Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Introduction to GIS
Dr. A.K.M. Saiful Islam
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Presentation outline
Introduction to GIS
Components of GIS
Sources of geospatial data
Geospatial databases
ESRI Data models
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Introduction to GIS
What is GIS ?
An Information System that is used to input,
store , retrieve, manipulate, analyze and
output geographically referenced data or
geospatial data, in order to support decision
making for planning and management of
land use, natural resources, environment,
transportation, urban facilities, and other
administrative records
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Why GIS is essential ?
Common problems of handing geospatial
information:
Geospatial data are poorly maintained.
Maps and statistics are out of date.
Data and information are inaccurate.
There is no data retrieval service.
There is no data sharing.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GIS Versus Manual Works
Maps
GIS
Manual works
Storage
Standardized
and
integrated
Different scales
on different
standard
Retrieval
Digital
Database
Paper Maps,
Census, Tables
Updating
Search by
Computer
Manual Check
Overlay
Very Fast
Expensive &
Time consuming
Spatial
Analysis
Easy
Complicated
Display
Cheap & Fast
Expensive
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Benefits once GIS is implemented
 Geospatial data are better maintained in a standard
format.
 Revision and updating are easier.
 Geospatial data and information are easier to search,
analysis and represent.
 More value added product.
 Geospatial data can be shared and exchanged freely.
 Productivity of the staff improved and more efficient.
 Time and money are saved.
 Better decision can be made.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Remote Sensing and GIS in Water Management © Dr. Saiful Islam, IWFM, BUET
Basic Functions of GIS
Functions
Sub-functions
Data Acquisition
and prepossessing
Digitizing, Editing , Topology Building, Projection
Transformation, Format Conversion etc.
Database
Management and
Retrieval
Data Archival, Hierarchical Modeling , Network
Modeling, Relational Modeling, Attribute Query,
Object-oriented Database etc.
Spatial
Measurement and
Analysis
Measurement operations, Buffering, Overlay
operations, connectivity Operations etc.
Graphic output and
Visualization
Scale Transformation, Generalization,
Topological Map, Statistical Map etc.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GIS as Multidisciplinary Science
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Area of GIS Applications
Area
GIS Applications
Facilities
Management
Locating underground pipes & cables, planning
facility maintenance, telecommunication network
services
Environmental and
Natural Resources
Management
Environmental impact analysis, disaster
management and mitigation
Street Network
Locating houses and streets, car navigation,
transportation planning
Planning and
Engineering
Urban planning, regional planning, development
of public facilities
Land Information
Taxation, zoning of land use, land acquisition
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GIS for
decision
support
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Components of GIS
• Key components of
GIS are:
– Computer system,
geospatial data, and
users
• Sources of geospatial
data are:
– Digitized maps, aerial
photographs, satellite
images, statistical tables,
and other related
documents
Computer
System
Geospatial
Data
Users
Figure: Key components of GIS
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Classification of Geospatial Data
 Graphical data
(called
geometric data)
 Attributes
(called thematic
data)
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Data Model
• A set of guidelines to convert the real world (called
entity) to the digitally and logically represented spatial
objects consisting of the attributes and geometry.
• Types of geometric data model
– Vector Model
- Model uses discrete points, lines and/or areas
corresponding to discrete objects with name or
code number of attributes
– Raster Model
- Model uses regularly spaced grid cells in
specific sequence. An element of grid cell is
called a pixel (picture cell)
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Example of vector based model
Vector model
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Example of raster representation
256 color
more colors
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Components of Raster model
Raster model, otherwise known as a
raster dataset (image), in its simplest
form is a matrix (grid) of cells.
Cell size- Each cell has a width and
height and is a portion of the entire
area represented by the raster
Cell value - Each cell has a value.
Cell location - The location of each
cell is defined by its row or column
location within the raster matrix.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Geometry and Topology of
Vector Data
• Geometry
– Spatial objects are classified into
• point object such as meteorological station,
• line object such as highway and
• area object such as agricultural land,
– which are represented geometrically by point, line and
area respectively
• Topology
– refers to the relationships or connectivity between
spatial objects
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Topological of Spatial Objects
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Attributes
• Attributes are often termed "thematic data" or
"non-spatial data", that are linked with spatial data
or geometric data.
• An attribute has a defined characteristic of entity in
the real world.
• Attribute can be categorized as normal, ordinal,
numerical, conditional and other characteristics.
• Attribute values are often listed in attribute tables
which will establish relationships between the
attributes and spatial data such as point, line and
area objects, and also among the attributes
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Map Layers
• Spatial objects in digital
representation can be
grouped into layers.
