Relationship between Geoinformatics ,IT and Sensor Tech

Geography has always
been important to humans. Stone-age hunters anticipated the location of their
quarry, early explorers lived or died by their knowledge of geography and
current societies live and work based on their understanding of who belongs
where. Applied geography, in the form of maps and spatial information, has
served discovery, planning, cooperation, and conflict for at least the past
3000 years and maps are among the most beautiful documents of our civilization. Most often our geographic knowledge is
applied to routine tasks, such as when we puzzle over a route through a maze of
city streets or search for the nearest gas station. Spatial information has a
much greater impact on our lives, often to an extent we don’t realize, to help
us produce the food we eat, the energy we burn, the clothes we wear, and the
diversions we enjoy.

Because
spatial information is so important, we have developed tools called geographic
information systems (GIS) to help us with our geographic knowledge. A GIS helps
us gather and use spatial data Some GIS components are purely technological;
they include space-age data collectors, advanced communications networks, and
sophisticated computing. Other GIS methods are very simple; for example, when a
pencil and paper are used to field verify a map.

As
with many aspects of life in the last five decades, how we gather and use
spatial data has been profoundly altered by modern electronics, and GIS
software and hardware are a primary result of these technological developments.
The capture and treatment of spatial data has quickened over the past three
decades, and continues to evolve.

Key
to all definitions of a GIS are “what” and “where”. GIS and spatial analyses
are concerned with the absolute and relative location of features, as well as
the properties and attributes of those features. The locations of important
spatial objects such as rivers and streams may be recorded, and also their
size, flow rate, water quality, or the kind of fish found in them. Indeed,
these attributes often depend on the spatial arrangement of “important”
features. A GIS aids in the analysis and display of these spatial relationships

Relation with information technology

Geoinformatics sometimes related to as
geographic information system/s. basically is the combination of two sciences
the geography   “  Geography (from Greek γεωγραφία – geographia,
lit. “earth describe-write”) is the science that studies the lands,
features, inhabitants, and phenomena of Earth A literal translation would be
“to describe or write about the Earth”. The first person to use the
word “geography” was Eratosthenes (276-194 BC). Four historical
traditions in geographical research are the spatial analysis of natural and
human phenomena (geography as a study of distribution), area studies (places
and regions), study of man-land relationship, and research in earth sciences.
Nonetheless, modern geography is an all-encompassing discipline that foremost
seeks to understand the Earth and all of its human and natural complexities—not
merely where objects are, but how they have changed and come to be. Geography
has been called “the world discipline” and “the bridge between
the human and the physical science”. Geography is divided into two main
branches: human geography and physical geography”, and information technology
“Information technology (IT) is the acquisition, processing, storage and
dissemination of vocal, pictorial, textual and numerical information by a
microelectronics-based combination of computing and telecommunications. The
term in its modern sense first appeared in a 1958 article published in the
Harvard Business Review, in which authors Leavitt and Whisler commented that
“the new technology does not yet have a single established name. We shall
call it information technology (IT). Some of the modern and emerging fields of
Information technology are next generation web technologies, bioinformatics,
Geoinformatics, cloud computing, global information systems,
large scale knowledgebase, etc.”

IT
is the area of managing technology and spans wide variety of areas that include
but are not limited to things such as processes, computer software, information
systems, computer hardware, programming languages, and data constructs. In
short, anything that renders data, information or perceived knowledge in any
visual format whatsoever, via any multimedia distribution mechanism, is
considered part of the IT domain. IT provides businesses with four sets of core
services to help execute the business strategy: business process automation, providing
information, connecting with customers, and productivity tools. Based on a global inventory of the world’s IT capacity,
Hilbert and Lopez identify the exponential pace of technological change (a kind
of Moore’s law): machines’ application-specific capacity to compute information
per capita has roughly doubled every 14 months between 1986-2007; the per
capita capacity of the world’s general-purpose computers has doubled every 18
months during the same two decades; the global telecommunication capacity per
capita doubled every 34 months; the world’s storage capacity per capita
required roughly 40 months to double (every 3 years); and per capita broadcast
information has doubled roughly every 12.3 years.

