Unit 4 Data Input and Analysis.pptx

AI8702 REMOTE SENSING
AND GEOGRAPHICAL
INFORMATION SYSTEM
Unit 4 Data Input and Analysis

GIS Data
Two kinds of data are usually associated with geographic features:
Spatial and Non- spatial data.
Spatial data refers to the shape, size and location of the
feature.
Non- spatial data refers to other attributes associated with the
feature such as name, length, area, volume, population, soil type,
etc ..

Spatial Data
Spatial data is the physical representation of earth features. It
represents the location, size, and shape of the object in the earth
i.e., building, ponds, mountains, administration, boundaries, etc.
Spatial Data is available in two primary formats
1. Vector
2. Raster

Raster Data
A raster data is a representation of images in a matrix of cells/ pixels into
rows and columns.
The raster data set and data values are stored in rows and columns.
To have high accuracy data, GIS professionals use high-resolution raster
datasets.
As it comes with the own challenges and difficulties to manage, Map info
advancement introduces to a specially designed data format, multi-
Resolution Raster (MRR).
There are different raster types, Image, Image Palette, Classified and
Continuous, or discrete. These types are stored as two significant formats,
single color data, and composite color data.

Raster File Formats
 Portable Network Graphics (PNG)
 Joint Photographic Experts Group (JPEG2000)
 JPEG File Interchange Format (JFIF)
 Multi-resolution Seamless Image Database (MrSID)
 Network Common Data Form (netCDF)
 Digital raster graphic(DRG)
 ARC Digitized Raster Graphic (ADRG)
 Enhanced Compressed ARC Raster Graphics (ECRG)
 Compressed ARC Digitized Raster Graphics (CADRG)

Raster File Formats
 Raster Product Format (RPF)
 Binary file – Band Interleaved by Pixel (BIP), Band Interleaved by
Line (BIL), Band Sequential (BSQ)
 Enhanced Compressed Wavelet (ECW)
 Extensible N-Dimensional Data Format (NDF)
 GDAL Virtual Format (VRT)
 Tagged Image File Formats (TIFF)
 Geo Tagged Image File Formats (GeoTIFF)
 Graphic Interchange Format (GIF)
 Digital Elevation Model (DEM)

Raster File Formats
 RS Landsat
 ArcInfo Grid
 Airborne Synthetic Aperture Radar (AIRSAR) Polarimetric
 Bitmap (BMP), device-independent bitmap (DIB) format, or Microsoft
Windows bitmap
 BSB
 Controlled Image Base (CIB)
 Digital Geographic Information Exchange Standard (DIGEST)
 File geodatabase
 ENVI Header

Raster File Formats
 Golden Software Grid (.grd)
 GRIB
 Hierarchical Data Format (HDF) 4
 HGT
 High-Resolution Elevation (HRE)
 Integrated Software for Imagers and Spectrometers (ISIS)
 Shuttle Radar Topography Mission (SRTM)
 Terragen terrain

Vector Data
Vector data are represented in points, lines and polygons.
Polygon data are used to describe areas such as the boundary of a
city (on a large scale map), forest, and lakes. Polygon features are
two dimensional. It can be used to measure the area and perimeter
of a geographic feature.

Vector Data
Line data represents the linear features. Some Common examples
for the representation of line features are rivers, roads, etc. The line
is a one-dimensional representation. It gives only the length of the
element.

Vector Data
Point data is used to represent non-adjacent features and to represent
discrete data points. Points have zero dimensions, and it gives latitude &
longitude of the respective location. The point feature will not provide the
length and area of the features. Examples would be schools, points of
interest such as hospitals, schools, colleges, worship centers, and more
other locations.

Vector File Formats
 Shapefiles
 ArcInfo Coverage
 E00 ArcInfo Interchange
 Spatial Database engine (ArcSDE)
 Digital Line Graph (DLG)
 GeoJSON
 AutoCAD DXF
 Keyhole Markup Language (KML)
 TIGER

Vector File Formats
 Vector Product Format (VPF)
 Esri TIN
 Geography Markup Language (GML)
 SpatiaLite
 OSM (OpenStreetMap)
 Scalable Vector Graphics
 National Transfer Format (NTF)
 SOSI
 MapInfo TAB format

Vector File Formats
 GPS exchange Format (GPX)
 IDRISI Vector
 Geographic Base File-Dual Independent Mask Encoding (GBF-
DIME)
 Delimited Text Files

Non-Spatial Data
 Non-spatial data are represented in table formats. For example,
the administrative boundary table has population information,
district name, provinces, sex ratio, etc.