• For example, a map
can be divided into a
set of map layers
consisting of contours,
boundaries, roads,
rivers, houses, forests
etc.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Remote Sensing and GIS in Water Management © Dr. Saiful Islam, IWFM, BUET
Sources for GIS data
 Analog maps
 Aerial photographs
 Satellite image
Ground survey with GPS
Reports and publications
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Data Acquisition Methods
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Concept of Spatial Database
• A spatial database is defined as a collection of interrelated geospatial data, that can handle and maintain a
large amount of data which is shareable between
different GIS applications.
• Required functions of a spatial database are as follows.
- consistency with little or no redundancy.
- maintenance of data quality including updating
- self descriptive with metadata.
- high performance by database management system
with database language.
- security including access control.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Design of Spatial Database
The following parameters should be well designed.
• storage media
Volume, access speed and on line service should be
considered.
• partition of data
Choice of administrative boundaries, map sheets,
watersheds etc. will be made in consideration of GIS
applications
• standards
Format, accuracy and quality should be standardized.
• change and updating
Add, delete, edit and update should be well controlled
by the database manager.
• scheduling
Data availability, priorities, data acquisition etc.
should be well scheduled.
• security
Copyright, back up system and responsibilities
should be well managed.
partition of data
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Spatial Data Models
1.
2.
Hierarchical
Relational
3.
Object oriented
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
1. Hierarchical Model
• Stores data as hierarchically related to
each other. Record shape are tree
structure.
BUET
Faculty of
Civil Engineering
CE
WRE
Faculty of
Architectural
URP
Archit..
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
(Contd..)
• Advantages
– High speed access to large databases
– Easy to update- (to add or delete new nodes)
• Disadvantages
– Links are only possible in Vertical Direction (from
top to bottom) but not for horizontal or diagonal
unless they have same parents.
– For example, it is hard to find what is the relation
between URP and DCE from this data model.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
2. Relational Model
Employee Table
• Based on two
important concepts:
– Key of relation one to one, one to
many, many to many
Employee Name
ID
Course
ID
1
Rahim
001
2
Karim
002
3
Sharmin 003
Course table
– Primary attribute –
which can’t be
duplicate
Employee Table *
*
Course Table
Many to many relationship
Cour
seID
Title
Fees
001
RS & GIS in WM
6,000
002
Risk Management
6,000
003
River Management 4,000
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Find the relationship between this
two tables in the BUET Library
Book Table
ISBN
Title
Author
050
Applied
David
Hydrology Maidmen
060
Irrigation
Cheng
Students Table
ID
Name
ISBN
1
Rahim
050
2
3
Karim
Sharmin
060
070
One to one
Many to Many
One to Many
?
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Structural Query Language
(SQL)
• SQL is used to perform query in relations databases.
• For example, find the name of the employee who have
spend more than 10,000 this year to attend different
courses.
• SELECT Employee.Name, Course.Fees FROM
Employee
• WHERE Employee.CourseID = Course.CourseID
• AND Fees >= 10,000
• The answer is : Rahim
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
3. Object Oriented Model
BUET
Part of
Part of
Departments
Is a
Is a
CE
Institutes
Is a
URP
DCE
IWFM
AIT
WRE
Is a = Inheritance
Part of = association
Attributes:
Faculty, Staff, Students
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Object Oriented Database
• An Object Oriented model uses functions to model spatial and
non-spatial relationships of geographic objects and the
attributes.
• An object is an encapsulated unit which is characterized by
attributes, a set of orientations and rules. An object oriented
model has the following characteristics.
• generic properties : there should be an inheritance
relationship.
• abstraction : objects, classes and super classes are to be
generated by classification, generalization, association and
aggregation.
• adhoc queries : users can order spatial operations to obtain
spatial relationships of geographic objects using a special
language.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
ESRI Data models
Advancements in GIS
Vector Data models
Shape file
Coverage
Composite Data model
TIN
Regions
Route
Database
Geodatabase
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
ESRI’s models
• Shapefiles – as non-topological data format. Shape file
treats points are pair of x, y coordinates, a line as a
series of points and a polygon as a series of lines.
– Can be displayed more rapidly on monitors.
– Interoperable among other software.
• Coverage – as topological based vector data format. A
coverage can be point coverage, line coverage or
polygon coverage.
– Connectivity: Arcs connect 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 polygons
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Data models for composite features
• TIN – Triangulated irregular network data model
approximates the terrain with a set of non-overlapping
triangles.
• Regions – is defined here as a geographic area with
similar characteristics. A coverage feature class that can
represent a single area feature as more than one
polygon.
• Routes - is a line feature such as highway, a bike path,
or a stream but unlike other linear features, a route has a
measurement system that allows linear measures to be
used on a projected coordinate system.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Triangulated Irregular Network (TIN)
 Triangulated irregular network. A
vector data structure used to store and
display surface models.
