Comprehensive Introduction to Geographic Information Systems (GIS) Fundamentals and Applications

(i)
Geographic Information
System
Dr. Gopikrishnan M
Professor,
Department of CSE
Prathyusha Engineering College,
Tiruvallur - 602 025
Strictly as per Revised Syllabus of
Anna University
Choice Based Credit System (CBCS)
Semester - V (CSE / IT / ECE / Mechanical) Open Elective-I

(ii)
AU 17
Semester - V (CSE / IT / ECE / Mechanical) Open Elective - I
Geographic Information
System
Subject Code : OCE552

Geographic Information System 1 - 2 Fundamentals of GIS
1.1 Introduction to GIS
A Geographic Information System (GIS) is a computer-based tool for mapping and analyzing
geographic phenomenon that exist, and events that occur, on Earth. GIS technology integrates
common database operations such as query and statistical analysis with the unique visualization
and geographic analysis benefits offered by maps. These abilities distinguish GIS from other
information systems and make it valuable to a wide range of public and private enterprises for
explaining events, predicting outcomes, and planning strategies. Map making and geographic
analysis are not new, but a GIS performs these tasks faster and with more sophistication than do
traditional manual methods.
Today, GIS is a multi-billion-dollar industry employing hundreds of thousands of people
worldwide. GIS is taught in schools, colleges, and universities throughout the world. Professionals
and domain specialists in every discipline are become increasingly aware of the advantages of
using GIS technology for addressing their unique spatial problems.
We commonly think of a GIS as a single, well-defined, integrated computer system. However,
this is not always the case. A GIS can be made up of a variety of software and hardware tools. The
important factor is the level of integration of these tools to provide a smoothly operating, fully
functional geographic data processing environment.
Overall, GIS should be viewed as a technology, not simply as a computer system.
In general, a GIS provides facilities for data capture, data management, data manipulation and
analysis, and the presentation of results in both graphic and report form, with a particular emphasis
upon preserving and utilizing inherent characteristics of spatial data.
The ability to incorporate spatial data, manage it, analyze it, and answer spatial questions is the
distinctive characteristic of geographic information systems.
A geographic information system, commonly referred to as a GIS, is an integrated set of
hardware and software tools used for the manipulation and management of digital spatial
(geographic) and related attribute data.
There are three integrating part in a GIS :
Geographic : The spatial realities of the real world
Information : The meaning and use of data
Systems : The computer technology and support infrastructure

1.2 Basic Spatial Concepts
Spatial concepts as the driving force for spatial thinking and for the selection and use of spatial
tools. Eight concepts are the focus of spatial reasoning in the use of geographical information.
These concepts are demonstrable at all levels of space and time.
 Location - Understanding formal and informal methods of specifying “where”
 Distance - The ability to reason from knowledge of relative position
 Network - Understanding the importance of connections
 Neighborhood and Region - Drawing inferences from spatial context
 Scale - Understanding spatial scale and its significance
 Spatial Heterogeneity - The implications of spatial variability
 Spatial Dependence - Understanding relationships across space
 Objects and Fields - Viewing phenomena as continuous in space-time or as discrete
1.3 Coordinate System
A coordinate system is a reference system used for locating objects in a two or three
dimensional space
Geographic Coordinate System
A geographic coordinate system, also known as global or spherical coordinate system is a
reference system that uses a three-dimensional spherical surface to determine locations on the
earth. Any location on earth can be referenced by a point with longitude and latitude.
We must familiarize ourselves with the geographic terms with respect to the Earth coordinate
system in order to use the GIS technologies effectively.
Pole : The geographic pole of earth is defined as either of the two points where the axis of
rotation of the earth meets its surface. The North Pole lies 90º north of the equator and the South
Pole lies 90º south of the equator
Latitude : Imaginary lines that run horizontally around the globe and are measured from 90º
north to 90º south. Also known as parallels, latitudes are equidistant from each other.
Equator : An imaginary line on the earth with zero degree latitude, divides the earth into two
halves-Northern and Southern Hemisphere. This parallel has the widest circumference.

Fig. 1.3.1 Division of earth into hemispheres
Longitude : Imaginary lines that run vertically around the globe. Also known as meridians,
longitudes are measured from 180º east to 180º west. Longitudes meet at the poles and are widest
apart at the equator
Prime meridian : Zero degree longitude which divides the earth into two halves-Eastern and
Western hemisphere. As it runs through the Royal Greenwich Observatory in Greenwich, England
it is also known as Greenwich meridian
Fig. 1.3.2 Latitude and longitude measurements
Equator (0º) is the reference for the measurement of latitude. Latitude is measured north or
south of the equator. For measurement of longitude, prime meridian (0º) is used as a reference.
Longitude is measured east or west of prime meridian. The grid of latitude and longitude over the
globe is known as graticule. The intersection point of the equator and the prime meridian is the
origin (0, 0) of the graticule.
Coordinate measurement
The geographic coordinates are measured in angles. The angle measurement can be understood
as per following :
A full circle has 360 degrees 360º
1 circle A degree is further divided into 60 minutes 1º = 60′
A minute is further divided into 60 seconds 1′ = 60″

An angle is expressed in Degree Minute Second.
While writing coordinates of a location, latitude is followed by longitude. For example,
coordinates of Delhi is written as 28° 36′ 50″ N, 77° 12′ 32″ E.
Decimal Degree is another format of expressing the coordinates of a location. To convert a
coordinate pair from degree minute second to decimal degree following method is adopted :
8º36’50” = 28 + (36*1)+50(50*1/60*1/60)
= 28 + 0.6 + 0.138
= 28.6138
We have 28 full degrees, 36 minutes - each 1/60 of a degree, and 50 seconds - each 1/60 of 1/60
of a degree.
Local Time and Time Zones
With rotation of earth on its axis, at any moment one of the longitudes faces the Sun (noon
meridian), and at that moment, it is noon everywhere on it. After 24 hours the earth completes one
full rotation with respect to the Sun, and the same meridian again faces the noon. Thus each hour
the Earth rotates by 360/24 = 15 degrees.
This implies that with every 15º of longitude change a new time zone is created which is
marked by a difference of one hour from the neighboring longitudes specified at 15º gap. The
earth's time zones are measured from the prime meridian (0º) and the time at Prime meridian is
called Greenwich Mean Time. Thus, there are 24 time zones created around the globe.
Date
The International Date Line is the imaginary line on the Earth that separates two consecutive
calendar days. Generally, it is said to be lying exactly opposite to the prime meridian having a
measurement of 180º meridian but it is not so. It zigs and zags the 180º meridian following the
political jurisdiction of the states but for sake of simplicity it is taken as 180º meridian. Starting at
midnight and going east to the International Date Line, the date is one day ahead of the date on the
rest of the Earth.
Projected Coordinate system
A projected coordinate system is defined as two dimensional representation of the Earth. It is
based on a spheroid geographic coordinate system, but it uses linear units of measure for
coordinates. It is also known as Cartesian coordinate system.
In such a coordinate system the location of a point on the grid is identified by (x, y) coordinate
pair and the origin lies at the centre of grid. The x coordinate determines the horizontal position
and y coordinate determines the vertical position of the point.

Fig. 1.3.3 Cartesian coordinate system
In such a coordinate system the location of a point on the grid is identified by (x, y) coordinate
pair and the origin lies at the centre of grid. The x coordinate determines the horizontal position
and y coordinate determines the vertical position of the point.
1.4 GIS and Information Systems
1.4.1 Definitions of GIS
“A geographic information system is a special case of information systems where the database
consists of observations on spatially distributed features, activities or events, which are definable in
space as points, lines, or areas. A geographic information system manipulates data about these
points, lines, and areas to retrieve data for ad hoc queries and analyses” (Kenneth Dueker,
Portland State University, 1979).
“A powerful set of tools for collection, storing, retrieving at will transforming and displaying
spatial data from the real world” Burrough,1986
“A system for capturing , storing, checking, integrating, manipulating, analyzing and displaying
data which are spatially referenced on the earth ” Chorley, 1987.
“GIS is a configuration of computer hardware and software specifically designed for the
acquisition, maintenance and use of cartographic data” Tomlin,1990
“A Geographic Information System (GIS) is a computer-based tool for mapping and analyzing
things that exist and events that happen on earth. GIS technology integrates common database
operations such as query and statistical analysis with the unique visualization and geographic
analysis benefits offered by maps.” ESRI

“GIS is an integrated system of computer hardware, software, and trained personnel linking
topographic, demographic, utility, facility, image and other resource data that is geographically
referenced.” NASA.
1.4.2 Objectives of GIS
Some of the major objectives of GIS are to
 Maximizing the efficiency of planning and decision making
 Integrating information from multiple sources
 Facilitating complex querying and analysis
 Eliminating redundant data and minimizing duplication
1.5 History of GIS
One of the first applications of spatial analysis in epidemiology is the 1832 "Rapport sur la
marche et les effets du choléra dans Paris et le department de la Seine". The French geographer
Charles Piquet represented the 48 districts of the city of Paris by halftone color gradient according
to the percentage of deaths by cholera per 1,000 inhabitants.
In 1854 John Snow depicted a cholera outbreak in London using points to represent the
locations of some individual cases, possibly the earliest use of a geographic methodology in
epidemiology. His study of the distribution of cholera led to the source of the disease, a
contaminated water pump (the Broad Street Pump, whose handle he disconnected, thus terminating
the outbreak).
The early 20th
century saw the development of photo zincography, which allowed maps to be
split into layers, for example one layer for vegetation and another for water. This was particularly
used for printing contours – drawing these was a labor-intensive task but having them on a separate
layer meant they could be worked on without the other layers to confuse the draughtsman.
The year 1960 saw the development of the world's first true operational GIS in Ottawa, Ontario,
Canada by the federal Department of Forestry and Rural Development. Developed by Dr. Roger
Tomlinson, it was called the Canada Geographic Information System (CGIS) and was used to
store, analyze, and manipulate data collected for the Canada Land Inventory – an effort to
determine the land capability for rural Canada by mapping information about soils, agriculture,
recreation, wildlife, waterfowl, forestry and land use at a scale of 1:50,000.
In 1986, Mapping Display and Analysis System (MIDAS), the first desktop GIS product
emerged for the DOS operating system. This was renamed in 1990 to MapInfo for Windows when
it was ported to the Microsoft Windows platform. This began the process of moving GIS from the
research department into the business environment.

The first known use of the term "Geographic Information System" was by Dr. Roger
Tomlinson in the year 1968 in his paper "A Geographic Information System for Regional
Planning“. Tomlinson is also acknowledged as the "father of GIS”.
1.6 Components of a GIS
1.6.1 Hardware
It consists of the equipments and support devices that are required to capture, store process and
visualize the geographic information. These include computer with hard disk, digitizers, scanners,
printers and plotters etc.
1.6.2 Software
Software is at the heart of a GIS system. The GIS software must have the basic capabilities of
data input, storage, transformation, analysis and providing desired outputs. The interfaces could be
different for different software’s.
Key software components are
 Tools for the input and manipulation of geographic information
 A database management system (DBMS)
 Tools that support geographic query, analysis, and visualization
 A graphical user interface (GUI) for easy access to tools
The GIS software’s being used today belong to either of the category –proprietary or open
source. ArcGIS by ESRI is the widely used proprietary GIS software. Others in the same category
are MapInfo, Microstation, Geomedia etc. The development of open source GIS has provided us
with freely available desktop GIS such as Quantum, uDIG, GRASS, MapWindow GIS etc., GIS
softwares.
1.6.3 Data
The data is captured or collected from various sources (such as maps, field observations,
photography, satellite imagery etc) and is processed for analysis and presentation.
1.6.4 Methods/Procedures
These include the methods or ways by which data has to be input in the system, retrieved,
processed, transformed and presented.

1.6.5 People
This component of GIS includes all those individuals (such as programmer, database manager,
GIS researcher etc.) who are making the GIS work, and also the individuals who are at the user end
using the GIS services, applications and tools
Fig. 1.6.1 Components of GIS
1.7 GIS Subsystems / Software Functional Elements
A GIS has four main functional subsystems. These are:
 a data input subsystem;
 a data storage and retrieval subsystem
 a data manipulation and analysis subsystem
 a data output and display subsystem
1.7.1 Data Input
Data input is the operation of encoding the data and writing them to the database and creates the
foundation for useful GIS. However, the process of good database creation is very time consuming
and complex operation upon which the usefulness of the GIS depends.
A data input subsystem allows the user to capture, collect, and transform spatial and thematic
data into digital form. Data input involves data acquisition including identification and collection
of data required for applications. It covers all aspects of transforming data captured from existing

maps, field observations, and sensors into a compatible digital form. A wide range of computer
tools is available for this purpose, including the digitizer, lists of data in text files, scanners and the
devices necessary for recording data already written on magnetic media such as tapes, drums and
disks
Various sources for data input may be :
 text files
 existing maps
 aerial photographs
 satellite imagery
 airborne scanners
 field measurements
Fig 1.7.1 Data input
1.7.2 Data Storage and Retrieval
The data storage and retrieval subsystem organizes the data, spatial and attribute, in a form
which permits it to be quickly retrieved by the user for analysis, and permits rapid and accurate
updates to be made to the database. This component usually involves use of a Database
Management System (DBMS) for maintaining attribute data. Spatial data is usually encoded and
maintained in a proprietary file format.

