SPATIAL DATABASES

Spatial information systems often must deal with different kinds of data imperfections:

Uncertainty. Uncertainty is a kind of data imperfection that arises from the lack of information about the state of the world (e.g., ``if the distance between Santiago and Concepción is unknown, the time that takes to travel from Santiago to Concepción is uncertain").

Imprecision/vagueness. Imprecision is a kind of data imperfection that arises from the granularity of the language used to make an imprecise statement (e.g., ``Santiago is located in América"). Vagueness is a kind of imprecision that arises from the use of terms when there are cases for which it is difficult to decide if they are covered or not by a particular concept (e.g., ``Santiago is close to Concepción");

Incompleteness. Incompleteness is a kind of imperfection that arises from the absence of some data values (e.g., a missing road in a transportation network)

Inconsistency. Inconsistency is a kind of data imperfection that arises from the coexistence of two contradictory facts (e.g., ``Concepción is located at 500 km from Santiago" and ``Concepción is located at 600 km from Santiago").

 

From the perspective of the type of characteristics the inconsistency refers to, inconsistency is related to what are called primery or secondary forms of error:

The primary form of error corresponds to a wrong description of location or characteristics/qualities of spatial objects. A typical case is the conflicting geometric representation of a spatial object; for example, having an integrity constraint that states that objects have only one location, there is an inconsistency derived from a primary type of error if there exist more than one location for a spatial object. This type of inconsistency occurs because there exist differences in data accuracy or precision, but also because many observations of spatial phenomena are essentially vague. For example, the boundaries of cities, mountains, and oceans cannot be determined with precision, which may make two observers record two different locations for the same object.

A spatial inconsistency related to a secondary form of error refers to a contradiction between stored data and constraints associated with structural definitions of geometric primitives. For example, a surface must be bounded by closed and non self-intersecting polylines. Inconsistency may also be related to semantic contradictions, such as when a road overlaps a body of water. These types of inconsistency, structural or semantic, depend on the spatial domain, and they are captured by rules that should be expressed within the data model.

Some relevant characteristics of spatial applications that should be considered in the treatment of consistency are:

• Spatial information deals with spatial and non-spatial data.
• Many spatial data are inherently vague, which may lead to conflicting data.
• Topological and other spatial relations are very important and are usually implicitly represented.
• A modification in a spatial database may cause simultaneous updates in a large number of records.
• Spatial databases may need to treat different levels of detail in the spatial representation.
• Many queries are defined in terms of combinations of functions that exist at both a low-level of abstraction (e.g., geometry types) and a high-level of abstraction (e.g., maps, configurations).

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