Although the two terms, data and information, are often used indiscriminately, they both have a specific meaning. Data can bedescribed as different observations, which are collected and stored. Information is that data, which is useful in answering queries or solving a problem.Map making is an old art but digitizing a large number of maps provides a large amount of data after hours of painstaking works, but thedata can only render useful information if it is used in analysis.

● GIS DATA TYPES:

1. Attribute Data:
The attributes refer to the properties of spatial entities. They are often referred to as non-spatial data since they do not in themselves represent location information. This type of data describes characteristics of the spatial features. These characteristics can be quantitative and/or qualitative in nature. Attribute data is often referred to as tabular data.

2. Spatial Data:
Geographic position refers to the fact that each feature has a location that must be specified in a unique way. To specify the position in an absolute way a coordinate system is used. For small areas, the simplest coordinate system is the regular square grid. For larger areas, certain approved cartographic projections are commonly used. Internationally there are many different coordinate systems in use. This Locational information is provided in maps by using Points, Lines and Polygons.These geometric descriptions are the basic data elements of a map. Thus spatial data describes the absolute and relative location of geographic features.

The coordinate location of a forest would be spatial data, while the characteristics of that forest, e.g. cover group, dominant species, crown closure, height, etc., would be attribute data. Other data types, in particular image and multimedia data, have become more prevalent with changing technology. Depending on the specific content of the data, image data may be considered either spatial, e.g. photographs, animation, movies, etc., or attribute, e.g. sound, descriptions, narration’s, etc.

● GIS DATA MODELS:

A GIS is based on data, hence there must be a data model that has to be followed to standardize procedures.
They are :
1. Spatial Data Models
2. Attribute Data Models

● SPATIAL DATA MODELS:
Traditionally spatial data has been stored and presented in the form of a map. Three basic types of spatial data models have evolved for storing geographic data digitally. These are referred to as:

Raster Vector Image

The selection of a particular data model, vector or raster, is dependent on the source and type of data, as well as the intended use of the data. Certain analytical procedures require raster data while others are better suited to vector data.

Raster Data Formats:

A simple raster data set is a regular grid of cells divided into rows and columns. In a raster data set, data values for a given parameter are stored in each cell – these values may represent an elevation in meters above sea level, a land use class, a plant biomass in grams per square meter, and so forth. The spatial resolution of the raster data set is determined by the size of the cell.
For example, Landsat TM satellite imagery data are raster data that are corrected to have a cell size of approximately 30 meterson a side. However, spatial resolution can be much finer, or much coarser than 30 meters. In general, spatial resolution is a function of the data collection techniques used, and the desired outcomes.

The size of cells in a tessellated data structure is selected on the basis of the data accuracy and the resolution needed by the user. There is no explicit coding of geographic coordinates required since that is implicit in the layout of the cell A raster data structure is in fact a matrix where any coordinate can be quickly calculated if the origin point is known, and the size of the grid cells is known. Since grid-cells can be handled as two-dimensional arrays in computer encoding many analytical operations are easy to program. This makes tessellated data structures a popular choice for many GIS software. Topology is not a relevant concept with tessellated structures since adjacency and connectivity are implicit in the location of a particular cell in the data matrix.

Since geographic data is rarely distinguished by regularly spaced shapes, cells must be classified as to the most common attribute for the cell. The problem of determining the proper resolution for a particular data layer can be a concern. If one selects too coarse a cell size then data may be overly generalized. If one selects too fine a cell size then too many cells may be created resulting in a large data volume, slower processing times, and a more cumbersome data set. As well, one can imply accuracy greater than that of the original data capture process and this may result in some erroneous results during analysis. As well,since most data is captured in a vector format, e.g. digitizing, data must be converted to the raster data structure. This is called vector-raster conversion. Most GIS software allows the user to define the raster grid (cell) size for vector-raster conversion. It is imperative that the original scale, e.g. accuracy, of the data be known prior to conversion. The accuracy of the data, often referred to as the resolution, should determine the cell size of the output raster map during conversion. Most raster based GIS software
requires that the raster cell contain only a single discrete value. Accordingly, a data layer, e.g. forest inventory stands, may be broken down into a series of raster maps, each representing an attribute type, e.g. a species map, a height map, a density map, etc. These are often referred to as one attribute maps. This is in contrast to most conventional vector data models that maintain data as multiple attribute maps.
A Simple Raster Data SetEach cell in the raster is assigned a single data value. In the above example simple binary data values have been used meaning that the possibilities are limited to two digit numbers – either 0 or 1. This is an example of a 1-bit raster data file. Mathematically, there are only two possibilities for each pixel, 0 or 1. By contrast in an 8-bit data file, there are 256 possibilities of data values for each pixel. In the above example, the computer “sees” the cells that contain 0 as “turned off”, while the cells that contain 1
as “turned on”.
A One Bit Raster Image                                 source