Triangulated Irregular Network (TIN)

Functions of GIS
 Data Entry
 Storing of Data
 Data Analysis

Functions of GIS
1. Data Entry
 Both spatial and attribute data are entered into computer system by
different input devices like scanner, digitizer, keyboard, mouse, etc.
 Scanner, digitizer, mouse are used for entering spatial data.
 The attribute data available as reports, tables, etc. are entered through
keyboard.
 As the data is drawn from different sources, they have different scales,
projections, referencing system, etc. Therefore, there is need to
standardize the database to common standard.
 GIS software enables this operation by ‘georeferencing’ method.

Functions of GIS
2. Storing of data
 The different spatial entities which represent different features of
real world can be stored in two different formats in the computer –
Raster format & Vector format
 The knowledge of these formats in which spatial data are stored,
is required for decision makers as it affects the accuracy of the
data, their analysis, storing capacity of computer, etc.

Functions of GIS
3. Data Analysis (Map Analysis)
 Different types of spatial data analysis can be performed by GIS, performing
queries, network analysis, overlay analysis, model building, etc.
 Since GIS stores both spatial and non-spatial data and links them together,
it can perform different types of queries.
 For example, by joining the spatial data and its attributes and then by
performing queries, one can see on map, the water of which tube wells
having chlorine content more than 200 mg/liter.
 Similarly, one can see on map, the roads constructed before 1980 which
needs to be repaired.
 In the same way, which area of a given forest having more than 60% tree
density on Map.

Functions of GIS
Proximity Analysis can be done
through buffering i.e., identifying a
zone of interest around a point, line
or polygon. For Ex, 10m around on
tube well can be marked for
planting flower plants, (or) 50m
along National Highways (both
sides) can be buffered for planting
trees. A specified distance around
the forest can be buffered as no
habitation zone.

Functions of GIS
Proximity Analysis

Functions of GIS
Network Analysis is another
important analysis done through
GIS. For example, optimum bus
routing can be determined by
examining all the field or attribute
data linked to road map/ spatial
data.

Functions of GIS
Network Analysis

Functions of GIS
Overlay Analysis can be done through GIS by
overlaying/ integrating a number of thematic
maps. Overlay operation allows creation of a
new layer of spatial data by integrating the
data from different layers. For example, a
particular land use class having saline soils,
slope less than 20%, drainage density less
than 10m per sq.km can be created from four
different thematic maps, through land use
map, soil map, topographic map and water
resource map.

Functions of GIS
Model building capability of GIS is
very helpful for decision makers. It
is usually referred to as ‘What if’
analysis. For example, if a certain
amount of water is released from a
dam, how much area would be
inundated?
GIS has the capabilities of analysing
a large amount of data within no
time.

Data Models
A data model is a description or view of the real world.
Data modeling is a process that formalizes the description or view
at different levels of data abstraction.
Since, the real world is made up of complex spatial objects and
phenomena, it is practically impossible for a single data model to
represent everything that is present.
This means that different users may have different data models
when they attempt to collect data in the same location.

Data Models
1. Conceptual models
The different views of the same urban area obtained by the
engineer, the developer and the geographer are called
conceptual models.
It represents the user’s perception of the real world. Here, data
abstraction is strictly limited to the description of the
information contents of the user’s view of the real world,
without any concern for computer implementation.

Data Models
2. Logical data models
It represents an implementation – oriented view of the
database.
It represents the real world by means of diagrams, lists and
tables designed to reflect the recording of data in terms of
some formal language.
It is software dependent.
There are three classic logical data models.
 The relational data model
 The network data model
 The hierarchical data model

Data Models
3. Physical data models
It represents the hardware implementation – oriented view of
the database.
It is the 3rd level of data abstraction.
It describes the physical storage (or file format) of the data in
the computer by record format, record ordering and access
paths.
It is hardware dependent.
It is intended for system programmer and database
administrator, and not for general end users.

Data Models
4. Spatial data models
The term spatial data model (geographic model) is used to
describe, how geographical data are organized within a GIS in
order to represent real world phenomena. GIS uses one of the
two spatial data models (sometimes both).
 Raster data models
 Vector data models

Data Models
Raster data models
 Raster models divide the study area into cells, usually
rectangular grid cells.
 It is location based because emphasis is placed upon the
location of each cell relative to other cells.
 It is frequently used to model field data.
 They correspond to regularly spaced points on a continuous
surface.

