1
Spatial Data on the Web: Issues and Challenges
Alberto Belussi
1 , Barbara Catania
2 , Eliseo Clementini
3 , and Elena Ferrari
4
1 University of Verona, Verona (Italy)
2 University of Genoa, Genoa (Italy)
3 University of L’Aquila, L’Aquila (Italy)
4 University of Insubria, Varese (Italy)
Spatial data are today needed in a wide range of application domains. Indeed, spatial
properties are included in several application contexts requiring the management of
very large data sets, such as, for instance, computer-aided design (CAD), very large
scale integration (VLSI), robotics, and image processing. However, the primary target of systems dealing with spatial data remains geographical applications, since
they served as the first motivation for the development of such technology and still
represent the most challenging application environment [19]. Spatial data can be defined as pieces of information describing quantitative and/or qualitative properties
that refer to space. Such properties can be represented as attributes of a set of objects
(like the path of a given highway or the technical drawing of the new version of a
car engine) or as functions of the space locations (like the temperature measured at
a given location on the European continent or the measured infrared emissions in a
remote sensing image).
This book considers spatial data in geographical applications; thus it is focused
on geographical data. This means that spatial data are used to describe objects or,
more generally, natural phenomena and human activities that occur on the Earth’s
surface. Often geographical data are described as composed of two parts: the spatial component, describing shape, extension, location, and orientation of an object
(or a phenomenon) existing on the Earth’s surface, and the non-spatial component
(also called “thematic,” “descriptive,” or “alphanumeric component”), describing
other properties of the considered object (or phenomenon), like traditional attributes
(e.g. the name of the object).
5 Therefore, in the chapters of this book, the term “spatial data” refers to the spatial component of some geographical information.
Even if we focus on a specific category of spatial data, heterogeneity is still very
high. Indeed, spatial data for geographical applications are often managed independently by various parties and specialized systems, such as systems for managing
images from remote sensing, raster data (grids) coming from environmental monitoring devices, or vector data collected by cartographers in geographical maps. The
5 Geographical objects are also called features in standards of International Organization for
Standardization (ISO) and Open Geospatial Consortium (OGC) [9] [14].
Spatial Data on the Web: Issues and Challenges
Alberto Belussi
1 , Barbara Catania
2 , Eliseo Clementini
3 , and Elena Ferrari
4
1 University of Verona, Verona (Italy)
2 University of Genoa, Genoa (Italy)
3 University of L’Aquila, L’Aquila (Italy)
4 University of Insubria, Varese (Italy)
Spatial data are today needed in a wide range of application domains. Indeed, spatial
properties are included in several application contexts requiring the management of
very large data sets, such as, for instance, computer-aided design (CAD), very large
scale integration (VLSI), robotics, and image processing. However, the primary target of systems dealing with spatial data remains geographical applications, since
they served as the first motivation for the development of such technology and still
represent the most challenging application environment [19]. Spatial data can be defined as pieces of information describing quantitative and/or qualitative properties
that refer to space. Such properties can be represented as attributes of a set of objects
(like the path of a given highway or the technical drawing of the new version of a
car engine) or as functions of the space locations (like the temperature measured at
a given location on the European continent or the measured infrared emissions in a
remote sensing image).
This book considers spatial data in geographical applications; thus it is focused
on geographical data. This means that spatial data are used to describe objects or,
more generally, natural phenomena and human activities that occur on the Earth’s
surface. Often geographical data are described as composed of two parts: the spatial component, describing shape, extension, location, and orientation of an object
(or a phenomenon) existing on the Earth’s surface, and the non-spatial component
(also called “thematic,” “descriptive,” or “alphanumeric component”), describing
other properties of the considered object (or phenomenon), like traditional attributes
(e.g. the name of the object).
5 Therefore, in the chapters of this book, the term “spatial data” refers to the spatial component of some geographical information.
Even if we focus on a specific category of spatial data, heterogeneity is still very
high. Indeed, spatial data for geographical applications are often managed independently by various parties and specialized systems, such as systems for managing
images from remote sensing, raster data (grids) coming from environmental monitoring devices, or vector data collected by cartographers in geographical maps. The
5 Geographical objects are also called features in standards of International Organization for
Standardization (ISO) and Open Geospatial Consortium (OGC) [9] [14].