Introduction to GIS theory Class work. studied by Dr Imtiaz Ahmed Chandio (chairman CRP MUET Jamshoro

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Topic no 1: Introduction to GIS
GIS, a technology and information handling strategy:
G stands for geographic, so we know that GIS has something to do with geography. it implies
that locations of the data items are known, or can be calculated, in terms of Geographic
coordinates (Latitude, Longitude).
I stands for information, so we know that GIS has something to do with information, namely
geographic information. it implies that the data in a GIS are organized to yield useful
knowledge, often as coloured maps and images, but also as statistical graphics, tables, and
various on-screen responses to interactive queries.
S stands for system, so we know that GIS is an integrated system of geography and information
tied together. it implies that a GIS is made up from several inter-related and linked components
with different functions. Thus, GIS have functional capabilities for data capture, input,
manipulation, transformation, visualization, combinations, query, analysis, modelling and
output.
History of GIS:
When we come across a topic called civilizations, we get to know that the people then were
equally good at planning things, they used to make road maps and paintings or any other type
of artwork that consisted at least a small information based on the geography . It can be
granaries, entertainment spots or any other place, we can just say GIS is as old as history.
Definition of GIS:
A Basic definition of GIS
"A system for capturing, storing, checking, integrating, manipulating, analyzing and displaying
data which are spatially referenced to the Earth".
This is normally considered to involve a spatially referenced computer database and
appropriate applications software.

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What makes GIS so special?
 GIS handles SPATIAL information – Information referenced by its location in space.
 GIS makes connections between activities based on spatial proximity.
 An Evidence to show GIS is quite old London cholera epidemic 1854 As per their
mapping the red spots were the infected spots and places of death , the dots indicate
the water wells.
Historical Background:
This technology has developed from:
1. Digital cartography and CAD
2. Data Base Management System
Fig: No: 01 to show the digital cartography and CAD relates to the Data Base Menagement
system.
Conclusion:
GIS has proved to be a really useful application, its evolution with the changing times has been
totally progressive . With the changes in the latest hardware's and the software's GIS will be
used for the best of its purposes…!
1
2
3
ATTRIBUT
E
ID X,
Y1
2
3
ID
1
2
3
CAD System Data Base Management System

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Applications of GIS:
Applications of GIS generally fulfill the five M‘s of GIS:
1.Mapping 2. Measurement
3. Monitoring 4. Modeling
5. Management
Applications of GIS in different disciplines are explained below:
Political Science:
a) Analysis of election results
b) Predictive Modeling
Real Estate:
a) Neighborhood Land Prices
b) Traffic Impact Analysis
Business:
a) Demographic Analysis
b) Site Selection
c) Market penetration
Health care:
a) Epidemiology
b) Needs Analysis
Education Administration:
a) Enrollment Projections
b) School Bus Routing
Urban Planning And Management:
a) Zoning, Subdivision Planning
b) Economic Development
c) Emergency Response
d) Code Enforcement
e) Tax Assessment

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Urban Planning And Management:
a) Zoning, Subdivision Planning
b) Economic Development
c) Emergency Response
d) Code Enforcement
e) Tax Assessment
General Application of GIS in the field of City and Regional Planning
a) Monitoring land use change
b) Assessing the impact of urban settlements
c) Simulation of processes in the urban and natural environment
SCOPE & IMPORTANCE
The many important benefits in using GIS in urban planning include;
Improved mapping – better access to maps, improved map currency, more effective thematic
mapping, and reduced storage cost; Greater efficiency in retrieval of information; Faster and
more extensive access to the types of geographical information important to planning and the
ability to explore a wider range of ‘what if’ scenarios; Improved analysis; Better communication
to the public and staff; Improved quality of services, for example speedier access to information
for planning application processing. GIS finds a vital role in the field of Urban Planning. The
features of GIS which makes it important in the Urban Planning are,
a) Multi-source data can be entered and integrated;
b) Data consistency can be maintained;
c) Data updating can be easily undertaken; and
d) Flexible data storage and retrieval can be achieved.
Management and planning of urban space requires spatially accurate and timely information
on land use and changing pattern. Monitoring provides the planners and decision-makers with
required information about the current state of development and the nature of changes that
have occurred. Geographical Information system (GIS) provides vital tools which can be applied
in the analysis at the district and as well as at the city level.

