Disparity of Spatial and Non Spatial Data

International Journal of Advance Foundation and Research in Computer (IJAFRC)

Volume 1, Issue 8, August2014. ISSN 2348 - 4853

50 | © 2014, IJAFRC All Rights Reserved www.ijafrc.org

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]

A B S T R A C T

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




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.




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 three-

dimensional 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 modeling-

The 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.




• 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.




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

• 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.




• 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.

Documents

Disparity of Spatial and Non Spatial Data