Vector data models are a subset of geographic information through which data are encoded as a geometric definition of a feature, organized within a framework that relates this geometry to the spatial location, context, and proximal relationship. Vector data models are often contrasted to raster data models, encoded in a regular grid spanning an extent with a fixed cell size. Vector data rely on geometric primitives, such as points, lines (sequences of linked points), and polygons (closed geometric forms), with variations on these and additional cases tailored to specific applications as they have emerged during the development of GIS. With the ability to define the location, placement, and interval of points that describe each feature, the resolution of the data are determined by the precision of placement of vertices from the sampled reality or when encoded in a processed form of the data. Due to their finite shapes, defined by their exact placement (point), the segment or overall length (line, polyline), or their area (polygon) vector data are commonly used to encode discrete, rather than continuous, features, with associated characteristics stored in an accompanying table of values corresponding to each shape feature.
Manipulating (e.g., reading, writing, and processing) geospatial data, the first step in geospatial analysis tasks, is a complicated step, especially given the diverse types and formats of geospatial data combined with diverse spatial reference systems. Geospatial data Input/Output (IO) libraries help facilitate this step by handling some technical details of the IO process. GDAL/OGR is the most widely-used, broadly-supported, and constantly-updated free library among existing geospatial data IO libraries. GDAL/OGR provides a single raster abstract data model and a single vector abstract data model for processing and analyzing raster and vector geospatial data, respectively, and it supports most, if not all, commonly-used geospatial data formats. GDAL/OGR can also perform both cartographic projections on large scales and coordinate transformation for most of the spatial reference systems used in practice. This entry provides an overview of GDAL/OGR, including why we need such a geospatial data IO library and how it can be applied to various formats of geospatial data to support geospatial analysis tasks. Alternative geospatial data IO libraries are also introduced briefly. Future directions of development for GDAL/OGR and other geospatial data IO libraries in the age of big data and cloud computing are discussed as an epilogue to this entry.