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Topic no 2: Definition, Key Components and Functional Subsystem
Definition:
“A system for capturing, storing, checking, integrating, manipulating, analysing and
displaying data which are spatially referenced to the Earth. This is normally considered to
involve a spatially referenced computer database and appropriate applications software”
We can also defined GIS as follows:
“A special case of information system where the database consists of observation son
spatially distributed features, activities or events, which are definable in space as points, lines
or area. A geographic information systems manipulates data about these points, lines and areas
to retrieve data for ad hoc queries and analyses”
GIS is Unique:
GIS is unique tool because
a) GIS handles SPATIAL information
 Information referenced by its location in space
b) GIS makes connections between activities based on spatial proximity.
Nature of GIS: The study of GIS is a multidisciplinary or interdisciplinary field
Fig: No:02 Nature of GIS

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Components of a GIS:
A working GIS integrates five key components: hardware, software, data, people, and methods.
Fig No: 03 components of GIS
Hardware
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.
Software
GIS software provides the functions and tools needed to store, analyze, and display geographic
information. 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
Data:
Possibly the most important component of a GIS is the data. Geographic data and
related tabular data can be collected in-house or purchased from a commercial data provider. A
GIS will integrate spatial data with other data resources and can even use a DBMS, used by
most organizations to organize and maintain their data, to manage spatial data.

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People:
GIS technology is of limited value without the people who manage the system and
develop plans for applying it to real-world problems. GIS users range from technical specialists
who design and maintain the system to those who use it to help them perform their everyday
work.
Methods:
A successful GIS operates according to a well-designed plan and business rules, which
are the models and operating practices unique to each organization.
GIS Subsystem:
A GIS has four main functional subsystems. These are:
1. a data input subsystem;
2. a data storage and retrieval subsystem;
3. a data manipulation and analysis subsystem; and
4. a data output and display subsystem.
Data Input:
A data input subsystem allows the user to capture, collect, and transform spatial and
thematic data into digital form. The data inputs are usually derived from a combination of hard
copy maps, aerial photographs, remotely sensed images, reports, survey documents, etc.
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.
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

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Multispectral scanner data
(1 band)
thought of as the heart of a GIS, and usually distinguishes it from other database information
systems and computer-aided drafting (CAD) systems.
Data Output:
The data output subsystem allows the user to generate graphic displays, normally maps, and
tabular reports representing derived information products.
Topic No: 03 Raster Data model, Vector data model, Attribute data
model.
Raster Data modal:
A format for storing, processing, and displaying graphic data in which graphic images are stored
as values for uniform grid cells or pixels is called raster data model.
Raster data models incorporate the use of a grid-cell data structure where the geographic area
is divided into cells identified by row and column. This data structure is commonly called Raster.
Fig No: 04 To show the Raster data model

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Fig No: 05 the Raster View of the World
Elements of Raster Data modal:
 A raster data model can be a grid, a raster map, a surface cover, or an image in GIS.
 A raster represents a continuous surface; however, for data storage and analysis, a
raster is divided into rows, columns and cells.
 Cells are called ‘pixels’ with images
 The origin of rows and columns is typically at the upper-left corner of the raster
 Rows represent y-coordinates
 Columns represent x-coordinates
Advantages of raster data model:
 It has a simple data structure
 Overlay operations are easily and efficiently implemented.
 Area and polygon analysis is simple.
 Overlaying and merging are easily performed.
 Image processing techniques produce data for integration to GIS in a raster format.
 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.

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Disadvantages of raster data model:
 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 exists. 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. poor at representing points, lines and areas; good at
surfaces
 must often include redundant or missing data
 network linkages are difficult to establish
Vector data modal:
All spatial datamodelsare approachesforstoringthe spatial locationof geographicfeaturesina
database.Vectorstorage impliesthe use of vectors(directional lines) torepresentageographic feature.
Advantages of vector data model:
 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
Disadvantages of vector data model:
 The location of each vertex needs to be stored explicitly.
 For effective analysis, vector data must be converted into a topological structure.

