Knowledge Discovery in Spatial Planning Data: A Concept for Cluster Understanding - Computational Approaches for Urban Environments - page 47

Chapter 3
Knowledge Discovery in Spatial Planning
Data: A Concept for Cluster Understanding

Martin Behnisch and Alfred Ultsch

Abstract The objective of this paper is to present a methodology for discovering
comprehensible, valid, potentially innovative, and useful patterns, i.e., new knowl-
edge, in multidimensional spatial data. Techniques from statistics, machine learning,
and data mining are applied in consecutive logical steps to allow the visualization
of results and the application of validation procedures at each stage. However,
the approach does not end with a data cluster; rather, if such a valid cluster
has been achieved, then the question is posed: “What do the clusters mean?”.
Symbolic machine learning methods are employed to produce an explanation of
the clusters in terms of rules employing an understandable subset of the high-
dimensional data variables. This combined with canonical representatives of a
cluster and consideration of the spatial distribution of the clusters lead to hypothesis
on emergent data structures, that is, potential new knowledge. The approach is
demonstrated on an exemplary data set of German urban districts featuring seven
dimensions of land use.

Keywords Knowledge discovery ￿ Data mining ￿ Cluster ￿ Spatial planning

3.1 Introduction

The rapid growth of freely available spatial data and advances in information
technology have made an application of the techniques of data mining and knowl-
edge discovery in databases (KDD) (Ultsch 2013 ; Laube 2011 ;Guo 2009 ; Fayyad
et al. 1996 ) both possible and necessary. The goal of this chapter is to present a
methodology for applying knowledge discovery to spatial planning data. Here our