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Attribute Data Input and

Management

Attribute data describe the characteristics of

the map feature.

Attribute data are stored in tables

Each row of a table represents a map feature.

Each column represents a characteristics.

The object-oriented data model stores both data in a single database, but can distinguish spatial data from attribute data.

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Linking Attribute Data and Spatial Data

The georelational data model store spatial

data and attribute data in separate files.

Each map feature has unique label ID (Figure

6.1). Linked by feature ID, the two sets of data files can be queried, analyzed and displayed. Attribute data are stored in a table called feature attribute table (Figure 6.2).

A row is called a record.

A column is called a field.

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Most GIS projects have many attributes.

To store all attributes in a single table is not

efficient both time and computer space and difficult to use and update.

A database management (DBMS)system

provides tools for data input, search, retrieval, manipulation and output to manage, integrate and share the database.

Most GIS packages include DBMS:

- INFO for Arc/Info - MS Access for IDRISI, ArcView and

ArcGIS

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An Example of IDRISI Attribute Table

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Soil Geodatabase in ArcGIS

Spatial Data

Attribute Data

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Types of Attribute Data

Defined by data types allowed in GIS

package. -Character string -Integers -Real numbers -Dates -Time interval

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Defined by measurement scale

-Nominal:describe different categories -Ordinal:data that differentiate data by ranking relationships, e.g. low, medium, high. -Interval:data that have known intervals between values, e.g., temperature 40C is warmer than 0C. -Ratio:same as interval but have meaningful or absolute values, e.g. population density of

0 means no population in the area.

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Nominal and ordinal data can be grouped as

categorical data. Interval and ratio data can be combined as numerical data.

Measurement scales are important for data display

and analysis in GIS.

They affect the choices of map symbols.

GIS analysis often involves computation, which is limited to numerical data.

Data types and measurement scales are related.

- Character strings are suitable for nominal data - Integer and real numbers are appropriate for interval and ratio data.

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The Relational Database Model

A database is the collection of interrelated tables in digital format (Figure 6.3).

A relational database is a collection of tables (relations) which can be connected to each other by keys.

A key: one or more attributes whose values can uniquely identify a record in a table.

- A key common to two tables can establish connection between corresponding records in the tables (Figure 6.3).

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Normalization

Normalization is the process of breaking

down a table into small tables while maintaining the necessary linkages between them.

Purposes:

- To avoid redundant data in the table. - To maintain and update data effectively in the separate tables. - To facilitate distributed database. 7

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Steps in normalization

First normal form:The table will no longer has

multiple values in the cell (Tables 6.1, 6.2).

Second step:Break down the table into small

ones, some redundancy still remain (Figure 6.5).

Final step:New tables are created no

redundancy exists (Figure 6.6).

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Types of Relationships

Three types (Figure 6.7)

- One-to-one relationship:one and only one record in the destination tableis relate to one and only one in the source table.

- One-to-many relationship:one in the destination table may be related to many records in the source table.

- Many-to-one relationship:two or more in the destination table may be related to one record in the source table.

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Designation of the source and destination

tables depend on the data stored in the tables and information sought.

See examples in Figures 6.8 and 6.9

Attribute Data Entry

Field definition

- Define data width, data type and number of decimal place.

Methods of data entry

- Import existing data - Typing and use look up table.

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Attribute Data Verification

Check to make sure that label ID is unique and

not contain null values.

Check the accuracy of attribute data.

Verification can be done by:

- Print and check manually - Use validation rule such as in ArcGIS 12

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Creating New Attribute Data from

Existing Data

Attribute Data Classification

- Reduce a dataset into a small number of classes, e.g. slope classes

Attribute Data Computation

- Perform computation with the existing data, e.g. create population density from population/area.quotesdbs_dbs17.pdfusesText_23

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