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 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
Characteristics
 Features are positioned accurately
 Shape of features can be represented correctly
 Features are represented discretely (no fuzzy boundaries)
 Not good for representing spatially continuous phenomena
 Potentially complex data structure (especially for polygons);
 can lead to long processing time for analytical operations.
Attribute data model
A separate data model is 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). A variety of different data
models exist for the storage and management of attribute data. The most common are:
 tabular
 hierarchical
 network
 relational
 object Oriented
The tabular model isthe mannerinwhichmostearly GIS software packages stored their attribute data.
The next three models are those most commonly implemented in database management systems
(DBMS). The object oriented is newer but rapidly gaining in popularity for some applications. A brief
review of each model is provided.

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Tabular Model
The simple tabular model 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 any 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.
Hierarchical Model
The hierarchical database organizes data in a tree structure. Data is structured
downward in a hierarchy of tables. Any level in the hierarchy can have unlimited children, but
any child can have only one parent. Hierarchical DBMS have not gained any noticeable
acceptance for use within GIS. They are oriented for data sets that are very stable, where
primary relationships among the data change infrequently or never at all. Also, the limitation on
the number of parents that an element may have is not always conducive to actual geographic
phenomenon.
Network Model
The network database organizes data in a network or plex structure. Any column in a
plex structure can be linked to any other. Like a tree structure, a plex structure can be
described in terms of parents and children. This model allows for children to have more than
one parent.
Network DBMS have not found much more acceptance in GIS than the hierarchical
DBMS. They have the same flexibility limitations as hierarchical databases; however, the more
powerful structure for representing data relationships allows a more realistic modelling of
geographic phenomenon. However, network databases tend to become overly complex too
easily. In this regard it is easy to lose control and understanding of the relationships between
elements.
Relational Model
The relational database organizes data in tables. Each table, is identified by a unique
table name, and is organized by rows and columns. Each column within a table also has a

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unique name. Columns store the values for a specific attribute, e.g. cover group, tree height.
Rows represent one record in the table. In a GIS each row is usually linked to a separate spatial
feature, e.g. a forestry stand. Accordingly, each row would be comprised of several columns,
each column containing a specific value for that geographic feature. The following figure
presents a sample table for forest inventory features. This table has 4 rows and 5 columns. The
forest stand number would be the label for the spatial feature as well as the primary keyfor the
database table. This serves as the linkage between the spatial definition of the feature and the
attribute data for the feature.
UNIQUE STAND
NUMBER
DOMINANT
COVER GROUP
AVG. TREE
HEIGHT
STAND SITE
INDEX
STAND AGE
001 DEC 3 G 100
002 DEC-CON 4 M 80
003 DEC-CON 4 M 60
004 CON 4 G 120
Data is often stored in several tables. Tables can be joined or referenced to each other
by common columns (relational fields). Usually the common column is an identification number
for a selected geographic feature, e.g. a forestry stand polygon number. This identification
number acts as the primary key for the table. The ability to join tables through use of a common
column is the essence of the relational model. Such relational joins are usually ad hoc in nature
and form the basis of for querying in a relational GIS product. Unlike the other previously
discussed database types, relationships are implicit in the character of the data as opposed to
explicit characteristics of the database set up.
The relational database model is the most widely accepted for managing the attributes
of geographic

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There are many different designs of DBMSs, but in GIS the relational design has been
the most useful. In the relational design, data are stored conceptually as a collection of tables.
Common fields in different tables are used to link them together. This surprisingly simple design
has been so widely used primarily because of its flexibility and very wide deployment in
applications both within and without GIS.
Fig No:06 Relational Model of Attributes
In the relational design, data are stored conceptually as a collection of tables. Common
fields in different tables are used to link them together.
In fact, most GIS software provides an internal relational data model, as well as support
for commercial off-the-shelf (COTS) relational DBMS'. COTS DBMS' are referred to as external
DBMS'. This approach supports both users with small data sets, where an internal data model is
sufficient, and customers with larger data sets who utilize a DBMS for other corporate data
storage requirements. With an external DBMS the GIS software can simply connect to the
database, and the user can make use of the inherent capabilities of the DBMS. External DBMS'
tend to have much more extensive querying and data integrity capabilities than the GIS'