Data Models
Vector data models
 Vector models are used to represent discrete phenomena,
represented by geometric primitives (points, lines &
polygons).
 It is object-based.
 Field based conceptualizations tends to favour a raster
model.
 Object based conceptualizations tends to favour a vector
model.
 3D surfaces can be represented by isolines (Ex: Contour lines)
or Triangulated irregular network (TIN)
 Isolines are familiar in cartography, but TINs are much more
efficient in GIS modelling.

Database Models
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 is the manner in which most early 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.

Database Models – Tabular Models
 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.

Database Models – Tabular Models

Database Models – Hierarchical Network
 A hierarchal database management system is a system in which
the data elements have a one to many relationship (1: N). This
DBMS organize data in a tree-like structure, similar to a folder
structure in your computer system.
 The hierarchy starts from the root node, connecting the child node
to the parent node. This DBMS is good for storing the data about
the items describing its features, attributes, and so on.

 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.

Database Models – Network Model
 A Network database management system is a system in which the
data elements have a one to one relationship (1: 1) or many to
many relationship (N: N). This DBMS also has a hierarchical
structure, but it organizes data in a graph-like structure, and is
allowed to have more than one parent for one single record.
 For example, a teacher in a college teaches in two departments.
Note: This DBMS is the most widely used database system before the
introduction of the relational database management system.

 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.

Database Models – Relational Model
 A relational database management system (RDBMS) is a system in
which the data is organized in the two-dimensional tables using rows
and columns. This database management system was introduced
by E.F Codd in 1970.
 It is called a ‘relational’ database because data within each table is
related to each other. Also, tables may be related to other tables in
the database by using certain concepts of keys. Each table in a
database has a key field that uniquely identifies each record. This
system is the most widely used DBMS. Relational database
management system software is available for large mainframe
systems as well as workstations and personal computers.
 For example, Oracle Database, MySQL, Microsoft SQL Server, and IBM
DB2.

Emp_id Emp_name Emp_salary Emp_address
101 Arun 42,000 Delhi
102 Aman 40,000 Moradabad
103 Rakesh 43,000 Meerut
104 Shivam 44,000 Noida
105 Tarun 42,000 Gurgaon
106 Yash 40,000 Delhi
In the above table employee, Emp_id, Emp_name, Emp_salary, and Emp_address are
the attributes containing their values. Here, Emp_id is a primary key attribute which is
uniquely identifying each record in the Employee table.

 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 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 key for 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 data.
 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.

The relational DBMS is attractive because of its:
 simplicity in organization and data modelling.
 flexibility - data can be manipulated in an adhoc 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 relational DBMS has emerged as the dominant commercial
data management tool in GIS implementation and application.

The following diagram
illustrates the basic linkage
between a vector spatial
data (topologic model) and
attributes maintained in a
relational database file.
Basic linkages between a
vector spatial data
(topologic model) and
attributes maintained in a
relational database file

Database Models – Object Oriented Model
 An object-oriented database management system is a system in
which information or data is represented in the form of objects, as
used in the object-oriented programming. It is a combination of
relational database concepts such as concurrency control,
transactions, etc. and OOPs principles, such as data encapsulation,
inheritance, and polymorphism.
 This database system permits data, information, software
components, computing environments, and products to be shared
easily.
 Object-Oriented Programming + Relational Database Features =
Object-Oriented Database management system

The object-oriented database model manages data
through objects.
An object is a collection of data elements and operations that
together are considered a single entity.
The object-oriented database is a relatively new model. This
approach has the attraction that querying is very natural, as
features can be bundled together with attributes at the
database administrator's discretion.
To date, only a few GIS packages are promoting the use of this
attribute data model.
However, initial impressions indicate that this approach may
hold many operational benefits with respect to geographic data
processing. Fulfilment of this promise with a commercial GIS
product remains to be seen.

Data Input and GIS
Data input is the procedure of encoding data into a computer-
readable form and writing the data to the GIS data base. There
are two types of data to be entered in a GIS - spatial (geographic
location of features) and non-spatial (descriptive or numeric
information about features).
There are three types of data entry:
•Manual (via typing on keyboard or importing text files);
•Digitizing;
•Scanning;

Data Input and GIS – Manual Data Entry
 Manual data entry can bring into GIS either collected or
measured data.
 These data exist as simple text files or binary files.
 Text files should have at least two columns with X and Y
coordinates.
 These columns allow georeferencing of the file i.e. association
of it with specific geographic coordinate system.
 Binary files are usually a product of the software package
associated with measuring device (for example files from
Global Positioning System data collection).
 They also have X and Y data, associated with description of the
collected features, but in encoded format that could be read
by special software.

