Vario-scale data structures supporting smooth zoom and progressive transfer of 2D and 3D data

GIS TechnologyOTB Research Institute for the Built Environmen

Data representation for smooth level of detail of spatial objects

Diplomová práce se zabývá konceptem úrovně detailu a jeho použitím v 3D GIS. Cílem práce je návrh datové struktury, která umožní spojité vykreslování diskrétních 3D modelů budov s odlišnou úrovní geometrického detailu, vytvořených pomocí generalizační metody založené na konceptu matematické morfologie. Navržené řešení nejprve vytváří linky odpovídajících si geometrických primitiv modelů v různé úrovni detailu a následně je rekonstruuje pomocí metody extruze. Takto vytvořená datová struktura je schopná vykreslovat libovolný model včetně přechodových modelů, které představují řezy skrz datovou strukturu napříč osou geometrického detailu.The thesis deals with the concept of level of detail and its use in 3D GIS. The aim of this work is to design a data structure that will allow continuous rendering of discrete 3D building models with different levels of geometric detail, which were created by generalization method based on mathematical morphology approach. The proposed solution creates links of corresponding geometric primitives of models at different level of detail at first and then reconstructs them using the extrusion method. The data structure created in this way is able to generate and render any model, including intermediate models, which are represented as slices through the data structure across the axis of geometric detail.Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Design and development of a system for vario-scale maps

Nowadays, there are many geo-information data sources available such as maps on the Internet, in-car navigation devices and mobile apps. All datasets used in these applications are the same in principle, and face the same issues, namely: Maps of different scales are stored separately. With many separate fixed levels, a lot of information is the same, but still needs to be included, which leads to duplication. With many redundant data throughout the scales, features are represented again and again, which may lead to inconsistency. Currently available maps contain significantly more levels of detail (twenty map scales on average) than in the past. These levels must be created, but the optimal strategy to do so is not known. For every user’s data request, a significant part of the data remains the same, but still needs to be included. This leads to more data transfer, and slower response. The interactive Internet environment is not used to its full potential for user navigation. It is common to observe lagging, popping features or flickering of a newly retrieved map scale feature while using the map. This research develops principles of variable scale (vario-scale) maps to address these issues. The vario-scale approach is an alternative for obtaining and maintaining geographical data sets at different map scales. It is based on the specific topological structure called tGAP (topological Generalized Area Partitioning) which addresses the main open issues of current solutions for managing spatial data sets of different scales such as: redundancy data, inconsistency of map scales and dynamic transfer. The objective of this thesis is to design, to develop and to extend the variable-scale data structures and it is expressed as the following research question: How to design and develop a system for vario-scale maps?  To address the above research question, this research has been conducted using the following outline: 1) Investigate state-of-the-art in map generalization. 2) Study development of vario-scale structure done so far. 3) Propose techniques for generating better vario-scale map content. 4) Implement strategies to process really massive datasets. 5) Research smooth representation of map features and their impact on user interaction. Results of our research led to new functionality, were addressed in prototype developments and were tested against real world data sets. Throughout this research we have made following main contributions to the design and development of a system of vario-scale maps. We have: studied vario-scale development in the past and we have identified the most urgent needs of the research. designed the concept of granularity and presented our strategy where changes in map content should be as small and as gradual as possible (e. g. use groups, maintain road network, support line feature representation). introduced line features in the solution and presented a fully-automated generalization process that preserves a road network features throughout all scales. proposed an approach to create a vario-scale data structure of massive datasets. demonstrated a method to generate an explicit 3D representation from the structure which can provide smoother user experience. developed a software prototype where a 3D vario-scale dataset can be used to its full potential. conducted initial usability test. All aspects together with already developed functionality provide a more complex and more unified solution for vario-scale mapping. Based on our research, design and development of a system for vario-scale maps should be clearer now. In addition, it is easier to identified necessary steps which need to be taken towards an optimal solution. Our recommendations for future work are: One of the contributions has been an integration of the road features in the structure and their automated generalization throughout the process. Integrating more map features besides roads deserve attention. We have investigated how to deal with massive datasets which do not fit in the main memory of the computer. Our experiences consisted of dataset of one province or state with records in order of millions. To verify our findings, it will be interesting to process even bigger dataset with records in order of billions (a whole continent). We have introduced representation where map content changes as gradually as possible. It is based on process where: 1) explicit 3D geometry from the structure is generated. 2) A slice of the geometry is calculated. 3) Final maps based on the slice is constructed. Investigation of how to integrate this in a server-client pipeline on the Internet is another point of further research. Our research focus has been mainly on one specific aspect of the concept at a time. Now all aspects may be brought together where integration, tuning and orchestration play an important role is another interesting research that desire attention. Carry out more user testing including; 1) maps of sufficient cartographic quality, 2) a large testing region, and 3) the finest version of visualization prototype. &nbsp

