GEOGRAPHIC INFORMATION SYSTEM (GIS)
Geographic Information System is a System containing
Information which is geographic in nature.
GIS can be defined as - A System which involves collecting/capturing, storing, processing and
manipulating, analyzing, managing, retrieving and displaying data (information) which is,
essentially, referenced to the real-world or the earth (i.e. geographically referenced).
Explanation of the Definition
The dataset collected for GIS may be in the form of hard copy maps, satellite images,
survey data or other data obtained from other primary and secondary sources.
Collection of data depends on the objective of the assignment. Data capturing involves
digitization of hard copy maps and satellite images.
In GIS Storage means not merely storing whatever data we have collected. The
collected data is converted in usable GIS format and then finally stored for further use
either on computer hard disk or in other storage devices (CD, DVD, magnetic tapes etc.)
3. Processing and Manipulation
The collected and stored dataset is imported and converted into layers. Then required
attributes are attached. Then data is processed for refinement, removing errors and
preparing it for further GIS-based analysis. Data manipulation is essential so that it can
be represented in proper understandable form.
Analysis of GIS data is required to convert it into desired outputs. There are many type of
analysis in GIS which is (or are) to be done is objective dependant. The analysis may be
statistical, spatial or specialized (like network analysis, utility analysis etc. Need not to say
GIS analysis requires skilled professionals.
Data management is essential and very important part of GIS for storing, managing and
properly maintaining GIS database.
In GIS, data can be retrieved through SQL or spatial queries. Some software provide tools to
retrieve data by simply selecting the features. Retrieval is used for getting information about the
features of our interest.
Displaying of final output may in many forms. These may be hard copy printouts, on-screen
display of maps, internet-based map display (through Internet Map Servers) or in the form of
presentation (like power point).
Computer hardware /
Specific applications /
decision making objectives
Following five essential components make a complete Geographic Information
System. Even imagine about GIS is not possible if we remove one of these
components. All components are important (however some may be more some
may be less).
Types of GIS Data
GIS data can be broadly described as
1. Spatial data and
2. Non-spatial data.
Spatial data is geographical representation of features. In other words, spatial
data is what we actually see in the form of maps (containing real-world features)
on a computer screen. Spatial data can further be divided into following two
a). Vector data
b) Raster data.
What makes data spatial?
Features /Characteristic of Spatial Data
Latitude / Longitude
Distance & bearing
It represent any geographical feature through point, line or polygon or
combination of these.
Point: A point in GIS is represented by one pair of coordinates (x & y). It is
considered as dimension-less object. Most of the times a point represent location
of a feature (like cities, wells, villages etc.).
Line: A line or arc contains at least two pairs of coordinates (say- x1, y1 & x2, y2).
In other words a line should connect minimum two points. Start and end points of a
line are referred as nodes while points on curves are referred as vertices. Points at
intersections are also called as nodes. Roads, railway tracks, streams etc. are
generally represented by line.
Polygon (or Area) : It is a closed line with area. It takes minimum three pairs of
coordinates to represent an area or polygon. Extent of cities, forests, land use etc.
is represented by polygon.
Raster data is made up of pixels. It is an array of grid cells with columns and rows.
Each and every geographical feature is represented only through pixels in raster
data. There is nothing like point, line or polygon. If it is a point, in raster data it will
be a single pixel, a line will be represented as linear arrangement of pixels and an
area or polygon will be represented by contiguous neighboring pixels with similar
In raster data one pixel contain only one value (unlike vector data where a point, a
line or a polygon may have number of values or attributes) that’s why only one
geographical feature can be represented by a single set of pixels or grid cells.
Hence a number of raster layers are required if multiple features are to be
considered (For example- land use, soil type, forest density, topography etc.).
As discussed earlier digital satellite images are also in raster format.
Attributes attached to spatial data are referred to as non-spatial data. Whatever
spatial data we see in the form of a colourful map on a computer screen is a
presentation of information which remains stored in the form attribute tables.
Attributes of spatial data must contain unique identifier for each object. There
may be other field also containing properties/information related a spatial
feature. Attribute table of spatial data also contains ‘x’ and ‘y’ location (i.e.
latitude/longitude or easting/northing) of features; however in some GIS
software these columns may remain ‘invisible’.
For example- if we are doing demographic analysis of villages then attributes of
each point (representing a village) must have a unique village ID and other
demographic information like total population, number of males & females,
number of children etc.