Data Input and GIS – Digitization & Scanning
 Digitizing is a process of entering digital codes of analyzed data
into computer.
 Digitizing can be manual (using digitizing tablet) or automatic
(using scanner).
 The difference between two methods is that digitizing tablet
allows to do georeferencing during the digitizing process, while
scanning require georeferencing later, after digital file (usually
TIFF, GIF or JPEG image) has been created.
 Another difference between methods is speed and accuracy of
the data processing.
 Apparent slowness of the work on digitizing tablet compensates
often for the amount of editing after scanning process.

Data Input and GIS
 At the same time good scanning allows automatic layer
separation (for example, separation of red-colored roads from
brown-colored contour lines), while digitizing of the map on a
tablet requires manual creation of separate themes.
 In this case the condition of the original hardcopy is very
important.
 Since human operator can use more cognitive tools and
knowledge than the software support for scanning device,
digitizer can handle better the hardcopy in a poor condition .
 Special kind of scanned data is remote sensing image, taken
either by satellite camera, digital camera or video camera.

Data Input and GIS - Digitization
 Digitizing in GIS is the process of converting geographic data
either from a hardcopy or a scanned image into vector data by
tracing the features. During the digitizing process, features
from the traced map or image are captured as coordinates in
either point, line, or polygon format.

Data Input and GIS - Digitization
 There are several types of digitizing methods. Manual
digitizing involves tracing geographic features from an external
digitizing tablet using a puck (a type of mouse specialized for
tracing and capturing geographic features from the
tablet). Heads up digitizing (also referred to as on-screen
digitizing) is the method of tracing geographic features from
another dataset (usually an aerial, satellite image, or scanned
image of a map) directly on the computer screen. Automated
digitizing involves using image processing software that
contains pattern recognition technology to generated vectors.

Data Input and GIS – Digitization Errors
 Since most common methods of digitizing involve the
interpretation of geographic features via the human
hand, there are several types of errors that can occur
during the course of capturing the data. The type of
error that occurs when the feature is not captured
properly is called a positional error, as opposed to
attribute errors where information about the feature
capture is inaccurate or false.

An open polygon caused by the
endpoints not snapping
together.
Dangles or Dangling Nodes
Dangles or dangling nodes are
lines that are not connected but
should be. With dangling nodes,
gaps occur in the linework where
the two lines should be
connected. Dangling nodes also
occur when a digitized polygon
doesn’t connect back to itself,
leaving a gap where the two end
nodes should have connected,
creating what is called an open
polygon.

Example of a weird polygon
where the line folds back on
itself.
Switchbacks, Knots, and Loops
These types of errors are
introduced when the digitizer has
an unsteady hand and moves the
cursor or puck in such a way that
the line being digitized ends up with
extra vertices and/or nodes. In the
case of switchbacks, extra vertices
are introduced and the line ends up
with a bend in it. With knots and
loops, the line folds back onto itself,
creating small polygon like
geometry known as weird polygons.

The circle represents the area of the snap
tolerance. The line being digitized will
automatically snap to the nearest nodes
within the snap tolerance area.
Overshoots and Undershoots
Similar to dangles, overshoots and
undershoots happen when the line digitized
doesn’t connect properly with the neighboring
line it should intersect with. During digitization
a snap tolerance is set by the digitizer. The
snap tolerance or snap distance is the
measurement of the diameter extending from
the point of the cursor. Any nodes of
neighboring lines that fall within the circle of
the snap tolerance will result in the end points
of the line being digitized automatically
snapping to the nearest node. Undershoots
and overshoots occur when the snap distance

Slivers
Slivers are gaps in a digitized polygon layer
where the adjoining polygons have gaps
between them. Again, setting the proper
parameters for snap tolerance is critical for
ensuring that the edges of adjoining polygons
snap together to eliminate those gaps. Where
the two adjacent polygons overlap in error, the
area where the two polygons overlap is called
a sliver.
Gap and Sliver Errors in Digitized Polygons

Data Input and GIS – Scanners
 Scanning coverts paper maps into digital format by
capturing features as individual cells, or pixels, producing
an automated image.
 Maps are generally considered the backbone of any GIS
activity.
 But many a time paper maps are not easily available in a
form that can be readily used by the computers.
 Most of the paper maps had been prepared on the basis of
old conventional surveys.
 New maps can be produced using improved technologies but
this requires time as it increases the volume of work. Thus,
we have to resort to the available maps.