Design and development of a system for vario-scale maps

Nowadays, there are many geo-information data sources available such as maps on the Internet, in-car navigation devices and mobile apps. All datasets used in these applications are the same in principle, and face the same issues, namely: Maps of different scales are stored separately. With many separate fixed levels, a lot of information is the same, but still needs to be included, which leads to duplication. With many redundant data throughout the scales, features are represented again and again, which may lead to inconsistency. Currently available maps contain significantly more levels of detail (twenty map scales on average) than in the past. These levels must be created, but the optimal strategy to do so is not known. For every user’s data request, a significant part of the data remains the same, but still needs to be included. This leads to more data transfer, and slower response. The interactive Internet environment is not used to its full potential for user navigation. It is common to observe lagging, popping features or flickering of a newly retrieved map scale feature while using the map. This research develops principles of variable scale (vario-scale) maps to address these issues. The vario-scale approach is an alternative for obtaining and maintaining geographical data sets at different map scales. It is based on the specific topological structure called tGAP (topological Generalized Area Partitioning) which addresses the main open issues of current solutions for managing spatial data sets of different scales such as: redundancy data, inconsistency of map scales and dynamic transfer. The objective of this thesis is to design, to develop and to extend the variable-scale data structures and it is expressed as the following research question: How to design and develop a system for vario-scale maps? To address the above research question, this research has been conducted using the following outline:  To address the above research question, this research has been conducted using the following outline: 1) Investigate state-of-the-art in map generalization. 2) Study development of vario-scale structure done so far. 3) Propose techniques for generating better vario-scale map content. 4) Implement strategies to process really massive datasets. 5) Research smooth representation of map features and their impact on user interaction. Results of our research led to new functionality, were addressed in prototype developments and were tested against real world data sets. Throughout this research we have made following main contributions to the design and development of a system of vario-scale maps. We have: studied vario-scale development in the past and we have identified the most urgent needs of the research. designed the concept of granularity and presented our strategy where changes in map content should be as small and as gradual as possible (e. g. use groups, maintain road network, support line feature representation). introduced line features in the solution and presented a fully-automated generalization process that preserves a road network features throughout all scales. proposed an approach to create a vario-scale data structure of massive datasets. demonstrated a method to generate an explicit 3D representation from the structure which can provide smoother user experience. developed a software prototype where a 3D vario-scale dataset can be used to its full potential. conducted initial usability test. All aspects together with already developed functionality provide a more complex and more unified solution for vario-scale mapping. Based on our research, design and development of a system for vario-scale maps should be clearer now. In addition, it is easier to identified necessary steps which need to be taken towards an optimal solution. Our recommendations for future work are: One of the contributions has been an integration of the road features in the structure and their automated generalization throughout the process. Integrating more map features besides roads deserve attention. We have investigated how to deal with massive datasets which do not fit in the main memory of the computer. Our experiences consisted of dataset of one province or state with records in order of millions. To verify our findings, it will be interesting to process even bigger dataset with records in order of billions (a whole continent). We have introduced representation where map content changes as gradually as possible. It is based on process where: 1) explicit 3D geometry from the structure is generated. 2) A slice of the geometry is calculated. 3) Final maps based on the slice is constructed. Investigation of how to integrate this in a server-client pipeline on the Internet is another point of further research. Our research focus has been mainly on one specific aspect of the concept at a time. Now all aspects may be brought together where integration, tuning and orchestration play an important role is another interesting research that desire attention. Carry out more user testing including; 1) maps of sufficient cartographic quality, 2) a large testing region, and 3) the finest version of visualization prototype

