CLASSIFYING TURKISH DISTRICT DATA WITH K-MEANS AND SOM ALGORITHMS
Classification in GIS Environment
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This book is the master degree thesis of Ece Aksoy, on An Attempt to Classify Turkish District Data: K-Means and Self-Organizing Map (SOM) Algorithms , at METU in Geodetic and Geographical Information Technologies (GIS) Department in 2004. In this book, it is tried to classify Turkish district data, which composed of 923 units, by using these two common techniques in GIS environment. It is aimed to reach a clustering by means of these techniques that can be used for several purposes such as regional politics, constructing statistical integrity or analyzing distribution of funds, and putting fo...
This book is the master degree thesis of Ece Aksoy,
on An Attempt to Classify Turkish District Data: K-
Means and Self-Organizing Map (SOM) Algorithms , at
METU in Geodetic and Geographical Information
Technologies (GIS) Department in 2004. In this book,
it is tried to classify Turkish district data, which
composed of 923 units, by using these two common
techniques in GIS environment. It is aimed to reach
a clustering by means of these techniques that can
be used for several purposes such as regional
politics, constructing statistical integrity or
analyzing distribution of funds, and putting forward
the facilitative usage of GIS in regional and
statistical studies. It further offers detailed
explanations on classification terminology, spatial
clustering concept and GIS usage in clustering
analysis.
on An Attempt to Classify Turkish District Data: K-
Means and Self-Organizing Map (SOM) Algorithms , at
METU in Geodetic and Geographical Information
Technologies (GIS) Department in 2004. In this book,
it is tried to classify Turkish district data, which
composed of 923 units, by using these two common
techniques in GIS environment. It is aimed to reach
a clustering by means of these techniques that can
be used for several purposes such as regional
politics, constructing statistical integrity or
analyzing distribution of funds, and putting forward
the facilitative usage of GIS in regional and
statistical studies. It further offers detailed
explanations on classification terminology, spatial
clustering concept and GIS usage in clustering
analysis.
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