Trends
in increasing Geoinformatics

Actually
Geography is an Information technology now a day and is growing enormously
around the world geographic information technology is a trillion dollar company
these days and is growing at the rate of 15% per year. Recent trend in the
growth of hardware and continuous decline in their cost has helped a lot both
in the growth of technology and Geoinformatics. Geoinformatics is wholly
dependent on Computers without the technological features available these days
one cannot think of Geoinformatics. The Development of Geoinformatics has been
driven at least in part by technology, particularly the specific technology to
support spatial and graphic data applications in computing. the use of advanced
DBMS have revolutionized the applications of Geoinformatics and the available
analysis are growing widely relation DBMS object oriented data basses high
level DBMS systems MS access SQL etc. are very useful in handling the
geographic as well as non-spatial data . Technology is evolving as from the
simple analytical engine to modern real time systems the technology in both
data storage and retrieval is increasing day by now we have terra best of Hard
discs some 60 Gb of Rams some 8-10 Gb of display adapters and recent Led display
devices are best displaying of geographic data. From the 8-bit computers we
have 64-bit + computers and having the speed in gaga hertz. Internet technology
also helped the GIS largely almost all the information is available on the
internet all the maps and other research projects are available on internet
also Open source software are supporting a lot in the spread of Geoinformatics
. Modern computers based on integrated circuits are millions
to billions of times more capable than the early machines, and occupy a
fraction of the space.Simple computers are small enough to fit into mobile
devices, and mobile computers can be powered by small batteries. Personal
computers in their various forms are icons of the Information Age and are what
most people think of as “computers”. However, the embedded computers
found in many devices from mp3 players to fighter aircraft and from toys to
industrial robots are the most numerous. Now we have the Graphic User Interface
Operating systems. The high standard peripheral devices and large storage
devices and also the advanced software Decision support systems also the WEBGIS
is emerging very rapidly. With the IT we have multitasking, Multiprocessing
Multi-User Computers which are the back bone of GIS. Strictly speaking if there
is no information Technology there would have been no GIS .

Geoinformatics
is a late bloomer among applications of computing technology in parts because
it is so demanding, and simply could not be supported in any useful fashion by
the resources available in the typical computer system of, say 1960. In
addition, the spatial nature of geographic data is not easily accommodated
within the essentially linear structure of conventional computers, and early
input and output devices lacked the spatial resolution to deal these kinds of
data. Indeed, it is often noted that the human eye and mind are still superior
to the best digital technology in such spatial tasks as pattern recognition.
The Geoinformatics development can be directly linked to the general advances
in hardware and software although other factors like education and awareness
and the action of key individuals have also been important.

 

The main power of Geoinformatics is the
linkage between geographic data and the information about it. GIS not only show
the feature but what exactly the feature is. This is only possible with the
help of data base management system which actually are the software and the
advancement in these systems has led to the easy collection and relation and
also supports the linkage between locational data and the attribute data. DBMs were
used for computing in late 1970 and were implemented in GIS in 1980s.

SENSOR technology and Geoinformatics

Without sensors most electronic applications would not exist
they perform a vital function, namely providing an interface to the real world.
The importance of sensors, however, contrasts with the limited information
available on them. Today’s smart sensors, wireless sensors, and
microtechnologies are revolutionizing sensor design and applications

A sensor is a device that measures a
physical quantity and converts it into a signal which can be read by an
observer or by an instrument. For example, a mercury-in-glass thermometer
converts the measured temperature into expansion and contraction of a liquid
which can be read on a calibrated glass tube. A thermocouple converts
temperature to an output voltage which can be read by a voltmeter. For
accuracy, most sensors are calibrated against known standards. Sensors are used
in everyday objects such as touch-sensitive elevator buttons (tactile sensor)
and lamps which dim or brighten by touching the base. There are also
innumerable applications for sensors of which most people are never aware.
Applications include cars, machines, aerospace, medicine, manufacturing and
robotics. A sensor is a device which receives and responds to a signal. A
sensor’s sensitivity indicates how much the sensor’s output changes when the
measured quantity changes. For instance, if the mercury in a thermometer moves
1 cm when the temperature changes by 1 °C, the sensitivity is 1 cm/°C (it is
basically the slope Dy/Dx assuming a linear characteristic). Sensors that
measure very small changes must have very high sensitivities. Sensors also have
an impact on what they measure; for instance, a room temperature thermometer
inserted into a hot cup of liquid cools the liquid while the liquid heats the
thermometer. Sensors need to be designed to have a small effect on what is
measured; making the sensor smaller often improves this and may introduce other
advantages. Technological progress allows more and more sensors to be
manufactured on a microscopic scale as microsensors using MEMS technology. In
most cases, a microsensor reaches a significantly higher speed and sensitivity
compared with macroscopic approaches.

