Principles of GIS unit 2

Prof. Sanjana Khemka
Prahladrai Dalmia Lions College

Unit 2:Data management and
processing systems
 Hardware and Software Trends
 Geographic Information Systems: GIS Software, GIS
Architecture and functionality, Spatial Data
Infrastructure (SDI)
 Stages of Spatial Data handling
 Database management Systems
 GIS and Spatial Databases

Hardware and software trends
 Advances in computer hardware seem to take place at an
ever-increasing rate.
 Computers are also becoming increasingly portable, while
offering this increased performance.
 Computers are also becoming increasingly affordable
 Hand-held computers are now commonplace in business
and personal use, equipping field surveyors with powerful
tools, complete with GPS capabilities for instantaneous
georeferencing.
 To support these hardware trends, software providers
continue to produce application programs and operating
systems that, while providing a lot more functionality,

 Alongside these trends, there have also been significant
developments in computer networks
 Today any computer can connect to some network, and contact
computers virtually anywhere else, allowing fast and reliable
exchange of (spatial) data
 Mobile phones are more and more frequently being used to
connect to computers on the Internet
 The UMTS protocol (Universal Mobile Telecommunications
System), allows digital communication of text, audio, and video
at a rate of approximately 2 Mbps.
 5G network is under development with a proposed speed of
500Mbps.
 Bluetooth version 2.0 is a standard that offers up to 3 Mbps
connections
 Wireless LANs under the WiFi standard, nowadays offer a
bandwidth of up to 108 Mbps on a single connection point
 Wide-area computer networks (national, continental, global)
have a capacity of several Gbps.

Geographic Information System
 GIS provides a range of capabilities to handle
georeferenced data, including:
1. Data capture and preparation,
2. Data management (storage and maintenance),
3. Data manipulation and analysis, and
4. Data presentation.
 For many years, analogue data sources were used,
processing was done manually, and paper maps were
produced.
 The introduction of modern techniques has led to an
increased use of computers and digital information in all
aspects of spatial data handling. The software technology
used in this domain is centered around geographic
information systems.

 GIS projects require data sources, both spatial and
non-spatial, from different national institutes.
 The data sources obtained may be in different scales or
projections. With the help of a GIS, the spatial data
can be stored in digital form in world coordinates.
 With the spatial data thus prepared, spatial analysis
functions of the GIS can then be applied to perform
the planning tasks.

GIS software
 GIS can be considered to be a data store, a toolbox, a
technology, an information source or a field of science.
 The main characteristics of a GIS software package are
analytical functions that provide means for deriving new
geoinformation from existing spatial and attribute data.
 All GIS packages available on the market have their
strengths and weaknesses,
 Some GISs have traditionally focused more on support for
raster-based functionality, others more on (vector-based)
spatial objects.
 Well known full fledged GIS packages include ILWIS,
Intergraph’s GeoMedia, ESRI’s ArcGis and MapInfo from
Map Info Corp.

GIS architecture and functionality
 GIS consists of software, data, people, and an
organization in which it is used.
 GIS consists of several functional components such
data capture and preparation, data storage, data
analysis, and presentation of spatial data.

 Data capture and preparation
 Data can be collected through first hand observation
called as primary source or through individual,
organization or published data called as secondary data.
 Capturing is done through scanning, photogrammetric,
remote sensing, digitization of analog map, field survey,
GPS survey or manual data entry.
 This data is then prepared for a project under study by
removing errors, rasterization, vectorization etc.
 Data Storage
 Spatial data is stored as themes, layers or coverage.
 Attribute data is the information about an object or
feature.

 Data Analysis
 It allows the user to define and execute spatial and
attribute procedures.
 Overlay, buffering, modeling and analysis are some of he
methods used in building a coverage or project.
 Presentation of spatial data
 Several mapping tools which are integrated with GIS, are
used to create map.
 The final maps are of high cartographic quality and are
brought out using a wide range of devices.

Spatial Data Infrastructure
 SDI deal with the sharing of spatial data between the
GISs in various organizations with the key importance
and aspects of data dissemination, security, copyright
and pricing require special attention
 SDI is defined as “the relevant base collection of
technologies, policies and institutional arrangements
that facilitate the availability of and access to spatial
data”.
 Fundamental to those arrangements are- in a wider
sense—the agreements between organizations and in
the narrow sense, the agreements between software
systems on how to share the geographic information

 standards are often the starting point for those
agreements
 Standards exist for all facets of GIS, ranging from data
capture to data presentation
 They are developed by different organizations, of
which the most prominent are the International
Organization for Standardisation (ISO) and the Open
Geospatial Consortium (OGC).
 SDI provides its users with different facilities for
finding, viewing, downloading and processing data.
 GIS has gradually become available as web-based
applications
 Much of the functionality is provided by so called geo-
webservices- software programs that act as an
intermediate between geographic data(bases) and the
users of the web

Stages of spatial data handling
 Spatial data capture and preparation
 Spatial data storage and maintenance
 Spatial query and analysis
 Spatial data presentation

Spatial data capture and preparation
 Traditional techniques for obtaining spatial data,
typically from paper sources, included manual
digitizing and scanning.
 The main methods and devices used for data capture
are-

 The data, once obtained in some digital format, may
not be quite ready for use in the system
 The format obtained from the capturing process is not
in the format required for storage and further use,
which means that some type of data conversion is
required.
 After data conversion it can be used to analysis and
present geoinformation.