Fig. 1.7.2 Data Storage
1.7.3 Data Manipulation and Analysis
The data manipulation and analysis subsystem allows the user to define and execute spatial and
attribute procedures to generate derived information. This subsystem is commonly thought of as
the heart of a GIS, and usually distinguishes it from other database information systems and
Computer-Aided Drafting (CAD) systems.
1.7.4 Data Output
The data output subsystem allows the user to generate graphic displays, normally maps, and
tabular reports representing derived information products.
The critical function for a GIS is, by design, the analysis of spatial data.
Fig. 1.7.3 Data Output

It is important to understand that the GIS is not a new invention. In fact, geographic
information processing has a rich history in a variety of disciplines. In particular, natural resource
specialists and environmental scientists have been actively processing geographic data and
promoting their techniques since the 1960's.
1.8 Applications of GIS
GIS is involved in various areas. These include topographical mapping, socioeconomic and
environment modeling, and education. The role of GIS is best illustrated with respect to some of
the representative application areas that are mentioned below
 Tax Mapping,
 Business,
 Logistics,
 Emergency evacuation,
 Environment
1.8.1 Tax Mapping
Raising revenue from property taxes is one of the important functions of the government
agencies. The amount of tax payable depends on the value of the land and the property. The correct
assessment of value of land and property determines the equitable distribution of the community
tax. A tax assessor has to evaluate new properties and respond to the existing property valuation.
To evaluate taxes the assessor uses details on current market rents, sale, maintenance, insurance
and other expenses. Managing as well as analyzing all this information simultaneously is time
consuming and hence comes the need of GIS. Information about property with its geographical
location and boundary is managed by GIS. Land units stored in parcel database can be linked to
their properties. Querying the GIS database can locate similar type of properties in an area. The
characteristics of these properties can then be compared and valuation can be easily done
1.8.2 Business
Approximately 80 percent of all business data are related to location. Businesses manage a
world of information about sales, customers, inventory, demographic profiles etc. Demographic
analysis is the basis for many other business functions: customer service, site analysis, and
marketing. Understanding your customers and their socioeconomic and purchasing behavior is
essential for making good business decisions. A GIS with relevant data such as number of
consumers, brands and sites they go for shopping can give any business unit a fair idea whether
their unit if set up is going to work at a particular location the way they want it to run.

1.8.3 Logistics
Logistics is a field that takes care of transporting goods from one place to another and finally
delivering them to their destinations. It is necessary for the shipping companies to know where
their warehouses should be located, which routes should the transport follow that ensures minimum
time and expenditures to deliver the parcels to their destinations. All such logistics decisions need
GIS support.
1.8.4 Emergency Evacuation
The occurrence of disasters is unpredictable. We as humans are unable to tell when, where and
what magnitude of disaster is going to emerge and therefore solely depend on disaster preparedness
as safety measures. It is important to know in which area the risk is higher, the number of
individuals inhabiting that place, the routes by which the vehicles would move to help in
evacuating the individuals. Thus preparing an evacuation plan needs GIS implementation.
1.8.5 Environment
GIS is being increasingly involved in mapping the habitat loss, urban sprawl, land-use change
etc. Mapping such phenomena need historical landuse data, anthropogenic effects which greatly
affect these phenomena are also brought into GIS domain. GIS models are then run to make
predictions for the future.
1.9 Proprietary and Open Source Software
1.9.1 Open-Source GIS Software
Many GIS tasks can be accomplished with open-source GIS software, which are freely
available over Internet downloads. With the broad use of non-proprietary and open data formats
such as the Shape File format for vector data and the Geotiff format 26 for raster data, as well as
the adoption of OGC standards for networked servers, development of open source software
continues to evolve, especially for web and web service oriented applications. Most widely used
open source applications:
 GRASS - Originally developed by the U. S. Army Corps of Engineers, open source : a
complete GIS
 MapServer - Web-based mapping server, developed by the University of Minnesota.
 Chameleon - Environments for building applications with MapServer.
 GeoNetwork opensource - A catalog application to manage spatially referenced resources
 GeoTools - Open source GIS toolkit written in Java, using Open Geospatial Consortium
specifications.

 gvSIG - Open source GIS written in Java.
1.9.2 Proprietary Software
With proprietary software there is one point of contact, with the open source software the
support comes from the community. License cost versus capacity development : potentially save
enough money on software to save jobs. License on tools are hard to sell, more profits from the
participation in projects that need improvements on the tool or capacity development using the
tool. Proprietary tools tend to lock the user in: hard to make improvements, stuck with formats.
Bugs are easier removed by the community. New developments are quicker implemented.
Most widely used notable proprietary software applications and providers :
 ESRI - Products include ArcView 3.x, ArcGIS, ArcSDE, ArcIMS, and ArcWeb services.
 GRAM++ GIS - Low-cost GIS software product developed by CSRE, IIT Bombay.
 Autodesk - Products include MapGuide and other products that interface with its flagship
AutoCAD software package.
 Cadcorp - Developers of GIS software and OpenGIS standard
 Intergraph - Products include GeoMedia, GeoMedia Profesional, GeoMedia WebMap
 ERDAS IMAGINE - A proprietary GIS, Remote Sensing, and Photogrammetry software
developed by Leica Geosystems Geospatial Imaging.
 SuperGeo - Products include SuperGIS Desktop & extensions, SuperPad Suite,
SuperWebGIS & extensions, SuperGIS Engine & extensions, SuperGIS Network Server and
GIS services.
1.10 Types of Data - Spatial, Attribute Data
1.10.1 Spatial Data
Geospatial data, spatial information or geographic information. Spatial data - describes the
absolute and relative location of geographic features. It is represented by vector and raster forms
(including imagery). Spatial data are generally multi-dimensional and auto correlated.
Spatial data also refers to all types of data objects or elements that are present in a geographical
space or horizon. It is the data or information that identifies the geographic location of features and
boundaries on Earth, such as natural or constructed features, oceans, and more.
Spatial data is usually stored as coordinates and topology, and the data that can be mapped. It
enables the global finding and locating of individuals or devices anywhere in the world.

1.10.1.1 Representation of Space
Burrough & McDonnell (1998) described two ways to represent the space (an area, landscape
or some bigger unit), which are as follows :
a. Discrete Entities : The space could be seen as occupied with entities that are described by
their properties and can be located on earth using coordinate systems. The entities have a
clear boundary. Buildings, roads, land parcels etc. are the example of discrete entities.
b. Continuous fields : The variation of an attribute over the space as a continuous field. No
physical boundary can ever be observed in such case. Temperature, pressure, elevation etc.
across an area are the examples of continuous fields
1.10.1.2 Types of Data Representation
The data can be represented in any of the format
 Numeric data
 Vector data
 Raster data
Numeric data
Numeric data is statistical data which includes a geographical component or field that can be
joined with vector files so the data can be queried and displayed as a layer on a map in a GIS.
The most common type of numeric data is demographic data from the US Census.
Vector data
Vector data is a data that has a spatial component, or X,Y coordinates assigned to it. Vector
files can contain sets of points, lines, or polygons that are referenced in a geographic space.
There are three types of features :
 Point (vertex, node) is a 0-dimensional object and has the property of location (x,y).
 Line (edge, link, chain, arc) is a one-dimensional object that has the property of length. An
arc starts with a node, has zero or more vertices, and ends with a node.
 Polygon is a two-dimensional object with properties of area and perimeter.
Fig. 1.10.1 Vector entities

Raster data
Raster data is a data in a .JPG, .TIF, .GIF or similar format.
Items scanned using a flatbed scanner like the map given below is examples of raster files.
Images taken with a digital camera produce these same types of files
Fig. 1.10.2 Rater data format
1.10.1.3 Data Content
Based on data content, spatial data can be classified as :
Temporal - Constantly changing data, i.e. data that represents the dynamic variables at
different time frames (t1
, t2
). Example of temporal data layers would be rainfall, stream discharge,
and land use.
Thematic - Contains information on some unique aspect or attribute class; i.e. watershed
boundaries, soils, geology.
1.10.1.4 Data Structure
Discrete Data - Discrete Data is object-based, categorical or discontinued data. It represents
objects defined as points, lines, or areas. Examples are weather stations, rivers, lakes.
In addition, we can find exact spatial objects (discrete features with well-defined boundaries)
and inexact spatial objects (discrete features also called “fuzzy entities” with no precise
boundaries, i.e. the boundaries are transitional).
Continuous Data - Continuous Data is field-based or surface data. It covers a continuous
space, represented by a large number of discrete units. An example is an elevation map
(represented as a raster).
1.10.2 Attribute Data
Attribute data describes characteristics of the spatial features. Also called as Non spatial data
and characteristic data.

These characteristics can be quantitative and/or qualitative in nature. Attribute data is often
referred to as tabular (Numeric) data. Non-spatial data are generally one-dimensional and
independent. It’s a separate data model 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).
Variety of different data models exist for the storage and management of attribute data.
The most common are :
 Tabular Model
 Hierarchical Model
 Network Model
 Relational Model
 Object Oriented Model
Most early GIS software packages stored their attribute data in Tabular model. The next three
models are those most commonly implemented in Database Management Systems (DBMS).
1.10.2.1 Tabular Model
The object oriented is newer but rapidly gaining in popularity for some applications.
It stores attribute data as sequential data files with fixed formats (or comma delimited for
ASCII data), for the location of attribute values in a predefined record structure.
This type of data model is outdated in the GIS arena.
It lacks in method of checking data integrity, as well as being inefficient with respect to data
storage,
e.g. limited indexing capability for attributes or records, etc.
Fig. 1.10.3 Example showing tabular model
1.10.2.2 Hierarchical Model
It is the earliest database model that is evolved from file system where records are arranged in a
hierarchy or as a tree structure. Records are connected through pointers that store the address of the

related record. Each pointer establishes a parent child relationship where a parent can have more
than one child but a child can only have one parent. There is no connection between the elements
at the same level. To locate a particular record, you have to start at the top of the tree with a parent
record and trace down the tree to the child.
Fig. 1.10.4 Example showing hierarchical data structure
The figure above describes the electronic gadgets in day today use. We can see that flash is a
child of mp3 players, which is a child of portable electronics, which is a child of electronics. The
topmost element electronics has no parent. Tube, LCD, plasma, CD players and 2 way radios are
leaf nodes (don’t have any children)
1.10.2.3 Network Data Structure Model
A network is a generalized graph that captures
relationships between objects using connectivity.
A network database consists of a collection of
records that are connected to each other through
links. A link is an association between two
records. It allows each record to have many
parents and many children thus allowing a natural
model of relationships between entities.
Fig. 1.10.5 Example showing network data
structure model

1.10.2.4 Relational Data Structure Model
The relational data model was introduced by Codd in 1970. The relational database relates or
connects data in different files through the use of a common field. A flat file structure is used with
a relational database model. In this arrangement, data is stored in different tables made up of rows
and columns. The columns of a table are named by attributes. Each row in the table is called a
tuple and represents a basic fact. No two rows of the same table may have identical values in all
columns.
There are two crucial data integrity constraints viz. primary key and foreign key. A primary key
is an attribute whose value is unique across all tuples (rows) in a relation (table). The primary key
of one table appearing as an attribute of another table is known as a foreign key in that table
Fig. 1.10.6 Example showing relational data structure model
1.10.2.5 Object Oriented Model
 The object-oriented database model manages data through objects.
 An object is a collection of data elements and operations that together are considered a
single entity.
 The object-oriented database is a relatively new model.
 This approach has the attraction that querying is very natural, as features can be bundled

together with attributes at the database administrator's discretion.
 To date, only a few GIS packages are promoting the use of this attribute data model.
Fig. 1.10.7 Example showing object oriented model
1.11 Scales/ Levels of Measurements
Measurement is defined as application of rules to assign numbers to objects (or attributes).
Measurement rules are the procedures used to transform the qualities of attributes into
numbers (e.g., type of scale used).
Three important properties :
 Magnitude is the property of “moreness”. Higher score refers to more of something.
 Equal interval is the difference between any two adjacent numbers referring to the same
amount of difference on the attribute?
 Absolute zero is the scale have a zero point that refers to having none of that attribute?
Measurement Scales
 The scale determines the amount of information contained in the data.
 The scale indicates the data summarization and statistical analyses that are most appropriate.
 The attributes shown in a thematic map can be recorded by four different scales.
 Numerical values may be defined with respect to nominal, ordinal, interval or ratio scales of
measurement.
 It is important to recognize the scales of measurement used in GIS data as this determines
the kinds of mathematical operations that can be performed on the data.

o Nominal scales - Qualitative, not quantitative distinction (no absolute zero, not equal
intervals, not magnitude)
o Ordinal scales - Ranking individuals (magnitude, but not equal intervals or absolute
zero)
o Interval scales - Scales that have magnitude and equal intervals but not absolute zero
o Ratio scales - Have magnitude, equal intervals, and absolute zero (so can compute
ratios)
Nominal Scale
Nominal Scales - There must be distinct classes but these classes have no quantitative
properties. Therefore, no comparison can be made in terms of one category being higher than the
other.
For example - There are two classes for the variable gender - males and females.
There are no quantitative properties for this variable or these classes and, therefore, gender is a
nominal variable.
Other Examples :
 Country of origin
 Biological sex (male or female)
 Animal or non-animal
 Married vs. Single
Sometimes numbers are used to designate category membership
Example : Country of Origin
 1 = United States
 3 = Canada
 2 = Mexico
 4 = Other
However, in this case, it is important to keep in mind that the numbers do not have intrinsic
meaning
Ordinal Scale
Ordinal Scales - There are distinct classes but these classes have a natural ordering or ranking.
The differences can be ordered on the basis of magnitude.
For example - Final position of horses in a race is an ordinal variable. The horses finish first,
second, third, fourth, and so on.

