There are two important data models used in GIS , one is Spatial and other is Attribute Data model. We have discussed in detail about the Spatial Data Model in our “Executive Introduction to Geographic Information System (GIS)“. Attribute Data Models in GIS, is a separate data model that is used to store and maintain attribute data for GIS software. These data models may exist internally within the GIS software, or may be reflected in external commercial Database Management Software (DBMS). A variety of different data models exist for the storage and management of attribute data. The most common are:
- Object Oriented
The tabular model is the manner in which most early GIS software packages stored their
attribute data. The next three models are those most commonly implemented in database
management systems (DBMS). The object oriented is newer but rapidly gaining in popularity for
some applications. A brief review of each model is provided.
The simple tabular model stores attribute data as sequential data files with fixed formats (or
comma delimited for ASCII data), for the location of attribute values in a predefined record
structure. This type of data model is outdated in the GIS arena. It lacks any method of checking
data integrity, as well as being inefficient with respect to data storage, e.g. limited indexing
capability for attributes or records, etc.
The hierarchical database organizes data in a tree structure. Data is structured downward in a
hierarchy of tables. Any level in the hierarchy can have unlimited children, but any child can
have only one parent. Hierarchical DBMS have not gained any noticeable acceptance for use
within GIS. They are oriented for data sets that are very stable, where primary relationships
among the data change infrequently or never at all. Also, the limitation on the number of
parents that an element may have is not always conducive to actual geographic phenomenon.
The network database organizes data in a network or plex structure. Any column in a network
structure can be linked to any other. Like a tree structure, a network structure can be described in
terms of parents and children. This model allows for children to have more than one parent.
Network DBMS have not found much more acceptance in GIS than the hierarchical DBMS.
They have the same flexibility limitations as hierarchical databases; however, the more
powerful structure for representing data relationships allows a more realistic modelling of
geographic phenomenon. However, network databases tend to become overly complex too
easily. In this regard it is easy to lose control and understanding of the relationships between
The relational database organizes data in tables. Each table, is identified by a unique table
name, and is organized by rows and columns. Each column within a table also has a unique
name. Columns store the values for a specific attribute, e.g. cover group, tree height. Rows
represent one record in the table. In a GIS each row is usually linked to a separate spatial
feature, e.g. a forestry stand. Accordingly, each row would be comprised of several columns,
each column containing a specific value for that geographic feature. The relational DBMS is attractive because of its:
- simplicity in organization and data modelling.
- flexibility – data can be manipulated in an ad-hoc manner by joining tables.
- efficiency of storage – by the proper design of data tables redundant data can be
- minimized; and the non-procedural nature – queries on a relational database do not need to take into account the internal organization of the data.
The object-oriented database model manages data through objects. An object is a collection of
data elements and operations that together are considered a single entity. The object-oriented
database is a relatively new model. This approach has the attraction that querying is very
natural, as features can be bundled together with attributes at the database administrator’s
discretion. To date, only a few GIS packages are promoting the use of this attribute data
model. However, initial impressions indicate that this approach may hold many operational
benefits with respect to geographic data processing.