Techniques for better vario-scale map content

The previous chapters covered the research where the vario-scale structure has been introduced. The main aim of the research was general functionality, performance and optimization. So far, the technical aspects had higher priority than the map content. Therefore, this chapter focuses on improving our development kit for generating varioscale content. It presents a strategy to provide good cartographic results throughout all scales and properly stored in the structure. First, Section 4.1 specifies our target. Section 4.2 presents solutions of other researchers. Section 4.3 introduces concepts and tools which are used later in newly designed process. This is demonstrated on road features in Section 4.4. The section presents the generalization approach for the whole scale range from large scale, where roads are represented as area objects, to mid and small scales, where roads are represented as line objects. In our suggested gradual approach even for one road the representation can be mixed, both area and line, which may provide better transition phase and b

Vario-scale data structures

The previous chapter presents state-of-the-art in map generalization at NMAs’ and continuous generalization. There is a noticeable technological shift towards continuous generalisation which supports interactive map use where users can zoom in, out and navigate more gradual way. Despite some research efforts there is no satisfactory solution yet. Therefore, this chapter introduces the truly smooth vario-scale structure for geographic information where a small step in the scale dimension leads to a small change in representation of geographic features that are represented on the map. With this approach there is no (or minimal) geometric data redundancy and there is no (temporal) delay any more between the availability of data sets at different map scales (as was and is the case with more traditional approaches of multi-scale representations). Moreover, continuous generalisation of real world features is based on the structure that can be used for presenting a smooth zoom action to the user. More specific, Section 3.1 and 3.2 provide historical overview of the development and the theoretical framework for vario-scale representations: the tGAP-structure (topological Generalized Area Partitioning). Section 3.3 describes the initial effort to generate the better cartographic content; the concept of constraint tGAP. Section 3.4 explains the 3D SSC (Space-Scale Cube) encoding of 2D truly vario-scale data. Section 3.5 shows idea how to combine more level of details in one map. Section 3.6 summarizes the open questions of the vario-scale concept and it indicates research covered in following chapters. Finally, Section 3.7 presents vario-scale data research in parallel to this PhD for progressive data transfer. Then, Section 3.8 summarises the chapter

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

State of the art in automated map generalization

Automated map generalization is a difficult, complex and computational very intensive problem. The aim of this chapter is to study existing solutions and state of the art. It also provides context and motivation for why we tackle this problem by applying varioscale approach. In Section 2.1, the paradigm shift in map generalization in a digital environment is studied. We investigate if requirements in the map making process have changed with the transformation from paper to digital environment and if so what are the consequences. Then Section 2.2 investigates how National Mapping Agencies are dealing with automated generalization process in general and what are their recent developments. In Section 2.3, the focus is on the issue of continuous map generalization, which is becoming more researched as an alternative to the map generalization for discrete predefined scales. Section 2.4 demonstrates another problem of digital map environment where the number of map scales available is not sufficient for user interactions. Final remarks are covered in 2.5

Large vario-scale datasets

In Chapter 3 the focus was on vario-scale data structure description. This was extended in Chapter 4, where generating better content for this structure was investigated. It showed how the structure has been developed and used in practice, and current technical limitations. One of them is processing really massive dataset with records in order of millions which do not fit in the main memory of computer. It is a notorious and challenging problem. This is especially true in the case of map generalization, where the relationships between (adjacent) features in the map must be considered. Therefore, this chapter presents our solution for automated generalization in vario-scale structure based on the idea of subdividing the workload according to a multi-level structure of the space, allowing parallel processing. More specifically: Section 5.1 specifies our goal. Section 5.2 presents related work and other options to handle large datasets. Section 5.3 explains the principles of our method in more detail. In Section 5.4 modifications of the process specific for road network generalization are introduced. Statistics and a test of real dataset with more than 800 thousand objects are given in Section 5.5, followed by conclusions and the future work related to processing large datasets in Section 5.6

Smooth zooming

Chapter 2 showed that current maps on the Internet are composed of the discrete set of LODs/scale pyramids with big changes of map content and representations. This can lead to confusion for the users when they navigate in the map. Therefore, a conceptual model (SSC) was proposed, see Chapter 3. We believe that, by capturing the whole generalization process in small smooth incremental changes, it is possible to achieve a better user experience e. g. when the user zooms in and out. To verify this hypothesis it is necessary: (1) generate a SSC dataset (2) develop a software prototype where a dataset can be used in its full potential and (3) perform usability test. Hence this chapter will describe the benefits of such as smooth representation in more detail. Section 6.1 gives an introduction to the problem and it suggests our solution. Section 6.2 covers the theoretical background, principle and example of smooth zooming. Section 6.3 presents possible conversion strategies to smooth representation. Section 6.4 explains the software prototype for possible usability testing. This is followed by our initial usability test, which was carry out addition to our plans. More specific, Section 6.5 defines all elements of the testing. Section 6.6 presents preliminary results, and Section 6.7 describes gained experiences. Then, Section 6.8 summarizes possible improvements for the future

Conclusion

This thesis has researched further design and development of a system for vario-scale maps. Section 7.1 presents our ultimate vario-scale goal, which will give us perspective to understand issues addressed in the previous chapters such as further development of current generalization tools considering better vario-scale content (Chapter 4), pro- cessing of a large dataset not fitting in main memory (Chapter 5) and smooth user in- teraction (Chapter 6). All these aspects are now brought together in this chapter where they will be summarized and critically evaluated. The main contributions will be put in the context of the thesis by answering the research questions in Section 7.2, mean- while new op r future research will be covered in Section 7.3

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 b

On-the-fly generalisation of cadastre data

Práce se zabývá zjednodušováním map v reálném čase. Řeší volbu libovolného měřítka mapy, redundanci dat a strukturu pro ukládání výsledků generalizace. V úvodu popisuje problematiku zjednodušování map on-line, následně hledá možnou datovou strukturu pro generalizaci v reálném čase, tedy on-the-fly. Pracuje na topologickém strukturování dat na uzly, hrany, plochy a vytváří další mapová měřítka zjednodušováním měřítek základních. Těžištěm práce je implementace vybrané datové struktury s názvem tGAP (topological Generalized Area Partition) v databázovém systému Oracle.Katedra matematikyObhájenoThe thesis is focused on map simplification in real time. It solves vario-scale, data redundancy and a structure for generalization data. The first, the thesis outlines the problem of on-line map simplification. The secondly, it is searching data structure for generalization in real time (on-the-fly). The structure works with topological data model, which is based on nodes, edges and faces. The structure also solves a creation of new maps scales by simplifying reference scale. The main content of thesis is implantation tGAP (topological Generalized Area Partition) structure in Oracle database