The difference between first and second is not necessarily equivalent to the difference between
second and third, or between third and fourth.
Does not assume that the intervals between numbers are equal
Fig. 1.11.1 Example : finishing place in a race (first place, second place)
Interval Scale
The data have the properties of ordinal data, and the interval between observations is expressed
in terms of a fixed unit of measure.
Designates an equal-interval ordering - The distance between, for example, a1
and a2 is the
same as the distance between a4 and a5.
Example - Celsius temperature is an interval variable. It is meaningful to say that 25 degrees
Celsius is 3 degrees hotter than 22 degrees Celsius, and that 17 degrees Celsius is the same amount
hotter (3 degrees) than 14 degrees Celsius. Notice, however, that 0 degrees Celsius does not have a
natural meaning. That is, 0 degrees Celsius does not mean the absence of heat!
Ratio Scale
Fig. 1.11.2 Levels of measurement

Ratio Scales - captures the properties of the other types of scales, but also contains a true zero,
which represents the absence of the quality being measured. Has an absolute zero that is
meaningful. Can construct a meaningful ratio (fraction), for example, number of clients in past six
months.
It is meaningful to say that “...we had twice as many clients in this period as we did in the
previous six months.
1.12 Two Marks Questions with Answers
Q.1 Define GIS
Ans. : GIS stands for Geographical Information System. According to Burrough,1986
GIS is defined as an integrated tool, capable of mapping, analyzing, manipulating and storing
geographical data in order to provide solutions to real world problems and help in planning for
the future.
GIS deals with what and where components of occurrences. For example, to regulate rapid
transportation, government decides to build fly-over (what component) in those areas of the
city where traffic jams are common (where component).
Q.2 List the components of GIS
Ans. : Hardware, Software, Data, Method, People.
Q.3 List out the three basic kinds vector entities in GIS.
Ans. : The Vector Model uses points and their coordinates (X, Y) to represent spatial features
(ESRI). There are three types of features:
Point (vertex, node) is a 0-dimensional object and has the property of location (x,y).
Line (edge, link, chain, arc) is a one-dimensional object that has the property of length. An
arc starts with a node, has zero or more vertices, and ends with a node.
A polygon is a two-dimensional object with properties of area and perimeter.
Q.4 What are the applications of GIS?
Ans. :
 Tax Mapping
 Business
 Logistics

 Emergency evacuation
 Environment
Q.5 Differentiate Spatial and Attribute data.
Ans. :
Sr.
No.
Spatial data Attribute data
1. Describes the absolute and relative
location of geographic features.
Describes characteristics of the spatial
features
2. It is represented by vector and raster
forms (including imagery).
These characteristics can be quantitative
and/or qualitative in nature
3. Spatial data are generally multi-
dimensional and auto correlated
Non-spatial data are generally one-
dimensional and independent.
Q.6 What are the 4M analysis in GIS?
Ans. : In a GIS, we measure environmental parameters, develop maps portraying earth
characteristics, monitor changes in surrounding space and time, and also model alternatives of
actions and processes operating in the environment. These are called four Ms of GIS
Q.7 Write any 4 advantages of GIS.
Ans. :
 Geospatial data better maintained in a standard format
 Revision and updating easier
 Geospatial data and information easier to search, analyze and represent
 Value added products can be generated
 Geospatial data can be shared and exchanged freely
 Productivity and efficiency of staff is improved
 Saving in time and money
 Better decisions making
Q.8 Which is called as characteristics data? Why?
Ans. : Attribute data is known as Characteristic Data. Attribute 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(Numeric) data.

Non-spatial data are generally one-dimensional and independent.
Q.9 What are the levels of measurement?
Ans. : The scale determines the amount of information contained in the data.
The scale indicates the data summarization and statistical analyses that are most appropriate.
Nominal scales - Qualitative, not quantitative distinction (no absolute zero, not equal
intervals, not magnitude)
Ordinal scales - Ranking individuals (magnitude, but not equal intervals or absolute zero)
Interval scales - Scales that have magnitude and equal intervals but not absolute zero
Ratio scales - Have magnitude, equal intervals, and absolute zero (so can compute ratios)
Q.10 Write the types of Coordinate Systems
Ans. : Cartesian Coordinate system - can be represented by a grid with a numbering system that
can locate information on a horizontal and vertical axis.
Polar coordinate system - information are located using only an angle and a distance (radius).
Global coordinate system - is where two numbers (latitude and longitude) are used to
reference a specific location on the earth.
Q.11 What is Geographical co-ordinate system?
Ans. : This is a one of true co-ordinate system .The location of any point on the earth surface
can be defined by a reference using latitude and longitude.
Q.12 What are the hardware components of a GIS?
Ans. : Hardware is the computer on which a GIS operates. Today, GIS software runs on a wide
range of hardware types, from centralized computer servers to desktop computers used in stand-
alone or networked configurations.
Q.13 What are the software components of a GIS?
Ans. :
 Data acquisition/Input
 Data processing and preprocessing
 Database management (storage and retrieval)
 Spatial data manipulation and analysis
 Product generation: output and visualization
Q.14 What are the data input devices used in a GIS?
Ans. : The different methods of input into a GIS are by
 Keyboard entry
 Manual digitizing
 Scanning and automatic digitizing.

Q.15 What are the data output devices used in a GIS?
Ans. : The important data output devices used ina GIS are
1. Plotter : Used to plot the graphical information after analysis on a paper
2. Printer : Used to print the information after analysis on a paper
3. VDU : Visual display unit - used to display the results after analysis
4. Tape Drive: Used to atore the results after analysis and take it to other systems.
Q.16 List the important GIS software’s.
Ans. : Standard GIS Softwares
 ARCGIS
 ARCVIEW
 ARCINFO
 MAPINFO
 ERDAS
 ENVI
 AUTOCADMAP
 IDRISI
Q.17 What is DBMS?
Ans. : A Database Management System (DBMS) is a software package with computer programs
that control the creation, maintenance, and use of a database. It allows organizations to
conveniently develop databases for various applications by Database Administrators (DBAs) and
other specialists. A database is an integrated collection of data records, files, and other objects. A
DBMS allows different user application programs to concurrently access the same database..
Q.18 Define attribute values
Ans. : An attribute-value system is a basic knowledge representation framework comprising a
table with columns designating "attributes" (also known as "properties", "predicates," "features,"
"dimensions," "characteristics" or "independent variables" depending on the context) and rows
designating "objects" (also known as "entities," "instances," "exemplars," "elements" or
"dependent variables"). Each table cell therefore designates the value (also known as "state") of
a particular attribute of a particular object.
Q.19 What are the input formats of GIS software?
Ans. :
 text files

 existing maps
 aerial photographs
 satellite imagery
 airborne scanners
 field measurements
 other GIS databases
Q.20 Differentiate Latitude and longitude.
Ans. : Latitude : Imaginary lines that run horizontally around the globe and are measured from
90º north to 90º south. Also known as parallels, latitudes are equidistant from each other.
Longitude : Imaginary lines that run vertically around the globe. Also known as meridians,
longitudes are measured from 180º east to 180º west. Longitudes meet at the poles and are
widest apart at the equator
1.13 Long Answered Questions with Answers
Q.1 Write about the components of GIS in detail. (Refer section 1.6)
Q.2 Illustrate the concept of Coordinate System. (Refer section 1.3)
Q.3 Explain the workflow of Geographic Information System in detail. (Refer section 1.7)
Q.4 Discuss briefly about Spatial and Attribute data. (Refer section 1.10)
Q.5 Discuss in detail about Software Functional elements. (Refer section 1.7)
Q.6 While writing coordinates of a location, latitude is followed by longitude. For example, coordinates of
Delhi is written as 28° 36′ 50″ N, 77° 12′ 32″ E. Convert a coordinate pair from degree minute second
to decimal degree. (Refer page No. 1 - 5)
Q.7 Write in detail about Proprietary and open source Software of GIS. (Refer section 1.9)
Q.8 Explain the types of data models in Attribute data. (Refer section 1.10.2)

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Notes

Geographic Information System 2 - 2 Spatial Data Models
Technical Publications
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- An up thrust for knowledge
2.1 Database Structures
Database in GIS:
A database is a collection of related information that permits the entry, storage, input, output
and organization of data. A database management system (DBMS) serves as an interface between
users and their database.
A spatial database includes location. It has geometry as points, lines and polygons.
GIS combines spatial data from many sources with many different people. Databases connect
users to the GIS database.
For example, a city might have the waste water division, land records, transportation and fire
departments connected and using datasets from common spatial databases.
The database structure is the collection of record type and field type definitions that comprise
your database:
 Record Types: These define the type of entities or research objects you wish to capture
(e.g. Person).
 Fields: These are the properties or attributes that describe your record types
(e.g. Gender, Age, Height etc.).
Collectively, these define the information or data that can be stored in any record of that type.
2.2 Data Structure Models (Structures of Databases)
 Data models are the conceptual models that describe the structures of databases.
 Structure of a database is defined by the data types, the constraints and the relationships for
the description or storage of data.
Following are the most often used data models:
 Relational Model
 Object oriented Model
 ER Diagram
2.2.1 Relational Model
The relational data model was introduced by Codd in 1970. The relational database relates or
connects data in different files through the use of a common field. A flat file structure is used with
a relational database model. In this arrangement, data is stored in different tables made up of rows
and columns. The columns of a table are named by attributes. Each row in the table is called a

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tuple and represents a basic fact. No two rows of the same table may have identical values in all
columns.
There are two crucial data integrity constraints viz. primary key and foreign key. A primary key
is an attribute whose value is unique across all tuples (rows) in a relation (table). The primary key
of one table appearing as an attribute of another table is known as a foreign key in that table.
Fig. 2.2.1 Relational Data Model
2.2.1.1 Relational Model Concepts
 Attribute: Each column in a Table. Attributes are the properties which define a relation.
e.g., Student_Rollno, NAME,etc.
 Tables – In the Relational model the, relations are saved in the table format. It is stored
along with its entities. A table has two properties rows and columns. Rows represent records
and columns represent attributes.
 Tuple – It is nothing but a single row of a table, which contains a single record.
 Relation Schema: A relation schema represents the name of the relation with its attributes.
 Degree: The total number of attributes which in the relation is called the degree of the
relation.
 Cardinality: Total number of rows present in the Table.

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 Column: The column represents the set of values for a specific attribute.
 Relation instance – Relation instance is a finite set of tuples in the RDBMS system.
Relation instances never have duplicate tuples.
 Relation key - Every row has one, two or multiple attributes, which is called relation key.
 Attribute domain – Every attribute has some pre-defined value and scope which is known
as attribute domain.
2.2.1.2 Operations in Relational Model
Four basic update operations performed on relational database model are
Insert, update, delete and select.
 Insert is used to insert data into the relation
 Delete is used to delete tuples from the table.
 Modify allows you to change the values of some attributes in existing tuples.
 Select allows you to choose a specific range of data.
Whenever one of these operations are applied, integrity constraints specified on the relational
database schema must never be violated.
2.2.1.3 Relational Integrity Constraints
Relational Integrity constraints are referred to conditions which must be present for a valid
relation. These integrity constraints are derived from the rules in the mini-world that the database
represents.
There are many types of integrity constraints. Constraints on the Relational database
management system are mostly divided into three main categories are:
 Domain constraints- Domain constraints specify that within each tuple, and the value of
each attribute must be unique. This is specified as data types which include standard data
types integers, real numbers,
 Key constraints- An attribute that can uniquely identify a tuple in a relation is called the
key of the table. The value of the attribute for different tuples in the relation has to be
unique.
 Referential integrity constraints- is base on the concept of Foreign Keys. A foreign key is
an important attribute of a relation which should be referred to in other relationships.
Advantages of using Relational model
 Simplicity: A relational data model is simpler than the hierarchical and network model.
 Structural Independence: The relational database is only concerned with data and not with

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a structure. This can improve the performance of the model.
 Easy to use: The relational model is easy as tables consisting of rows and columns is quite
natural and simple to understand.
 Query capability: It makes possible for a high-level query language like SQL to avoid
complex database navigation.
 Data independence: The structure of a database can be changed without having to change
any application.
 Scalable: Regarding a number of records, or rows, and the number of fields, a database
should be enlarged to enhance its usability.
Disadvantages of using Relational model
 Few relational databases have limits on field lengths which can't be exceeded.
 Relational databases can sometimes become complex as the amount of data grows, and the
relations between pieces of data become more complicated.
 Complex relational database systems may lead to isolated databases where the information
cannot be shared from one system to another
2.2.2 Object Oriented Model
This data model is another method of representing real world objects. It considers each object
in the world as objects and isolates it from each other. It groups its related functionalities together
and allows inheriting its functionality to other related sub-groups.
Elements of Object oriented data model
 Objects
The real world entities and situations are represented as objects in the Object oriented
database model.
 Attributes and Method
Every object has certain characteristics. These are represented using Attributes. The
behaviour of the objects is represented using Methods.
 Class
Similar attributes and methods are grouped together using a class. An object can be called as
an instance of the class.
 Inheritance
A new class can be derived from the original class.
The derived class contains attributes and methods of the original class as well as its own.

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For an example consider an Employee database to understand this model better. In this database
we have different types of employees – Engineer, Accountant, Manager, Clerk. But all these
employees belong to Person group. Person can have different attributes like name, address, age and
phone.
Fig. 2.2.2 Object oriented model
Advantages
 Because of its inheritance property, we can re-use the attributes and functionalities. It
reduces the cost of maintaining the same data multiple times. Also, these informations are
encapsulated and, there is no fear being misused by other objects. If we need any new
feature we can easily add new class inherited from parent class and adds new features.
Hence it reduces the overhead and maintenance costs.
 Because of the above feature, it becomes more flexible in the case of any changes.
 Codes are re-used because of inheritance.
 Since each class binds its attributes and its functionality, it is same as representing the real
world object. We can see each object as a real entity. Hence it is more understandable.
Disadvantages
 It is not widely developed and complete to use it in the database systems. Hence it is not

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accepted by the users.
 It is an approach for solving the requirement. It is not a technology. Hence it fails to put it in
the database management systems.
2.2.3 Entity-Relationship Model
Entity-Relationship (ER) Model is based on the notion of real-world entities and relationships
among them. While formulating real-world scenario into the database model, the ER Model creates
entity set, relationship set, general attributes and constraints.
ER Model is best used for the conceptual design of a database.
ER Model is based on −
Entities and their attributes.
Relationships among entities.
These concepts are explained below.
Fig. 2.2.3 ER Diagram
 Entity − An entity in an ER Model is a real-world entity having properties called attributes.
Every attribute is defined by its set of values called domain. For example, in a school
database, a student is considered as an entity. Student has various attributes like name, age,
class, etc.
 Relationship − The logical association among entities is called relationship. Relationships
are mapped with entities in various ways. Mapping cardinalities define the number of
association between two entities.
Mapping cardinalities
1. one to one 2. one to many
3. many to one 4. many to many
Attributes
Entities are represented by means of their properties, called attributes. All attributes have
values. For example, a student entity may have name, class, and age as attributes.

