This ontology pattern describes geodata types representing core concepts of spatial information (Kuhn 2012) as well as further semantic distinctions relevant for geocomputation and geo-analysis. This includes measurement levels of attributes, in particular extensive and intensive attributes, as well as basic geometric models (raster and vector geometry, tessellations). The ontology can be used to automate wporkflow synthesis by using its classes as signature (input/output) types for GIS tools. The ontology was introduced and is explained in: Scheider et la. (2020): Ontology of core concept data types for answering geo-analytical questions. Journal of Spatial Information Science 20 (2020): 167-201. https://josis.org/index.php/josis/article/view/125
This ontology pattern describes geodata types representing core concepts of spatial information (Kuhn 2012) we well as further semantic attribute distinctions relevant for geocomputation and geo-analysis.
Scheider et la. (2020): Ontology of core concept data types for answering geo-analytical questions. Journal of Spatial Information Science 20 (2020): 167-201.
This ontology pattern describes geodata types representing core concepts of spatial information (Kuhn 2012) we well as further semantic attribute distinctions relevant for geocomputation and geo-analysis. The ontology was introduced in: Scheider et la. (2020): Ontology of core concept data types for answering geo-analytical questions. Journal of Spatial Information Science 20 (2020): 167-201. https://josis.org/index.php/josis/article/view/125
Dataset of amounts of objects
AmountDS
An attribute that represents a (quantified) amount, which is a quantity defined by an arbitrary part of space. This can be either an amount of objects or an amount of some field.
AmountQ
A raster data set where each cell represents an amount of objects
AmountRasterDS
A dataset where each geometry denotes an amount of objects
AmountVectorDS
Attributes of a tabular dataset. Each attribute corresponds to a column in the table.
Attribute
Boolean attributes are spatial attributes within the range of True/False. They are special types of (bi)nominal attributes
BooleanA
This is a spatial attribute which denotes a bounded phenomenon. Can be either a object vector or an existence raster
BoundedPhen
Contour layers are coverages that denote intervals of the reprepresented continuous field. For example, temperature intervals, represented as regions bounded by isolines of a temperature field.
Contour
A dataset representing a field using contours, which denote the boundaries of value intervals. For example, a terrain contour map.
ContourDS
A data set which represents a core concept of spatial information. The attributes of this dataset are assumed to be of corresponding types.
CoreConceptDataSet
Superclass of all core concept qualities.
CoreConceptQ
An attribute that denotes counts of discrete entities (compare Chrisman 1998)
CountA
Coverages are tessellated layers that represent a continuous field. For example, landuse polygons.
Coverage
A dataset representing a field using (possibly irregular) patches of homogeneous values. For example, a landuse map or a soil map.
CoverageDS
An attribute that denotes a cyclical measure, like an angle. Note that cyclical measures don't have a natural ordering and thus are not ordinal scaled. (compare Chrisman 1998)
CyclicA
Extensive region attribute. Since attributes are extensive, their values increase with the size of their support regions. Zero size regions are zero then defined to be zero, which implies a ratio scale level.
ERA
A spatial dataset representing events
EventDS
This attribute represents a quality of an event (core concept). For example, the duration.
EventQ
Events represented in terms of an attribute of a vector data set. For example, the magnitude of earthquakes represented as an attribute of a point data set.
EventVector
Existence rasters are rasters representing boolean fields (fields denoting whether something is present at a cell location or not).
ExistenceRaster
A vector layer attribute that satisfies a boolean expression and thus denotes the 'existence' of some attribute encoded phenomenon across space. Example is a layer of polygons of ''forest'' landuse or a layer of "parks".
ExistenceVector
This type of lattice has an extensive attribute. For example, a spatial dataset about the population number of municipalities
ExtLattice
A spatial dataset representing a field
FieldDS
An attribute which represents the quality of a field (core concept). This implies that the spatial data items in this data set are self-similar (their parts have the same attribute)
FieldQ
Field rasters are raster representations of continuous fields. For example, raster layers representing a terrain height (DTM).
FieldRaster
A dataset representing a field by homogeneous values using raster cells
FieldRasterDS
A vector representation of a field. Can be a point or line measure, or a coverage, or a Patch
FieldVector
Intensive region attribute. An attribute that is independent from the size of its support region.
