Data input and transformation

APPLICATION OF REMOTE SENSING AND
GEOGRAPHICAL INFORMATION SYSTEM IN
CIVIL ENGINEERING
Date:
INSTRUCTORS
DR. MOHSIN SIDDIQUE
ASSIST. PROFESSOR
DEPARTMENT OF CIVIL ENGINEERING

Lecture
No
Description Seminar
1 Geographic Information System (GIS): Introduction, Basic data
operations and data structures for GIS
2 Geographic Information System (GIS): Concept and theories
of Database, Representation of Geo-objects
3 Geographic Information System (GIS): Basic data structure
and data operations, Advanced data models/structures.
4 Introduction of GIS software: (ArcGIS/ArcView/ArcInfo), Data
input and transformation
5 Introduction of GIS software: Spatial query and analysis:
Spatial query and related data structures
6 Introduction of GIS software: Spatial Operations and analysis
7 Practice Sessions: Data generation for Hydrological Modeling:
Filling DEM, Flow direction map, Flow accumulation map,
Catchment boundaries, River network generation, Slope etc
grids.
Tentative Course Schedule (GIS)
2

Spatial information is presented in two ways:
as vector data (Vector Model) in the form of
points, lines, and areas (polygons);
or as grid data (Raster Model) in the form of
uniform, systematically organized cells.
Basic Data Models
3
Modelling the real world

The basis of the vector model is the assumption that the real world can be
divided into clearly defined elements where each element consists of an
identifiable object with its own geometry
In principle, every point on a map and every point in the terrain it represents
is uniquely located using two or three numbers in a coordinate system, such as
in the northing, easting, and elevation Cartesian coordinate system
Vector Data model
4
On maps, coordinate systems are
commonly displayed in grids with location
numbers along the map edges.
On the ground, coordinate systems are
imaginary, yet marked out by survey
control stations.
Data usually may be transformed from one
coordinate system to another

In any map, data are traditionally
coded. Roads, contour lines,
property boundaries, and other
data indicated by lines are
usually shown in lines of various
widths and colours.
Symbols designate the locations
of mosques, airports, and other
buildings and facilities. In other
words, coordinates and coding
information identify all objects
shown on a map
Coding Digital Data For Map Production

Similarly, the digital data used to
produce maps are also coded,
usually by the assignment of
numerical codes used throughout
the production process – from the
initial data to computer
manipulation and on to the drawing
of the final map.
Each numerical code series contains
specific codes assigned to the
objects in the group.

In thematic coding, data are divided into single-topic groups, such as all
property boundaries. Information on symbol types, line widths, colours, and so
on, may be appended to each thematic code, and various combinations of
themes may be drawn.
Data may be presented jointly in this way only if all objects are registered,
using a common coordinate system.
Theme codes in the digital map data can be send to separate data
into thematic layers

Point objects may easily be realized in a database because a given number
of attributes and coordinates is associated with each point
Line and polygon objects are more difficult to realize in a database because
of the variation in the number of points composing them
Object spatial information and object attributes are often stored in different
databases to ease the manipulation of lines and areas, but in some systems
they are stored together
Coding Digital Data for GIS
Each object is assigned attributes and coordinates. The number of coordinates
for lines and polygons depends on the length or circumference of the object

Coding Digital Data for GIS
Representation of geographic details,
point, line and area features

Points and Lines
The location information from these features is stored using location
coordinates
Storing Vector Data
The vector data model is based around the storage of coordinate pairs
UTM (Universal Transverse Mercator) system gives a standard way of representing
positions for any feature

Area boundary
One way to store this area is by storing
the line which defines its boundary, as
shown in Figure
Other is using attributes as being
associated with the interior of an area
rather than its boundary. e.g., centroid of
area
It helps to add a label to the boundary
of an area
The centroid is commonly used to give a
position for labels when drawing maps
of area features and for this reason
centroids are normally positioned near the
centre of the area
Storing Vector Data
Storage of area feature.