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internal relational model. The emergence and use of the external DBMS is a trend that has
resulted in the proliferation of GIS technology into more traditional data processing
environments.
The relational DBMS is attractive because of its:
 Simplicity in organization and data modelling.
 Flexibility: data can be manipulated in an ad hoc manner by joining tables.
 Efficiency of storage: by the proper design of data tables redundant data can be
minimized and
 The non-procedural nature: queries on a relational database do not need to take into
account the internal organization of the data.
The following diagram illustrates the basic linkage between a vector spatial data (topologic
model) and attributes maintained in a relational database file.
Fig NO: 07 Basic linkages between a vector spatial data (topologic model) and attributes
maintained in a relational database file (From Berry).

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Topic No: 4 Data Acquisition
Data Acquisition Means Acquiring or Collection Data
To, Collect Data it is very expensive in GIS activities
Types of Data Collection:
1. Data Capture (Direct Collection)
2. Data transfer
Topic No: 5 Data Capturing techniques and procedures, Data Transformation
Data Capture (Direct Collection)
 Capture specifically for GIS use
 Raster – remote sensing (e.g. SPOT & aerial photography)
 Passive and active sensors
 Resolution is key consideration
 Spatial
 Spectral
 Temporal
Two broad capture methods;
1-Primary(directmeasurement)
2-Secondary (indirectderivation)
VECTOR PRIMARY DATA CAPTURE
Surveying
 Locations of objects determines by angle and distance measurements from known
locations
 Uses expensive field equipment and crews
 Most accurate method for large scale, small areas

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GPS
 Collection of satellites used to fix locations on Earth’s surface
 Differential GPS used to improve accuracy
Imagery for GIS
Secondary Geographic Data Capture
 Data collected for other purposes can be converted for use in GIS
 Raster conversion
 Scanning of maps, aerial photographs, documents, etc
 Important scanning parameters are spatial and spectral (bit depth) resolution
Data transformation
Functions to transform a layer of one feature type to another.
or
Spatial analyses between different data (stored in different layers)
Some examples: – Point to line: interpolation (contour mapping)
• Point to polygon: buffering –
Polygon to polygon: dissolve/merge
Point-in-Polygon and Line-In-Polygon
 Point-in-Polygon is a topological overlay procedure which determines the spatial
coincidence of points and polygons.
 Points are assigned the attributes of the polygons within which they fall.
 Line-in-Polygon is a spatial operation in which lines in one coverage are overlaid with
polygonsof anothercoverage todetermine which lines, or portions of lines, are contained
within the polygons.
 Polygon attributes are associated with corresponding lines in the resulting line coverage.

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Geometric Transformations
Thisfunctionisconcernedwiththe registeringof a data layer to a common coordinate scheme.
This usually involves registering selected data layers to a standard data layer already registered. The
term rubber sheeting is often used to describe this function. Rubber sheeting involves stretching one
data layerto meetanotherbasedonpredefinedcontrol pointsof knownlocations. Two other functions
may be categorizedundergeometrictransformations.These involve warping a data layer stored in one
data model, either raster or vector, to another data layer stored in the opposite data model.
Map Projection Transformations
Thisfunctionalityconcernsthe transformationof datain geographic coordinates for an existing
map projection to another map projection. Most GIS software requires that data layers must be in the
same map projectionforanalysis.Accordingly,if dataisacquiredina differentprojectionthanthe other
data layers it must be transformed. Typically 20 or more different map projections are supported in a
GIS software offering.
Topic No: 06 Visualization of spatial data, layers and projections
Spatial Data
Spatial Data 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 is the data can be
mapped.
Characteristics of Spatial Data
 “Mappable” characteristics:
 Location (coordinate system)
 Size is calculated by the amount (length, area, perimeter) of the data
 Shape is defined as shape (point, line, area) of the feature
 Discrete or continuous
 Spatial relationship

