPC cluster, ArcGIS Engine cluster and model repository. The computing management toolset provides computing task scheduling, load balancing, computing node management, computing monitoring and other functions. GeoHPC is a distributed parallel spatial analysis computing engine developed by GeoWay. The GeoHPC cluster consists of multiple GeoHPC computing nodes.