Decimal data type ¶
The decimal data type is used to specify a numeric value.
The following is an example of a decimal number declaration in a scheme.
<xs:element name="prize" type="xs:decimal"/>
The elements in the document should look like this:
<prize>999.50prize>
Or something like this:
<prize>+999.5450prize>
Or something like this:
<prize>-999.5230prize>
Or something like this:
<prize>0prize>
Or something like this:
<prize>14prize>
注意: The maximum number of decimal digits you can specify is 18 digits.
Integer data type ¶
Integer data types are used to specify values that have no decimal components.
The following is an example of an integer declaration in a scheme.
<xs:element name="prize" type="xs:integer"/>
The elements in the document should look like this:
<prize>999prize>
Or something like this:
<prize>+999prize>
Or something like this:
<prize>-999prize>
Or something like this:
<prize>0prize>
Numeric data type ¶
Note that all of the following data types are derived from decimal data types (except decimal itself)!
First name
Number of seconds
Byte
8-bit integers with plus or minus
Decimal
Decimal number
Int
32-bit integers with plus or minus
Integer
Integer value
Long
64-bit integers with plus or minus
NegativeInteger
Integers that contain only negative values (.,-2,-1.)
NonNegativeInteger
Contains only integers with non-negative values (0,1,2,..)
NonPositiveInteger
Only integers that contain non-positive values (.,-2,-1, 0)
PositiveInteger
Integers containing only positive values (1, 2,..)
Short
16-bit integers with plus or minus
UnsignedLong
64-bit integers without positive or negative
UnsignedInt
32-bit integers without positive or negative
UnsignedShort
16-bit integers without positive or negative
UnsignedByte
An 8-bit integer with no positive or negative
Qualification of numeric data types (Restriction) ¶
Restrictions that can be used with numeric data types:
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Enumeration
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FractionDigits
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MaxExclusive
-
MaxInclusive
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MinExclusive
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MinInclusive
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Pattern
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TotalDigits
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WhiteSpace
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In recent years, Geographic Information Systems (GIS) have undergone rapid development in both theoretical and practical dimensions. GIS has been widely applied for modeling and decision-making support across various fields such as urban management, regional planning, and environmental remediation, establishing geographic information as a vital component of the information era. The introduction of the “Digital Earth” concept has further accelerated the advancement of GIS, which serves as its technical foundation. Concurrently, scholars have been dedicated to theoretical research in areas like spatial cognition, spatial data uncertainty, and the formalization of spatial relationships. This reflects the dual nature of GIS as both an applied technology and an academic discipline, with the two aspects forming a mutually reinforcing cycle of progress.
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