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Mining Statistically Significant Co-location and
Segregation Patterns
Abstract
In spatial domains, interaction between features gives rise to two types of
interaction patterns: co-location and segregation patterns. Existing
approaches to finding co-location patterns have several shortcomings: (1)
They depend on user specified thresholds for prevalence measures; (2)
they do not take spatial auto-correlation into account; and (3) they may
report co-locations even if the features are randomly distributed.
Segregation patterns have yet to receive much attention. In this paper, we
propose a method for finding both types of interaction patterns, based on a
statistical test. We introduce a new definition of co-location and segregation
pattern, we propose a model for the null distribution of features so spatial
auto-correlation is taken into account, and we design an algorithm for
finding both co-location and segregation patterns. We also develop two
strategies to reduce the computational cost compared to a naïve approach
based on simulations of the data distribution, and we propose an approach
to reduce the runtime of our algorithm even further by using an
approximation of the neighborhood of features. We evaluate our method
empirically using synthetic and real data sets and demonstrate its
advantages over a state-of-the-art co-location mining algorithm.

2. Existing system
In spatial domains, interaction between features gives rise to two types of
interaction patterns: co-location and segregation patterns. Existing
approaches to finding co-location patterns have several shortcomings: (1)
They depend on user specified thresholds for prevalence measures; (2)
they do not take spatial auto-correlation into account; and (3) they may
report co-locations even if the features are randomly distributed.
Segregation patterns have yet to receive much attention. In this paper, we
propose a method for finding both types of interaction patterns, based on a
statistical test.
Proposed system
We introduce a new definition of co-location and segregation pattern, we
propose a model for the null distribution of features so spatial auto-correlation
is taken into account, and we design an algorithm for finding
both co-location and segregation patterns. We also develop two strategies
to reduce the computational cost compared to a naïve approach based on
simulations of the data distribution, and we propose an approach to reduce
the runtime of our algorithm even further by using an approximation of the
neighborhood of features. We evaluate our method empirically using
synthetic and real data sets and demonstrate its advantages over a state-of-
the-art co-location mining algorithm.
System Configuration:-
Hardware Configuration:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz

3.  RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
Software Configuration:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.