A spatial index is a data structure that allows for accessing a spatial object efficiently. It is a common technique used by spatial databases. Without indexing, any search for a feature would require a "sequential scan" of every record in the database, resulting in much longer processing time. In a spatial index construction process, the minimum bounding rectangle serves as an object approximation. Various types of spatial indices across commercial and open-source databases yield measurable performance differences. Spatial indexing techniques are playing a central role in time-critical applications and the manipulation of spatial big data.
Spatial query is a crucial GIS capability that distinguishes GIS from other graphic information systems. It refers to the search for spatial features based on their spatial relations with other features. This article introduces a spatial query's essential components, including target feature(s), reference feature(s), and the spatial relation between them. The spatial relation is the core component in a spatial query. The document introduces the three types of spatial relations in GIS: proximity relations, topological relations, and direction relations, along with query examples to show the translation of spatial problems to spatial queries based on each type of relations. It then discusses the characteristics of the reasoning process for each type of spatial relations. Except for topological relations, the other two types of spatial relations can be measured either quantitatively as metric values or qualitatively as verbal expressions. Finally, the general approaches to carrying out spatial queries are summarized. Depending on the availability of built-in query functions and the unique nature of a query, a user can conduct the query by using built-in functions in a GIS program, writing and executing SQL statements in a spatial database, or using customized query tools.
Spatial information retrieval is fundamental in modern applications that enhance data analysis by enabling the representation, storage, and management of spatial data. Spatial database systems and Geographical Information Systems are essential tools for those applications, offering efficient spatial data representation and spatial query processing. Spatial data is usually represented by geometries in Euclidean space, such as points, lines, and regions. Spatial query processing mainly relies on the task of retrieving spatial objects meeting specific spatial conditions. These conditions are often defined by spatial relationships, which describe how spatial objects relate within a given space and have different semantics, such as topological relationships (e.g., overlap, inside), metric relationships (e.g., distance), and direction relationships (e.g., cardinal directions). A typical spatial query finds all objects in a specified relationship with a search object. This article systematically explores the design and definition of spatial queries by using spatial relationships as their main foundation. By introducing both intuitive and formal definitions of various types of spatial queries, it helps users to understand each query’s practical use and underlying mechanics.
The increasing accessibility of geospatial data in the form of knowledge graphs, developed in alignment with the Semantic Web vision and employing Linked Data principles, is becoming a prominent feature of the Web. The multitude of available geospatial knowledge graphs demonstrates their indispensable role within the Web of Data Cloud. Such graphs serve as central nexuses, interconnecting events, people, and objects, offering an ever-growing semantic representation of the geospatial information wealth. The resulting resources and capabilities are being leveraged to utilize, consume, and capitalize on geospatial information through the strategic deployment of knowledge graphs. The geospatial graphs are stored and managed by triple stores, which are also known as RDF stores or knowledge bases. As examples, SPARQL and GeoSPARQL are semantic query languages that are used to retrieve and process knowledge graphs. Some developments and experiences in the GIScience community have demonstrated the feasibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from disparate and distributed sources. These efforts have facilitated the consumption of geospatial knowledge graphs through lightweight web applications or GIS applications.