Because the world is much more complex than can be represented on a computer, all geospatial data are incomplete approximations of the world. Therefore, most geospatial data models encode some form of strategy for collecting a finite sample from an often infinite domain and a structure for organizing the sample in such a way as to allow for interpolation of the nature of the unsampled portion. For example, a building consists of an infinite number of points in space; a vector polygon represents it with a few ordered points, which are connected in a closed contour by straight lines and assuming that all interior points are part of the building; Furthermore, a "height" attribute may be the only representation of its three-dimensional volume.[5]​.

The process of designing geospatial data models is similar to data modeling in general, at least in its general pattern. For example, it can be segmented into three different levels of model abstraction:

Each of these models can be designed in one of two situations or scopes:.

Generic geospatial conceptual models attempt to capture both the physical nature of geographic phenomena and the way people think about and work with them. Unlike the standard modeling process described above, the data models on which GIS is built were not originally designed based on a general conceptual model of geographic phenomena, but were designed largely according to technical convenience, probably influenced by common sense conceptualizations that had not yet been documented.

That said, an early conceptual framework that was very influential in the early development of GIS was the recognition by Brian Berry and others that geographic information can be decomposed into the description of three very different aspects of each phenomenon: space, time, and attribute/property/theme. As a further development in 1978, David Sinton presented a framework that characterized different measurement, data and mapping strategies by holding one of the three aspects constant, controlling a second and measuring the third.[7]​.

During the 1980s and 1990s, a body of spatial information theories gradually emerged as a major subfield of geographic information science incorporating elements from philosophy (especially ontology), linguistics, and the sciences of spatial cognition. By the early 1990s, a basic dichotomy of two alternative ways of making sense of the world and its contents had emerged:

These two conceptual models are not intended to represent different phenomena, but are often different ways of conceptualizing and describing the same phenomenon. For example, a lake is an object, but the temperature, clarity, and pollution rate of the water in the lake are fields.

The raster logical model represents a field by tessellation of geographic space into a two-dimensional array of regularly spaced locations (each called cell), with a single attribute value for each cell (or more than one value in a multiband raster). Typically, each cell represents a sample of a single center point (in which the measurement model for the entire raster is called grid) or represents a summary (usually the mean) of the field variable over the square area (in which the model is called grid).[5]​ The overall data model is essentially the same as that used for images and other raster graphics, with the addition of capabilities for geographic context. A small example follows:

To represent a raster grid in a computer file, it must be serialized into a single (one-dimensional) list of values. Although there are several possible sorting schemes, the most used is the main row in which the cells of the first row, immediately followed by the cells of the second row, are as follows:.

6 7 10 9 8 6 7 8 6 8 9 10 8 7 7 7 7 8 9 10 9 8 7 6 8 8 9 11 10 9 9 7 . . .

To reconstruct the original grid, a header with general parameters for the grid is required. At a minimum, it requires the number of rows in each column so it knows where to start each new row and the data type of each value (that is, the number of bits in each value before starting the next value).

While the raster model is closely related to the conceptual field model, objects can also be represented in raster, essentially transforming an object X into a discrete (boolean) field of presence/absence of X. Alternatively, a layer of objects (usually polygons) could be transformed into a discrete field of object identifiers. In this case, some raster file formats allow you to join a vector-like attribute table to the raster by matching the ID values. Raster representations of objects are often temporary, only created and used as part of a modeling procedure, rather than in a permanent data store.

To be useful in GIS, a raster file must be georeferenced to correspond to real-world locations, since a raw raster can only express locations in terms of rows and columns. This is typically done with a set of metadata parameters, either in the file header (such as the GeoTIFF format) or in a sidecar file (such as a world file).

At a minimum, georeferencing metadata must include the location of at least one cell in the chosen coordinate system and the resolution or cell size distance between each cell. A linear affine transformation is the most common type of georeferencing, allowing rotating and rectangular cells. More complex georeferencing schemes include polynomial and spline transformations.

Raster data sets can be very large, so image compression techniques are often used. Compression algorithms identify spatial patterns in the data, then transform the data into parameterized representations of the patterns, from which the original data can be reconstructed. In most GIS applications, lossless compression algorithms (e.g., Lempel-Ziv) are preferred over lossy ones (e.g., JPEG), because the complete original data is needed, not interpolation.

Contenido

A partir de la década de 1990, a medida que maduraban los modelos de datos originales y el software GIS, uno de los enfoques principales de la investigación del modelado de datos fue el desarrollo de extensiones de los modelos tradicionales para manejar información geográfica más compleja.

Spatiotemporal models

Time has always played an important role in analytical geography, since at least the regional science matrix of Brian Berry (1964) and the geography of time of Torsten Hägerstrand (1970). At the dawn of the era of GIS science in the early 1990s, Gail Langran's work opened the doors to research into methods to explicitly represent change over time in GIS data; This led to the emergence of many conceptual and data models in the following decades. Some forms of temporal data began to be supported in off-the-shelf GIS software in 2010.

Several common models for representing time in vector and raster GIS data include:.

Three-dimensional models

There are several approaches to representing three-dimensional map information and managing it in the data model. Some of these were developed specifically for GIS, while others were adopted from 3D computer graphics or computer-aided drafting (CAD).

