Statistical Techniques For Spatial Data Analysis

Páginas: 16 (3797 palabras) Publicado: 7 de julio de 2012
STATISTICAL TECHNIQUES FOR
SPATIAL DATA ANALYSIS
Prachi Misra Sahoo
I.A.S.R.I., Library Avenue, New Delhi-110 012
rprachi@iasri.res.in
1. Introduction
Spatial data analysis aims at extracting implicit knowledge such as spatial relations and
patterns that is not explicitly stored in spatial databases. It distinguishes itself from classical
data analysis in that it associates with eachobject the attributes under consideration including
both non-spatial and spatial attributes.
We distinguish three prevalent spatial data types, defined by the topology of the entity to
which the recorded information refers. These are point, lines and area. Features having a
specific location, but without extent in any direction are considered as points. A pair of
coordinates represents a point.Village locations, industrial locations, cities etc. are the
examples of the point data. Lines features consist of series of x, y coordinate pairs with
discrete beginning and ending points. Features like rivers, road networks, represents lines.
Features defined by a set of linked lines enclosing an area are known as polygons. Polygons
are characterized by area and perimeter. Administrativeboundaries, land use, soil map etc. are
the polygon features.
Statistical analysis which deals with spatial data is termed as the science of Spatial statistics.
Spatial statistics span many disciplines, with methods varying in relation to the specific
research questions being addressed, whether predicting ore quality in mining, examining
suspiciously high frequencies of disease events, orhandling the vast data volumes being
generated by GPS (global positioning system) and satellite remote sensing. A unique feature
of spatial data is that geographical location provides a key shared either exactly or
approximately between data sets of different origins. Census data can be overlayed over
patient or customer data; environmental data can be integrated with disease frequencies;
problemswhich hitherto did not admit ready empirical testing are becoming approachable It
is an area of spatial analysis that has grown significantly in the last twenty years. It
encompasses an impressive array of sophisticated methods and techniques for visualization,
exploration and modeling of spatial data which are described here.
2. Descriptive Spatial Statistics
A set of descriptive spatialstatistics has been developed (Table 1) that are areal or locational
equivalents to the nonspatial measures.
Table 1: Nonspatial and Spatial Descriptive Statistics
Statistic
Central tendency
Absolute
Relative
Dispersion
Dispersion
Nonspatial
Mean
Standard Deviation
Coefficient of
Variation
Spatial
Mean Center or
Standard Distance
Relative Distance
Median Center or
Euclidean Median Statistical Techniques for Spatial Data Analysis

2.1 Spatial Measures of Central Tendency
Mean Center
The mean is an important measure of central tendency for a set of data. If this concept of
central tendency is extended to locational point data in two dimensions (X and Y coordinates),
the average location, called the mean centre, can be determined.
Consider the spatial distribution ofpoints shown in Fig. 1. These points might represent any
spatial distribution of interest, the only stipulation is that the phenomenon can be displayed
graphically as a set of points in a two-dimensional coordinates system.
Once a coordinate system has been established and the coordinates of each point determined,
the mean center can be calculated by separately averaging the X and Ycoordinates, as
follows:
ΣX i
ΣYi
Xc =

and Yc =

n

n

where

X c = mean center of X ,

Yc = mean center of Y

X i = X coordinate of point i , Y i = Y coordinate of point i
n = number of points in the distribution
For the point pattern shown in Fig. 1, the mean centre coordinates are
X c = 3.81 and Yc = 2.51 .

Y

Fig. 1: Graph of Locational Coordinates and Mean Center

4
3...
Leer documento completo

Regístrate para leer el documento completo.

Estos documentos también te pueden resultar útiles

  • Software For Data Analysis
  • A Technique For Effective Shade
  • DEA
  • Like Water For Chocolate Analysis
  • Guidelines For Analysis Of Rules
  • Base De Datos Foro
  • data base for objects
  • Foro Caso Salida de Datos

Conviértase en miembro formal de Buenas Tareas

INSCRÍBETE - ES GRATIS