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Nature of spatial data:
 Spatially referenced data “georeferenced”
 “Attribute” data associated with location.
 Example: spatial objects
 Points: x, y coordinates
 Cities stores, crimes, accidents.
 Lines: arcs, from node to node
 Road network, transmission lines
 Polygons: series of connected arcs
 Provinces, cities, census tracts
Point Spatial Data:
 A point is a 0 dimensional object and has only the property of location (x, y)
 Points can be used to Model features such as a well, building, power, pole, sample
location etc.
 Other name for a point are vertex, node.
Fig No: 08 Shows the Point Spatial Data
Line Spatial Data:
 A line is a one dimensional object that has the property of length.
 Lines can be used to represented road, streams, faults, maker beds, boundary, contacts
etc.
 In an arc info coverage an arc starts with a node, has zero of more vertices, and ends
with a node.
Fig No: 09 Shows the Line Spatial Data

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Polygon Spatial Data:
 A polygon is a two dimensional object with properties of area and perimeter.
 A polygon can represent a city, geologic formation, lake, river, etc.
 Other names for polygons are face and zone.
Fig No: 10 Show the Polygon Spatial Data
Three Classes of Spatial Data
1. Geo statistical Data
 Points as sample locations (“field” data as opposed to “objects”)
 Continuous variation over space
2. Lattice/ Regional Data
 Polygons or points (centroids)
 Discrete variation over space, observations associated with regular or irregular
areal units.
3. Point patterns
 Points on map (occurrences of events at locations in space)
 Observation of a variable are made at location X
 Assumption that spatial arrangement is directly related to the interaction
between units of abservation

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Geostatistical Data:
 Spatial Process
 Index set D is fixed subset of Rd (continuous)
 Data
 Sample points from underlying continuous surface
 Example: mining, air quality, house sales price.
Point patterns:
 Spatial process
 Index set D is point process, s is random
 Data
 Mapped pattern Fig No: 11 Geographic data
 Example: location of disease, gang shootings
 Research question
 Interest focuses on detecting absence of spatial randomness (cluster statistics)
Fig No: 12 show the gang relatedv/s Nongang related

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Lattice or Regional Data:
 Spatial process
 Index set D fixed collection of countably many points in Rd
 Finite, discrete spatial units.
 Data
 Fixed points or discrete location (region)
 Example: county tax rates, state unemployment
 Research question
 Interest focuses on statistical inference
 Estimation, specification tests.
Visualizationof Spatial Data
Fig No: 13 show the Visualizationof spatial data

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Layers:
In ArcGIS, a reference to a data source, such as a shapefile, coverage, geodatabase
feature class, or raster, that defines how the data should be symbolized on a map. Layers can
also define additional properties, such as which features from the data source are included.
Layers can be stored in map documents (.mxd) or saved individually as layer files (.lyr). Layers
are conceptually similar to themes in ArcView 3.
What are projections
Projections are a mathematical transformation that take spherical coordinates (latitude
and longitude) and transform them to an XY (planar) coordinate system. This enables you to
create a map that accurately shows distances, areas, or directions. With this information, you
can accurately work with the data to calculate areas and distances and measure directions. As
implemented in Geographic Information Systems, projections are transformations from
spherical coordinates to XY coordinates systems and transformations from one XY coordinate
system to another.
How can they help you?
Projections are chosen based on the needs of the map or data analysis and on the area
of the world. Projections are useful for a limited set of purposes or scales. Finally, projections
are based on local needs and standards.
Topic no 7: Map Design: symbols to portray Points, Lines and Volumes:
Maps
A map is a picture or representation of the Earth's surface, showing how things are
related to each other by distance, direction, and size. Maps are a way of showing many things
about a portion of the earth's surface on a flat piece of paper that can be carried and
transported easily.
A map is not a photograph of the Earth's surface. It can show many things that a picture
cannot show, and as a result, a map looks different in many ways from a photograph of the
Earth's surface.