Ideal sensors are designed to be linear or
linear to some simple mathematical function of the measurement, typically
logarithmic. The output signal of such a sensor is linearly proportional to the
value or simple function of the measured property. The sensitivity is then
defined as the ratio between output signal and measured property. For example,
if a sensor measures temperature and has a voltage output, the sensitivity is a
constant with the unit [V/K]; this sensor is linear because the ratio is
constant at all points of measurement. Actually the GIS uses the sensor to
Acquire the information and on this information the whole system is dependent

Sensor deviations

If the sensor is not ideal, several
types of deviations can be observed:

  • Ø The sensitivity may in practice differ from the value
    specified. This is called a sensitivity error, but the sensor is still linear.
  • Ø Since the range of the output signal is always limited, the
    output signal will eventually reach a minimum or maximum when the measured
    property exceeds the limits. The full scale range defines the maximum and
    minimum values of the measured property.
  • Ø If the output signal is not zero when the measured property
    is zero, the sensor has an offset or bias. This is defined as the output of the
    sensor at zero input.
  • Ø If the sensitivity is not constant over the range of the
    sensor, this is called nonlinearity. Usually this is defined by the amount the
    output differs from ideal behavior over the full range of the sensor, often
    noted as a percentage of the full range.
  • Ø If the deviation is caused by a rapid change of the measured
    property over time, there is a dynamic error. Often, this behaviour is
    described with a bode plot showing sensitivity error and phase shift as
    function of the frequency of a periodic input signal.
  • Ø If the output signal slowly changes independent of the
    measured property, this is defined as drift (telecommunication).
  • Ø Long term drift usually indicates a slow degradation of
    sensor properties over a long period of time. Noise is a random deviation of
    the signal that varies in time.
  • Hysteresis is an
    error caused by when the measured property reverses direction, but there is
    some finite lag in time for the sensor to respond, creating a different offset
    error in one direction than in the other.
  • Ø If the sensor has a digital output, the output is
    essentially an approximation of the measured property. The approximation error
    is also called digitization error.
  • Ø If the signal is monitored digitally, limitation of the
    sampling frequency also can cause a dynamic error, or if the variable or added
    noise noise changes periodically at a frequency near a multiple of the sampling
    rate may induce aliasing errors.

The sensor may to some extent be sensitive to
properties other than the property being measured. For example, most sensors
are influenced by the temperature of their environment.

All these deviations can be classified
as systematic errors or random errors. Systematic errors can sometimes be
compensated for by means of some kind of calibration strategy. Noise is a
random error that can be reduced by signal processing, such as filtering,
usually at the expense of the dynamic behavior of the sensor. The more the
sensor is error free the more information will be accurate and the more
analysis will be really developed. Actually the GIS displays the real world in
a model recorded by the sensors of various types the range on EMR to which the
sensor is sensitive depends upon the technology used in that sensor. Recent
sensors are sensitive to hundreds of wavelengths Hyper spectral sensors

Resolution

The resolution of a sensor is the smallest change it can
detect in the quantity that it is measuring. Often in a digital display, the
least significant digit will fluctuate, indicating that changes of that
magnitude are only just resolved. The resolution is related to the precision
with which the measurement is made. For example, a scanning tunneling probe (a
fine tip near a surface collects an electron tunneling current) can resolve
atoms and molecules. The more the sensor resolution the more accurate the data
will be and the technology has grown when we have the spatial resolution of .5
m which helps to detect a small change/feature

Types

All living organisms contain biological
sensors with functions similar to those of the mechanical devices described.
Most of these are specialized cells that are sensitive to:

Light, motion, temperature, magnetic fields,
gravity, humidity, vibration, pressure, electrical fields, sound, and other
physical aspects of the external environment

Physical aspects of the internal environment,
such as stretch, motion of the organism, and position of appendages
(proprioception)

Environmental molecules, including toxins,
nutrients, and pheromones

Estimation of biomolecules interaction and
some kinetics parameters

Internal metabolic milieu, such as glucose
level, oxygen level, or osmolality

Internal signal molecules, such as hormones,
neurotransmitters, and cytokines

Differences between proteins of the organism
itself and of the environment or alien creatures.