 A TIN partitions geographic space
using a set of irregularly spaced data
points, each of which has an x-, y-,
and z-value.
 These points are connected by edges
into a set of contiguous, nonoverlapping triangles, creating a
continuous surface that represents the
terrain.
TIN
TIN & Contour
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Components of TIN
 The nodes originate from the points and line vertices contained in
the input data sources. Every node is incorporated in the TIN
triangulation. Every node in the TIN surface model must have a z
value.
Nodes
 Every node is joined with its nearest neighbors by edges to form
triangles which satisfy the Delaunay criterion. Each edge has two
nodes, but a node may have two or more edges. Because edges
have a node with a z value at each end, it is possible to calculate
a slope along the edge from one node to the other.
 Each triangular facet describes the behavior of a portion of the
TIN's surface. The x,y,z coordinate values of a triangle’s three
nodes can be used to derive information about the facet, such as
slope, aspect, surface area, and surface length.
 The hull of a TIN is formed by one or more polygons containing
the entire set of data points used to construct the TIN. The hull
polygons define the zone of interpolation of the TIN. Inside or on
the edge of the hull polygons, it is possible to interpolate surface
z values, perform analysis, and generate surface displays.
Outside the hull polygons, it is not possible to derive information
about the surface. The hull of a TIN can be formed by one or
more polygons which can be non-convex.
Edges
Triangles
Hull
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Delaunay Triangulation for TIN
 A method of fitting triangles to a set of points. The triangles
are defined by the condition that the circumscribing circle
of any triangle does not contain any other points of the
data except the three defining it.
 It is a method which produces triangles with a low
variance in edge length. The resulting triangles may be
used as an irregular tessellation for interpolation of other
points on a surface.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Region and polygon -1
 Polygons do not overlap and completely cover the area
being represented (do not contain any void areas).
 In a region, the polygons representing geographic
features can be freestanding, they can overlap, and they
need not exhaust the total area.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Region and polygon -2
Another premise of polygons is that each
geographic feature is represented by one
polygon.
This is extended for regions, so that a single
geographic feature can be represented by
several polygons.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Region and polygon -3
 As with points, lines, and polygons, each region is given a
unique identifier. As with polygons, area and perimeter are
maintained for each region.
 Constructing regions with polygons is similar to
constructing polygons from arcs. Whereas a polygon is a
list of arcs, a region is simply a list of polygons. One
important distinction exists: the order of the polygons is not
significant.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Route
In ArcGIS, the term route
refers to any linear feature,
such as a city street,
highway, river, or pipe, that
has a unique identifier and a
common measurement
system along each linear
feature.
A collection of routes with a
common measurement system is a
route feature class. Each route in
the feature class will also have a
unique identifier. Line features with
the same unique identifier are
considered to be part of the same
route:
Route feature classes are created and managed as line feature classes in the
geodatabase. You can also use route feature classes from ArcInfo coverages and
polyline shapefiles that include route identifiers and measured features.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Point events along Route
• Point events occur at a
precise point location along a
route. Accident locations
along highways, signals along
rail lines, bus stops along bus
routes, Wells or gauging
stations along river reaches,
pumping stations along pipe
lines, Manholes along city
streets and valves along pipes
are all examples of point
events. Point events use a
single measure value to
describe their location.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Line events along Route
• Line events describe
portions of routes.
Pavement quality, salmon
spawning grounds, bus
fares, pipe widths, and
traffic volumes are all
examples of line events.
Line events use two
measure values to
describe their location.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Polygon events along Route
• Locating polygon features along
routes computes the route and
measure information at the
geometric intersection of polygon
data and route data. Once
polygon data has been located
along routes, the resulting event
table can be used, for example,
to calculate the length of route
that traveled through each
polygon.
Examples:
 Soils, spillways, areas of inundation, or hazard zones along river reaches
 Wetlands, hazard zones, or town boundaries along highways
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Example of Route
 Hydrologists and ecologists use linear referencing on
stream networks to locate various types of events
The route feature
class for streams
provides measures
along the streams
using river reach mile.
Point and line event
tables record the route
ID and location along
each river reach.
These event tables
can be used to locate
point and line events.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Route system
 A collection of routes with a common system of
measurement is called a route system. Route systems
usually define linear features with similar attributes. For
example, a set of all bus routes in a county would be a
route system.
 Many route systems can exist within a single coverage.
For example, school bus, truck, and ambulance route
systems could exist in a coverage of a city.
 Route systems are built using arcs, routes, and
sections, and can accurately model linear features
without having to modify the underlying arc-node
topology.
 The route below is defined using four arcs. Notice how
the route's endpoints fall along the arcs. Routes need
not begin and end at nodes.