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Reference maps:
Maps that give general information about the location of features are reference maps.
Maps in a road atlas are an example of reference maps, as are topographic maps.
Thematic or statistical maps:
Those that show the distribution of a specific topic are thematic or statistical maps.
A map showing population distribution by county is a thematic map.
Components of maps:
 Title It
The most basic component of a map is its title. The title should refer to everything the map
covers.
 Add a Legend
The legend should explain every feature or symbol contained on the map.
 Provide Perspective With a Scale
A scale is important to put into perspective for the reader the distances on the map and to
provide accurate navigational information for the user.
 Determine Compass Orientation
 All maps must have a compass orientation. Because the primary purpose of a map is to
provide and insight into directions, a map has to be able to show which way is which on
a compass.
 Date It
To give context to a map, the date of publication should be present.

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Map Types: Point Data:
Reference Topographic
Dot Picture Symbol
Graduated Symbol
Fig No: these figures shows the points data

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Map Types: Line Data
Network Flow
Isoline Fig No: 15 figures shows the line data Reference
Map Types: Volume Data
Gridded fishnet map Hill-shaded

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Image map
Fig No: 16 these figures shows the Volume data
Topic no 8: Graphic Variables, Visual hierarchy:
Graphic Variables
Graphic representation of spatial data or maps, thematic data, tables and network with
geographic reference and topology is very important to communicate geospatial data and the
results of spatial analysis to all users.
Graphic representation in GIS will be implemented in the form of graphs, maps and images with
XY plotters, dot printers, color monitors, color plotters etc. based on the knowledges of
cartography, computer graphics, color theory, semiology and psychology.
Following graphic variables are used to display quantity, order, difference or similarity.
Location: geographic location and spatial relation of points, lines and areas are displayed in 2D
space or map.
Size: Size of symbols and thickness of line represent quantitative difference. Physical difference
does not coincide with psychological impression.
Density: density, intensity or gray scale is used to represent order and difference. Density or
spacing of dot pattern or screen mesh should be carefully selected for the optimum gray
scaling.
Texture: cyclic or repeated pattern of data, lines or symbols will represent difference as well as
similarity.
Color: hue (H), intensity (I) and saturation (S) are aesthetically selected.
Orientation: directional pattern with hatching will represent difference as well as similarity.
Symbol: form of symbols will represent similarity of class or group.

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Visual hierarchy
Visual survey is the order in which the human eye perceives what it sees. It is used
in cartography to help the map designer create a product where the viewer processes the
information presented in order from the most important to the least important. This can be
achieved by manipulating different pieces of a map such as its color contrast, symbology,
texture, shape, position, scale, orientation and size. Jacques Bertin's graphic variables, or visual
variables, also work together to make the visual hierarchy of a map.
By focusing on visual hierarchy while designing a map, a cartographer can ensure the
main purpose for creating the map is understood by those viewing it. The viewers will also be
more persuaded to accept the message the map is trying to convey.
When an object is disconnected from the ‘whole’ the human eye notices the
disassociated object before it notices the ‘whole
An example in cartography is represented in the following map:
Fig No: 17 shows the cartography is represented in the map

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Topic No: 09 Spatial analysis: Overlay analysis, Neighborhood function:
Spatial analysis
Spatial analysis or spatial statistics includes any of the formal techniques which study
entities using their topological, geometric, or geographic properties. Spatial analysis includes a
variety of techniques, many still in their early development, using different analytic approaches
and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in
the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to
build complex wiring structures. In a more restricted sense, spatial analysis is the technique
applied to structures at the human scale, most notably in the analysis of geographic data.
Complex issues arise in spatial analysis, many of which are neither clearly defined nor
completely resolved, but form the basis for current research. The most fundamental of these is
the problem of defining the spatial location of the entities being studied.
Classification of the techniques of spatial analysis is difficult because of the large
number of different fields of research involved, the different fundamental approaches which
can be chosen, and the many forms the data can take.
Overlay analysis:-
One basic way to create or identify spatial relationships is through the process of spatial
overlay. Spatial overlay is accomplished by joining and viewing together separate data sets that
share all or part of the same area. The result of this combination is a new data set that
identifies the spatial relationships.
Fig No: 18 shows the overlay analysis