In another example- if we are doing some GIS analysis related to road then
each road must have its unique Road ID. Other attributes may include like road
length, road width, current traffic volume, number of stations etc.
Input data for GIS (or Data sources/Data acquisition)
Input data for GIS cover all aspects of capturing spatial data and attribute data. The
sources of spatial data are existing maps, aerial photographs, satellite imageries, field
observations, and other sources as shown in figure. The spatial data not in digital form
are converted into standard digital form using digitizer or scanner for use in GIS. The
entire process is reffered as data aquisition
Working with GIS Database
Data storage sub
Data and analysis
Reporting and output
as per User
Spatial Data Modelling
To make geographic data useful, it should be encoded in digital form, and
organized as a digital geographical database that creats a perception of
the real world similar to the perception created by the paper maps.
Type of spatial data modelling :
1. object based model. The geographic space is treated to be filled by
discrete and identifiable objects. A object which is a spatial
feature, has identifiable boundaries ,relevance to some intended
application, and can be described by one or moor more
characteristics known as attributes.
spatial object such as
• Exact objects (building ,roads etc)
• Inexact objects(landform fearture , and wildlife habitat)
1. Field based model. The field based model treats geographic space
as populated by one or more spatial phenomena of real world
feartures varing continuously over space with no obvious or specific
Model is a set of plans for a building, therefore, modelling of database means a methodologies
to be followed for some specified purpose. It specify the structure of database. Common
approach for this purpose include;
1. Digital Elevation Model (DEM)
2. Triangulated Irregular Network (TIN)
Digital Elevation Model (DEM)
It is a sampled array of elevations (z) that are at regularly spaced intervals in the x and y
directions. Two approaches for determining the surface z value of a location between sample
points are followed.
a). Lattice: each mesh point represents a value on the surface only at the center of the
grid cell. The z-value is approximated by interpolation between adjacent sample
points; it does not imply an area of constant value.
b). Surface grid: considers each sample as a square cell with a constant surface value.
• Simple conceptual model
• Data cheap to obtain
• Easy to relate to other raster data
• Irregularly spaced set of points can be
converted to regular spacing by
• Linear features not well represented
Does not conform to variability of
Representation of DEM
After a satellite image has been combined with a DEM, one gets a representation like
that shown here and known as Digital Terrain Model (DTM).
Triangulated Irregular Network (TIN)
It is a set of adjacent, nonoverlapping triangles
computed from irregularly
spaced points, with x, y
horizontal coordinates and z
– Can capture significant
slope features (ridges,
– Efficient since require
few triangles in flat areas
– Easy for certain
analyses: slope, aspect,
– Analysis involving
comparison with other
GIS Data Models
The real world can only be depicted in a GIS through the use of models that define
phenomena in a manner that computer systems can interpret and perform meaningful
analysis. There are two types of GIS Data Models
1. Vector Model
2. Raster Model
Raster data model
– location is referenced by a grid
cell in a rectangular array (matrix)
– attribute is represented as a single
value for that cell
– much data comes in this form
• images from remote sensing
• scanned maps
• elevation data from USGS
– best for continuous features:
Vector data model
– location referenced by x,y
coordinates, which can be linked
to form lines and polygons
– attributes referenced through
unique ID number to tables
– much data comes in this form
• DIME and TIGER files from US
• DLG from USGS for streams,
• census data (tabular)
– best for features with discrete
• property lines
• political boundaries
The application of geospatial sciences has spread very fast and wide over the past few
decades. User's of GIS's range from indigenous people, communities, research
institutions, environmental scientists, health organizations, land use
planners, businesses, and government agencies at all levels.
1. Conservation & Monitoring
2. Planning & Policy
3. Wetland Management
4. Wildlife Management
5. Forest Management
6. Water Pollution
7. Air Pollution
8. Climate Change
1. Mineral & Mining
2. Crop Production
3. Crop Pattern
4. Crop Yield
6. Soil Management
1. Urban Sprawl
2. Fringe Area Development
3. Urban Agglomeration
4. Emerging Technologies
Natural Resource Management
2. Water Resources
4. Coastal Zone Management
Land Information System
2. Rural & Cadastral
5. Corporate Case Studies
Natural Hazard Management
4. Flood & Cyclones
5. Landslide & Soil Erosion