Database Reference
In-Depth Information
objects (points, lines, polygons, etc.) and includes specifications for various
spatial operators to derive new objects from existing ones. PostGIS makes use
of the proj.4 library
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for converting geographic data between different map
projections, an important functionality to integrate geospatial objects that
are based on different reference systems.
GRASS provides a variety of functions to manage raster data and topolog-
ical vector data. It natively uses and supports a number of vector and raster
formats, which are expanded with several other formats using the Geospa-
tial Data Abstraction Library (GDAL).
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GRASS offers the option to manage
nonspatial attributes associated with geographic objects and raster images in
either files or an SQL-based database management system.
Besides the above GIS type of data management infrastructures, geospa-
tial data are also often managed just at the file level. That is, applications
generate geospatial data and simply record them in standard file formats for
consumption by and exchange with other programs. One can basically distin-
guish between file formats for vector data (object-based data) and file formats
for raster or gridded data (field-based data). One of the most common formats
for vector data are
shapefiles
, which have been developed by ESRI and are
used to exchange data among ESRI products and other software.
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Another
important, although less widely used, format for vector data is the Topologi-
cally Integrated Geographic Encoding and Referencing (TIGER) format used
by the U.S. Census Bureau. It is employed for modeling geographic informa-
tion such as roads, rivers, lakes and census tracts.
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For raster and gridded data, widely used file formats are the Network Com-
mon Data Form (NetCDF),
36
the Hierarchical Data Format (HDF5),
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and
GeoTIFF.
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These file formats only represent a small but important portion
of a large collection of scientific data formats (many of which also come in an
XML framework) that have been developed over the past decades in different
disciplines.
The above discussions about the variety of commercial and open-source
geospatial data management software as well as file formats for the exchange
of complex (geo)spatial data clearly illustrate that achieving interoperability
among heterogeneous geospatial data sources is a great challenge.
10.2.3 Schemas and Metadata
An essential ingredient to any data integration approach is to have information
about the schemas as well as metadata for schema components and the data
managed in heterogeneous scientific data repositories. In the following, we first
discuss an emerging standard for geospatial data to represent both schema
information and data and then detail some prominent metadata frameworks
used in the context of geospatial data.
10.2.3.1
GML Application Schemas
The Geography Markup Language (GML) is an XML-based specification de-
veloped by the OGC for representing geographic features.
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,
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GML serves as