Data Input and GIS – Scanners
 These paper maps have to be first converted into a digital format
usable by the computer.
 This is a critical step as the quality of the analog document must be
preserved in the transition to the computer domain.
 The technology used for this kind of conversions is known as scanning
and the instrument used for this kind of operation is known as a
scanner.
 A scanner can be thought of as an electronic input device that converts
analog information of a document like a map, photograph or an overlay
into a digital format that can be used by the computer. Scanning
automatically captures map features, text, and symbols as individual
cells, or pixels, and produces an automated image.

Data Input and GIS – Working of a Scanner
 The most important component inside a scanner is the scanner
head which can move along the length of the scanner.
 The scanner head contains either a charged-couple device
(CCD) sensor or a contact image (CIS) sensor.
 A CCD consists of a number of photosensitive cells or pixels
packed together on a chip.
 The most advanced large format scanners use CCD’s with 8000
pixels per chip for providing a very good image quality.

 While scanning a bright white light from the scanner strikes the
image to be scanned and is reflected onto the photosensitive
surface of the sensor placed on the scanner head.
 Each pixel transfers a gray tone value (values given to the
different shades of black in the image ranging from 0 (black) –
255 (white) i.e. 256 values to the scan board (software).
 The software interprets the value in terms of 0 (Black) or 1
(white), thereby, forming a monochrome image of the scanned
portion.
 As the head moves ahead, it scans the image in tiny strips and
the sensor continues to store the information in a sequential
fashion. The software running the scanner pierces together the
information from the sensor into a digital form of the image. This
type of scanning is known as one pass scanning.

 Scanning a colour image is slightly different in which the
scanner head has to scan the same image for three different
colours i.e. red, green, blue.
 In older colour scanners, this was accomplished by scanning the
same area three times over for the three different colours. This
type of scanner is known as three-pass scanner.
 However, most of the colour scanners now scan in one pass
scanning all the three colours in one go by using colour filters.

 In principle, a colour CCD works in the same way as a
monochrome CCD. But in this each colour is constructed by
mixing red, green and blue. Thus, a 24-bit RGB CCD presents
each pixel by 24 bits of information. Usually, a scanner using
these three colours (in full 24 RGB mode) can create up to 16.8
million colours.
 Nowadays a new technology: full width, single-line contact
sensor array scanning has emerged in which the document to
be scanned passes under a line of LED’s which capture the
image. This new technology enables the scanner to operate at
previously unattainable speeds.

Data Input and GIS – Types of Scanner
There are several different types of scanners
performing the same job but handling the job
differently using different technologies and
producing results depending on their varying
capabilities.
Hand-held scanners although portable, can only
scan images up to about four inches wide. They
require a very steady hand for moving the scan
head over the document. They are useful for
scanning small logos or signatures and are virtually
of no use for scanning maps and photographs.

Flatbed scanners
The most commonly used scanner is a flatbed
scanner also known as desktop scanner. It has a
glass plate on which the picture or the document
is placed. The scanner head placed beneath the
glass plate moves across the picture and the
result is a good quality scanned image. For
scanning large maps or toposheets wide format
flatbed scanners can be used.

Drum scanners
Then there are the drum scanners which are
mostly used by the printing professionals. In this
type of scanner, the image or the document is
placed on a glass cylinder that rotates at very
high speeds around a centrally located sensor
containing photo-multiplier tube instead of a CCD
to scan. Prior to the advances in the field of sheet
fed scanners, the drum scanners were
extensively used for scanning maps and other
documents.

Sheet fed scanners
Finally, there are the Sheet fed scanners which work
on a principle similar to that of a fax machine. In this,
the document to be scanned is moved past the
scanning head and the digital form of the image is
obtained. The disadvantage of this type of scanner
is that it can only scan loose sheets and the
scanned image can easily become distorted if the
document is not handled properly while scanning.
However, the new generation of the wide format
sheet fed scanners has overcome this problem and
have become indispensable for scanning maps,
imageries and other large sized documents.

Unit 4 Data Input and Analysis.pptx

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Unit 4 Data Input and Analysis.pptx