Although the two terms, information and data, are frequently utilized aimlessly, the two of them have a particular significance. Information can be described as various perceptions, which are gathered and stored. Data is that information, which is valuable in noting inquiries or solving problems. Data is the most significant part of the Geographic Information System, known as fuel for GIS and mapmaking. The GIS data is separated into three broad categories.

  1. Spatial Data
  2. Attribute Data
  3. Metadata


Generally speaking, the spatial information, also called geospatial information, is alluded to the area, size, and position of an element on planet Earth, such as a structure, lake, mountain, or district. Spatial data may likewise incorporate attributes that give more detailed information about the GIS environment’s components.

GIS information is a mix of graphical and tabular information. Graphical information can be arranged into two distinct classes vector and raster. Contingent upon the particular substance of the information, image data might be considered either spatial, for example, photos, animations, movies, etc. The attributes represented in tabular format can be sound, descriptions, and narration.

  • Vector Data

Vector information is discrete, and it is best depicted as a graphical representation of this real-world in the form of X-Y coordinates. There are three fundamental classes of vector data: Points, lines or arc, and polygons.

  • Point Vector Data

Point information is most regularly used to portray nonadjacent features and represent discrete data points such as X-Y coordinates. Mostly, they are  Latitude and longitude with spatial reference.

Point data is used where features are too tiny to be portrayed as polygons; for instance, city limit lines cannot be seen worldwide. For this situation, maps frequently use location points to show urban communities. Points are dimensionless; therefore, neither area nor length can be measured with this dataset.


  • Line Vector Data

To portray line features such as waterways, trails, and roads, railway lines, pipelines, outlines of objects, and powerlines, line (or arc) data is used, which is also known by the name polyline. The line is one dimensional hence must be utilized to measure length. Vector lines made up of multiple line segments are a progression of vector points. The two common words associated with the line are the node and vertex.

A node speaks of  where a line starts or closes. A vertex shows where a line alters its course. Any little portion of the line comprises of two nodes, a starting node, and an end node, while long queues can have two nodes and different vertices in where the line changes the directions.

Attributes may be connected either to the entire line, particular node, or vertex; subsequently, each line may have various attributes in the information attribute table. For example, if a line speaks to a road, each road divide between two unions may have its area information.

  • Polygon Vector Data

Polygons are made from a progression of vertices associated with a continuous line and enclose the path. In a polygon, the first and last vertices ought to consistently be at a similar spot! Making a shape. Polygons symbolize areas such as the encased zones, for instance,  the boundary line of a city, lake, forest, dams, island,s, etc.

Polygons are two dimensional and subsequently can calculate the area and length of a geographic element.

They share the same geometry with neighboring polygons with a shared boundary line, such as in the city map. Likewise, points and polylines, polygons have attributes describing each polygon. For instance, a dam may have characteristics of water depth and water quality. The attribute data is linked to the polygon’s focal point regardless of how complex the polygon is. The Polygon features distinguished either using thematic mapping symbology (color schemes), patterns, or color gradation.

  • Raster Data

Raster data gives a portrayal of the real world in a cell-based way composed of rows and columns, as a surface divided into a matrix of cells, where every cell has a corresponding value known as cell pixels. These cell values may speak of an elevation in meters above ocean level, a land-use class.


The cell size dictates the spatial goal of the raster informational collection; these cells are utilized to speak to geographic information. Each cell contained in the raster map has either numeric or feature attribute data related to it. Thematic Data includes Digital Elevation Models, Spectral Data such as satellite images, and Pictures such as aerial photographs or scanned maps are all known examples of Raster datasets. There are two main classes of Raster data; Discrete rasters and Continuous Rasters.

  • Discrete Data

Discrete raster data is also known as categorical data, have distinct values, themes, and categories. In discrete raster s,  each thematic class can be distinguished; for instance,  each class depicts a land cover value in terms of numbers in soil map or land use map such as value 1 may represent urban land, 2 represent forest land, etc.

Each class can be discretely characterized, where it starts and finishes. To put it another way, each land spread cell is quantifiable, filling the cell’s whole area.

  • Continuous Data

Continuous rasters are also known as non-discrete raster data, with a matrix of cells with gradually changing information, such as height and temperature.

A continuous raster surface can be acquired from a fixed point. For example, Digital Elevation Models use the ocean level as a fixed point, and every cell shows the incentive above or underneath the ocean level. Another example can be stream direction or expect cell portraying directions connected with values, for example, north, east, south, or west.


Attribute data is the second type of data utilized in GIS. Attribute data describes the traits of spatial elements, as spatial information is more than just the location’s information. Thus, any extra data or non-spatial information describing the feature is named attribute data, such as a city map showing structures. Besides location, substructure may have some extra information, such as buildings types including housing, business, government, etc.), year of construction, and floors. The attribute data are either quantitative in numbers or qualitative in the text are organized in tabular form represent what part of what is where they are sometimes also referred to as variables or fields.


Metadata is the most overlooked type. It is defined as data about the data. Metadata is like a guidance manual for information because it portrays the who, what, when, where, why, and how about the data.  It is significant because if someone utilizes information about someone else, they must know how, when, where the data is presented.

Metadata can be stored as an inherited part of  GIS information, or it might be put away as a different report, but it must be detailed, reliable, and well documented.  A few metadata examples are the creation date of the GIS information, the creator of GIS information, contact data, source office, map projection and coordinate system, errors, description of symbology and properties, information limitations, and license. Metadata can be found with satellite images when they are downloaded.


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