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There exists a domain or range of values that can be assigned to attributes. For example, a
student's name cannot be a numeric value. It has to be alphabetic. A student's age cannot be
negative, etc.
Types of Attributes
 Simple attribute − Simple attributes are atomic values, which cannot be divided further. For
example, a student's phone number is an atomic value of 10 digits.
 Composite attribute − Composite attributes are made of more than one simple attribute.
For example, a student's complete name may have first_name and last_name.
 Derived attribute − Derived attributes are the attributes that do not exist in the physical
database, but their values are derived from other attributes present in the database. For
example, average_salary in a department should not be saved directly in the database,
instead it can be derived. For another example, age can be derived from data_of_birth.
 Single-value attribute − Single-value attributes contain single value. For example −
Social_Security_Number.
 Multi-value attribute − Multi-value attributes may contain more than one values. For
example, a person can have more than one phone number, email_address, etc.
 In the below diagram, Entities or real world objects are represented in a rectangular box.
Their attributes are represented in ovals. Primary keys of entities are underlined. All the
entities are mapped using diamonds. This is one of the methods of representing ER model.
There are many different forms of representation.
Fig. 2.2.4 Example - ER diagram

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Basically, ER model is a graphical representation of real world objects with their attributes and
relationship. It makes the system easily understandable. This model is considered as a top down
approach of designing a requirement.
Advantages
 It makes the requirement simple and easily understandable by representing simple diagrams.
 One can covert ER diagrams into record based data model easily.
 Easy to understand ER diagrams
Disadvantages
 No standard notations are available for ER diagram. There is great flexibility in the notation.
It’s all depends upon the designer, how he draws it.
 It is meant for high level designs. We cannot simplify for low level design like coding.
2.3 Spatial Data Models
Spatial data refers to the data or information that describes the absolute or relative location of
geographic features on the earth. The non spatial data or the attribute data on the other hand
describes the characteristics of the spatial features. These characteristics can be quantitative or
qualitative.
2.3.1 Representation of Space
Burrough & McDonnell (1998) described two ways to represent the space (an area, landscape
or some bigger unit), which are as follows:
a) Discrete Entities: The space could be seen as occupied with entities that are described by
their properties and can be located on earth using coordinate systems. The entities have a
clear boundary.
Buildings, roads, land parcels etc. are the example of discrete entities.
b) Continuous fields: The variation of an attribute over the space as a continuous field. No
physical boundary can ever be observed in such case.
Temperature, pressure, elevation etc. across an area are the examples of continuous fields
The term spatial data model is used to describe, how geographical data are organized within a
GIS in order to represent real world phenomena.
2.3.2 Data Models
 A data model is a description or view of the real world.
 Data modeling is a process that formalizes the description or view at different levels of data
abstraction.

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 Since, the real world is made up of complex spatial objects and phenomena, it is practically
impossible for a single data model to represent everything that is present.
 This means that different users may have different data models when they attempt to collect
data in the same location.
GIS uses one of two spatial data models
a) Raster data model
 It divides the study area into cells, usually rectangular grid cells.
 It is location based because emphasis is placed upon the location of each cell relative to
other cells.
 It is frequently used to model field data.
 They correspond to regularly spaced points on a continuous surface.
b) Vector data model
 It is used to represent discrete phenomena, represented by geometric primitives ( point, line
and polygon).
 It is object – based.
 3D (TIN) Triangular irregular Network.
Data models are conceptual models of the real world. These describe us the representation and
storage of the geographic data. The data models used in GIS are described below:
2.3.2.1 Vector Data Model
The vector data model is closely linked with the discrete object view. In vector data model,
geographical phenomena are represented in three different forms;-point, line and polygon. The
shape of a spatial entity is stored using two-dimensional (x, y) coordinate system.
Point : A location depicted by a single set of (x, y) coordinates at the scale of abstraction.
The wells in a village, electricity poles in a town and cities in the world map are the examples
of spatial features described by points
Note : A city can be marked as a single point on a world map but would be marked as a
polygon on a state map. The scale plays an important role in deciding the geometry of a
geographical feature.

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Fig. 2.3.1 Vector data model
Line/Arc : Ordered sets of (x, y) coordinate pairs arranged to form a linear feature. The curves
in a linear feature are generated by increasing the density of points/vertices.
The roads, rails and telephone cables are the examples of the spatial features described by lines.
Polygon : The set of (x, y) coordinate pairs enclosing a homogeneous area
The land parcels, agricultural farms and water bodies are the examples of the spatial features
described by polygons.
2.3.2.2 Raster Data Model
The raster data model is commonly associated with the field conceptual model. Here,
geographic space is represented by array of cells or pixels (aka picture elements) which are
arranged in rows and columns. Each pixel has a value that represents information. The value can be
in the form of integer, floating points or alphanumeric.
A point can be represented by a single pixel in raster model. A line is a chain of spatially
connected cells with the same value. Similarly, a water body in raster data is represented as a set of
contiguous pixels having same value that represents a homogeneous area.
Fig. 2.3.2 Raster data model
Comparison between Vector and Raster Data Models
Data
Model
Advantages Disadvantages
Simple data structure Cell size determines the resolution at which
the data is represented
Compatible with remote sensing or Requires a lot of storage space

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Raster
scanned data
Spatial analysis is easier Projection transformations are time
consuming
Simulation is easy because each unit has
the same size and shape
Network linkages are difficult to establish
Vector
Data is represented at its original
resolution and form without
generalization
The location of each vertex is to be stored
explicitly
Require less storage space Overlay based on criteria is difficult
Editing is faster and convenient Spatial analysis is cumbersome
Network analysis is fast Simulation is difficult because each unit has
a different topological form
Projection transformations are easier
2.4 Raster Data Structure
In a simple raster data structure the geographical entities are stored in a matrix of rectangular
cells. A code is given to each cell which informs users which entity is present in which cell.
The simplest way of encoding a raster data into computers can be understood as follows:
 Entity model
 Pixel values
 File structure
a) Entity model :
Fig. 2.4.1 Entity model
 It represents the whole raster data.

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 Let us assume that the raster data belongs to an area where land is surrounded by water.
 Here a particular entity (land) is shown in SHADED color and the area where land is not
present is shown by white.
b) Pixel values :
 The pixel value for the full image is shown.
 Cells having a part of the land are encoded as 1 and others where land is not present are
encoded as 0.
Fig. 2.4.2 Pixel values
(c) File structure:
 It demonstrates the method of coding raster data.
 The first row of the file structure data tells that there are 5 rows and 5 columns in the image,
and 1 is the maximum pixel value.
 The subsequent rows have cells with value as either 0 or 1 (similar to pixel values).
Fig. 2.4.3 File structure
 The huge size of the data is a major problem with raster data.
 An image consisting of twenty different land-use classes takes the same storage space as a
similar raster map showing the location of a single forest.

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 To address this problem many data compaction (Compression) methods have been
developed.
2.5 Raster Data Compression
 Data compression is the process of modifying, encoding or converting the bits structure of
data in such a way that it consumes less space on disk.
 It enables reducing the storage size of one or more data instances or elements. Data
compression is also known as source coding or bit-rate reduction.
 Data compression enables sending a data object or file quickly over a network or the Internet
and in optimizing physical storage resources.
 Data compression has wide implementation in computing services and solutions, specifically
data communications. Data compression works through several compressing techniques and
software solutions that utilize data compression algorithms to reduce the data size.
A common data compression technique removes and replaces repetitive data elements and
symbols to reduce the data size. Data compression for graphical data can be lossless compression
or lossy compression, where the former saves all replaces but save all repetitive data and the latter
deletes all repetitive data.
Compression techniques
 Run length encoding
 Block encoding
 Chain encoding
 Quadtree
2.5.1 Run Length Coding (Lossless)
 Geographical data tends to be "spatially autocorrelated", meaning that objects which are
close to each other tend to have similar attributes:
 "All things are related, but nearby things are more related than distant things"
(Tobler 1970) Because of this principle, we expect neighboring pixels to have similar values.
Therefore, instead of repeating pixel values, we can code the raster as pairs of numbers -
(run length, value).
 The run length coding is a widely used compression technique for raster data. The primary
data elements are pairs of values or tuples, consisting of a pixel value and a repetition count
which specifies the number of pixels in the run. Data are built by reading successively row
by row through the raster, creating a new tuple every time the pixel value changes or the end
of the row is reached. Describes the interior of an area by run-lengths, instead of the
boundary.

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 In the example , the first row is blank and is stored as (0,8). This means there are 8 cells and
they are all zeros. In the second row, there are 4 consecutive zeros so it gets a value of (0,4).
After this, we have three consecutive cells with the value 1 so it gets a value of (1,3). This
continues until it reaches the bottom-right cell.
Fig. 2.5.1 Example for Run length coding
2.5.2 Block Coding-Grouping Blocks of Data
 The block coding raster storage technique assigns areas that are blocks to reduce
redundancy.
 The block coding raster image compression method subdivides an entire raster image
into hierarchical blocks. It’s an extension of the run length encoding technique, but extends
it to two dimensions.
In the example :
Instead of storing 64 grid cells, all it takes is just 7 blocks. Using block coding, it requires one
3×3 block, two 2×2 blocks and four 1×1 cell blocks to encode this raster image.
In this block coding example, the top-left corner is used as a reference for each block.
Fig. 2.5.2 Example for Block coding

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2.5.3 Chain Coding (Freeman Coding)-Defining the Exterior Boundary
 Chain coding defines the outer boundary using relative positions from a start point. The
sequence of the exterior is stored where the endpoint finishes at the start point.
 During the encoding, the direction is stored as an integer. However, in this example we use
cardinal directions for simplicity. For example, the value 0 is north and 1 is east, 2 is south,
3 is west
 In the example, we start at position (5,2). From here we define the border using cardinal
directions and number of movements. We move east 3 positions until we hit the edge. At
this location, we move south 4 positions. This process continues until the end point hits the
start point.
Note : Only for the purpose of this exercise, we used north, east, south and west as alphabetical
values. When encoded, it is a numerical value.
Fig 2.5.3 Example for chain coding
2.5.4 Quadtree Encoding - Subdividing Data into Quarters
 Quadtrees are raster data structures based on the successive reduction of homogeneous cells.
It recursively subdivides a raster image into quarters. The subdivision process continues
until each cell is classed.
 It reduces raster storage requirements. It also is dependent on the complexity of the feature
and the resolution of the smallest grid cell.
 In the example, the top-left and bottom-right 8×8 grids do not need to be subdivided further
because they are homogeneous. The top-right 8×8 grid is subdivided into three 4×4 grid. The
remaining 4×4 grid is separated into 4 individual classes.

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Fig. 2.5.4 Example for quadtree coding
2.5.5 Image Compression Reduces File size
 GIS data is abundant. With satellites acquiring images each day, raster data is the spatial
model of choice.
 Deploying efficient raster image compression techniques means reducing storage space. This
is the primary benefit of compressing your data.
 It can save money and time. You can also improve your network performance because you
are working with a reduced amount of data.
2.6 Vector Data Structure
 Geographic entities encoded using the vector data model, are called features. The features
can be divided into two classes:

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a. Simple features
 These are easy to create, store and are rendered on screen very quickly.
 They lack connectivity relationships and so are inefficient for modeling phenomena
conceptualized as fields.
This is also called Feature data. Shapefiles are ArcView's native file format for geographic
features and attribute data. ArcView can also display Arc/Info Coverages, which comprises a
more complex representation of vector data. Vector comprise the following:
 Point - a pair of x and y coordinates.
 Line - a sequence of points
 Polygon - a closed set of lines
Attribute information is stored in Feature Tables.
Point entities : These represent all geographical entities that are positioned by a single XY
coordinate pair. Along with the XY coordinates the point must store other information such as
what does the point represent etc.
Line entities : Linear features made by tracing two or more XY coordinate pair.
 Simple line: It requires a start and an end point.
 Arc: A set of XY coordinate pairs describing a continuous complex line. The shorter the line
segment and the higher the number of coordinate pairs, the closer the chain approximates a
complex curve.
Simple Polygons : Enclosed structures formed by joining set of XY coordinate pairs. The
structure is simple but it carries few disadvantages which are mentioned below:
 Lines between adjacent polygons must be digitized and stored twice, improper digitization
give rise to slivers and gaps
 Convey no information about neighbor
 Creating islands is not possible
b. Topological features
 A topology is a mathematical procedure that describes how features are spatially related and
ensures data quality of the spatial relationships.
 Topological relationships include following three basic elements:
I. Connectivity : Information about linkages among spatial objects
II. Contiguity : Information about neighbouring spatial object
III. Containment : Information about inclusion of one spatial object within another spatial
object

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Connectivity- Information about linkages among spatial objects
 Arc node topology defines connectivity
 arcs are connected to each other if they share a common node.
 This is the basis for many network tracing and path finding operations.
 Arcs represent linear features and the borders of area features.
 Every arc has a from-node which is the first vertex in the arc and a to-node which is the last
vertex.
 These two nodes define the direction of the arc.
 Nodes indicate the endpoints and intersections of arcs.
 They do not exist independently and therefore cannot be added or deleted except by adding
and deleting arcs.
Fig. 2.6.1 Arc-node Topology
 Nodes can be used to represent point features which connect
segments of a linear feature.
 (e.g., intersections connecting street segments, valves
connecting pipe segments).
 Arc-node topology is supported through an arc-node list.
 For each arc in the list there is a from node and a to node.
 Connected arcs are determined by common node numbers.
Fig. 2.6.3 Arc-Node Topology with list
 Contiguity- Information about neighbouring spatial object
 Polygon topology defines contiguity.
Fig. 2.6.2 Node showing
intersection

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 The polygons are said to be contiguous if they share a common arc.
 Contiguity allows the vector data model to determine adjacency.
Fig. 2.6.4 Polygon Topology
 The from node and to node of an arc indicate its direction, and it helps determining the
polygons on its left and right side.
 Left-right topology refers to the polygons on the left and right sides of an arc.
 In the illustration above, polygon B is on the left and polygon C is on the right of the arc 4.
 Polygon A is outside the boundary of the area covered by polygons B, C and D.
 It is called the external or universe polygon, and represents the world outside the study area.
 The universe polygon ensures that each arc always has a left and right side defined.
Containment
Geographic features cover distinguishable area on the surface of the earth. An area is
represented by one or more boundaries defining a polygon. The polygons can be simple or they can
be complex with a hole or island in the middle. In the illustration given below assume a lake with
an island in the middle. The lake actually has two boundaries, one which defines its outer edge and
the other (island) which defines its inner edge. An island defines the inner boundary of a polygon.
The polygon D is made up of arc 5, 6 and 7. The 0 before the 7 indicates that the arc 7 creates an
island in the polygon.
Fig. 2.6.5 Polygon ArcTopology