IRA
An interval scaled attribute
IntervalA
A lattice is a spatial tessellation that represents objects and their qualities. For example, a set of municipalities with average income.
Lattice
A dataset of objects which tesselate space and contain statistical summaries. For example, the municipalities of the Netherlands
LatticeDS
Superclass of of attribute types that characterize a geometric layer property
LayerA
A generalized notion of a line in 2D and 3D
Line
Attribute of some Line data set
LineA
Line data set
LineDS
Line data
LineData
Linear representations of fields. Examples are isoline maps
LineMeasures
A representation of a field as a line of homogeneous values
LineMeasuresDS
A network relation where the complete cross-product of an object domain forms the key. Examples would be origin-destination matrices.
MatrixDS
an attribute of a matrix. For example, distance in an origin-destination matrix.
MatrixQ
A spatial dataset representing a spatial network. Note thate a network in the sense of a core concept is a quantified relation between objects. For example, the traffic flow between municipalities or between two intersections. Streets themselves can also be seen as objects if they do not encode such relations
NetworkDS
This attribute represents the quality of a network edge. Note that spatial networks (as core concepts) are regarded as quantified relations between objects. So O-IRA-O would be an intensive quality measured between two objects, such as their distance from each other.
NetworkQ
A raster dataset where NoData is interpreted as boolean "false".
NoDataExistenceRaster
A spatial attribute on nominal scale level
NominalA
A spatial data set representing objects
ObjectDS
An attribute of an object line dataset
ObjectLine
An attribute of an object point dataset
ObjectPoint
An attribute that represents the quality of an object (core concept)
ObjectQ
An attribute that represents an object that has a region
ObjectRegion
Object vectors are vector representation of a set of objecs and their quality. For example, a point layer denoting points of Interest (POI) or buildings in a city together with their quality, e.g. the building height.
ObjectVector
A spatial attribute on ordinal scale level
OrdinalA
An attribute of some dataset representing homogeneous patches of a field as regions.
Patch
A dataset which represents a field in terms patches of homogeneous values. Not necessarily tessellated
PatchDS
An interval attribute that is plain/proper
PlainIntervalA
A nominal attribute that is plain/proper (not ordinal or boolean)
PlainNominalA
An ordinal attribute that is plain/proper (not interval)
PlainOrdinalA
A ratio attribute that is plain/proper
PlainRatioA
Vector regions that are not tessellated
PlainVectorRegion
An attribute of a vector region layer that is not a tessellation
PlainVectorRegionA
A generalized notion of a point in 2D or 3D
Point
Attribute of some point data set
PointA
Point data set
PointDS
Point data
PointData
Point measures are point-like representations of fields. For example, point data sets denoting the locations of measured temperature values.
PointMeasures
A dataset representing a field in terms of point-wise measures. For example, temperature sensor measurements.
PointMeasuresDS
A single value of some quality
QualityValue
A raster data set is a special kind of tessellation, where regions are regular squares.
Raster
Attribute of some Raster
RasterA
Attribute on ratio scale level
RatioA
A generalized geometry, including 2D and 3D extended regions. In 2D, this might be a Polygon.
Region
Atrribute of some region data sets
RegionA
Region data set
RegionDS
Region data
RegionData
A dataset that represents some quantified realtionb between other concepts. For example, a network (being a relation between objects).
RelationalDS
An attribute of a dataset that represents some quantified relation between other core concepts. For example a network is relation between objects.
RelationalQ
A region data set where spatial regions are tessellated (jointly covering and mutually non-overlapping) within a spatial extent
Tessellation
An attribute of a tesselation data set
TessellationA
A type for a spatial track. A spatial track is a vector representation of "fix" events of some moving object. For example, a GPS track of points of a person moving.
Track
A dataset representing tracks, i.e. point wise events (fixes) of the trajectory of some objects.
TrackDS
Vector data sets are spatial data sets that are not raster
Vector
Attribute of some vector data set
VectorA
Dataset with vector regions as geometry
VectorRegion
An attribute of a region vector layer
VectorRegionA
This is a vector data set which is tessellated (and no raster)
VectorTessellation
An attribute of a vector tessellation
VectorTessellationA
A raster dataset where 0 is interpreted as boolean "false"
ZeroExistenceRaster
ofDataSet o hasElement o hasMeasure o valueOf
ofDataSet o hasElement o hasSupport o valueOf