Among the techniques and tools capable of
assisting in resources identification, mapping, and
utilization, GIS ( Geographic Information System) has
had the most spectacular growth. Within a few years
of its formal introduction, GIS has come to influence
each and every dimension of resource science.
GIS are designed to accept, organize, statistically
analyse, and display diverse types of spatial data. These
aspects are digitally referenced to a common
coordinate system of particular projection and scale.
4. GIS and its subsystems
Marble and Peuquet (1983) defined four main
subsystems of GIS.
Input- get spatial and attribute data into the GIS. The
data is collected from various sources.
Preprocessing- organise data for retrieval and editing.
It allows managing, viewing and editing the database.
Analysis- perform tasks on the data. With this
subsystem, spatial analysis is conducted to create
output – create thematic maps, models and statistics.
5. Data Analysis Functions
Analyzing geographic data requires critical
thinking and reasoning. Patterns,
associations, connections, interactions, and
evidence of change through time and over
space are sought for. GIS functions assist in
analysis that helps to evaluate, estimate,
predict, interpret, and understand spatial
7. Selection and measurement
Selection is not an analysis function, but it is
an important first step for many analysis
functions. Due to its heavy use in the analytical
phase, however, it is included. Measurement is
easier to justify as an analytical process because
numbers that describe features are generated by
There are two selection processes-
1) Attribute query
2) Spatial selection
8. Attribute query(Boolean Selection)
An attribute query is a way to search for and retrieve
records of features in a set of data based on its
attribute values. Attribute query is a vector process.
An attribute query is any SQL ( Standard Query
Language) query used to select map features based on
their attribute values. Attribute queries use Boolean
algebra (AND, OR, XOR, NOT), set algebra (>, <, =, >=,
<=), arithmetic operators (=, -, *, /), and user-defined
values. Such queries are fundamental and an important
first step in defining, working with, and
analyzing GIS data. The GIS compares the values in an
attribute field with a query expression that the user
9. What is spatial analysis?
• Spatial analysis the crux of GIS because it
includes all of the transformations,
manipulations, and methods that can be
applied to geographic data to add value to
them, to support decisions, and to reveal
patterns and anomalies that are not
• Spatial analysis is the process by which we
turn raw data into useful information.
10. Types of Spatial Analysis
• Queries and reasoning
– Aspects of geographic data, length, area, etc.
– New data, raster to vector, geometric rules
• Descriptive summaries
– Essence of data in 1 or 2 parameters
• Optimization - ideal locations, routes
• Hypothesis testing - sample to entire pop.
11. Types of Spatial Analysis
• Queries and reasoning are the most basic of
analysis operations, in which the GIS is used
to answer simple questions posed by the user.
• No changes occur in the database and no new
data are produced.
12. • Measurements are simple numerical values
that describe aspects of geographic data.
• They include measurement of simple
properties of objects, such as length, area, or
shape, and of the relationships between pairs
of objects, such as distance or direction.
13. • Transformations are simple methods of
spatial analysis that change data sets by
combining them or comparing them to obtain
new data sets and eventually new insights.
• Transformations use simple geometric,
arithmetic, or logical rules, and they include
operations that convert raster data to vector
data or vice versa.
14. • Descriptive summaries attempt to capture
the essence of a data set in one or two
• They are the spatial equivalent of the
descriptive statistics commonly used in
statistical analysis, including the mean and
15. • Optimization techniques are normative in
nature, designed to select ideal locations for
objects given certain well-defined criteria.
• They are widely used in market research, in
the package delivery industry, and in a host of
16. • Hypothesis testing focuses on the process of
reasoning from the results of a limited sample to
make generalizations about an entire population.
• It allows us, for example, to determine whether a
pattern of points could have arisen by chance
based on the information from a sample.
• Hypothesis testing is the basis of inferential
statistics and forms the core of statistical analysis,
but its use with spatial data can be problematic.
17. Spatial analysis can be
• inductive, to examine empirical evidence in
the search for patterns that might support
new theories or general principles, in this case
with regard to disease causation.
• deductive, focusing on the testing of known
theories or principles against data
• normative, using spatial analysis to develop or
prescribe new or better designs
18. Spatial Selection
Spatial selection chooses features from the
In most cases, it selects features from one layer
that fall within or touches an edge
of polygon features in a second layer (or an
interactively drawn graphic polygon). Spatial
query operations generally are not available in
raster-based GIS packages even though these
packages have Standard Query Language
attribute data queries.
19. Overlay Analysis
Map overlay is an important technique for integrating
data derived from various sources and perhaps is the basic
key function in GIS data analysis and modelling surfaces.
Map overlay is a process by which it is possible to take two or
more different thematic maps of the same area and
overlay them on top of the other to form a composite new
This technique is used for the overlay of vector data on a
raster background image overlays where new spatial data
sets are created involving the merger of data from two or
more input data layers to create a new output data layer.
20. One of the most
important benefits of
overlay analysis of GIS
data is the ability to
multiple types of
from a range of sources.
Intersection computes the
geometric intersection of all
of the polygons in the input
layers (Figure A). Any
polygon or portion of a
polygon that falls outside of
the common area is
discarded from the output
layer. The new polygon
layer can possess the
attribute data of the
features in the input layers.