Spatial data storage and
maintenance
 The way that data is stored plays a central role in the
processing and the eventual understanding of that
data
 In a GIS, features are represented with their
(geometric and non-geometric) attributes and
relationships.
 The storage of a raster is, in principle, straightforward.
It is stored in a file as a long list of values, one for each
cell, preceded by a small list of extra data

 GIS software packages provide support for both spatial and
attribute data
 They accommodate spatial data storage using a vector
approach, and attribute data using tables
 Database management systems (DBMSs) have been based
on the notion of tables for data storage.
 All major GIS packages provide facilities to link with a
DBMS and exchange attribute data with it.
 Spatial (vector) and attribute data are still sometimes
stored in separate structures, although they can now be
stored directly in a spatial database
 Maintenance of (spatial) data can best be defined as the
combined activities to keep the data set up-to-date and as
supportive as possible to the user community.
 It deals with obtaining new data, and entering them into
the system, possibly replacing outdated data.
 The purpose is to have an up-to-date stored data set
available.

Spatial query and analysis
 The most distinguishing parts of a GIS are its functions
for spatial analysis, i.e. operators that use spatial data
to derive new geoinformation
 Spatial queries and process models play an important
role in this functionality
 Spatial decision support systems (SDSS) are a category
of information systems composed of a database, GIS
software,models, and a so-called knowledge engine
which allow users to deal specifically with locational
problems.
 The analysis functions of a GIS use the spatial and
non-spatial attributes

 Analysis of spatial data can be defined as computing
new information that provides new insight from the
existing, stored spatial data.
 Analysis of spatial data can be defined as computing
new information that provides new insight from the
existing, stored spatial data.

Spatial data presentation
 The presentation of spatial data, whether in print or
on-screen, in maps or in tabular displays, or as ‘raw
data’, is closely related to the disciplines of
cartography, printing and publishing
 The presentation may either be an end-product, for ex-
 ample as a printed atlas, or an intermediate product, as
in spatial data made available through the internet.

Data
 The smallest piece of information is called data
 Data is the building block on which every organization
is built to operate.
 Data is raw fact or figures or entities
 It can be in the form of text, numbers, pictures and
sound.

Database
 A database is a collection of related data
 In ordinary word it is a table which is a collection of
rows and columns
 Database is a collection of various objects tables,
query, report, form, macro etc.
 Database is a coherent collection of data with inherent
meaning, designed, built and populated with data for a
specific purpose.

Database Management System (DBMS)
 It is a collection of program that enables user to create
and maintain the information.
 DBMS is a general purpose software system that
facilitates the process of defining, constructing and
manipulating database for various applications.
 DBMS is a specialized computer program to manage
data efficiently.
 DBMS can simply be described as a system involving
 Data
 The software that utilizes the hardware
 The user who turns the data into information

Advantages of using DBMS
 Controlling data Redundancy
 Controlling data inconsistency
 Restricting unauthorized access
 Providing multiple user interface.
 Enforcing integrity constraints
 Providing backup and recovery
 Enforce user defined rules
 Support storage and manipulation of very large data
set
 DBMS provides a high level, declarative query
language.
 It supports the use of a data model.

Restrictions of DBMS
1. High initial investment in hardware, software and
training
2. Overheads on account of
1. Security
2. Recovery
3. Integrity functioning
3. DBMS responded to the request very sluggishly and
were not suitable when the no. of users exceeded
four or five

Alternatives for data management
 The decision whether or not to use a DBMS will depend on
how much data there is or will be, what type of use will be
made of it, and how many users might be involved.
 when the data set is small, its use relatively simple, and
with just one user—we can use simple text files, and a text
processor.
 If our data set is still small and numeric by nature, and we
have a single type of use in mind, a spreadsheet program
can be used.
 When a large amount of data is involved both text and
numeric and different types of analysis is required then
dedicated DBMS or RDBMS software can be used.

The relational data model
 A data model is a language that allows the definition of:
• The structures that will be used to store the base data,
• The integrity constraints that the stored data has to obey at
all moments in time, and
• The computer programs used to manipulate the data.
 For the relational data model, the structures used to
define the database are attributes, tuples and relations.
 Computer programs either perform data extraction from
the database without altering it, in which case we call
them queries, or they change the database contents, and
we speak of updates or transactions.

RDBMS
 It is a database management system where all data
visible to the user is organized strictly as tables of data
values, and where all database operations work on
these tables.
 The relation model is based on the concept that data is
organized and stored in two dimensional tables called
relations.
 Concept of RDBMS has been developed by Dr. E. F.
Codd at IBM in the late 1960’s.
 He has specified a set of 12 rules that has become
popular as Codd’s rule.

