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International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
Disparity of Spatial and Non Spatial Data.
Rupali B. Surve*, Bhaskar Y. Kathane
Kamla Nehru Mahavidyalaya, Nagpur (MS), India*
VMV Commerce, JMT Arts and JJP Science College, Nagpur(MS), India
[email protected]*, [email protected]
ABSTRACT
This paper presents the variation in spatial data and non spatial data. The technical progress of
computerized data model gaining and storage result in the growth of vast database. Increase
the use of spatial data and gathering the huge amount of computerized data have far exceeded
human ability to completely interpret and used. There are different fields which need to manage
geometric, geographic type of data in which data is related to space. Non spatial data also called
as conventional data are not particularly suitable for geographic applications because they do
not efficiently support the types of operations that are required for spatial applications
and they are not suitable for the storage and manipulation of spatial data and
graphical data. Spatial data are the data related to objects that occupy space.
Index Terms: Spatial data, Non-spatial data, Vector model, Raster mode, GIS, Vector data, Raster
data .
I.
INTRODUCTION
Spatial data, also known as geospatial data, is information about a physical object that can be represented
by numerical values in a geographic coordinate system. Generally speaking, spatial data represents the
location, size and shape of an object on planet, earth such as a building, lake, mountain or township.
Spatial data may also include attributes that provide more information about the entity that is being
represented. A spatial database is a database that is optimized to store and query data that represents
objects defined in a geometric space. Most spatial databases allow representing simple geometric objects
such as points, lines and polygons. Some spatial databases handle more complex structures such as 3D
objects, topological coverage, linear networks etc. While typical databases are designed to manage
various numeric’s and character types of data, additional functionality needs to be added for databases to
process spatial data types efficiently. These are typically called geometry or feature. Spatial data are data
that have a spatial component; it means that data are connected to a place in the Earth. A Geographic
Information System (GIS) integrates hardware, software, data, and people to capture, manipulate,
analyze and display all forms of geographically referenced information or spatial data. A GIS allows see,
understand, consult and interpret data to reveal relationships, patterns and trends. Most of the human
activities are linked directly or indirectly to location. GIS or other specialized software applications can
be used to access, visualize, manipulate and analyze geospatial data. Microsoft introduced two spatial
data types with SQL Server 2008: geometry and geography. Geometry types are represented as points on
a planar, or flat-earth, surface. Geography spatial data types, on the other hand, are represented as
latitudinal and longitudinal degrees, as on Earth or other earth-like surfaces. There are different fields
which need to manage geometric, geographic type of data in which data is related to space [1]. Spatial
data are the data related to objects that occupy space. Spatial data carries topological and distance
information. A major difference between data mining in ordinary relational database and in spatial
database is that attribute of neighbors of the some object of interest may have an influence on the object
50 | © 2014, IJAFRC All Rights Reserved
www.ijafrc.org
International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
and have to be considered as well. A spatial database is a database that offers spatial data types in its
data model and query language and supports spatial data types in its implementation, providing at least
spatial indexing and spatial join methods. Spatial data may be accessed using queries containing spatial
operator such as near, north, south, adjacent and contained in whereas non spatial data has accessed
using queries containing operators such as insert, select, project, update, delete. A spatial database is
optimized to store and query data that represents objects defined in a geometric space. Most spatial
databases allow representing simple geometric objects such as points, lines and polygons. The
development of specialized software for spatial data analysis has seen rapid growth as the lack of such
tools was lamented in the late 1980s by Haining (1989) and cited as a major impediment to the adoption
and use of spatial statistics by geographic information systems (GIS) researchers. Initially, attention
tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS
environment (loosely versus tightly coupled, embedded versus modular, etc.), and which techniques
would be most fruitfully included in such a framework. Any data which are directly or indirectly
referenced to a location on the surface of the earth are spatial data. The presence or absence of
Latitude/Longitude or an OS Grid reference in the data is not a determining factor. For example, an
experiment carried out in a laboratory may not appear to yield spatial data; however, if soil, water or
vegetation samples used in the experiment were collected from a known location(s) the resulting data
are spatial [2].
II.
SPATIAL DATA VS NON SPATIAL DATA
Spatial data are the data related to objects that occupy space. Spatial data carries topological and
distance information. Non spatial data model are not particularly suitable for geographic applications
because they do not efficiently support the types of operations that are required for spatial
applications and, they are not suitable for the storage and manipulation of spatial data and
graphical data.