Because the world is much more complex than can be represented on a computer, all geospatial data are incomplete approximations of the world. Therefore, most geospatial data models encode some form of strategy for collecting a finite sample from an often infinite domain and a structure for organizing the sample in such a way as to allow for interpolation of the nature of the unsampled portion. For example, a building consists of an infinite number of points in space; a vector polygon represents it with a few ordered points, which are connected in a closed contour by straight lines and assuming that all interior points are part of the building; Furthermore, a "height" attribute may be the only representation of its three-dimensional volume.[5]​.

The process of designing geospatial data models is similar to data modeling in general, at least in its general pattern. For example, it can be segmented into three different levels of model abstraction:

Each of these models can be designed in one of two situations or scopes:.

Generic geospatial conceptual models attempt to capture both the physical nature of geographic phenomena and the way people think about and work with them. Unlike the standard modeling process described above, the data models on which GIS is built were not originally designed based on a general conceptual model of geographic phenomena, but were designed largely according to technical convenience, probably influenced by common sense conceptualizations that had not yet been documented.

That said, an early conceptual framework that was very influential in the early development of GIS was the recognition by Brian Berry and others that geographic information can be decomposed into the description of three very different aspects of each phenomenon: space, time, and attribute/property/theme. As a further development in 1978, David Sinton presented a framework that characterized different measurement, data and mapping strategies by holding one of the three aspects constant, controlling a second and measuring the third.[7]​.

During the 1980s and 1990s, a body of spatial information theories gradually emerged as a major subfield of geographic information science incorporating elements from philosophy (especially ontology), linguistics, and the sciences of spatial cognition. By the early 1990s, a basic dichotomy of two alternative ways of making sense of the world and its contents had emerged:

These two conceptual models are not intended to represent different phenomena, but are often different ways of conceptualizing and describing the same phenomenon. For example, a lake is an object, but the temperature, clarity, and pollution rate of the water in the lake are fields.

The raster logical model represents a field by tessellation of geographic space into a two-dimensional array of regularly spaced locations (each called cell), with a single attribute value for each cell (or more than one value in a multiband raster). Typically, each cell represents a sample of a single center point (in which the measurement model for the entire raster is called grid) or represents a summary (usually the mean) of the field variable over the square area (in which the model is called grid).[5]​ The overall data model is essentially the same as that used for images and other raster graphics, with the addition of capabilities for geographic context. A small example follows:

To represent a raster grid in a computer file, it must be serialized into a single (one-dimensional) list of values. Although there are several possible sorting schemes, the most used is the main row in which the cells of the first row, immediately followed by the cells of the second row, are as follows:.

6 7 10 9 8 6 7 8 6 8 9 10 8 7 7 7 7 8 9 10 9 8 7 6 8 8 9 11 10 9 9 7 . . .

To reconstruct the original grid, a header with general parameters for the grid is required. At a minimum, it requires the number of rows in each column so it knows where to start each new row and the data type of each value (that is, the number of bits in each value before starting the next value).

While the raster model is closely related to the conceptual field model, objects can also be represented in raster, essentially transforming an object X into a discrete (boolean) field of presence/absence of X. Alternatively, a layer of objects (usually polygons) could be transformed into a discrete field of object identifiers. In this case, some raster file formats allow you to join a vector-like attribute table to the raster by matching the ID values. Raster representations of objects are often temporary, only created and used as part of a modeling procedure, rather than in a permanent data store.

To be useful in GIS, a raster file must be georeferenced to correspond to real-world locations, since a raw raster can only express locations in terms of rows and columns. This is typically done with a set of metadata parameters, either in the file header (such as the GeoTIFF format) or in a sidecar file (such as a world file).

At a minimum, georeferencing metadata must include the location of at least one cell in the chosen coordinate system and the resolution or cell size distance between each cell. A linear affine transformation is the most common type of georeferencing, allowing rotating and rectangular cells. More complex georeferencing schemes include polynomial and spline transformations.

Raster data sets can be very large, so image compression techniques are often used. Compression algorithms identify spatial patterns in the data, then transform the data into parameterized representations of the patterns, from which the original data can be reconstructed. In most GIS applications, lossless compression algorithms (e.g., Lempel-Ziv) are preferred over lossy ones (e.g., JPEG), because the complete original data is needed, not interpolation.

Contenido

A partir de la década de 1990, a medida que maduraban los modelos de datos originales y el software GIS, uno de los enfoques principales de la investigación del modelado de datos fue el desarrollo de extensiones de los modelos tradicionales para manejar información geográfica más compleja.

Spatiotemporal models

Time has always played an important role in analytical geography, since at least the regional science matrix of Brian Berry (1964) and the geography of time of Torsten Hägerstrand (1970). At the dawn of the era of GIS science in the early 1990s, Gail Langran's work opened the doors to research into methods to explicitly represent change over time in GIS data; This led to the emergence of many conceptual and data models in the following decades. Some forms of temporal data began to be supported in off-the-shelf GIS software in 2010.

Several common models for representing time in vector and raster GIS data include:.

Three-dimensional models

There are several approaches to representing three-dimensional map information and managing it in the data model. Some of these were developed specifically for GIS, while others were adopted from 3D computer graphics or computer-aided drafting (CAD).