The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
169 
Number of sites 
in each quadrat 
Observed 
frequency 
Observed 
Proportions 
Cumulative 
proportion 
Expected 
(poisson) 
proportion 
Cumulative expected 
(poisson) proportion 
Proportion 
differences 
0 
38 
0.567 
0.567 
0.1308 
0.1308 
0.4362 
1 
8 
0.119 
0. 686 
0.2660 
0.3968 
0.2892 
2 
4 
0.059 
0.745 
0.2705 
0.6673 
0.0777 
3 
8 
0.119 
0.867 
0.1834 
0.8507 
0.0163 
4 
1 
0.0149 
0.878 
0.0935 
0.9442 
0.0662 
5 
2 
0.029 
0.9079 
0.0379 
0.9821 
0.0742 
6 
2 
0.029 
0.9369 
0.0128 
1.00 
0.0631 
7 
3 
0.044 
0.9809 
0.000 
1.00 
0.0191 
>7 
1 
0.0149 
1.00 
0.000 
1.00 
0.00 
Table2. K-Sstatistical inferences based on a comparison of the observed pattern with Poisson probability distribution 
Table 1 shows site cluster distribution where it is only here we 
see the number of quadrats containing more amounts than the 
sites. This theory is borne out by site dispersion frequencies 
where quadrat alone has almost more sites than all the other 
quadrats combined. Other than this, site distribution isn’t the 
same in all the quadrats and based on this we can draw the 
conclusion that site distribution across the area being studied 
hasn’t been considered as the same. Analysis of table 2 in which 
K-S test was performed as well, establishes acceptable bases for 
rejecting random site distribution. In K-S measurement, the 
value of the test was equal: 
D = Max\O i -E i \ (3) 
Dmax = 0. 4362 
thus 
Dmax = 0.5= 0.1660.4362 
Therefore, the similarity between site distribution pattern and 
Poisson random pattern is also rejected. Furthermore, 
considering the statistical process of table 3, the ratio of 
variance and a mean of 8/824 are big enough to confirm site 
cluster distribution (equation 4). 
a =■ 
ji^x-Ay 
(4) 
Where x, is the number of archaeological sites in a quadrat, n., 
is the number of quadrat with x. points, and n is the total number 
of quadrats. 
On the other hand, the fact that statistical value of t is also 
44/950 repudiates the possibility of a pattern other than 
clustering for site distributions (equation 5). 
K-)-i 
= r -y ~ 
(5) 
(n-l) 
Where df is the number of degree of freedom, and n is the 
number of quadrats. 
Number of sites in each quadrat 
Observed frequency 
(x, 
2 
n (x - X) 
i i 
0 
38 
4.137 
157.206 
1 
8 
1.067 
8.553 
2 
4 
1.156 
4.624 
3 
8 
0.9331 
7.465 
4 
1 
3.865 
3.865 
5 
2 
8.797 
17.594 
6 
2 
15.729 
31.458 
7 
3 
24.661 
73.983 
32 
1 
897.961 
897.961 
Table 3 Variance mean ratio of the observed and expected patterns of archaeological sites in the eastern shores of Urmia Lake 
5- DISCUSSION 
Today, it has become frequently prevalent to use point pattern 
analysis in archaeology to show the location of artifacts, 
features, and archaeological sites. Therefore, point pattern 
analysis is seen as an important tool for describing, interpreting, 
and analyzing spatial distribution features of the above 
archaeological phenomena. (Conolly and Lake 2006:162) 
Analysis of archaeological settlement pattern is a brilliant 
approach as far as site dispersion settlement is concerned. This 
approach has carved a special niche for itself both on 
intellectual and practical levels in the development of analytic 
tools such as GIS within archaeology. For instance, in the case 
of settlement pattern analysis, regular spacing of sites has been 
taken to reflect either a form of competition between 
settlements, the existence of site catchments, or a combination 
of both as a result of demographic growth from an initial 
random distribution. By contrast, clustering of sites may result 
from a number of factors, but localized distribution of resources