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The power of spatial overlay is illustrated by the project highlighted in the figure below.
Three layers of data were used in the analysis which was designed to identify the development
potential of land within the County. Polygons (enclosed areas) were assigned a rating based on
vegetation type, soil type and whether they were in the 100-year floodplain. Then the three
layers were combined to create a new layer which contained all the previous information.
Finally, a comprehensive rating was determined by performing a weighted average of the three
separate rating items. The result was a map contrasting suitable and unsuitable areas for
development based on the land characteristics.
Neighborhood function:
Neighborhood functions create output values for each cell location based on the location value
and the values identified in a specified neighborhood. The neighborhood can be of two types:
moving or search radius.
Moving neighborhoods can either be overlapping or non-overlapping. Overlapping
neighborhood functions are also referred to as focal functions and generally calculate a
specified statistic within the neighborhood. For example, you may want to find the mean or
maximum value in a 3 x 3 neighborhood. Variations of the overlapping neighborhood statistics
function are the high and low pass filter functions to smooth and accentuate data. The non-
overlapping neighborhood functions, or block functions, allow for statistics to be calculated in a
specified non-overlapping neighborhood. Block functions are particularly useful for changing
the resolution of a raster to a coarser cell size. The values assigned to the coarser cells can be
based on another calculation, such as the maximum value in the coarser cell as opposed to
using the default nearest neighbor interpolation.
Neighborhood operations are a method of analyzing data in a GIS environment. They are
especially important when a situation requires the analysis of relationships between locations,
rather than interpret the characteristics at individual locations.
The Scanning Cell and its Neighbourhood:
Neighborhood operations are commonly called ‘Focal Functions’ since each operation
performed generates a value for the ‘focus’ of a neighborhood. The neighborhood focus is
generally called the scanning cell and its neighbors – that is the cells surrounding it – are known

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as the scanning neighborhood. The scanning neighborhood can take on various sizes and
shapes, which are defined by selecting the appropriate options in the GIS package.
The most common neighborhood shapes are:
Fig No: 19 shows the Common neighborhood shapes
How Neighborhood Operations Work:
Neighborhood operations work by moving across a raster grid map, one cell at a time.
As each cell is visited, it becomes the scanning cell and a new value is computed for that cell as
a function of its scanning neighborhood. All computed values are then placed into the
corresponding cells of the output map/theme
Topic No: 10 Networks, Overlay Analysis and Buffering:
Measurements of a layer
 Distance (e.g., measure tool in ArcMap, a little “measure” icon on the main tool
bar)
 Areas (sometimes stored in the attribute table) average)
 Geo statistics
 Histogram
 Trend analysis
 Semivariograms
(Variance based on nearby samples; a check for spat
 In ArcMap,
 Customize, toolbars, geostatistical analyst
 Geostatistical analyst, explore data option

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7
Bufferzonesaround (a) point, (b) line,and (c) area
features
• Bufferat a specifieddistance;Ata distance fromanattribute field;
and Asmultiple ringsata definedincrement.
• In ArcMap: Geoprocessing,buffer;InArcToolbox:Analysistools,Proximity,
Buffer;
Vector Overlay –
Point-in-Polygon
• Point-in-Polygonisusedtofindoutthe polygoninwhichapoint
Falls.
• Example:Whichland coverdoeseachmeteorological stationfall into?
• Whyis there a probleminthe alternativeorder –polygontopoint?
Hint:What wouldthe attribute table looklike?

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Buffering
Buffering usually creates two areas: one area that is within a specified distance to selected real
world features and the other area that is beyond. The area that is within the specified distance
is called the buffer zone.
A buffer zone is any area that serves the purpose of keeping real world features distant from
one another. Buffer zones are often set up to protect the environment, protect residential and
commercial zones from industrial accidents or natural disasters, or to prevent violence.
Common types of buffer zones may be greenbelts between residential and commercial areas,
border zones between countries (see figure buffer zone), noise protection zones around
airports, or pollution protection zones along rivers.
in a GIS Application, buffer zones are always represented as vector polygons enclosing other
polygon, line or point features
Fig No: showthe bufferof the Points
Variations in buffering
There are several variations in buffering. The buffer distance or buffer size can
vary according to numerical values provided in the vector layer attribute table for each feature.
The numerical values have to be defined in map units according to the Coordinate Reference
System (CRS) used with the data