Flow mapping is a cartographic method of representing movement of phenomena. Maps of this type often depict the vector movement of entities (imports and exports, people, information) between geographic areas, but the general method also encompasses a range of graphics illustrating networks (e.g., transit and communications grids) and dynamic systems (e.g., wind and water currents). Most flow maps typically use line symbols of varying widths, lengths, shapes, colors, or speeds (in the case of animated flow maps) to show the quality, direction, and magnitude of movements. Aesthetic considerations for flow maps are numerous and their production is often done manually without significant automation. Flow maps frequently use distorted underlying geography to accommodate the placement of flow paths, which are often dramatically smoothed/abstracted into visually pleasing curves or simply straight lines. In the extreme, such maps lack a geographic coordinate space and are more diagrammatic, as in Sankey diagrams, alluvial diagrams, slope graphs, and circle migration plots. Whatever their form, good flow maps should effectively visualize the relative magnitude and direction of movement or potential movement between a one or more origins and destinations.

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.