Most remote sensing instruments (sensors) are designed to
measure photons. The fundamental principle underlying sensor operation centers
on what happens in a critical component – the detector. This is the concept of
the photoelectric effect (for which Albert Einstein, who first explained
it in detail, won his Nobel Prize [not for Relativity which was a much
greater
achievement]; his discovery was, however, a key step in the
development of quantum physics). This, simply stated, says that there will be
an emission of negative particles (electrons) when a negatively charged plate
of some appropriate light-sensitive material is subjected to a beam of photons.
The electrons can then be made to flow as a current from the plate, are
collected, and then counted as a signal. A key point: The magnitude of the
electric current produced (number of photoelectrons per unit time) is directly
proportional to the light intensity. Thus, changes in the electric current can
be used to measure changes in the photons (numbers; intensity) that strike the
plate (detector) during a given time interval. The kinetic energy of the
released photoelectrons varies with frequency (or wavelength) of the impinging
radiation. But, different materials undergo photoelectric effect release of
electrons over different wavelength intervals; each has a threshold wavelength
at which the phenomenon begins and a longer wavelength at which it ceases. The
first is a functional treatment of several classes of sensors, plotted as a
triangle diagram, in which the corner members are determined by the principal
parameter measured: Spectral; Spatial; Intensity.

The growth in technology has been started for early human
age and is increasing day by day .In early days the images which were available
for GIS were only panchromatic (black/white) and the analysis were not so good
enough now we have the sensor sensitive to almost every band in EMR and the
data is available in lots of colours and shades by thermal sensor the image s
are taken day and night also the active sensors are employed now a days we have
the real time access to the features changing on the earth. The Hyper spectral
images give large information about the feature and the microwave sensor can sense
through the clouds even in rain and is sensitive to the geometric features as
well.  As in the advancement in sensor
technology the applications of Geoinformatics are increased.

 

Summary

From the technological perspective, the
development of GIS is heavily dependent on the general trends of the hardware
and software evolution in the computer industry. In recent years, growing
awareness of the importance of Geoinformatics in cooperate information resource
management has attracted many main stream companies to GIS arena. There is a
particularly strong interest among database software vendors to include spatial
data-handling capabilities within the conventional database environments. Big
information technology companies such as Oracle, Hewlett Packard .IBM and
Microsoft have now all had a presence in the GIS marketplace. This has led to
several breakthroughs in database technology that make the integration of
spatial and descriptive data a reality.  However,
it has also created a substantial amount of hype and confusion among the
ordinary users. For example, the entry of large technology companies into the
GIS marketplace has engendered many take overs, mergers , partnerships and
joint development pacys among them and conventional  GIS vendors. It has also given to rise to
intense marketing efforts with claims and counter claims about the capabilities
of products and the future directions of technology. Users are sometimes
overwhelmed with excessive information when trying to configure their system
for implementation projects.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

  • Ø Geographic Information System By
    Michael F. Goodchild University of California Santa Barbara
  • Ø Concept and Techniques of GIS by C. P.
    Lo Albert K. W. Yeung
  • Ø Fundamentals of GIS by Michael N.
    DeMers.
  • Ø GIS Tools Prakash T.N
  • Ø Sensor Technology Hand Book by John S.
    Wilson
  • Ø WEB
    • Wikipedia
    • Nasa Website
    • Usgs
    • Nmsu.edu
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GIS DATA MODLES AND TYPES

GIS stores information about the world as a collection of thematic layers that can be linked together by geography. This simple but extremely powerful and versatile concept has proven invaluable for solving many real-world problems from tracking delivery vehicles, to recording details of planning applications, to modelling global atmospheric circulation. The thematic layer approach allows us to organize the complexity of the real world into a simple representation to help facilitate our understanding of natural relationships.