Spatial data: There are following features of spatial data
•
•
•
Spatial data consist of location, shape, size and orientation.
Spatial data includes spatial relationships.
Spatial data are generally multi-dimensional and auto related.
For example - points, lines and polygons on a geographic reference system on the earth.
Non-spatial data: There are following features of non-spatial data
•
•
•
•
•
Non-spatial data has no specific location in space. It can have a geographic component and be linked
to a geographic location
Tabular and attribute data are non-spatial but can be linked to location.
Non-spatial data also called attribute or characteristic data is the information which is independent of
all geometric considerations.
Non-spatial data are generally one-dimensional and independent.
Non-spatial data has no direct reference to a position on an object. We often call that tabular data.
For example - a person’s height and age are non-spatial data because they are independent of the
person’s location.
51 | © 2014, IJAFRC All Rights Reserved
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International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
Spatial database and non-spatial database
The Spatial data is designed to make spatial data management easier and more natural to users or
applications such as a Geographic Information System (GIS). Once this data is stored in an Oracle
database, it can be easily manipulated, retrieved, and related to all the other data stored in the database.
Spatial data refers to geographic areas or features. Features occupy a location whereas Non-spatial data
has no specific location in space.
Spatial database and non spatial database contain following type of data.
Spatial information
•
•
•
•
•
Locations of objects (are separate, individual points in space)
Space occupied by objects (continuous)
Example of objects
Lines (e.g., roads, rivers)
regions (e.g., buildings, crop maps, polyhedra)
Non-spatial information
•
•
•
Region names, postal codes etc
City population, year founded etc
Road names, speed limits, etc [3].
III. SPATIAL DATA MODEL
The main application driving research in spatial database systems are GIS (Geographical Information
System). Hence we consider some modeling needs in this area which is typical also for other applications.
Examples are given for two dimensional spaces, but almost everywhere, extension to the threedimensional or more-dimensional is possible. There are two important alternative views of what needs to
be represented [1].
We are interested in distinct entities arranged in space each of which has its own geometric description.
We wish to describe space itself, that is, say something about every point in space. The first view allows
one to model, for example, cities, forests, or rivers. The second view is the one of thematic maps
describing e.g. land use or the partition of a country into districts. Since raster images say something
about every point in space, they are also closely related to the second view. We can merge the views to
some extent by offering concepts for modelingThe fundamental abstractions of spatial data models are point, line, and region.
•
Point: A point represents the geometric aspect of an object for which only its location in space is
important but not its size. For example, in fig 1. A Nagpur city map may be modeled as a point in a
model describing a large geographic area.
•
Line: Line is the basic concept for facilities for moving through space, or connections in space. For
example in fig. 1. Roads, Rivers route, Cables for phone, electricity etc.
52 | © 2014, IJAFRC All Rights Reserved
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International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
•
Polygon: A region is the abstraction for something having an extent in 2d-space, e.g. a country
map, a lake map, or a national park map. A region may have holes and may also consist of
several disjoint pieces.
Figure 1 shows the three basic abstractions for objects [1].
Point
Line
Polygon
Nagpur City Map Using Point, Line and Polygon
Figure 1. Object Specification Method
The two most important instances of spatially related collections of objects are partitions (of the
plane) and networks.
(a) A partition can be viewed as a set of region objects that are required to be disjoint. The adjacency
relationship is of particular interest, that is, there exist often pairs of region objects with a
common boundary. Partitions can be used to represent thematic maps.
(b) A network can be viewed as a graph embedded into the plane, consisting of a set of point objects,
forming its nodes, and a set of line objects describing the geometry of the edges. Networks are
ubiquitous in geography, for example, highways, rivers, public transport, or power supply lines [1].
a) Partitions
b) Network
Figure 2 . Structure of Spatial data
Spatial data representation: There are two different ways of representing spatial data.
Vector data model: A representation of the world using points, lines, and polygons. Vector models are
useful for storing data that has discrete boundaries, such as country borders, land parcels, and streets.
Vector data consists of individual points, which (for 2D data) are stored as pairs of (x, y) co-ordinates.
The points may be joined in a particular order to create lines, or joined into closed rings to create
polygons, but all vector data fundamentally consists of lists of co-ordinates that define vertices, together
with rules to determine whether and how those vertices are joined.