Relational database
 Relation – Relations can be represented as two dimensional
data tables with rows and columns.
 A table or relation is itself a collection of tuples (or
records). Each table is a collection of tuples that are
similarly shaped.
 Tuples – The rows of a relation are called Tuples.
 Attributes – columns of a relation are called attribute.
 Cardinality – the number of tuples in a relation is called its
cardinality
 Degree – the no. of attributes in a relation is called its
degree.

Finding tuples and building links
between them
 The relational data model uses the notion of a key for
quick search among many tuples.
 A key of a relation comprises one or more attributes. A
value for these attributes uniquely identifies a tuple.
 Every relation has a key.

Querying a relational database
 All query operator require input and produce output,
and both input and output are relations
 The three most elementary query operators
1. Tuple selection- Tuple selection works like a filter: it
allows tuples that meet the selection condition to
pass, and disallows tuples that do not meet the
condition. Ex – Select * from parcel where area>1000;
2. Attribute projection- it works like a tuple formatter: it
passes through all tuples of the input, but reshapes
each of them in the same way. Ex – select pid,
Location from parcel.

 Queries like the two above do not create stored tables
in the database. This is why the result tables have no
name: they are virtual tables.
 Join operator- The join operator takes two input
relations and produces one output relation, combining
two tuples together (one from each input relation), to
form a bigger tuple, if they meet a specified condition.
 Ex - SELECT ∗ FROM TitleDeed, Parcel WHERE
TitleDeed.Plot = Parcel.PId
 ex - SELECT Owner, DeedDate FROM TitleDeed,
Parcel WHERE TitleDeed.Plot = Parcel.PId AND
AreaSize > 1000

GIS and spatial databases
 GIS software provides support for spatial data and
attribute data
 GISs have traditionally stored spatial data and
attribute data separately
 This required the GIS to provide a link between the
spatial data (represented with rasters or vectors), and
their non-spatial attribute data.
 GIS software has inbuilt feature to store and analyze
data and produce map.
 GIS packages themselves can store tabular data,
however, they do not always provide a full-fledged
query language to operate on the tables.

 DBMSs offer much better table functionality, since
they are specifically designed for this purpose.
 A lot of the data in GIS applications is attribute data,
so it made sense to use a DBMS for it.
 For this reason, many GIS applications have made use
of external DBMSs for data support.
 In this role, the DBMS serves as a centralized data
repository for all users, while each user runs her/his
own GIS software that obtains its data from the DBMS.
 A GIS had to link the spatial data represented with
raster's or vectors, and the attribute data stored in an
external DBMS.

 With raster representations, each raster cell stores a
characteristic value. This value can be used to look up
attribute data in an accompanying database table.

 With vector representations, our spatial objects—
whether they are points, lines or polygons—are
automatically given a unique identifier by the system.
 This identifier is usually just called the object ID or
feature ID and is used to link the spatial object (as
represented in vectors) with its attribute data in an
attribute table.

Spatial database functionality
 DBMS vendors have recognized the need for storing more
complex data, like spatial data.
 During the 1990’s, object-oriented and object-relational
data models were developed for this purpose.
 These extend standard relational models with support for
objects, including ‘spatial’ objects.
 Currently, GIS software packages are able to store spatial
data using a range of commercial and open source DBMSs
such as Oracle, Informix, IBM DB2, Sybase, and
PostgreSQL
 Some GIS software have integrated database ‘engines’, and
therefore do not need these extensions. Ex ESRI’s ArcGIS.

 Spatial databases, also known as geodatabases,3 are
implemented directly on existing DBMSs, using
extension software to allow them to handle spatial
objects.
 A spatial database allows users to store, query and
manipulate collections of spatial data
 There are several advantages in doing this
 spatial data can be stored in a special database column,
known as the geometry column
 GISs can rely fully on DBMS support for spatial data,
making use of a DBMS for data query and storage (and
multi-user support), and GIS for spatial functionality
 A geodatabase allows a wide variety of users to access
large data sets

 The Open Geospatial Consortium (OGC) has released
a series of standards relating to geodatabases that
(amongst other things), define:
• Which tables must be present in a spatial database (i.e.
geometry columns table and spatial reference system
table)
• The data formats, called ‘Simple Features’ (i.e. point,
line, polygon, etc.)
• A set of SQL-like instructions for geographic analysis.

Querying a spatial database
 A Spatial DBMS provides support for geographic co-
ordinate systems and transformations.
 It also provides storage of the relationships between
features, including the creation and storage of topological
relationships.
 As a result one is able to use functions for ‘spatial query’
(exploring spatial relationships). To illustrate, a spatial
query using SQL to find all the Thai restaurants within 2
km of a given hotel would look like this:
SELECT R.Name
FROM Restaurants AS R,
Hotels as H
WHERE R.Type = “Thai” AND
H.name = “Hilton” AND
ST Intersects(R.Geometry, ST Buffer(H.Geometry, 2000))

References
 Principles of Geographic Information System –Sheth
Publication
 Principles of Geographic Information Systems - An
Introductory Text Book – Publication-The
international Institute of Geo Information Science and
Earth Observation

Principles of GIS unit 2

Recommended

More Related Content

What's hot (20)

Similar to Principles of GIS unit 2 (20)

Recently uploaded (20)

Principles of GIS unit 2