BK 10-15:

Over the years 2010-2015 TU Delft's Faculty of Architecture and the Built Environment (in Dutch: Bouwkunde or BK) made good progress with its research by: merging the Architecture faculty and the Built Environment research institute; streamlining its PhD research by setting-up a graduate school for doctoral education; co-founding an institute for metropolitan solutions in Amsterdam together with MIT and Wageningen University (targeted yearly budget: 25 M€); implementing good research management; increasing the scientific output; managing a project portfolio with a yearly income of 1.5 M€ in research grants, 5 M€ in contract research and up to 2 M€ in other external funding; ranking 3rd in the QS World University Rankings by Subject 2015 - Architecture / Built Environment. Presented in this book is an overview of research data and policies, together with a selection of our finest research results: activities, organisations, facilities/assets, output, including indications of their use and recognition. Now it is not the time to become complacent. Instead, we should look ahead to face new academic and societal challenges and opportunities, knowing we can always do better

Simulated cognitive topologies: automatically generating highly contextual maps for complex journeys

As people traverse complex journeys, they engage in a number of information interactions across spatial scales and levels of abstraction. Journey complexity is characterised by factors including the number of actions required, and by variation in the contextual basis of reasoning such as a transition between different modes of transport. The high-level task of an A to B journey decomposes into a sequence of lower-level navigational sub-tasks, with the representation of geographic entities that support navigation during, between and across sub-tasks, varying relative to the nature of the task and the character of the geography. For example, transitioning from or to a particular mode of transport has a direct bearing on the natural level of representational abstraction that supports the task, as well as on the overall extent of the task’s region of influence on the traveller’s focus. Modern mobile technologies send data to a device that can in theory be context-specific in terms of explicitly reflecting a traveller’s heterogeneous information requirements, however the extent to which context is explicitly reflected in the selection and display of navigational information remains limited in practice, with a rigid, predetermined scale-based hierarchy of cartographic views remaining the underlying representational paradigm. The core subject of the research is the context-dependent selection and display of navigational information, and while there are many and varied considerations in developing techniques to address selection and display, the central challenge can simply be articulated as how to determine the probability, given the traveller’s current context, that a feature should be in the current map view. Clearly this central challenge extends to all features in the spatial extent, and so from a practical perspective, research questions centre around the initial selection of a subset of features, and around determining an overall probability distribution over the subset given the significance of features within the hierarchically ordered sequence of tasks. In this thesis research is presented around the use of graph structures as a practical basis for modeling urban geography to support heterogenous selections across viewing scales, and ultimately for displaying highly context-specific cartographic views. Through an iterative, empirical research methodology, a formalised approach based on routing networks is presented, which serves as the basis for modeling, selection and display. Findings are presented from a series of 7 situated navigation studies that included research with an existing navigation application as well as experimental research stimuli. Hypotheses were validated and refined over the course of the studies, with a focus on journey-specific regions that form around the navigable network. Empirical data includes sketch maps, textual descriptions, video and device interactions over the course of complex navigation exercises. Study findings support the proposed graph architecture, including subgraph classes that approximate cognitive structures central to natural comprehension and reasoning. Empirical findings lead to the central argument of a model based on causal mechanisms, in which relations are formalised between task, selection and abstraction. A causal framework for automatically determining map content for a given journey context is presented, with the approach involving a conceptual shift from treating geographic features as spatially indexed records, to treating them as variables with a finite number of possible states. Causal nets serve as the practical basis of reasoning, with geographic features being represented by variables in these causal structures. The central challenge of finding the probability that a variable in a causal net is in a particular state is addressed through a causal model in which journey context serves as the evidence that propagates over the net. In this way, complex heterogeneous selections for interactive multi-scale information spaces are expressed as probability distributions determined through message propagation. The thesis concludes with a discussion around the implications of the approach for the presentation of navigational information, and it is shown how the framework can support context-specific selection and disambiguation of map content, demonstrated through the central use case of navigating complex urban journeys