Union combines the
features of input polygon
layers ( Figure B). All
polygons from the input
layers are included in the
output polygon layer. It
can also possess the
combined attribute data
of the input polygon
24. • Clip-
Clip removes those
features (or portions of
features) from an input
polygon layer that overlay
with features from a clip
polygon layer (Figure
C). The clip layer removes
features (and portions of
features) that fall inside
the clip layer.
25. Vector overlay
Vector GIS displays the locations or all objects
stored using points and arcs. Attributes and entity
types can be displayed by varying colours, line
patterns, and point symbols.
There are three types of vector overlay operations:
1) Polygon on polygon is where one polygon layer is
superimposed over another polygon layer to create a
new output polygon layer.
The resultant polygons may contain some or all of the
attributes from the polygons in which they were
26. • Several types of polygon on polygon overlay
exist, including intersection (A and B), union
(A or B), and clip (A not B).
• The Boolean operators work both on the
attribute table and the geography.
27. 2)Point in polygon is where a layer of point features is
superimposed over a layer of polygon features. The
two layers produce a point layer that includes
attributes from the surrounding input layer polygons
. Other point attributes can be aggregated (summed,
averaged, etc.) and included as attributes in the
polygon’s data file. The transferring of attributes based
on their geographic position is called a spatial join.
3) Line on polygon is similar to point in polygon, but lines
are superimposed on polygons. This type of spatial
join either joins polygon attributes to line features
falling within them or counts and aggregates line
attribute data to the polygon layer’s data file.
28. Raster Overlay
Raster data structure is represented by grid
cells. A point is represented by a single cell, a
line, by a string of cells and an area, by a
group of cells. Raster overlay can be
performed by using map algebra or
mathematics. Using map algebra, input layers
may be added, subtracted, multiplied or
divided to produce an output value.
33. Neighbourhood Operations
• Neighbourhood operations, also called proximity
analyses, consider the characteristics of
neighbouring areas around a specific
location. These functions either modify existing
features or create new feature layers, which are
influenced, to some degree, by the distance from
• All GIS programs provide some neighbourhood
analyses, which include buffering, interpolation,
Theissen polygons, and various topographic
Buffering creates physical zones around
features. These “buffers” are usually
based on specific straight-line distances
from selected features ( in
Figure). Buffers, common to both raster
and vector systems, are created around
point, line, or polygon features. The
resulting buffers are placed in an output
polygon feature layer. Once complete,
buffer layers are used to determine which
features (in other layers) occur either
within or outside the buffers (spatial
queries), to perform overlay, or to
measure the area of the buffer
zone. They are the most used
Figure a: Buffering around a selected line
Interpolation is a method of predicting
unknown values using the known values at
neighbouring locations. Since it is impractical
to take measurements at all locations across
an area due to money, time, legal, and physical
constraints, interpolation between known
pixel values (sampled locations) is done. With
interpolation, a continuous surface like
elevation, temperature, and soil characteristics
can be created. Because of its continuous
nature, interpolation is only available within
Figure b: Interpolating between point features.
The red dots are the points where values are
The grey cells are the estimated data based on
36. • Theissen polygons
Theissen polygons are
boundaries created around
a set of points in such a way
that the polygon boundaries
are equidistant from the
points. Thiessen Polygons
are basically used to predict
the values at surrounding
points from a single point
Figure c: Creating Theissen
polygons from point
37. • Topographic Functions-
Topography refers to the surface
characteristics ie the hills, valleys and plains of
which it is comprised, the topography is
defined by the elevation of each location
within the area.
Topographic functions are used to
calculate values that describe the topography
at a specific geographic location.
Digital Elevation Models (DEMs) are
used to represent a terrain’s surface.
38. The most commonly calculated terrain
parameters by using the elevation data of the
neighbouring points are
• Slope ( rate of change of elevation)
• Aspect ( The direction that a surface faces)
• Hillshading ( A lighting effect which mimics
the sun to highlight hills and valleys)
40. Connectivity Analysis
Connectivity analysis use functions that
accumulate values over an area travelled. Most
often, these include the analysis of surfaces and
networks. Connectivity analyses include network
analysis, spread functions, and visibility analysis.
Vector-based systems generally focus on
network analysis capabilities. Raster-based
systems provide visibility analysis and
sophisticated spread function capabilities.
41. • Spread Functions-
Spread Functions are raster analysis
techniques that determine paths through space
by considering how phenomena spread over an
area in all directions but with different
• Network Analysis-
Network Analysis involve analysing the flow
of networks- a connected set of lines and point
nodes. These linear networks most often
represent features such as rivers, transportation
corridors and utilities.
• GIS uses spatial analysis functions to answer
questions about the real world.
• Once the data input process is complete and
the GIS layers are preprocessed, the analysis
stage can be initiated.
• Data analysis helps to evaluate, estimate,
predict, interpret, and understand spatial
• M. Anji Reddy ,Textbook of Remote sensing and GIS,
Fourth Edition, pg 421-439
• P. A. Burrough , Principles of Geographical Information
Systems for Land Resources Assessment