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A spatial database is a general-purpose database (usually a relational database) that has been enhanced to include spatial data that represents objects defined in a geometric space, along with tools for querying and analyzing such data. Most spatial databases allow the representation of simple geometric objects such as points, lines and polygons.
A grid-based spatial index has the advantage that the structure of the index can be created first, and data added on an ongoing basis without requiring any change to the index structure; indeed, if a common grid is used by disparate data collecting and indexing activities, such indices can easily be merged from a variety of sources.
R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 [2] and has found significant use in both theoretical and applied contexts. [3]
Geographic information systems (GIS) and the underlying geographic information science that advances these technologies have a strong influence on spatial analysis. The increasing ability to capture and handle geographic data means that spatial analysis is occurring within increasingly data-rich environments.
CRAN site for Analysis of Spatial Data, R-Forge site: Analysis Full integration of spatial data analysis tools with the R: classes for spatial data; handling spatial data; reading and writing spatial data; point pattern analysis; geostatistics; disease mapping and areal data analysis; spatial regression and ecological analysis. R GPL-2: Google ...
The origin of the geodatabase was in the mid-1990s during the emergence of the first spatial databases.One early approach to integrating relational databases and GIS was the use of server middleware, a third-party program that stores the spatial data in database tables in a custom format, and translates it dynamically into a logical model that can be understood by the client software.
It is generally created within the constraints of chosen generic data models, so that existing GIS software can be used. For example, a data model for a city would include a list of data layers to be included (e.g., roads, buildings, parcels, zoning), with each being specified with the type of generic spatial data model being used (e.g. raster ...
From 2016, development continues at the Center for Spatial Data Science (CSDS) at the University of Chicago. [1] GeoDa has powerful capabilities to perform spatial analysis, multivariate exploratory data analysis, and global and local spatial autocorrelation. It also performs basic linear regression.
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