"Mapping is a way of visualizing parts of the world and maps are largely diagrammatic and two dimensional. There is usually a one-to-one correspondence between places in the world and places on the map, but while there are limitless aspects to the world, the cartographer can only select a few to map" Daniel Dorling, 1996 Map generalization is the process by which small scale maps are derived from large scale maps. This requires the application of operations such as simplification, selection, displacement and amalgamation to map features subsequent to scale reduction. The work is concerned with the problem of effective rendering of large scale datasets on small display devices by developing appropriate map generalization techniques for generating schematic maps. With the advent of high-end miniature technology and large scale digital geographic data products it is essential to devise proper methodologies and techniques for the automated generation of schematic maps specifically tailored for mobile GIS applications. Schematic maps are diagrammatic representation based on linear abstractions of networks. Transportation networks are the key candidates for applying schematization to help ease the interpretation of information by the process of cartographic abstraction. This study looks at how simulated annealing optimisation technique can be successfully applied for automated generation of schematic maps from large scale digital geographic datasets tailored specifically for mobile GIS applications. The software developed makes use of a simulated annealing based schematic map generator algorithm to generate route maps from OSCAR® dataset corresponding to a series of user defined start and end points. The generated schematic route maps are displayed and tested on mobile handheld devices shows promising results for mobile GIS applications. This work concentrates on the automatic generation of schematic maps, which, in the context of mobile mapping, are seen as being a particularly useful means of displaying routes for way finding type and utility network applications.

Automated map generalization is a necessary technique for the construction of multi-scale vector map databases that are crucial components in spatial data infrastructure of cities, provinces, and countries. Nevertheless, this is still a dream because many algorithms for map feature generalization are not parameter-free and therefore need human’s interference. One of the major reasons is that map generalization is a process of spatial similarity transformation in multi-scale map spaces; however, no theory can be found to support such kind of transformation. This thesis focuses on the theory of spatial similarity relations in multi-scale map spaces, aiming at proposing the approaches and models that can be used to automate some relevant algorithms in map generalization. After a systematic review of existing achievements including the definitions and features of similarity in various communities, a classification system of spatial similarity relations, and the calculation models of similarity relations in the communities of psychology, computer science, music, and geography, as well as a number of raster-based approaches for calculating similarity degrees between images, the thesis achieves the following innovative contributions. First, the fundamental issues of spatial similarity relations are explored, i.e. (1) a classification system is proposed that classifies the objects processed by map generalization algorithms into ten categories; (2) the Set Theory-based definitions of similarity, spatial similarity, and spatial similarity relation in multi-scale map spaces are given; (3) mathematical language-based descriptions of the features of spatial similarity relations in multi-scale map spaces are addressed; (4) the factors that affect human’s judgments of spatial similarity relations are proposed, and their weights are also obtained by psychological experiments; and (5) a classification system for spatial similarity relations in multi-scale map spaces is proposed. Second, the models that can calculate spatial similarity degrees for the ten types of objects in multi-scale map spaces are proposed, and their validity is tested by psychological experiments. If a map (or an individual object, or an object group) and its generalized counterpart are given, the models can be used to calculate the spatial similarity degrees between them. Third, the proposed models are used to solve problems in map generalization: (1) ten formulae are constructed that can calculate spatial similarity degrees by map scale changes in map generalization; (2) an approach based on spatial similarity degree is proposed that can determine when to terminate a map generalization system or an algorithm when it is executed to generalize objects on maps, which may fully automate some relevant algorithms and therefore improve the efficiency of map generalization; and (3) an approach is proposed to calculate the distance tolerance of the Douglas-Peucker Algorithm so that the Douglas-Peucker Algorithm may become fully automatic. Nevertheless, the theory and the approaches proposed in this study possess two limitations and needs further exploration. • More experiments should be done to improve the accuracy and adaptability of the proposed models and formulae. The new experiments should select more typical maps and map objects as samples, and find more subjects with different cultural backgrounds. • Whether it is feasible to integrate the ten models/formulae for calculating spatial similarity degrees into an identical model/formula needs further investigation. In addition, it is important to find out the other algorithms, like the Douglas-Peucker Algorithm, that are not parameter-free and closely related to spatial similarity relation, and explore the approaches to calculating the parameters used in these algorithms with the help of the models and formulae proposed in this thesis.

Map generalization is a traditional cartographical issue which should be particularly considered in today’sinformation age. The aim of this study is to find some characteristics about head/tail breaks which worksas generalization method compared with the well known Topfer’s method. A questionnaire survey wasconducted to let 30 users choose either of the series maps of both methods and the reason(s) for thatchoice. Also to test their understanding of the series maps histograms were added for them to match.Afterwards the sample results were analyzed using both univariate and bivariate analysis approaches. Itshows that the head/tail breaks method was selected by 58%, compared with 38.7% of Topfer’s method,because of its simplicity. By checking the correctness of histogram question it also shows that those whowell understood answers choose the head/tail breaks rather than the Topfer’s method. However in somecases, where the amount of geographical features is relatively small, Topfer’s method is more selectedbecause of its informative characteristic and similar structure to the original map. It was also found that inthe comparison the head/tail breaks is more advantageous in line feature type generalization than in arealfeature type. This is probably because Topfer’s method changes its minority selection rule to half selectionin line feature type, whereas the head/tail breaks keeps the scaling property. Any difference between thetwo tested scales, Finland level and Helsinki level, is not found in this comparison study. However, futurework should explore more regarding this and other issues.