Note that whereas raster data consists of an array of regularly spaced cells, the points in a vector dataset
need not be regularly spaced.
53 | © 2014, IJAFRC All Rights Reserved
www.ijafrc.org
International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
Raster data model: A representation of the world as a surface divided into a regular grid of cells. Raster
models are useful for storing data that varies continuously, as in an aerial photograph, a satellite image, a
surface of chemical concentrations, or an elevation surface.
Raster data is made up of pixels (or cells), and each pixel has an associated value. Simplifying slightly, a
digital photograph is an example of a raster dataset where each pixel value corresponds to a particular
color. In GIS, the pixel values may represent elevation above sea level, or chemical concentrations, or
rainfall etc.
Figure 3. Spatial Data Representation
There are following advantages and disadvantages of Vector data.
Advantages of vector data
•
•
•
Data can be represented at its original resolution and form without generalization.
Most data, e.g. hard copy maps, is in vector form no data conversion is required.
Accurate geographic location of data is maintained [4].
Disadvantages of vector data
•
•
•
•
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The location of each vertex needs to be stored explicitly.
For effective analysis, vector data must be converted into a topological structure. This is often
processing intensive and usually requires extensive data cleaning. As well, topology is static, and any
updating or editing of the vector data requires re-building of the topology.
Algorithms for manipulative and analysis functions are complex and may be processing intensive.
Often, this inherently limits the functionality for large data sets, e.g. a large number of features.
Continuous data, such as elevation data, is not effectively represented in vector form. Usually
substantial data generalization or interpolation is required for these data layers.
Spatial analysis and filtering within polygons is impossible [4].
There are following advantages and disadvantages of raster data.
Advantages of raster data
•
•
The geographic location of each cell is implied by its position in the cell matrix. Accordingly, other
than an origin point, e.g. bottom left corner, no geographic coordinates are stored.
Due to the nature of the data storage technique data analysis is usually easy to program and quick to
perform.
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•
•
•
International Journal of Advance Foundation and Research in Computer (IJAFRC)
Volume 1, Issue 8, August2014. ISSN 2348 - 4853
The inherent nature of raster maps, e.g. one attribute maps, is ideally suited for mathematical
modeling and quantitative analysis.
Discrete data, e.g. forestry stands, is accommodated equally well as continuous data, e.g. elevation
data, and facilitates the integrating of the two data types.
Grid-cell systems are very compatible with raster-based output devices, e.g. electrostatic plotters,
graphic terminals [4].
Disadvantages of raster data
•
•
•
•
•
The cell size determines the resolution at which the data is represented.
It is especially difficult to adequately represent linear features depending on the cell resolution.
Accordingly, network linkages are difficult to establish.
Processing of associated attribute data may be cumbersome if large amounts of data exist. Raster
maps inherently reflect only one attribute or characteristic for an area.
Since most input data is in vector form, data must undergo vector-to-raster conversion. Besides
increased processing requirements this may introduce data integrity concerns due to generalization
and choice of inappropriate cell size.
Most output maps from grid-cell systems do not conform to high-quality cartographic needs [4].
IV. ACKNOWLEDGMENT
We are very much thankful to Dr. P. K. Butey, Head, Associate Professor, Kamla Nehru Mahavidyalaya,
Nagpur, for his valuable inputs, constant guidance and his extensive support an encouragement for this
work.
V. CONCLUSION
This paper presents the variation in spatial data and non spatial data. There are different fields which
need to manage geometric, geographic type of data in which data is related to space. Spatial data are the
data related to objects that occupy space. Non spatial data are not particularly suitable for geographic
applications because they do not efficiently support the types of operations that are required for
spatial applications and they are not suitable for the storage and manipulation of spatial data
and graphical data.
VI. REFERENCES
[1]
Ralf Hartmut Güting Praktische Informatik IV, Fern Universität Hagen D-58084 Hagen, Germany,
An Introduction to spatial database system, Vol 3, No 4, October 1994.
[2]
M. H. Dunham, S. Sridhar, Data Mining, Introductory and Advanced Topics.
[3]
Hanan Samet, Spatial database and Geographic Information System, University Of Maryland
College Park, Maryland 20742-3411 USA.
[4]
David J. Buckey, BGIS Introduction to GIS.
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