DATA TYPES:-

The basic data type in a GIS reflects traditional data found on a map. Accordingly, GIS technology utilizes two basic types of data. These are 1. The spatial data and 2. The attribute data to be more precise u can call it meta data ( for convince only)

Spatial data:- In simple terms the Spatial data means the data about the location. It describes  the absolute and relative location of geographic features. it is further divided into the following three basic types :-

 

1. Vector                                                      2. Raster                                             3. Image.

1. Vector data model:-

All spatial data models are approaches for storing the spatial location of geographic features in a database. Vector storage implies the use of vectors (directional lines) to represent a geographic feature. Vector data is characterized by the use of sequential points or vertices to define a linear segment. Each vertex consists of an X coordinate and a Y coordinate. The most popular method of retaining spatial relationships among features is to explicitly record adjacency information in what is known as the topologic data model. Topology is a mathematical concept that has its basis in the principles of feature adjacency and connectivity. Vector lines are often referred to as arcs and consist of a string of vertices terminated by a node. A node is defined as a vertex that starts or ends an arc segment. Point features are defined by one coordinate pair, a vertex. Polygonal features are defined by a set of closed coordinate pairs. In vector representation, the storage of the vertices for each feature is important, as well as the connectivity between features, e.g. the sharing of common vertices where features connect.

The topologic data structure is often referred to as an intelligent data structure because spatial relationships between geographic features are easily derived when using them. Primarily for this reason the topologic model is the dominant vector data structure currently used in GIS technology. Many of the complex data analysis functions cannot effectively be undertaken without a topologic vector data structure. Topology is reviewed in greater detail later on in the book.

The secondary vector data structure that is common among GIS software is the computer-aided drafting (CAD) data structure. This structure consists of listing elements, not features, defined by strings of vertices, to define geographic features, e.g. points, lines, or areas. There is considerable redundancy with this data model since the boundary segment between two polygons can be stored twice, once for each feature. The CAD structure emerged from the development of computer graphics systems without specific considerations of processing geographic features. Accordingly, since features, e.g. polygons, are self-contained and independent, questions about the adjacency of features can be difficult to answer. The CAD vector model lacks the definition of spatial relationships between features that is defined by the topologic data model.

GIS MAP Structure – VECTOR systems (Adapted from Berry)

 

2. Raster Data Model:-

Raster data models incorporate the use of a grid-cell data structure where the geographic area is divided into cells identified by row and column. This data structure is commonly called raster. While the term raster implies a regularly spaced grid other tessellated data structures do exist in grid based GIS systems. In particular, the quadtree data structure has found some acceptance as an alternative raster data model.

The size of cells in a tessellated data structure is selected on the basis of the data accuracy and the resolution needed by the user. There is no explicit coding of geographic coordinates required since that is implicit in the layout of the cells. A raster data structure is in fact a matrix where any coordinate can be quickly calculated if the origin point is known, and the size of the grid cells is known. Since grid-cells can be handled as two-dimensional arrays in computer encoding many analytical operations are easy to program. This makes tessellated data structures a popular choice for many GIS software. Topology is not a relevant concept with tessellated structures since adjacency and connectivity are implicit in the location of a particular cell in the data matrix.

Several tessellated data structures exist, however only two are commonly used in GIS’s. The most popular cell structure is the regularly spaced matrix or raster structure. This data structure involves a division of spatial data into regularly spaced cells. Each cell is of the same shape and size. Squares are most commonly utilized.

Since geographic data is rarely distinguished by regularly spaced shapes, cells must be classified as to the most common attribute for the cell. The problem of determining the proper resolution for a particular data layer can be a concern. If one selects too coarse a cell size then data may be overly generalized. If one selects too fine a cell size then too many cells may be created resulting in a large data volume, slower processing times, and a more cumbersome data set. As well, one can imply accuracy greater than that of the original data capture process and this may result in some erroneous results during analysis.

As well, since most data is captured in a vector format, e.g. digitizing, data must be converted to the raster data structure. This is called vector-raster conversion. Most GIS software allows the user to define the raster grid (cell) size for vector-raster conversion. It is imperative that the original scale, e.g. accuracy, of the data be known prior to conversion. The accuracy of the data, often referred to as the resolution, should determine the cell size of the output raster map during conversion.

