Karl-Heinrich Anders

Linking Objects of Different Spatial Data Sets by Integration and Aggregation

In order to solve spatial analysis problems, nowadays a huge amount of digital data sets can be accessed: cadastral, topographic, geologic, and environmental data, in addition to all kinds of other types of thematic information. In order to fully exploit and combine the advantages of each data set, they have to be integrated. This integration has to be established at an object level leading to a multiple representation scheme. Depending on the type of data sets involved, it can be achieved using different techniques.Such a linking has many benefits. First, it helps to limit redundancies and inconsistencies. Furthermore, it helps to take advantage of the characteristics of more than one data set and therefore greatly supports complex analysis processes. Also, it opens the way to integrated data and knowledge processing using whatever information and processes are available in a comprehensive manner. This is an issue currently addressed under the heading of ‘interoperability’.Linking has basically two aspects: on the one hand, the links characterize the correspondence between individual objects in two representations. On the other hand, the links also can carry information about the differences between the data sets and therefore have a procedural component, allowing the generation of a new data set based on given information (i.e., database generalization).In the paper three approaches for the linking of objects in different spatial data sets are described. The first defines the linking as a matching problem and aims at finding a correspondence between two data sets of similar scale. The two other approaches focus on the derivation of one representation from the other one, leading to an automatic generation of new digital data sets of lower resolution. All the approaches rely on methodologies and techniques from artificial intelligence, namely knowledge representation and processing, search procedures, and machine learning.

Pattern recognition in road networks on the example of circular road detection

The paper will introduce into the subject of recognition of typical patterns in road networks. Especially we will describe the search for ring structures and its implementation in detail. Applications to detect these patterns and to use them for eliciting additional implicit knowledge in vector data are shown. We will familiarise the reader with different methods and approaches for the automatic detection of those patterns in vector data. The retrieval of implicit information in vector data can be very helpful for many tasks, ranging from generalisation of maps to the spatial analysis and enrichment of GIS data to make it searchable by search engines.

Characterising Space via Pattern Recognition Techniques: Identifying Patterns in Road Networks

Publisher Summary The chapter introduces the subject of pattern recognition in road networks. It describes the concepts of random and scale-free graphs, since scale-free graphs contain important information that can be used to control generalization processes. The legibility of maps and their accuracy with respect to content depends to a great extent on the quality of the original data. However, the presentation and thematic fidelity of the data can vary considerably depending on the scale and the theme. The generalization process seeks to highlight salient information while omitting information not pertinent to the particular theme. Different criteria are used to control elimination, presentation, and emphasis of geometrical elements—criteria such as measures of object density and shape of geometric elements. Approaches to the automatic detection of these patterns in vector data are reviewed and different patterns of graphs are presented. The chapter also presents methods for identifying the center of cities using a combination of various pattern characteristics. It is argued that pattern recognition techniques are critical to the automatic characterization and generalization of higher order structures in geographical data.

Chapter 12 – Characterising Space via Pattern Recognition Techniques: Identifying Patterns in Road Networks

Publisher Summary The chapter introduces the subject of pattern recognition in road networks. It describes the concepts of random and scale-free graphs, since scale-free graphs contain important information that can be used to control generalization processes. The legibility of maps and their accuracy with respect to content depends to a great extent on the quality of the original data. However, the presentation and thematic fidelity of the data can vary considerably depending on the scale and the theme. The generalization process seeks to highlight salient information while omitting information not pertinent to the particular theme. Different criteria are used to control elimination, presentation, and emphasis of geometrical elements—criteria such as measures of object density and shape of geometric elements. Approaches to the automatic detection of these patterns in vector data are reviewed and different patterns of graphs are presented. The chapter also presents methods for identifying the center of cities using a combination of various pattern characteristics. It is argued that pattern recognition techniques are critical to the automatic characterization and generalization of higher order structures in geographical data.