Storing Vector Data
Drawing instructions are designated in look-up tables. Thematic
code values or attribute values are input values in the tables, while
output values can be symbol types, colours, line thickness etc

Spaghetti data is a term used to describe digital map data with crossing lines,
loose ends, double digitization of common boundaries between adjacent
polygons, etc. These data lie in a pile, just like spaghetti.
Spaghetti data are a collection of points and line segments with no real
connection and several line segments are found at odd places in the data file.
Spaghetti Model
There are no specific points that
designate where lines might cross, nor are
there any details of logical relationships
between objects
Polygons are represented by their
circumscribing boundaries, as a string of
coordinates so that common boundaries
between adjacent polygons are
registered twice
The lines of data are unlinked and
together are a confusion of crossings
Drawback of this model is that both data storage and data searches are sequential. Hence
search times are often unduly long for such routine operations as finding commonality
between two polygons

15
Common Vector Data Structures
Whole Polygon Structure
Dual Independent Map Encoding
Arc-Node Structure
Relational Structure
Digital Line Graph
TopologyModel

Each layer in the database is dissolved in number of polygons
Each polygon is encoded as sequence of locations (points) that define the
boundaries of each closed area.
Each polygon is stored as a separate entity
No explicit mean to define neighborhood
Attributes of each polygon can be stored with the list of the coordinates using
TOPOLOGY Model.
Several points that are shared by the polygons are repeated, as such
enhancing the redundancy in data storage
Whole Polygon Structure (Storing Area Boundaries)

Topology deals with geometric properties which remain invariable under
certain transformations, such as stretching or bending.
The topology model is one in which the connections and relationships between
objects are described independent of their coordinates; their topology
remains fixed as geometry is stretched and bent.
Hence, the topology model overcomes the major weakness of the spaghetti
model, which lacks the relationships requisite to many GIS manipulations and
presentations.
Topological information permits automatic verification of data consistency to
detect such errors as the incomplete closing of polygons during the encoding
process
Topology Model – Connections and Relationships between Object

Unique identities are assigned to all
links, nodes, and polygons, and attribute
data describing connections are
associated with all identities. Topology
can therefore be described in three
tables
i. The polygon topology table lists the
links comprising all polygons, each of
which is identified by a number.
ii. The node topology table lists the links
that meet at each node.
iii. The link topology table lists the
nodes on which each link terminates and
the polygons on the right and left of
each link, with right and left defined in
the direction from a designated start
node to a finish node. The system
creates these tables automatically.
Topology Model

i. The computational time required to identify all nodes may be relatively
long.
ii. Uncertainties and errors may easily arise in connection with the closing of
polygons and formation of nodes in complex networks (such as in road
interchanges). Operators must solve such problems.
iii. When new data are entered and existing data updated, new nodes must
be computed and the topology tables brought up to date.
iv. Topological data may require a longer plotting time than spaghetti data
because of the separation of lines into nodes and links.
However, the overall advantages of the topology model over the spaghetti
model make it the prime choice in most GISs
Today, efficient software and faster computers enable topology to be
established on-the-fly; thus the disadvantages of topological data as
compared to spaghetti data have become less important
Drawbacks of Topology Model

Dual Independent Map Encoding (DIME) is an encoding scheme developed
by the US Bureau of the Census for efficiently storing geographical data
We can construct two graphs
One based on the streets surrounding a block,
Other based on the blocks surrounding a street intersection.
Mathematically, the two graphs are exactly the same, since both will
consist of a single closed loop.
If we can automatically create these graphs from our street map, and
check that they do form closed loops we will have a way of checking the
data.
Details on DIME are Assignment to be submitted in next class
Dual Independent Map Encoding (DIME)

Data Compression
Simple data compression. The volume of
data to be stored is reduced to a single
entry, assigning the value common to all
coordinate values
Douglas - Peucker Method, this
helps in saving storage space
The most basic technique involves the elimination of repetitive characters
There are other automatic methods of removing points. One simple means is
to keep only every nth point on a line

The raster model (also termed as grid model)represents reality through
selected surfaces arranged in a regular pattern
Reality is thus generalized in terms of uniform, regular cells, which are usually
rectangular or square but may be triangular or hexagonal
Raster Data Model
22
The cells of a model are given in a
sequence determined by a hierarchy of
rows and columns in a matrix, with
numbering usually starting from the
upper left comer
The geometric location of a cell, and
hence of the object it represents, is
stated in terms of its row and column
numbers.
This identification corresponds to the
directional coordinates of the vector
model.

