Chapter 8.
ATTRIBUTE DATA INPUT AND MANAGEMENT
8.1 Attribute Data in GIS
8.1.1 Type of Attribute Table
8.1.2 Database Management
8.1.3 Type of Attribute Data
Box 8.1 Categorical and Numeric Data
8.2 The Relational Model
8.2.1 SSURGO: A Relational Database Example
8.2.2 Normalization
8.2.3 Types of Relationships
8.3 Joins, Relates, and Relationship Classes
8.3.1 Joins
8.3.2 Relates
8.3.3 Relationship Classes
8.4 Attribute Data Entry
8.4.1 Field Definition
8.4.2 Methods of Data Entry
8.4.3 Attribute Data Verification
8.5 Manipulation of Fields and Attribute Data
8.5.1 Adding and Deleting Fields
Box 8.2 Add and Delete Fields in ArcGIS Desktop
8.5.2 Classification of Attribute Data
8.5.3 Computation of Attribute Data
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Key Concepts and Terms
Review Questions
Applications: Attribute Data Entry and Management
Task 1: Enter Attribute Data of a Geodatabase Feature Class
Task 2: Join Tables
Task 3: Relate Tables
Task 4: Create New Attribute by Data Classification
Task 5: Use Advanced Method for Attribute Data Classification
Task 6: Create New Attribute by Data Computation
Task 7: Create Relationship Class
Challenge Question
References
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Attribute Data
zAttribute data are stored in tables.
zAn attribute table is organized by row and column.
zEach row represents a spatial feature, each column
describes a characteristic, and the intersection of a column
and a row shows the value of a particular characteristic for
a particular feature.
Figure 8.1
Each street segment in the TIGER/Line files has a set of associated
attributes. These attributes include street name, address ranges on the
left side and the right side, as well as ZIP codes on both sides.
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Feature Attribute Table
zA feature attribute table has access to the spatial data. Every
vector data set must have a feature attribute table.
zFor the georelational data model, the feature attribute table uses
the feature ID to link to the feature’s geometry.
zFor the object-based data model, the feature attribute table has a
field that stores the feature’s geometry.
Figure 8.2
As an example of the georelational data model, the soils coverage uses
SOIL-ID to link to the spatial and attribute data.
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Figure 8.3
The object-based data model uses the Shape field to store the
geometry of soil polygons. The table therefore contains both spatial
and attribute data.
Value Attribute Table
An integer raster has a value attribute table, which lists
the cell values and their frequencies (count).
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Figure 8.4
A value attribute table lists the attributes of value and count. The value field
refers to the cell value, and the count field refers to the number of cells. A value
attribute table differs from the feature attribute tables in Figures 8.2 and 8.3.
Figure 8.5
A feature attribute table consists of rows and columns. Each row
represents a spatial feature, and each column represents a
property or characteristic of the spatial feature.
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Type of Attribute Data
zOne method for classifying attribute data is by data type.
Common data types are number, text (or character), date, and
binary large object (BLOB).
zAnother method is to define attribute data by measurement
scale. The measurement scale concept groups attribute data into
nominal, ordinal, interval, and ratio data, with increasing
degrees of sophistication.
Type of Database Design
There are at least four types of database designs that have been
proposed in the literature: flat file, hierarchical, network, and
relational.
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Figure 8.6
Four types of database design:
(a) flat file, (b) hierarchical, (c)
network, and (d) relational.
Normalization
Designing a relational database must follow certain rules.
An important rule is called normalization. Normalization is
a process of decomposition, taking a table with all the
attribute data and breaking it down to small tables while
maintaining the necessary linkages between them .
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PIN Owner Owner address Sale date Acres Zone code Zoning
P101 Wang 101 Oak St 1-10-98 1.0 1 residential
Chang 200 Maple St
P102 Smith 300 Spruce Rd 10-6-68 3.0 2 commercial
Jones 105 Ash St
P103 Costello 206 Elm St 3-7-97 2.5 2 commercial
P104 Smith 300 Spruce Rd 7-30-78 1.0 1 residential
TABLE 8.1 An Unnormalized Table
PIN Owner Owner address Sale date Acres Zone code Zoning
P101 Wang 101 Oak St 1-10-98 1.0 1 residential
P101 Chang 200 Maple St 1-10-98 1.0 1 residential
P102 Smith 300 Spruce Rd 10-6-68 3.0 2 commercial
P102 Jones 105 Ash St 10-6-68 3.0 2 commercial
P103 Costello 206 Elm St 3-7-97 2.5 2 commercial
P104 Smith 300 Spruce Rd 7-30-78 1.0 1 residential
TABLE 8.2 First Step in Normalization
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Figure 8.7
Separate tables from the second step in normalization.
The keys relating the tables are highlighted.
Figure 8.8
Separate tables after normalization. The keys relating the tables
are highlighted.
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Type of Relationship
A relational database may contain four types of relationships (also
called cardinalities) between tables, or more precisely, between
records in tables: one-to-one, one-to-many, many-to-one, and
many-to-many.
Figure 8.9
Four types of data relationship
between tables: one-to-one,
one-to-many, many-to-one, and
many-to-many.
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Join and Relate
zTwo common operations for linking tables in a relational
database are join and relate.
zA join operation brings together two tables by using a key
that is common to both tables.
zA relate operation temporarily connects two tables but
keeps the tables physically separate.
Figure 8.10
Primary key and foreign key provide the linkage to join the table on the
right to the feature attribute table on the left.
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Figure 8.11
This example of a many-to-one relationship in the SSURGO database relates
three tree species in cotreestomng to the same soil component in component.
Figure 8.12
This example of a one-to-many relationship in the SSURGO database relates one
soil map unit in mapunit to two soil components in component.
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Natural Resources Conservation Service: SSURGO
http://soils.usda.gov/
SSURGO metadata
http://soildatamart.nrcs.usda.gov/SSURGOMetadata.aspx
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