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Polygons are represented as an ordered list of arcs and not in terms of X, Y coordinates. This is
called Polygon-Arc topology. Since arcs define the boundary of polygon, arc coordinates are stored
only once, thereby reducing the amount of data and ensuring no overlap of boundaries of the
adjacent polygons.
Topologic Features
Networks : A network is a topologic feature model which is defined as a line graph composed
of links representing linear channels of flow and nodes representing their connections. The
topologic relationship between the features is maintained in a connectivity table. By consulting
connectivity table, it is possible to trace the information flowing in the network.
Polygons with explicit topological structures : Introducing explicit topological relationships
takes care of islands as well as neighbors. The topological structures are built either by creating
topological links during data input or using software. Dual Independent Map Encoding (DIME)
system of US Bureau of the Census is one of the first attempts to create topology in geographic
data.
Fig. 2.6.6 Polygons with explicit topological structures
 Polygons are formed using the lines and their nodes.
 Once formed, polygons are individually identified by a unique identification number.
 The topological information among the polygons is computed and stored using the
adjacency information (the nodes of a line, and identifiers of the polygons to the left and
right of the line) stored with the lines.
Poly
ID
Arcs Arc ID From To ARCID Left Poly Right
Poly
A 1, 2 1 x y 1 A U
B 2, 3, 4 2 x y 2 B A
C 3 3 z z 3 B B
4 x y 4 U B
Fig. 2.6.7 Arc node Topology, Polygon Topology, Polygon Arc Topology

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Fully topological polygon network structure
A fully topological polygon network structure is built using boundary chains that are digitized
in any direction. It takes care of islands and lakes and allows automatic checks for improper
polygons. Neighborhood searches are fully supported. These structures are edited by moving the
coordinates of individual points and nodes, by changing polygon attributes and by cutting out or
adding sections of lines or whole polygons. Changing coordinates require no modification to the
topology but cutting out or adding lines and polygons requires recalculation of topology and
rebuilding the database.
2.7 Vector and Raster - Advantages and Disadvantages
There are several advantages and disadvantages for using either the vector or raster data model
to store spatial data. These are summarized below.
Vector : Advantages
 Data can be represented at its original resolution and form without generalization.
 Graphic output is usually more aesthetically pleasing (traditional cartographic
representation);
 Since most data, e.g. hard copy maps, is in vector form no data conversion is required.
 Accurate geographic location of data is maintained.
 Allows for efficient encoding of topology, and as a result more efficient operations that
require topological information, e.g. proximity, network analysis.
Vector : Disadvantages
 The location of each vertex needs to be stored explicitly.
 For effective analysis, vector data must be converted into a topological structure. This is
often processing intensive and usually requires extensive data cleaning. As well, topology is
static, and any updating or editing of the vector data requires re-building of the topology.
 Algorithms for manipulative and analysis functions are complex and may be processing
intensive. Often, this inherently limits the functionality for large data sets, e.g. a large
number of features.
 Continuous data, such as elevation data, is not effectively represented in vector form.
Usually substantial data generalization or interpolation is required for these data layers.
 Spatial analysis and filtering within polygons is impossible.
Raster Advantages :
 The geographic location of each cell is implied by its position in the cell matrix.

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Accordingly, other than an origin point, e.g. bottom left corner, no geographic coordinates
are stored.
 Due to the nature of the data storage technique data analysis is usually easy to program and
quick to perform.
 The inherent nature of raster maps, e.g. one attribute maps, is ideally suited for mathematical
modeling and quantitative analysis.
 Discrete data, e.g. forestry stands, is accommodated equally well as continuous data, e.g.
elevation data, and facilitates the integrating of the two data types.
 Grid-cell systems are very compatible with raster-based output devices, e.g. electrostatic
plotters, graphic terminals.
Raster : Disadvantages
 The cell size determines the resolution at which the data is represented.
 It is especially difficult to adequately represent linear features depending on the cell
resolution. Accordingly, network linkages are difficult to establish.
 Processing of associated attribute data may be cumbersome if large amounts of data exist.
Raster maps inherently reflect only one attribute or characteristic for an area.
 Since most input data is in vector form, data must undergo vector-to-raster conversion.
Besides increased processing requirements this may introduce data integrity concerns due to
generalization and choice of inappropriate cell size.
 Most output maps from grid-cell systems do not conform to high-quality cartographic needs.
It is often difficult to compare or rate GIS software that use different data models. Some
personal computer (PC) packages utilize vector structures for data input, editing, and display but
convert to raster structures for any analysis. Other more comprehensive GIS offerings provide both
integrated raster and vector analysis techniques. They allow users to select the data structure
appropriate for the analysis requirements. Integrated raster and vector processing capabilities are
most desirable and provide the greatest flexibility for data manipulation and analysis.
2.8 Triangular Irregular Network (TIN)
 A TIN is a data structure that defines geographic space as a set of contiguous, non-
overlapping triangles, which vary in size and angular proportion. Like grids, TINs are used
to represent surfaces such as elevation, and can be created directly from files of sample
points.

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Fig. 2.8.1 Anatomy of a TIN
 The TIN data structure is defined by two elements: a set of input points with x,y, and z
values, and a series of edges connecting these points to form triangles. Each input point
becomes the node of a triangle in the TIN structure, and the output is a continuous faceted
surface of triangles
 The triangles are constructed according to a mathematical technique called Delaunay
triangulation. The technique guarantees that a circle drawn through the three nodes of any
triangle will contain no other input point.
Fig. 2.8.2 Delaunay Triangulation
 Because points can be placed irregularly over a surface a TIN can have higher resolution in
areas where surface is highly variable. The model incorporates original sample points
providing a check on the accuracy of the model. The information related to TIN is stored in a
file or a database table. Calculation of elevation, slope, and aspect is easy with TIN but these
are less widely available than raster surface models and more time consuming in term of
construction and processing.

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Node attribute table
Vertex ID X Y Z
a 780,034 33,020 90
b 780,017 33,035 102
c 780,007 33,052 115
d 780,023 33,070 135
…
Fig. 2.8.3 TIN model
Polygon attribte table Arc attribute table
Triangle
ID
Area Edge1 Edge2 Edge3 Nighbors Edge
ID
Length From
Node
To
node
A 8200 1 2 12 B, F 1 160 f a
B 7040 3 4 2 C, A 2 140 a g
C 6000 5 6 4 D, B 3 130 a b
D 5440 7 8 6 E, C 4 140 b g
… …
The TIN model is a vector data model which is stored using the relational attribute tables. A
TIN dataset contains three basic attribute tables: Arc attribute table that contains length, from node
and to node of all the edges of all the triangles.
 Node attribute table that contains x, y coordinates and z (elevation) of the vertices
 Polygon attribute table that contains the areas of the triangles, the identification number of
the edges and the identifier of the adjacent polygons.
Storing data in this manner eliminated redundancy as all the vertices and edges are stored only
once even if they are used for more than one triangle. As TIN stores topological relationships, the
datasets can be applied to vector based geoprocessing such as automatic contouring, 3D landscape
visualization, volumetric design, surface characterization etc.
TIN GRID
Advantages  Ability to describe the surface at
different
 Efficiency in storing data
 Easy to store and manipulate
 Easy integration with raster database
 Smoother, more natural appearance of
derived terrain features
Disadvantages  In many cases require visual
inpection and manual control of
the network.
 Inability to use various grid sizes to reflect
areas of different complexity of relief.

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2.9 The Open Geospatial Consortium
The Open Geospatial Consortium (OGC) is an international not for profit organization
committed to making quality open standards for the global geospatial community. These standards
are made through a consensus process and are freely available for anyone to use to improve sharing
of the world's geospatial data.
Description of the OGC
The OGC provides open standard specifications with the aim to facilitate and encourage the use
of these standards when organisations develop their own geospatial software, or online geoportals
offering data and software services online. The collection of geoportals and various other
complimentary services, create a Spatial Data Infrastructure (SDI).
Benefits of the OGC
Interoperability of geospatial data and reduced fragmentation in data delivery.
Consensus based approach. Participation of organisations from the public sector, private sector,
academia and research when developing standards assures the interests and needs of the geospatial
community are considered.
OGC helps to bring together geospatial data and services from multiple sectors.
2.9.1 Common OGC standards
OGC Web Services (OWS) are the OGC standards that use the internet or alternatively the
World Wide Web to view, edit, manage and share geospatial data. To understand OGC web
services you must be able to understand how web services work.
How do web services work?
A fully operable web service requires the service itself, a server, a client and the internet. The
client is an application that will use the web service (e.g. an internet browser) and the server is
where the data and information is stored. The web service will process a request made by the client
and then collect the appropriate service from the server and return this to the client via the internet
for the client to retrieve. Facebook is a good example where the SERVER is Facebook's servers,
the CLIENT is either the website you access on a computer or the app you have on your phone or
tablet, while the WEB SERVICE is the image you requested or the status update you posted.

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Fig. 2.9.1 Working of web service
The requests being made by the client are commonly known as Hypertext Transfer Protocol
(HTTP) requests. HTTP is the protocol used for data communication on the World Wide Web and
there are two types of HTTP requests that need to be defined for this course:
Fig. 2.9.2 Working of HTTP
So to appropriately define the use of web services: A web service is used to provide access to
data and information from a server via the internet to a client.
2.9.2 OGC Standards
● Web Map Service (WMS)
This standard visualises geographic data that can be displayed across the web and multiple
platforms. This standard does not provide the actual geospatial data; instead it just provides a
georeferenced image (e.g. PNG, JPEG or GIF files) of the data.

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● Web Feature Service (WFS)
This standard allows the sharing of geographic data at a feature level (i.e. sharing vector based
data for example ESRI Shapefiles (.shp)). This standard allows the user to request specified
geographic data from a client and receives the requested data via the web.
● Web Coverage Service (WCS)
This standard allows the visualisation and provision of geospatial data according to temporal
and spatial characteristics from a web server and the data can be in multiple raster based data
formats (e.g. GeoTiffs, .img, ENVI (.hdr) file types)
● Web Processing Service (WPS)
This standard allows the potential for geospatial processing tools and applications to be used on
geographic data within an interface via the web. For example this standard could use digitising,
spatial analysis and network analysis tools to edit geospatial data.
● Web Map Context (WMC)
This standard allows for the creation of a XML document that will save the layers and
parameters of a web map project so that it can be used at a different time and/or location. The OGC
service layers being used in the map (e.g. WMS layers) and the map parameters (e.g. map extent,
projection scheme information) are saved within the xml context document.
● Web Map Tile Service (WMTS)
This standard allows the pre-rendering of image tiles for a web map application. The pre-
rendering allows the web map to add or remove data layers between different map extents. This
tiling can provide higher or lower detailed maps depending on the map extent being viewed and the
specific web map properties being used.
● Catalogue Service for the Web (CS-W)
This standard allows a user to find, request and modify geospatial metadata that is stored within
a spatial database via the web. The metadata can be utilised from multiple sources and is an
important part of understanding and applying data that is being shared to different users, locations
and to varying platforms.
● Styled Layer Descriptor (SLD)
This standard allows for the application of style properties to the geographic features of a web
map and also allows the retrieval of the web map style legend.

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● KML
This standard is a XML language that is based around the visualisation of geographic data, and
is used within Google Earth and Google Maps. Geographic data that this standard commonly
represents include place marks, image overlays, polygon features and paths.
● Geography Markup Language (GML)
This standard is an encoding standard similar to XML, but is used for the description and
representation of points, lines and polygons to be made into specified geographic features. For
example lines could represent roads, points could represent and polygons could represent specific
buildings.
● Sensor Model Language (SensorML)
This standard allows for the encoding of models and XML for sensor and observation
processing. This standard was established for the OGC Sensor Web Enablement (SWE) that aims
to enable applications and services to gain access to sensors of all types overthe web.
● OpenGlS@ Open Location Service (OpenLS)
This standard allows for the sharing of Location Based Services (LBS) through various
interfaces. LBS include emergency response (E-911), personal navigation, traffic information
service, and travel directions.
● open GeoSMS
This standard provides an encoding and interface that offers the potential for the
communication of location content betvveen different Location Based Service (LBS) devices or
applications via a Short Message Service (SMS).
● GeoAP1
This standard defines a Java language application programming interface (API) that can be used
to manipulate geographic information via the use of a Java based standard library that contains the
types and methods that can be implemented.
2.10 Data Quality
Data quality is the degree of data excellency that satisfy the given objective. In other words,
completeness of attributes in order to achieve the given task can be termed as Data Quality. Data
created from different channels with different techniques can have discrepancies in terms of
resolution, orientation and displacements.

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Data quality is a pillar in any GIS implementation and application as reliable data are
indispensable to allow the user obtaining meaningful results. The following review of data quality
focuses on three distinct components, data accuracy, quality, and error.
2.10.1 Accuracy
The fundamental issue with respect to data is accuracy. Accuracy is the closeness of results of
observations to the true values or values accepted as being true. This implies that observations
of most spatial phenomena are usually only considered to estimates of the true value. The
difference between observed and true (or accepted as being true) values indicates the accuracy of
the observations.
Basically two types of accuracy exist. These are positional and attribute accuracy
Positional accuracy is the expected deviance in the geographic location of an object from its
true ground position. There are two components to positional accuracy. These
are relative and absolute accuracy.
Absolute accuracy concerns the accuracy of data elements with respect to a coordinate scheme.
Relative accuracy concerns the positioning of map features relative to one another.
Attribute accuracy is equally as important as positional accuracy. It also reflects estimates of
the truth. Interpreting and depicting boundaries and characteristics for forest stands or soil
polygons can be exceedingly difficult and subjective.
2.10.2 Quality
Quality can simply be defined as the fitness for use for a specific data set. Data that is
appropriate for use with one application may not be fit for use with another. It is fully dependent
on the scale, accuracy, and extent of the data set, as well as the quality of other data sets to be used.
The recent U.S. Spatial Data Transfer Standard (SDTS) identifies five components to data quality
definitions. These are :
 Lineage
 Positional Accuracy
 Attribute Accuracy
 Logical Consistency
 Completeness
Lineage
The lineage of data is concerned with historical and compilation aspects of the data such as the:
 source of the data;

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 content of the data
 data capture specifications
 geographic coverage of the data
 compilation method of the data, e.g. digitizing versus scanned;
 transformation methods applied to the data; and
 the use of an pertinent algorithms during compilation, e.g. linear simplification, feature
generalization
● Positional Accuracy
The identification of positional accuracy is important. This includes consideration of inherent
error (source error) and operational error (introduced error).
● Attribute Accuracy
Consideration of the accuracy of attributes also helps to define the quality of the data. This
quality component concerns the identification of the reliability, or level of purity (homogeneity), in
a data set.
● Logical Consistency
This component is concerned with determining the faithfulness of the data structure for a data
set. This typically involves spatial data inconsistencies such as incorrect line intersections,
duplicate lines or boundaries, or gaps in lines. These are referred to as spatial or topological errors.
● Completeness
The final quality component involves a statement about the completeness of the data set. This
includes consideration of holes in the data, unclassified areas, and any compilation procedures that
may have caused data to be eliminated.
The ease with which geographic data in a GIS can be used at any scale highlights the
importance of detailed data quality information. Although a data set may not have a specific scale
once it is loaded into the GIS database, it was produced with levels of accuracy and resolution that
make it appropriate for use only at certain scales, and in combination with data of similar scales.
2.10.3 Error
Two sources of error, inherent and operational, contribute to the reduction in quality of the
products that are generated by geographic information systems.
Inherent error is the error present in source documents and data.
Operational error is the amount of error produced through the data capture and manipulation
functions of a GIS.