Raster Data Structures: Tesselations
Square grid: equal length sides
conceptually simplest
cells can be recursively divided into
cells of same shape
4-connected neighborhood (above,
below, left, right) (rook’s case)
all neighboring cells are
equidistant
8-connected neighborhood (also
include diagonals) (queen’s case)
all neighboring cells not
equidistant
center of cells on diagonal is 1.41
units away (square root of 2)
rectangular
commonly occurs for lat/long
when projected
data collected at 1degree by 1
degree will be varying sized
rectangles
triangular (3-sided) and
hexagonal (6-sided)
all adjacent cells and points are
equidistant
triangulated irregular network
(tin):
vector model used to represent
continuous surfaces (elevation)
more later under vector

Using a raster GIS we could store a set of spatial data in the form of a grid
of pixels. Each pixel will hold a value which relates to some feature of
interest at that point in space.
These values are normally one of three possible types.
I. Binary – A value which indicates the presence or absence of a feature of
interest. For example, in a layer representing roads, we might use 1 for
pixels which contained part of road, and 0 for pixels which did not.
II. Enumeration – A value from some classification. For example, a layer
representing soils might contain codes representing the different soil types–1
for alluvial, 2 for red soil etc.
III. Numerical – An integer or floating point number recording the value of a
geographical phenomenon. In the soil example, we might have measurements
of soil moisture content.
Storing Raster Data Structures

Storing Raster Data Structures
Only one attribute value is assigned to each cell. Objects that have several attributes
are therefore represented with a number of raster layers, one for each attribute.

The raster data model has the great virtue of simplicity but it can produce
very large files. The precision with which, 1 raster layer can represent spatial
data is related to the size of the pixel – we cannot represent anything which
is smaller than a pixel
The simplest method of storing a raster layer in the memory of the computer is
using a data structure called an array.
All programming languages have a structure called an array, which can be
used to store and access lists of items
Raster Data Structure: The Array
An example of simple raster array

Even with modern computers with enormous amounts of disk space and
memory, it still makes sense to reduce data sizes for a number of reasons.
First, the transfer of data from disk to memory is considerably slower than the
speed with which the same information can be processed once it is held in
memory – therefore smaller files means quicker execution times.
Second, the smaller the file size, the more images can be held in memory at
one time.
Data Compression Techniques
Run Length Encoding
Chain Encoding
Black Code
Regional Quad Tree
Compression of Raster Data

Run Length Encoding
Original data is replaced with data pairs or tuples
Example
Original data: 12,12,15,15,15,15,17,17,17,17,17
Encoded data:
(2,12), (4, 15), (5,17)
Benefit: Reduction from 11 elements to 6
Good compression if repeating data is available

Chain Encoding
Represents the boundary of a
region by using a series of
cardinal direction and cells
Example to encode the image.
N1 E1 N1 E1 N3 E2 S2 E1 S1
W3 S1 W1
Starting at lower left cell of
the region, the chain code
records the regions boundary
by using the principle direction
and number of cells.
Clockwise rotation

Block Code
Represents the boundary of a
region by using square blocks
Example to encode the image.
One four square (1,2)
Four One square (3,2;3,3; 3,4;
4,1)
Specify how many square
blocks are there
And specify the upper left
corner of each square block
(0,0)
(6,0)
(0,6)
(6,6)

31
Regional Quad Tree
Recursive decomposition to divide a
grid into hierarchy of quadrants
A quadrant with cells having same
value will not be subdivided.
Example:
Divide the grid into hierarchy of
quadrant. The division stops if the
quadrant has all the cells of same
value
(0,0)
(8,0)
(0,8)
(8,8)

32
Grass and IDRISI use Run Length Encoding (RLE)
SPANS uses a Quad Tree Structure
ARC/Info grids uses a Hierarchical Tile Block Cell Data Structure
Each grid is divided into a number of tiles
Tiles are divided into series of rectangular blocks
Blocks are divided into cells if required.

33
Other Techniques of Compression
Tagged Image File Format (Tiff), (Graphics Interchange Format ) GIF can
compress data without loosing info
Joint Photographic Experts Group (JPEG) however can compress more but
loose some info
MrSid (Multi-resolution Seamless Image) is a techniques which stores image at
different resolution within image. Encoding of high detail parts of the image
are done with high resolution and other at low resolution.
Good compression w/o loosing info
Assignment: One page about types of Image file formats
You may use Wikipedia as reference

