GIS DATA MODEL

In the earlier blog we seen the history of GIS from on paper cartography to digital cartography. So in this blog we will see some of the concepts involved in GIS on the blocks of which this field is built.

GIS stands for Geographic Information System. As we know information system is nothing but a computerised system which helps in capturing, analysing, manipulating and representing data. So when we talk about GIS this data becomes spatial or we can call it geographic i.e. locational data. On the 2D plan, we represent location of any point using its x & y coordinate. In the same way, for each location on earth they have their location assigned in the form of Latitude and Longitude. Whenever we intend to use this lat, long information along with their description, it means we are using GIS.

Whenever you search for any of the shortest route, it means you are actually utilizing the extension of GIS. So, what does that mean ? Where the data came from ? What form of data used ? these and such type of questions will be answered in subsequent blogs.

So, GIS, OR Geospatial data comprise the spatial and attribute components. Today we will discuss only about spatial component. Spatial Data or geospatial data is data or information that identifies location of physical features which means it stores coordinates of features. As the definition says spatial data has locational information involved in it.

A GIS data model enables a computer to represent real geographical elements as graphical elements. Two representational models are possible; raster (grid-based) and vector (geometrical shape-based)

GIS stores spatial in the vector and raster data formats. The vector data format or data model uses the geometric objects of point, line and area to represent simple spatial features while raster data model uses cells i.e. collection of rows and columns to store data.

Vector data, it is a representation of world using point, line and polygon(area). Dimensionality and property distinguish these three types of geometric objects as well as the features they represent. Vector data models are useful for storing data that has discrete boundaries, such as country borders, land parcels, and streets.

Vector Data Model

A point has 0(zero) dimension and has only property of location. A point may also be called a node, vertex, or 0-cell. A point feature is made of a point or a set of separate points. Wells, benchmarks, and gravel points are some of the examples of point features.

A line is one dimensional (1D) and had the property of line. A line has two end points and points in between to mark the shape of the line. The shape of the line may be smooth curve or a connection of straight line segments depends on feature you are designing. A line is also called an edge, link, chain or 1-cell. Roads, streams, and contour lines are examples of line features.

An area is two dimensional (2D) and has the properties of area (size) and perimeter. Made of connected lines, an area may be alone or share boundaries with other areas. An area is also called a polygon, face, zone or 2-cell. Land parcels, water bodies, state and nation boundaries are some of the examples of area features.

Raster Data Model

Raster Data Representation

Raster data model is variously called a grid, a raster map, a surface cover or an image in GIS. A raster represents continuous surface, but for data storage and analysis, a raster is divided into rows, columns, and cells. Cells are also called pixels with images. The origin of rows and columns is typically at the upper-left corner of the raster. Rows function as y-coordinates and columns as x-coordinates. Each cell in raster is explicitly defined by its rows and column position.

Raster data represents points with single cells, lines with sequences of neighbouring cells and areas with collection of contiguous cells. Although the raster data model lacks the vector model’s precision in representing the location of spatial features, it has the distinct advantage of having fixed cell locations.

In computing algorithms, a raster can be treated as a matrix with rows and columns and its cell values can be stored in a two-dimensional (2D) array. Raster data are therefore much easier to manipulate, aggregate and analyse than vector data.

So this is how GIS represents real world data. There is one other representation technique (TIN), which I will explain in my upcoming blogs. There are many other concepts involved in GIS which like storage of data, analysis on data etc. which I will discuss in my further blogs.