 Sections, as shown below, are the arcs or portions of
arcs used to define each route. They form the
infrastructure of the route system.
 The diagram below shows an example of attributes
containing distance measurements, such as milepost
numbers or addresses, which can be used to locate
events, such as accidents or pavement quality.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Dynamic segmentation
 Dynamic segmentation
(DynSeg) is the process
of computing the map
location (shape) of
events stored in an
event table.
 Dynamic segmentation
is what allows multiple
sets of attributes to be
associated with any
portion of a linear
feature.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Geodatabase
• A spatial and
attribute data
container
– Relational database
management system
(RDBMS)
– Maintains data integrity
– Apply Rules and Behavior
• Native data format
for ArcGIS
Relational Database - A
method of structuring data as
collections of tables that are
logically associated to each
other by shared attributes. Any
data element can be found in a
relation by knowing the name of
the table, the attribute (column)
name, and the value of the
primary key.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
First Generation
Storage/Linking
AS400
Database
Access
Database
Tabular Data
Spatial Data
•Tabular/Spatial data is linked outside the database
•Links occur using unique IDs….Parcel Numbers
•Storage is still in separate locations
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Second Generation Storage/Linking
Geodatabases
Tabular Data
Spatial Data
•Tabular/Spatial data is stored/linked in a single location!!
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Benefits of a GeoDatabase
o Spatial & attribute data integrity
o Intelligent Behavior
o Centralized Data Storage
o Increased Performance
o Advanced Analysis Capabilities
o Multi-user editing (SDE format)
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Benefits of Migrating to a
Geodatabase
Data Integrity
• Maintain tabular
data more efficiently
– Reduce typological
data errors
• Maintain spatial data
more efficiently
– Reduce spatial errors
Pro-West & Associates
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
2 Types of Geodatabase
• Personal Geodatabase
– Stand alone PC, MS Access database
– Supports individual and small groups on
moderate size datasets
• Enterprise Geodatabase
– Exists on underlying RDBMS through Spatial
Database Engine (SDE) e.g. SQL Server
– Usually runs on a dedicated server
– Supports many users and massive datasets
– Supports raster datasets
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Two types of GeoDatabases
• Personal
– Access
• Multi-user
– SDE
GIS
SDE
SQL
View/Analyze
Interpreter
Data
Storage
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
The Personal Geodatabase
It’s not Scary!
• Stores spatial and tabular
data in an Access database
format
• Sets the stage for future
SDE geodatbase migration
• Edit in ArcView, ArcEditor or
ArcInfo
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Geodatabase Features
Feature Dataset
• Contains
Topology
tables, feature
classes,
feature
Feature Classes
datasets,
topology
rules, etc.
Tables
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Geodatabase Elements
Geodatabase
Feature data set
Geometric network
Feature class
Relationship class
Table
Annotation class
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GeoDatabase (GDB) structure
• Stores
– Feature datasets
– Feature classes
– Tables
– Raster
– More
• A unique structure within the GDB
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Feature Dataset
• Contains Feature
Classes
– Must have same
coordinate system
• Required for
Topology
– Behavior
relationships
between feature
classes.
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GDB Objects: Feature Dataset
• A collection of feature classes
– Environment for spatial reference
– Environment for topology
– Environment for coincident geometry and linked
annotation
– Feature classes inherit spatial reference
• Data loaded are
projected on the fly,
if necessary
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Feature Class
• Stores a single
feature type
– Point, Line, Polygon
• Can be standalone
or member of a
Feature dataset
Feature Dataset
Feature Class
Stand Alone
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
GDB Objects: Feature Class (FC)
• A collection of features
– Each feature class has one geometry type (point,
multi-point, line, polygon)
• Can be stored in a
feature dataset or
‘stand-alone’
• Attributes are
stored with
coordinate data
in one table
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Spatial Reference
A
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Domain
• A property of a feature dataset or feature
class (cannot change once set)
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Coordinate domain
• Extent of available
coordinates
– Min and max X,Y coordinates
– Precision = storage units per
map unit
• Example, 1000 mm per meter
• Make sure it covers study
area
– Allow for growth
• ArcCatalog default
– Import: data plus room for
growth
• Set your own
– Import from existing data
– Type in extent for study area
2.14 billion storage units
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
The Spatial Domain
• The Geodatabase stores all geometry
coordinates as positive integers
– Faster Display, Processing, and Analysis
– Better Compression (DBMS only)
– Efficient for managing topologic relationships
• Limited to 2,147,423,647 storage units.
– 2.14x109 meters, or miles, or inches, or ...
Remote Sensing and GIS in Water Management © Dr. Akm Saiful Islam
Thank you !