Geospatial visualizations are becoming a larger part of society. From using maps to go from one location to another to using battlefield visualizations to help the military, geospatial visualizations are becoming a larger part of people's lives. At the same time, large displays are becoming more prominent in people's lives. From large fifty-monitor tiled displays to dual monitor desktop systems people are using larger displays more often in their daily lives. This dissertation summarizes our work with large displays and geospatial visualizations. We show dramatic increases in performance of more than ten times performance improvement when using larger displays that offer a greater number of pixels. We show performance improvements for a range of tasks from simple navigation to complex pattern finding tasks. This dissertation contributes to the fields of human-computer interaction and information visualization in that it shows performance improvements as analytical force multipliers and explains why such performance exists. It explains how virtual navigation (mouse and keyboard input) correlates to physical navigation (body movement) to explain performance improvements. In addition, this dissertation explains how semantic zooming, space scale, task scale, and task type all are variables that influence human behavior in both navigation and performance. This dissertation addresses primarily geospatial information visualizations, but extends to other generic spatially oriented visualizations. The impacts of large displays for both geospatial information visualizations and generic spatially oriented visualizations are explained. / Ph. D.

The process of performing cartographic generalization in an automatic way applied on geographic information is of highly interest in the field of cartography, both in academia and industry. Many research e↵orts have been done to implement di↵erent automatic generalization approaches. Being able to answer the research question on automatic generalization, another interesting question opens up: ”Is it possible to retrieve and visualize geographic information in any arbitrary scale?” This is the question in the field of vario-scale geoinformation. Potential research works should answer this question with solutions which provide valid and efficient representation of geoinformation in any on-demand scale. More brilliant solutions will also provide smooth transitions between these on-demand arbitrary scales. Space-Scale-Cube (Meijers and Van Oosterom 2011) is a reactive tree (Van Oosterom 1991) data structure which shows positive potential for achieving smooth automatic vario-scale generalization of area features. The topic of this research work is investigation of adaptation of this approach on an interesting class of geographic information: road networks datasets. Firstly theoretical background will be introduced and discussed and afterwards, implementing the adaptation would be described. This research work includes development of a hierarchical data structure based on road network datasets and the potential use of this data structure in vario-scale geoinformation retrieval and visualization.

The buildings of a city such as shopping malls, plazas, world trade centers, hotels or even residential complexes are not only alternative urban building typologies but they represent power in social, economical, political and even religious terms. In this sense buildings should not be seen as specific design and research areas limited with single building scale but rather should be seen as urban statements in city scale. However the eclectic existence of these buildings in urban fabric causes a series of unexpected transformations in a larger scale. The impact of a building in urban scale takes a very important place in the modern city &ndash / their architectural expression is not limited with their individual scale but rather it becomes an integrated part of the whole city which is open to transform function, infrastructure, architectural meaning, image ability and other social problems. This building behaves as a cultural and social symbol and it is inevitable to consider the design process as an urban experience. However many of the contemporary examples are designed as individual architectural buildings&hellip / The integration of Turkey, but especially the city of Ankara to the global economic network providing new cultural identities presents a transformation of the city which natures could be seen &ldquo / in terms of rent theory&rdquo / and makes this city &ldquo / a place of competition for profit.&rdquo / To better present these transformations one of the most important regions EskiSehir Highway will be analyzed for the power it reflects as the buildings are set on the two sides of the highway as a new type of urban architecture proceeding spontaneously and reconfiguring boundaries based on the limits of the capital. The limits economic power decides about social, economic and physical order of places shapes the city as an urban product to be sold.

The process of performing cartographic generalization in an automatic way applied on geographic information is of highly interest in the field of cartography, both in academia and industry. Many research e↵orts have been done to implement di↵erent automatic generalization approaches. Being able to answer the research question on automatic generalization, another interesting question opens up: ”Is it possible to retrieve and visualize geographic information in any arbitrary scale?” This is the question in the field of vario-scale geoinformation. Potential research works should answer this question with solutions which provide valid and efficient representation of geoinformation in any on-demand scale. More brilliant solutions will also provide smooth transitions between these on-demand arbitrary scales. Space-Scale-Cube (Meijers and Van Oosterom 2011) is a reactive tree (Van Oosterom 1991) data structure which shows positive potential for achieving smooth automatic vario-scale generalization of area features. The topic of this research work is investigation of adaptation of this approach on an interesting class of geographic information: road networks datasets. Firstly theoretical background will be introduced and discussed and afterwards, implementing the adaptation would be described. This research work includes development of a hierarchical data structure based on road network datasets and the potential use of this data structure in vario-scale geoinformation retrieval and visualization.:Declaration of Authorship i Abstract iii Acknowledgements iv List of Figures vii Abbreviations viii 1 Introduction 1 1.1 Problem Definition 2 1.1.1 Research Questions 2 1.1.2 Objectives 3 1.2 Proposed Solution 3 1.3 Structure of the Thesis 4 1.4 Notes on Terminology 4 2 Cartographic Generalization 6 2.1 Cartographic Generalization: Definitions and Classifications 6 2.2 Generalization Operators 9 2.3 Efforts on Vario-Scale Visualization of Geoinformation 10 2.4 Efforts on Generalization of Road Networks and Similar Other Networks 16 2.4.1 Geometric Generalization of Networks 17 2.4.2 Model Generalization of Networks 18 2.5 Clarification of Interest 20 3 Theory of Road Network SSC 21 3.1 Background of an SSC 21 3.1.1 tGAP 21 3.1.2 Smoothing tGAP 23 3.2 Road Network as a ’Network’ 24 3.2.1 Short Background on Graph Theory 5 3.3 Formation of Road Network SSC 26 3.3.1 Geometry 26 3.3.2 Network Topology 27 3.3.3 Building up tGAP on The Road Network 28 3.3.4 Smoothing of Road Network SSC 31 3.3.4.1 Smoothing Elimination 32 3.3.4.2 Smoothing Simplification 32 3.4 Reading from a road network SSC 34 3.4.1 Discussion on Scale 34 3.4.2 Iterating Over The Forest 35 3.4.3 Planar Slices 35 3.4.4 Non-Planar Slices 36 4 Implementation of Road Network SSC 37 4.1 General Information Regarding The Implementation 37 4.1.1 Programming Language 37 4.1.2 RDBMS 38 4.1.3 Geometry Library 39 4.1.4 Graph Library 39 4.2 Data Structure 40 4.2.1 Node 40 4.2.2 Edge 41 4.2.3 Edge-Node-Relation 41 4.3 Software Architecture 42 4.3.1 More Detail on Building The SSC 42 4.3.1.1 Initial Data Processing 42 4.3.1.2 Network Processing 43 4.3.2 More Detail on Querying The SSC 46 4.3.2.1 Database Query 46 4.3.2.2 Building Geometry 46 4.3.2.3 Interface and Visualization 47 4.4 Results 48 5 Conclusions and Outlook 49 Bibliography 51