Spatial data in GIS represents features that have a known location on the earth.

• Points:             X & Y Locations
• Polygon:         Connected X & Y Locations making a close figure.
• Line:               Connected X & Y Locations without closing
• Raster:            Row and column matrix represent geographic space.

## Vector Data Structure

The method of representing geographic features by the basic graphical elements of points, lines and polygon is said to be the vector method, or vector data model. Vector data represent geographic space that is intuitive and reminiscent of analog maps.

Vector data mostly incorporate the use to topological structure, especially the Arc/Node structure explained below.

## Raster Data Structure

Raster data models incorporate the use of a grid-cell data structure where the geographic area is divided into cells identified by row and column. A raster is a tesselation of a surface.• (A tesselation is defined as the process to cover a surface through the repeated use of a single shape.)

### What is a raster data?

A raster based system stores data by using a grid of cells. A unique reference coordinate represents each pixel either at a corner or in the middle of the cell. Each cell or pixel has discrete attributes assigned to it

Raster data resolution is dependent on the pixel or grid size and may vary from sub-meter to many kilometers. raster data stores different information in layers; elevation, soil type, geology, forest type, rainfall rate, etc.

Generally, raster data requires less processing than vector data, but it consumes more computer storage space. Examples include:

1. Remote sensors on satellites store data in raster format
2. Digital terrain models (DTM) and digital elevation models (DEM)
3. Continuous data (FIELD) suit a raster structure

A raster can use any reasonable geometric shape, as long as it can be connected in such a way as to create a continuous surface.

## Attribute Data and Attribute Data Model

Attribute data are the information linked to the geographic features (spatial data) that describe them. That is, attribute data are the “[n]on-graphic information associated with a point, line, or area elements in a GIS.”

Attribute data model is a separate data model 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: tabular, hierarchical, network, relational and object oriented models. Only relational models is commonly used in GIS currently.

## Relational Model

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 following figure presents a sample table for forest inventory features. This table has 4 rows and 5 columns. The forest stand number would be the label for the spatial feature as well as the primary key for the database table. This serves as the linkage between the spatial definition of the feature and the attribute data for the feature.

Data is often stored in several tables. Tables can be joined or referenced to each other by common columns (relational fields). Usually the common column is an identification number for a selected geographic feature, e.g. a forestry stand polygon number. This identification number acts as the primary key for the table. The ability to join tables through use of a common column is the essence of the relational model. Such relational joins are usually ad hoc in nature and form the basis of for querying in a relational GIS product.

The relational database model is the most widely accepted for managing the attributes of geographic data.

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
• 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.