Raster data are converted to vector data through vectorization. The reverse
process is rasterization.
In vectorization, areas containing the same cell values are converted to
polygons with attribute values equivalent to the pre – conversion cell values.
In the reverse process of converting polygons to cells, each cell falling within
a polygon is assigned a value equal to the polygon attribute value
Automatic Conversion Between Vector and Raster Models
Raster to vector Vector to Raster

The models (basic data models) discussed in the previous slide/lecture
describe limited parts of two-dimensional real world
Several other data models used in GIS can extend the real world to include
the terrain surface, the time factor, and movable objects
The digital representation of a terrain surface is called a digital elevation
model (DEM)
DEMs may be subdivided into:
Digital surface models (DSM) that contain elevation information about all
features in the landscape, such as vegetation, buildings, and other
structures;
Digital terrain models (DTM) that contain elevation information about the
bare-Earth surface without the influence of vegetation or man-made
structures.
Surface Representation

DTMs comprise various arrangements of individual points in x , y & z
coordinates
the term 2.5 – dimensional is often used to describe the DTM dimension in
which height (Z-coordinate is specified as attribute) and handle only one z
value for each point
In 3-dimensional elevation model, elevation is an integral part of position (x,
y, z) and the model can handle several z values for the same x, y pair
Thus 3D DEM can handle different geological layers, roof heights on buildings,
roads that cross each other, together with the terrain surface.
Digital Elevation Models

The z value of a new point is calculated by interpolation from the z value to
the closest existing points.
If the points are stored in an unstructured way, all registered points will have
to be searched to be able to calculate the z values to a new point. This can
be very time consuming even for a powerful computer.
It is therefore usual to use data structures which also describe the contiguity
between the points. This is achieved by using data structures based on
single points in a raster (grid) or
triangles covering a surface (e.g., TIN)
Digital Elevation Models

A systematic grid, or raster, of spot heights at fixed mutual spaces is often
used to describe terrain
Grid Model
Elevation data are acquired through (A) Mass points (lattices) (B) Contour lines (C)
GRIDs (interpolated from points or lines; or created currently from digital imagery)
Elevation is assumed constant within each cell of the grid, so small cells detail
terrain more accurately than large cells.
The size of cells is constant in a model, so areas with a greater variation of
terrain may be described less accurately than those with less variation

In TIN model, the area is an array of triangular areas with their corners
stationed at points for which the elevations are known.
The inclination of the terrain is assumed to be constant within each triangle.
The area of the triangles may vary, with the smallest representing those areas
in which the terrain varies the most.
As far as possible, small equilateral triangles are preferable.
To construct a TIN, all measured points are built and the model thus represents
lines of fracture, single points, and random variations in the terrain
Triangulated irregular network (TIN) Model
An example of TIN model.

The points are established by
triangulation and in such away
that no other points are located
within each triangle’s converted
circle.
In the TIN model, the x – y – z
coordinates of all points, as well
as the triangle attributes of
inclination and direction, are
stored.
The triangles are stored in a
topological data storage structure
comprising polygons and nodes,
thereby preserving the triangle’s
contiguity
TIN Model
TIN model: the triangles are stored in
a topological structure

Continuous lines connecting points
of the same elevation may
represent terrain in much the same
way as contour lines depict terrain
on conventional maps
In principle closer the lines, the
greater the accuracy of description
Isolines Models
A combination of isolines and individual points may also be used to
describe terrain, especially when specifying such point features as
peaks and valley floors, or vital terrain lines, such as the top and
bottom of a fill.

In the real world, time is a factor that concerns us deeply
In practice, it is difficult to create a data model that is capable of
incorporating all imaginable changes
The time factor is relatively often neglected in GIS probably because we are
more concerned with documenting our current situation than we are with
historical changes
However, If the time factor is not incorporated in the data model for GIS, we
run the risk of losing important historical data.
Representation of Time

The most usual way of handling the time factor in GIS is to look on time as an
attribute to the objects in the same way as for other attributes
This view corresponds with the usual way of presenting geographical data
(i.e., as two-dimensional time overlays) and can thus be realized for both
vector and raster data.
However, this simple approach will not necessarily create a logical connection
between the various time layers
Possible practical solutions will therefore be:
1. The attributes of the objects will be changed.
2. The geometry of objects is changed.
Representation of Time

Comments….
Questions….
Suggestions….
46
I am greatly thankful to all the information sources
(regarding remote sensing and GIS) on internet that I
accessed and utilized for the preparation of present
lecture.
Thank you !

Data input and transformation

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