Clustering Contextual Neural Gas: A New Approach for Spatial Planning and Analysis Tasks - Computational Approaches for Urban Environments - page 74

Chapter 4
Clustering Contextual Neural Gas: A New
Approach for Spatial Planning and Analysis
Ta s k s

Julian Hagenauer

Abstract Spatial clustering is a method that can reveal structures and identify
groupings in large spatial data sets, which is in particular useful for spatial planning
and analysis tasks. A recent and powerful clustering algorithm for spatial data is
contextual neural gas (CNG). The CNG algorithm is closely related to the basic self-
organizing map algorithm but additionally takes spatial dependence into account.
However, like most clustering algorithms, CNG requires the analyst to specify
the number of clusters beforehand. Even though the chosen number of clusters
critically affects the results of the clustering, it is unclear how to determine it. This
study introduces a new method which combines CNG, the learning of the CNG's
topology, and graph clustering. It can be used to cluster spatial data without any
prior knowledge of present clusters in the data. The proposed method is in particular
useful for spatial planning and analysis tasks, because it provides means to find
groupings in the data and identify homogeneous regions. To evaluate the method,
this study draws from two experiments which are based on a synthetic and a real-
world data set. The results of the synthetic data set show that it can correctly identify
clusters in a predefined setting. The results of the real-world data set demonstrate
that the proposed method outlines meaningful and theoretically sound regions.

Keywords Artificial neural networks ￿ Cluster analysis ￿ Spatial planning

4.1 Introduction

Clustering is the task of organizing observations into clusters such that the similarity
of observations within a cluster is maximized and the dissimilarity between the
clusters is maximized. It is particularly useful if no categorization or labeling of
the observations is available, but some structural organization is needed. Many
different clustering algorithms have been developed in the past, mainly in the fields