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Possible sources of operational errors include:
 Mis-labelling of areas on thematic maps;
 misplacement of horizontal (positional) boundaries;
 human error in digitizing
 classification error;.
 GIS algorithm inaccuracies; and
 human bias.
An awareness of the error status of different data sets will allow user to make a subjective
statement on the quality and reliability of a product derived from GIS processing.
The validity of any decisions based on a GIS product is directly related to the quality
and reliability rating of the product.
Depending upon the level of error inherent in the source data, and the error operationally
produced through data capture and manipulation, GIS products may possess significant amounts of
error.
2.10.4 Sources of Spatial Data Discrepancy
● DataInformationExchange:
Data information exchange is basically the information about the data provided by the client to
organization. The degree of information provided by the client defines the accuracy and
completeness of data.
● TypeandSource:
Data type and source must be evaluated in order to get appropriate data values. There are many
spatial data formats and each one of them is having some beneficiary elements as well as some
drawbacks.
● DataCapture:
There are many tools that incorporate manual skills to capture the data using various softwares
like ArcGIS. These softwares allows user to capture information from the base data. During this
data capture, the user may misinterpret features from the base data and captures the features with
errors. Data capture must be performed on a perfect scale where one must be able to view the
features distinctly.

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● CartographicEffects :
After capturing the data, some cartographic effects like symbology, pattern, colors, orientation
and size are assigned to the features. This is required for a better representation of reality. These
effects must be assigned according to the domain of the features
● DataTransfer:
Some discrepancies may occur while transferring the data from one place to another “There is
no bad or good data. There are only data which are suitable for a specific purpose.” So, Data must
be evaluated according to the domain for which it is supposed to be used.
● Metadata:
Sometimes metadata is not updated according to the original features. So, metadata must be
updated with the original data.
2.10.5 Data Quality Improvement Techniques
 Choice of relevant data from a relevant source.
 Derive precisions in the origin itself.
 Data quality testing in each phase of data capture.
 Using automated software tools for spatial and non-spatial data validation.
 Assessment of the mode of data uses and user.
 Determining the map elements like scale, visualization and feature orientation.
2.11 Two Marks Questions with Answers
Q.1 What is data model?
Ans. : Data Models: Vector and Raster Spatial data in GIS has two primary data formats: raster
and vector.
Raster uses a grid cell structure, whereas vector is more like a drawn map. Raster and Vector
Data Vector format has points, lines, polygons that appear normal, much like a map. Raster
format generalizes the scene into a grid of cells, each with a code to indicate the feature being
depicted. The cell is the minimum mapping unit. Raster has generalized reality: all of the
features in the cell area are reduced to a single cell identity.
Q.2 What is vector data?
Ans. : Vector data uses two dimensional cartesian coordinates to store the shape of spatial
entity. Vector based features are treated as discrete geometric objects over the space. In the
vector data base point is the basic building block from which all the spatial entities are

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constructed. The vector spatial entity, the point is represented by a single x,y coordinate pair.
Line and area entities are constructed by a series of points into chains.
Q.3 Define Raster data.
Ans. : Raster is a method for the storage, processing and display of spatial data. Each area is
divided into rows and columns, w hich form a regular grid structure. Each cell must be
rectangular in shape, but not necessarily square. Each cell within this matrix contains location
co-ordinates as well as an attribute value.
The origin of rows and column is at the upper left corner of the grid. Rows function as the
“y”coordinate and column as”x”coordinate in a two dimensional system. A cell is defined by its
location in terms of rows and columns.
Q.4 Why is compression needed for remote sensing data?
Ans. : Data compression method provides compact raster representation using a variable sized
grid. Large cells are used in areas of low detail while small cells are used in areas of high detail
Q.5 What is Vectorization?
Ans. : Vectors are data elements describing position and direction. In GIS, vector is the maplike
drawing of features, without the generalizing effect of a raster grid. Therefore, shape is better
retained. Vector is much more spatially accurate than the raster format.
Q.6 What is raster coding?
Ans. : In the data entry process, maps can be digitized or scanned at a selected cell size and each
cell assigned a code or value. The cell size can be adjusted according to the grid structure or by
ground units, also termed resolution. There are three basic and one advanced scheme for
assigning cell codes.
Presence/Absence: is the most basic method and to record a feature if some of it occurs in the
cell space.
Q.7 Compare vector and raster data structure.
Ans. : Vectors are data elements describing position and direction. In GIS, vector is the maplike
drawing of features, without the generalizing effect of a raster grid. Therefore, shape is better
retained. Vector is much more spatially accurate than the raster format.
In the data entry process, maps can be digitized or scanned at a selected cell size and each cell
assigned a code or value. The cell size can be adjusted according to the grid structure or by
ground units, also termed resolution. There are three basic and one advanced scheme for
assigning cell codes. Presence/Absence: is the most basic method and to record a feature if some
of it occurs in the cell space.

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Q.8 What do you understand about data compression?
Ans. : Data compression method provides compact raster representation using a variable sized
grid. Large cells are used in areas of low detail while small cells are used in areas of high detail
Q.9 What is buffering?
Ans. : Buffering is the creation of polygons that surround other points, lines or polygons.
Buffers M/J be created either to exclude a certain amount of area around a point, line or polygon
or to include only the buffer area in a study
Q.10 List the advantages of raster data.
Ans. : Raster data also known as grid based system or cellular system consists of rectangular
cell. The raster system used minimum time for process in a and easy to program
Advantages : It is a simple data structure, Overaly operations are easily and efficiently
implemented, High spatial variability is efficiently represented in a raster format.
Disadvantage : 1. Less compact 2. Topographical relationship are more difficult to represent
Q.11 What you mean by data compression?
Ans. : Reducing the 'electronic space' (data bits) used in representing a piece of information, by
eliminating the repetition of identical sets of data bits (redundancy) in an audio/video, graphic,
or text data file. White spaces in text and graphics, large blocks of the same color in pictures, or
other continuously recurring data, is reduced or eliminated by coding(encryption) with a
program that uses a particular type of compression algorithm. The same program is used to
decompress (decrypt) the data so that it can be heard, read, or seen as the original data.
Q.12 List out the basics elements of GIS modeling.
Ans. : Geographic Information systems have three important components. They are
1. Computer hardware,
2. Set of application software modules
3. Spatial data
4. Data management and analysis procedures
5. Personnel to operate the GIS
Q.13 Write short notes on topographical overlay.
Ans. : Map overlay is the process by which it is possible to take two or more different
topographical layers of the same area and overlay them on top of the other and form a composite
new layer this techniques is used to overlay vector data on a raster image. In Vector base

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systems map overlay is time consuming, complex and computationally expensive. In raster
based systems it is quick, straightforward and efficient
Q.14 Discuss about network model.
Ans. : A network is a generalized graph that captures relationships between objects using
connectivity. A network database consists of a collection of records that are connected to each
other through links. A link is an association between two records. It allows each record to have
many parents and many children thus allowing a natural model of relationships between entities.
Q.15 Describe about object oriented model.
Ans. : The aim of object oriented model is to allow data modeling which is closer to real world.
An object-oriented database uses objects as elements within database files.
An object is a logical grouping of related data that represents a real world entity.
Each object is a distinct entity which is identified using a key attribute called ObjectID.
The object can be grouped together to form a class.
Objects of the same class have same attributes, behavior and relationships with other objects
Q.16 Write about relational model.
Ans. : The relational data model was introduced by Codd in 1970.
The relational database relates or connects data in different files through the use of a common
field. A flat file structure is used with a relational database model. In this arrangement, data is
stored in different tables made up of rows and columns. The columns of a table are named by
attributes. Each row in the table is called a tuple and represents a basic fact. No two rows of the
same table may have identical values in all columns. There are two crucial data integrity
constraints viz. primary key and foreign key. A primary key is an attribute whose value is
unique across all tuples (rows) in a relation (table). The primary key of one table appearing as
an attribute of another table is known as a foreign key in that table.
Q.17 What are the three basic elements of topological relationships?
Ans. : Topological features
A topology is a mathematical procedure that describes how features are spatially related and
ensures data quality of the spatial relationships. Topological relationships include following
three basic elements:
I. Connectivity: Information about linkages among spatial objects
II. Contiguity: Information about neighbouring spatial object
III. Containment: Information about inclusion of one spatial object within another spatial object

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Q.18 List the three models of encoding raster data.
Ans. : The simplest way of encoding a raster data into computers can be understood as follows:
 Entity model
 Pixel values
 File structure
Q.19 Define raster data model.
Ans. : The raster data model is commonly associated with the field conceptual model. Here,
geographic space is represented by array of cells or pixels (aka picture elements) which are
arranged in rows and columns. Each pixel has a value that represents information. The value can
be in the form of integer, floating points or alphanumeric.
A point can be represented by a single pixel in raster model. A line is a chain of spatially
connected cells with the same value. Similarly, a water body in raster data is represented as a set
of contiguous pixels having same value that represents a homogeneous area.
Q.20 What are the classifications of vector structures?
Ans. : Geographic entities encoded using the vector data model, are often called features. The
features can be divided into two classes:
a. Simple features : These are easy to create, store and are rendered on screen very quickly.
They lack connectivity relationships and so are inefficient for modelling phenomena
conceptualized as fields.
b. Topological features : A topology is a mathematical procedure that describes how
features are spatially related and ensures data quality of the spatial relationships.
2.12 Long Answered Questions with Answers
Q.1 Explain about Relational and Object oriented model in detail. (Refer section 2.2)
Q.2 Compare raster and vector data representation with suitable examples. (Refer section 2.7)
Q.3 With neat sketch explain briefly about Raster Data structures. (Refer section 2.4)
Q.4 Explain briefly about E-R diagrams. (Refer section 2.2.3)
Q.5 Discuss in detail about the data compression techniques. (Refer section 2.5)
Q.6 Describe TIN. Give the difference between TIN and GRID. (Refer section 2.8)
Q.7 Explain the concepts of Data quality. (Refer section 2.10)
Q.8 Write in detail about OGC standards for GIS. (Refer section 2.9)


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Notes

Geographic Information System 3 - 2 Data Input and Topology
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3.1 Data Input
Data input is the procedure of encoding data into a computer-readable form and writing the data
to the GIS data base.
There are two types of data to be entered into a GIS :
1. Spatial data
2. Associated non-spatial attribute data.
The spatial data represents the geographical location of the features.
 The non-spatial attribute data provide descriptive information like the name of a street,
salinity of the lake or the type of tree stand. It must be logically attached to the features they
describe.
 The data input and output functions are the means by which a GIS communicates with the
world outside.
 The objective in defining GIS input and output requirements is to identify the mix of
equipment and methods needed to meet the required level of performance and quality. No
one device or approach is optimum for all situations.
 Data entry is usually the major bottleneck in implementing a GIS. The initial cost of building
the database is commonly 5 to 10 times to cost of the GIS hardware and software.
 The creation of an accurate and well-documented database is critical to the operation of the
GIS.
 Accurate information can only be generated if the data on which it is based were accurate to
begin with.
 Data quality information includes the date of collection, the positional accuracy,
completeness, and the method used to collect and encode the data.
There are five types of data entry systems commonly used in a GIS :
 keyboard entry
 coordinate geometry
 manual digitizing
 scanning
 input of existing digital files
3.1.1 Keyboard Entry
 It involves manually entering the data at a computer terminal. Attribute data are commonly
input by keyboard whereas spatial data are rarely input this way.

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 Keyboard entry may also be used during manual digitizing to enter the attribute information.
However this is usually more efficiently handled as a separate operation.
 Roads files versus the census file -- roads file will use codes for the various road types while
the census file uses exact numbers for things like total population, age range, etc.
3.1.2 Coordinate Geometry (COGO)
 This technique is also called as COGO method.
 In this method survey measurements such as bearings and lengths are taken as input and
entered into GIS using keyboard.
 Coordinates of objects and features are calculated by the GIS.
 This input technique produces highly accurate results and is useful in preparing cadastral
maps.
 However, it takes lot of time, manpower and cost to produce the maps compared with
normal digitizing process.
 Surveyors and engineers want the higher accuracy of COGO for their applications. Planners
and most others are happy with the lower accuracy provided by manual digitizing.
3.1.3 Manual Digitizing
 Digitizing is the process of interpreting and converting paper map or image data to vector
digital data.
 Digitizing is the process by which coordinates from a map, image, or other sources of data
are converted into a digital format in a GIS.
 This process becomes necessary when available data is gathered in formats that cannot be
immediately integrated with other GIS data.
 Digitization results in shape files, which are vector features.
 Manual digitization is a tedious job and if operator is not efficient it may lead to several
digitizing errors. Hence, it has to be done with most skill and caution.
 Manual digitizing is a tedious job. Operator fatigue (eye strain, back soreness, etc.) can
seriously degrade the data quality.
 Managers must limit the number of hours an operator works at one time.
 A commonly used quality check is to produce a verification plot of the digitized data that is
visually compared with the map from which the data were originally digitized.

