Data mining in geographical contexts and texts

An increasing number of institutions, acting at different scales and within different sectors, create
in-house geographical information systems, e.g. for regional statistics, for land and transport management, for local urban planning, etc. In addition, with the advent of new technologies, such as GPS's or web-mapping facilities, the use of such geographical data is being more and more popular and data is made more easily accessible (sometimes even contributed by the end-users). Geographers find themselves in rather data rich environments today (irrespective of homogeneity and quality). Also geographical objects require specific visualization and statistical methods. The application and adaptation of data mining approaches in geographical contexts is an increasingly important research topic.
In this lecture we will start from theoretical considerations on data mining in geography, particularly emphasizing what is special with exploratory spatial data analysis. We will then refer to ongoing research related to geographical data mining undertaken at the University of Luxembourg in collaboration with colleagues from other institutions. A first example will refer to a large and homogeneous dataset of all dwellings within a Belgian province. Using graph theory and local spatial statistics, the data is used to identify and categorize urbanisation patterns across scales in an iterative way. A second example will depict an application of 'self-organizing maps' to understand patterns of 'territorial cohesion' in Europe using a rather small and lacunary dataset. The third example will be dedicated to a text-mining application to a rather large corpus of documents related to spatial development in Europe. This work funded under the ESPON (European Spatial Observatory Network) aims at producing a relevant thematic structure to the online regional statistics database of the ESPON network.