Most raster based GIS software requires that the raster cell contain only a single discrete value. Accordingly, a data layer, e.g. forest inventory stands, may be broken down into a series of raster maps, each representing an attribute type, e.g. a species map, a height map, a density map, etc. These are often referred to as one attribute maps. This is in contrast to most conventional vector data models that maintain data as multiple attribute maps, e.g. forest inventory polygons linked to a database table containing all attributes as columns. This basic distinction of raster data storage provides the foundation for quantitative analysis techniques. This is often referred to as raster or map algebra. The use of raster data structures allow for sophisticated mathematical modelling processes while vector based systems are often constrained by the capabilities and language of a relational DBMS.

GIS MAP Structure – RASTER systems (Adapted from Berry)

This difference is the major distinguishing factor between vector and raster based GIS software. It is also important to understand that the selection of a particular data structure can provide advantages during the analysis stage. For example, the vector data model does not handle continuous data, e.g. elevation, very well while the raster data model is more ideally suited for this type of analysis. Accordingly, the raster structure does not handle linear data analysis, e.g. shortest path, very well while vector systems do. It is important for the user to understand that there are certain advantages and disadvantages to each data model.

The selection of a particular data model, vector or raster, is dependent on the source and type of data, as well as the intended use of the data. Certain analytical procedures require raster data while others are better suited to vector data.

3. Image:-

Image data is most often used to represent graphic or pictorial data. The term image inherently reflects a graphic representation, and in the GIS world, differs significantly from raster data. Most often, image data is used to store remotely sensed imagery, e.g. satellite scenes or orthophotos, or ancillary graphics such as photographs, scanned plan documents, etc. Image data is typically used in GIS systems as background display data (if the image has been rectified and georeferenced); or as a graphic attribute. Remote sensing software makes use of image data for image classification and processing. Typically, this data must be converted into a raster format (and perhaps vector) to be used analytically with the GIS.

Image data is typically stored in a variety of de facto industry standard proprietary formats. These often reflect the most popular image processing systems. Other graphic image formats, such as TIFF, GIF, PCX, etc., are used to store ancillary image data. Most GIS software will read such formats and allow you to display this data.

Image data is most often used for remotely sensed imagery such as satellite imagery or digital orthophotos.

Attribute data:- It describes characteristics of the spatial features. These characteristics can be quantitative and/or qualitative in nature. Attribute data is often referred to as tabular data. For example The coordinate location of a forestry stand would be spatial data, while the characteristics of that forestry stand, e.g. cover group, dominant species, crown closure, height, etc., would be attribute data. Other data types, in particular image and multimedia data, are becoming more prevalent with changing technology. Depending on the specific content of the data, image data may be considered either spatial, e.g. photographs, animation, movies, etc., or attribute, e.g. sound, descriptions, narration’s, etc.

It is further divide into the following types:-

1. Tabular                  2.Hierarchical                        3. Network                    4. Relational                      5.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. A brief review of each model is provided.

Tabular Model

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.

Hierarchical Model

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. Hierarchial 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.

Network Model

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.

Relational Model

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 No Dominant cover group Avg.Tree height Stand site index Stand age
1 DEC 3 G 100
2 DEC-CON 4 M 80
3 DEC-CON 4 M 60
4 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.

pic097

In the relational design, data are stored conceptually as a collection of tables. Common fields in different tables are used to link them together.

In fact, most GIS software provides an internal relational data model, as well as support for commercial off-the-shelf (COTS) relational DBMS’. COTS DBMS’ are referred to as external DBMS’. This approach supports both users with small data sets, where an internal data model is sufficient, and customers with larger data sets who utilize a DBMS for other corporate data storage requirements. With an external DBMS the GIS software can simply connect to the database, and the user can make use of the inherent capabilities of the DBMS. External DBMS’ tend to have much more extensive querying and data integrity capabilities than the GIS’ internal relational model. The emergence and use of the external DBMS is a trend that has resulted in the proliferation of GIS technology into more traditional data processing environments.

The relational DBMS is attractive because of its:

simplicity in organization and data modelling.

flexibility – data can be manipulated in an ad hoc 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 (From Berry)

Object-Oriented Model

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.

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