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3.1.4 Scanning
 Scanning provides a faster means of data entry compared to manual digitizing.
 The process of conversion of paper maps into digital format usable by computer is known as
scanning.
 It is used to convert an analog map into a scanned file, which is again converted to vector
format through tracing.
 Scanning automatically captures map features, text and symbols as individual cells, or pixels
and produces an automated image.
 The scanned file shows map features as raster lines (a series of connected pixels). And must
be vectorized to complete the process of digitizing.
 Vectorization is converting raster lines into vector lines in a process known as tracing.
3.1.5 Inputting Existing Digital Files
 There are many companies and organizations on the market that provide or sell digital data
files often in a format that can be read directly into a GIS.
 These digital data sets are priced at a fraction of the cost of digitizing existing maps.
 Over the next decade, the increased availability of data should reduce the current high cost
and lengthy production times needed to develop digital geographic data bases.
3.2 Scanner - Raster Data Input
 The process of conversion of paper maps into digital format usable by computer is known as
scanning.
 It is used to convert an analog map into a scanned file, which is again converted to vector
format through tracing. Scanning automatically captures map features, text and symbols as
individual cells, or pixels and produces an automated image.
 The scanned file shows map features as raster lines (a series of connected pixels). And must
be vectorized to complete the process of digitizing.
 Vectorization is converting raster lines into vector lines in a process known as tracing.
 A variety of scanning devices exist for the automatic capture of spatial data. While several
different technical approaches exist in scanning technology, all have the advantage of being
able to capture spatial features from a map at a rapid rate of speed.
 Scanners are generally expensive to acquire and operate. As well, most scanning devices
have limitations with respect to the capture of selected features, e.g. text and symbol
recognition.

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3.2.1 Operation of Scanner
 The primary function of any scanner is to convert measured quantities of light to electrical
analogs. The light that is measured may be light that has been transmitted through the
material, as would be the case when film transparencies are scanned, or the light that is
measured could be that which is reflected from the surface of a map or photograph.
 For GIS and other computer applications, the electrical analogs are subsequently converted
to a binary form suitable for computer processing. If the output of the scanner is to be used
as input to a GIS, care must be taken to preserve the spatial integrity of the item being
scanned.
 Preservation of the spatial integrity is normally accomplished by describing the scanned
document as an orthogonal array of grid cells (raster array). Each grid cell represents an
instantaneous field of view within which the scanner makes a measurement. The manner in
which the grid cell is defined depends upon the particular scanner being used.
 The following four types of scanner are commonly used in GIS and remote sensing.
Fig. 3.2.1 Major types of scanner
a. Mechanical scanner
It is called drum scanner since a map or an image placed on a drum is digitized mechanically
with rotation of the drum and shift of the sensor as shown in Fig. 3.2.1(a). It is accurate but slow.

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b. Video Camera
Video camera with CRT (cathode ray tube) is often used to digitize a small part of map of firm.
This is not very accurate but cheap. (See Fig. 3.2.1(b))
c. CCD Camera
Area CCD camera (called digital still camera) instead of video camera will be also convenient
to acquire digital image data (See Fig. 3.2.1 (c)). It is more stable and accurate than video camera.
d. CCD Scanner
Flat bed type or roll feed type scanner with linear CCD (charge coupled device) is now
commonly used to digitize analog maps in raster format, either in mono-tone or color mode. It is
accurate but expensive. (See Fig. 3.2.1 (d)).
Type Resolution Accuracy Cost
Mechanical scanner Very high
( 25 - 100 μm)
Very high High
Video camera Low
(500 x 500 pixels)
Low Cheap
CCD camera Medium cheap
(500 x 500 pixels) High
( 4000 x 4000 pixels)
Medium Cheap (low resolution)
High ( High resolution)
CCD scanner Very High High High
Table 3.2.1 Shows the performance of major scanners
3.2.2 Types of Scanners
 There are several different types of scanners performing
the same job but handling the job differently using
different technologies and producing results depending on
their varying capabilities.
 Hand-held scanners although portable, can only scan
images up to about four inches wide. They require a very
steady hand for moving the scan head over the document.
They are useful for scanning small logos or signatures and
are virtually of no use for scanning maps and photographs.
Fig. 3.2.2 Hand held scanner

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Flatbed Scanner
 The most commonly used scanner is a flatbed scanner also
known as desktop scanner. It has a glass plate on which the
picture or the document is placed. The scanner head placed
beneath the glass plate moves across the picture and the result is
a good quality scanned image. For scanning large maps or
toposheets wide format flatbed scanners can be used.
Fig.3.2.3 Flatbed scanner
Drum Scanner
 Then there are the drum scanners which are mostly used by
the printing professionals. In this type of scanner, the image
or the document is placed on a glass cylinder that rotates at
very high speeds around a centrally located sensor
containing photo-multiplier tube instead of a CCD to scan.
Prior to the advances in the field of sheet fed scanners, the
drum scanners were extensively used for scanning maps and
other documents.
Fig. 3.2.4 Rotating drum scanner
3.2.3 Methods of Scanning
 Scanning captures map features, text, and symbols as individual cells, or pixels, and produce
an automated image.
 Based on the document to be scanned there are different scanning procedures followed.
Black and White Raster Scanning :
 Image scanned in B&W
 Image scanned in B&W Black and white or “binary” scanning is the simplest method of
converting any document and can be performed on line drawings, reduced media, text or any
one colour document.
 This is the appropriate solution for archiving and storage projects, in which the documents
will be viewed and printed but never changed.
 It is, therefore, an ideal solution as the first stage in a planned document conversion project.
Grey Scale and Colour Raster Scanning :
 Image scanned in greyscale.

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 Image scanned in Gray colour scale and (especially) colour images can be quite large.
 It must be made sure that the system is capable of handling files whose size is often
measured in tens of megabytes.
 Because virtually every pixel is populated with a value, an attempt to compress the file
results in little or no reduction in file size.
3.2.4 Limitations in use of Scanners
 Hard copy maps are often unable to be removed to where a scanning device is available, e.g.
most companies or agencies cannot afford their own scanning device and therefore must
send their maps to a private firm for scanning;
 Hard copy data may not be in a form that is viable for effective scanning, e.g. maps are of
poor quality, or are in poor condition;
 Geographic features may be too few on a single map to make it practical, cost-justifiable, to
scan;
 Often on busy maps a scanner may be unable to distinguish the features to be captured from
the surrounding graphic information, e.g. dense contours with labels;
 With raster scanning there it is difficult to read unique labels (text) for a geographic feature
effectively; and
 Scanning is much more expensive than manual digitizing, considering all the
cost/performance issues.
3.3 Raster Data File Formats
 Raster data represents the world as a surface divided into regular grid of cells. Raster data
models are useful for storing data that varies continuously, as in an aerial photograph, a
satellite image or an elevation surface.
 There are two types of raster data : Continuous and discrete. Raster stores the data in the
type of digital image represented by reducible and enlargeable grids and these grid of
cells contains a value representing information, such as temperature, discrete data represents
features such as land-use or soils data.
 Raster data provides a matrix of cells with values representing a coordinate and sometimes
linked to an attribute table and it is much simpler for many layers combinations. Raster data
is very easy to modify or program due to simple data structure.
 Rasters are in part defined by their pixel depth. Pixel depth defines the range of distinct
values the raster can store. For example, a 1-bit raster can only store 2 distinct values :
0 and 1.

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 There is a wide range of raster file formats used in the GIS world. Some of the most popular
ones are listed below.
Tagged Image File Formats (TIFF)
 This format is associated with scanners. It saves the scanned images and reads them. TIFF
can use run length and other image compression schemes. It is not limited to 256 colors like
a GIF.
GEO-TIFF
 As part of a header in a TIFF format it puts Lat/Long at the edges of the pixels.
Graphic Interchange Format (GIF)
 Graphic Interchange Format. A file format for image files, commonly used on the Internet. It
is well-suited for images with sharp edges and relatively few gradations of color.
Joint Photograph Experts Group (JPEG)
 JPEG is a common picture format. It uses a variable-resolution compression system offering
both partial and full resolution recovery.
DEM
 Digital Elevation Models or DEM have two types of displays. The first is 30-meter elevation
data from 1:24,000 seven-and-a-half minute quadrangle map. The second is the 1:250,000 3
arc-second digital terrain data. DEMs are produced by the National Mapping Division of
USGS.
Band Interleaved by Pixel (BIP), Band Interleaved by Line (BIL)
 BIP and BIL are formats produced by remote sensing systems. The primary difference
among them is the technique used to store brightness values captured simultaneously in each
of several colors or spectral bands.
RS Landsat
 Landsat satellite imagery and BIL information are used in RS Landsat. In one format, using
BIL, pixel values from each band are pulled out and combined. Programs that use this kind
of information include IDRISI, GRASS, and MapFactory. It is fairly easy to exchange
information from within these raster formats.
Portable Network Graphics (PNG)
 Provides a well-compressed, lossless compression for raster files. It supports a large range of
bit depths from monochrome to 64-bit color. Its features include indexed color images of up
to 256 colors and effective 100 percent lossless images of up to 16 bits per pixel.
Single file - extension *.png

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JPEG File Interchange Format (JFIF)
A standard compression technique for storing full-color and grayscale images. Support for
JPEG compression is provided through the JFIF file format.
Single file - extension *.jpg, *.jpeg, *.jpc, or *.jpe
World file - extension *.jgw
ArcCatalog only recognizes the .jpg file extension by default. To add .jpeg or .jpe files to
ArcMap without renaming them, add those file extensions to ArcCatalog or drag those files from
Windows Explorer into your map.
3.4 Digitizer - Vector Data Input
 Digitizing is the process of interpreting and converting paper map or image data to vector
digital data.
 Digitizing is the process by which coordinates from a map, image, or other sources of data
are converted into a digital format in a GIS. This process becomes necessary when available
data is gathered in formats that cannot be immediately integrated with other GIS data.
 Digitization results in shape files, which are vector features.
 Manual digitization is a tedious job and if operator is not efficient it may lead to several
digitizing errors. Hence, it has to be done with most skill and caution.
 Manual digitizing is a tedious job. Operator fatigue (eye strain, back soreness, etc.) can
seriously degrade the data quality. Managers must limit the number of hours an operator
works at one time. A commonly used quality check is to produce a verification plot of the
digitized data that is visually compared with the map from which the data were originally
digitized.
 Tablet digitizers with a free cursor connected with a personal computer are the most
common device for digitizing spatial features with the plainmetric coordinates from analog
maps. The analog map is placed on the surface of the digitizing tablet as shown in figure.
The size of digitizer usually ranges from A3 to A0 size.
Fig. 3.4.1 Tablet digitizer

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The digitizing operation is as follows
Step 1 : A map is affixed to a digitizing table.
Step 2 : Control points or tics at four corners of this map sheet should be digitized by the
digitizer and input to PC together with the map coordinates of the four corners.
Step 3 : Map contents are digitized according to the map layers and map code system in either
point mode or stream mode at short time interval.
Step 4 : Editing errors such as small gaps at line junctions, overshoots, duplicates etc. should
be made for a clean dataset without errors.
Step 5 : Conversion from digitizer coordinates to map coordinates to store in a spatial database.
Major problems of map digitization are :
- The map will stretch or shrink day by day which makes the newly digitized points slightly off
from the previous points.
- The map itself has errors.
- Discrepancies across neighbouring map sheets will produce disconnectivity.
Manual digitizing has many advantages. These include :
 Low capital cost, e.g. digitizing tables are cheap;
 Low cost of labour;
 Flexibility and adaptability to different data types and sources;
 Easily taught in a short amount of time - an easily mastered skill
 Generally the quality of data is high;
 Digitizing devices are very reliable and most often offer a greater precision that the data
warrants; and
 Ability to easily register and update existing data.
Heads-up digitization
This method uses scanned copy of the map or image and digitization is done on the screen of
the computer monitor. The scanned map lays vertical which can be viewed without bending the
head down and therefore is called as heads up digitization. Semi-automatic and automatic methods
of digitizing requires post processing but saves lot of time and resources compared to manual
method .

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Heads-down digitization
Digitizers are used to capture data from hardcopy maps. Heads down digitization is done on a
digitizing table using a magnetic pen known as Puck. The position of a cursor or puck is detected
when passed over a table inlaid with a fine mesh of wires. The function of a digitizer is to input
correctly the coordinates of the points and the lines. Digitization can be done in two modes.
Point mode : In this mode, digitization is started by placing a point that marks the beginning of
the feature to be digitized and after that more points are added to trace the particular feature (line or
a polygon). The number of points to be added to trace the feature and the space interval between
two consecutive points are decided by the operator
Stream mode : In stream digitizing, the cursor is placed at the beginning of the feature, a
command is then sent to the computer to place the points at either equal or unequal intervals as per
the position of the cursor moving over the image of the feature
3.5 Topology
 Topology expresses explicitly the spatial relationships between connecting or adjacent
vector features (points, polylines and polygons) in a GIS, such as two lines meeting perfectly
at a point and directed line having an explicit left and right side.
 Topological or topology based data are useful for detecting and correcting digitizing error in
geographic data set and are necessary for some GIS analyses.
 Topologic data structures help insure that information is not unnecessarily repeated. The
database stores one line only in order to represent a boundary (as opposed to two lines, one
for each polygon). The database tells us that the line is the “left side” of one polygon and the
“right side” of the adjacent polygon.
 Topology is the study of those properties of geometric objects that remain invariant under
certain transformations such as bending or stretching.
 Topology is often explained through graph theory.
 Topology has least two main advantages.
i) The assurance of data quality
ii) Enhance GIS analysis
 Topological relationships are built from simple elements into complex elements: points
(simplest elements), arcs (sets of connected points), areas (sets of connected arcs), and routes
(sets of sections, which are arcs or portions of arcs).

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3.5.1 Components of Topology
Topology has three basic components :
1. Connectivity (Arc - Node Topology) :
 Points along an arc that define its shape are called vertices.
 Endpoints of the arc are called nodes.
 Arcs join only at the nodes.
Fig. 3.5.1 Arc-Node topology with list
2. Area Definition / Containment (Polygon - Arc Topology) :
 An enclosed polygon has a measurable area.
 Lists of arcs define boundaries and closed areas are maintained.
 Polygons are represented as a series of (x, y) coordinates that connect to define an area.
Fig. 3.5.2 Polygon arc topology
3. Contiguity (Adjacency) :
 Every arc has a direction
 A GIS maintains a list of Polygons on the left and right side of each arc.
 The computer then uses this information to determine which features are next to one another.

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Fig. 3.5.3 Polygon topology
Generally, topology is employed to do the following :
 Manage coincident geometry (constrain how features share geometry). For example,
adjacent polygons, such as parcels, have shared edges; street centerlines and the boundaries
of census blocks have coincident geometry; adjacent soil polygons share edges; etc.
 Define and enforce data integrity rules (such as no gaps should exist between parcel features,
parcels should not overlap, road centerlines should connect at their endpoints).
 Support topological relationship queries and navigation (for example, to provide the ability
to identify adjacent and connected features, find the shared edges, and navigate along a
series of connected edges).
 Support sophisticated editing tools that enforce the topological constraints of the data model
(such as the ability to edit a shared edge and update all the features that share the common
edge).
 Construct features from unstructured geometry (e.g., the ability to construct polygons from
lines sometimes referred to as "spaghetti").
3.5.2 Topology in Different GIS Format
1. Coverage
 Coverage is a topology based vector data format. Coverage can be a point coverage, line
coverage, or polygon coverage.
 The coverage model supports three basic topological relationships.
Connectivity : Arc connects to each other at nodes.
Area definition : An Area is defined by a series of connected arcs.
Contiguity : Arcs have directions and left and right polygon.
2. Shapefile
 Shapefile is a standard non topological data format. Shape file are a first attempt an object
spatial features.

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 They are very simple floating point geometry feature. A Shapefile is a digital vector storage
format for storing geometric location and associated attribute information.
A shapefile is actually a set of several files
 .shp - shape format; the feature geometry itself
 .shx - shape index format; a positional index of the feature geometry to allow seeking
forwards and backwards quickly
 .dbf - attribute format; columnar attributes for each shape, in dBase III format
The geometry of a shapefile is stored in two basic files .shp and .shx :
3. DXF (Drawing exchange format)
It maintains data in separate layers. But it does not support topology. It is AutoCAD format.
4. Geodatabase
A geodatabase is a relational database that store geographic information.
 It is object-oriented model not a georelational.
 A relational database is a collection of tables logically associated with each other by
common key attribute field.
 A geodatabase can store geographic information because, besides storing a number or a
string in a attribute field; tables in a geodatabase can also store geometric coordinates to
define the shape and locations of points, lines or polygon.
Georelational Object based
Topological Coverage Geodatabase
Non-Topological Shapefile Geodatabase
3.6 Topological Consistency Rules
 Geodatabase topology rules allow you to define relationships between features in the same
feature class or subtype or between two feature classes or subtypes. The status of a topology,
including errors and exceptions, is saved to the source geodatabase.
Points
Must be Coincident With
Points in one feature class or subtype must be coincident
with points in another feature class or subtype. Use this rule
when points from one feature class or subtype should be
aligned with points from another feature class or subtype.
for example, when service meters must be coincident with
service points in an electric utility network.

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Must Be Disjoint
Points cannot overlap within the same feature class or
subtype. Use this rule when points within one feature class
or subtype should never occupy the same space, for
example, when fittings in a water distribution network
should not overlap.
Must Be Covered By Boundary Of
Points in one feature class or subtype must touch
boundaries of polygons from another feature class or subtype.
Use this rule when you want points to be on or inside the
boundaries of polygons, for example, when utility service
points are required to be within the boundary of a parcel.
Must Be Properly Inside Polygons
Points in one feature class or subtype must be inside
polygons of another feature class or subtype. Use this rule
when you want points to be completely within the boundaries
of polygons, for example, when state capitals must be inside
each state.
Must Be Covered By Endpoint Of
Points in one feature class or subtype must be covered by the
ends of lines in another feature class or subtype. Use this rule
when you want to model points that are coincident with the ends
of lines, for example, when street intersections must be covered
by the endpoints of street centerlines.

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Point Must be Covered by Line
Points in one feature class or subtype must be covered by lines in
another feature class or subtype. Use this rule when you want to model
points that are coincident with lines, for example, when monitoring
stations must fall along streams.
POLYLINE
Must be Larger than Cluster Tolerance
The cluster tolerance is the minimum distance between the vertices that make up a feature.
Vertices that fall within the cluster tolerance are determined to be
coincident. This rule is mandatory for a topology and applies to all
polyline feature classes.
Must Not Overlap
Lines must not overlap any part of another line within a feature class or
subtype. Lines can touch, intersect, and overlap themselves. Use this rule with
lines that should never occupy the same space with other lines, for example,
when lot lines cannot overlap one another.
Must Not Intersect
Lines must not cross or overlap any part of another line within the same
feature class or subtype. Use this rule with lines whose segments should never
cross or occupy the same space with other lines, for example, when lot lines
cannot intersect or overlap, but the endpoint of one feature can touch the
interior of another feature.
Must Not Have Dangles
The end of a line must touch any part of one other line or any part of itself within
a feature class or subtype. Use this rule when you want lines in a feature class or
subtype to connect to one another, for example, when a street network has line
segments that connect. In this example, you can set exceptions to this rule for road
segments that end at terminate with dead-ends.

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POLYGON
Must be Larger than Cluster Tolerance
The cluster tolerance is the minimum distance between the
vertices that make up a feature. Vertices that fall within the cluster
tolerance are determined to be coincident. This rule is mandatory
for a topology and applies to all polygon feature classes.
Must Not Overlap
Requires that polygons must not overlap within a feature class or subtype. Polygons can be
disconnected, touch at a point, or touch along an edge. Use this rule to
make sure that no polygon feature overlaps another polygon feature in
the same feature class or subtype, for example, when administrative
boundaries such as ZIP Codes or voting districts, or mutually exclusive
area classifications such as land form types cannot have any overlaps.
Must Not Have Gaps
Requires that polygons must not have a void between them within a
feature class or subtype. Use this rule when all of your polygons should
form a continuous surface with no voids or gaps, for example, when soil
polygons cannot include gaps or form voids and must form a continuous
fabric.
Must Not Overlap with
Polygons of the first feature class or subtype must not overlap
polygons of the second feature class or subtype. Use this rule when
polygons from one feature class or subtype should not overlap
polygons of another feature class or subtype, for example, when
lakes and land parcels from two different feature classes must not
overlap.
Must be Covered by Feature Class of
The polygons in the first feature class or subtype must be covered
by the polygons of the second feature class or subtype. Use this rule
when each polygon in one feature class or subtype should be covered
by all the polygons of another feature class or subtype, for example,
when states are covered by counties.

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Must Cover Each other
All polygons in the first feature class and all polygons in the second feature class must cover
each other. This means that feature class one (1) must be
covered by feature class two (2), and feature class two (2)
must be covered by a feature class of feature class one (1). Use
this rule when you want the polygons from two feature classes
or subtypes to cover the same area, for example, when
vegetation and soils must cover each other.
Must be Covered by
Polygons in one feature class or subtype must be covered by a
single polygon from another feature class or subtype. Use this rule
when you want one set of polygons to be covered by some part of
another single polygon in another feature class or subtype, for
example, when countries must be covered by states.
Boundary Must be Covered by
Polygon boundaries in one feature class or subtype must be covered
by the lines of another feature class or subtype. Use this rule when
polygon boundaries should be coincident with another line feature class
or subtype, for example, when major road lines form part of outlines
for census blocks.
Area Boundary Must be Covered by Boundary of
The boundaries of polygons in one feature class or subtype must be
covered by the boundaries of polygons in another feature class or
subtype. Use this rule when the boundaries of polygons in one feature
class or subtype should align with the boundaries of polygons in another
feature class or subtype, for example, when subdivision boundaries are
coincident with parcel boundaries but do not cover all parcels.
Contains Point
Each polygon of the first feature class or subtype must contain
within its boundaries at least one point of the second feature class or
subtype. Use this rule to make sure that all polygons have at least one
point within their boundaries. Overlapping polygons can share a
point in that overlapping area, for example, when school district
boundaries must contain at least one school.

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3.7 Attribute Data Input and Management
 Attribute data describe the characteristics of the map feature.
 Attribute data are stored in tables
 Each row of a table represents a map feature.
 Each column represents a characteristic.
 The object-oriented data model stores both data in a single database, but can distinguish
spatial data from attribute data.
Linking Attribute Data and Spatial Data
 The georelational data model store spatial data and attribute data in separate files.
 Each map feature has unique label ID (Fig 3.7.1).
 Linked by feature ID, the two sets of data files can be queried, analyzed and displayed.
 Attribute data are stored in a table called feature attribute table (Fig 3.7.2).
 A row is called a record.
 A column is called a field.
Fig. 3.7.1 Attribute data linked to spatial data
Fig. 3.7.2 Attribute data table

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 Most GIS projects have many attributes.
 Data from both structures are linked together for use through unique identification numbers,
e.g. feature labels and DBMS primary keys.
 This coupling of spatial features with an attribute record is usually maintained by an internal
number assigned by the GIS software.
 A label is required so the user can load the appropriate attribute record for a given
geographic feature.
 Most often a single attribute record is automatically created by the GIS software once a
clean topological structure is properly generated.
 This attribute record normally contains the internal number for the feature, the user's label
identifier, the area of the feature, and the perimeter of the feature.
 Linear features have the length of the feature defined instead of the area.To store all
attributes in a single table is not efficient both time and computer space and difficult to use
and update. Most GIS packages include DBMS :
o INFO for Arc/Info
o MS Access for IDRISI, ArcView and ArcGIS
3.8 Open Database Connectivity
ODBC “Open Data Base Connectivity” A standard software API specification for using
database management systems (DBMS). A component of Windows Open Services Architecture
Independent of any programming language, database system and operating system
Open Database Connectivity-or ODBC-is an application programming interface (API) that lets
software connect with database management systems while remaining independent of them. This is
important, because it allows applications to interact with multiple databases simultaneously
using SQL (Structured Query Language).
Goals of ODBC
 Access any data from any application, regardless of which DBMS is handling the data Insert
a middle layer between an application and the database management system.
 A database driver this layer translates the application's data queries into commands that the
DBMS understands
 Allow application programs to use SQL to access data from any kinds of sources

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Some of the advantages of ODBC are :
 ODBC provides a consistent interface regardless of the kind of database server used.You can
have more than one concurrent connection.
 Applications do not have to be bound to each database on which they will run.
 Although COBOL for AIX does this bind for you automatically, it binds automatically to
only one database. If you want to choose which database to connect to dynamically at run
time, you must take extra steps to bind to a different database.
The ODBC architecture has four components :
 Application
 Driver Manager
 Driver
 Data Source
Application
Performs processing and calls ODBC functions to submit SQL statements and retrieve results.
A number of tasks are common to all applications, no matter how they use ODBC.
Taken together, they largely define the flow of any ODBC application. The tasks are :
 Selecting a data source and connecting to it.
 Submitting an SQL statement for execution.
 Retrieving results (if any).
 Processing errors.
 Committing or rolling back the transaction enclosing the SQL statement.
 Disconnecting from the data source.
Driver Manager
 Loads and unloads drivers on behalf of an application. Processes ODBC function calls or
passes them to a driver.
 The Driver Manager exists mainly as a convenience to application writers and solves a
number of problems common to all applications. These include determining which driver to
load based on a data source name, loading and unloading drivers, and calling functions in
driver
 Driver Processes ODBC function calls, submits SQL requests to a specific data source, and
returns results to the application. If necessary, the driver modifies an application's request so
that the request conforms to syntax supported by the associated DBMS.

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Drivers are libraries that implement the functions in the ODBC API. Each is specific to a
particular DBMS; Drivers expose the capabilities of the underlying DBMSs; they are not required
to implement capabilities not supported by the DBMS. The only major exception to this is that
drivers for DBMSs that do not have stand-alone database engines, such as Xbase, must implement
a database engine that at least supports a minimal amount of SQL.
Data Source consists of the data the user wants to access and its associated operating system,
DBMS, and network platform (if any) used to access the DBMS.
A data source is simply the source of the data. It can be a file, a particular database on a
DBMS, or even a live data feed. The data might be located on the same computer as the program,
or on another computer somewhere on a network.
The purpose of a data source is to gather all of the technical information needed to access the
data - the driver name, network address, network software, and so on - into a single place and hide
it from the user.
Fig.3.8.1 Four Components of Open Database Connectivity(ODBC)
3.9 GPS or Global Positioning System
What is GPS ?
GPS or Global Positioning System is a satellite navigation system that furnishes location and
time information in all climate conditions to the user. GPS is used for navigation in planes, ships,
cars and trucks also. The system gives critical abilities to military and civilian users around the
globe. GPS provides continuous real time, 3-dimensional positioning, navigation and timing
worldwide.
How does GPS System Work ?
The GPS system consists of three segments :
1) The space segment: the GPS satellites

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2) The control system, operated by the U.S. military,
3) The user segment, which includes both military and civilian users and their GPS equipment.
Fig 3.9.1 Three elements of GPS
Space Segment :
The space segment is the number of satellites in the constellation. It comprises of 29 satellites
circling the earth every 12 hours at 12,000 miles in altitude. The function of the space segment is
utilized to route/navigation signals and to store and retransmit the route/navigation message sent by
the control segment. These transmissions are controlled by highly stable atomic clocks on the
satellites. The GPS Space Segment is formed by a satellite constellation with enough satellites to
ensure that the users will have, at least, 4 simultaneous satellites in view from any point at the
Earth surface at any time.
Control Segment :
The control segment comprises of a master control station and five monitor stations outfitted
with atomic clocks that are spread around the globe. The five monitor stations monitor the GPS
satellite signals and then send that qualified information to the master control station where
abnormalities are revised and sent back to the GPS satellites through ground antennas. Control
segment also referred as monitor station.
User Segment :
The user segment comprises of the GPS receiver, which receives the signals from the GPS
satellites and determine how far away it is from each satellite. Mainly this segment is used for the
U.S military, missile guidance systems, civilian applications for GPS in almost every field. Most of

Comprehensive Introduction to Geographic Information Systems (GIS) Fundamentals and Applications

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