How to eat a graph

Chimani, Markus; Dijk, Thomas C. van; Haunert, Jan‐Henrik

doi:10.1145/2666310.2666414

Cited by 18 publications

(8 citation statements)

References 23 publications

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“…Line schematization has also been addressed by ontology‐based semantic analysis [KDE05] and categorization of geometric features [PY11]. Schematization techniques have also been applied to various map styles, such as metro maps [NW11, WC11, WTLY12, WTH*13], road networks [HS11, CvDH14], and urban area maps [GASP08, QWC*09,HWAT13]. Map schematization was also employed to attract visual attention, with improved map readability [vDvGH*13].…”

Section: Related Workmentioning

confidence: 99%

Interactive Optimization for Cartographic Aggregation of Building Features

Takahashi,

Kokubun,

Nishimura

et al. 2024

Computer Graphics Forum

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Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale‐aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation.

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Section: Related Workmentioning

confidence: 99%

Interactive Optimization for Cartographic Aggregation of Building Features

Takahashi,

Kokubun,

Nishimura

et al. 2024

Computer Graphics Forum

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“…Based on these requirements, they wanted to show as many points as possible for a given scale interval. Reference Chimani et al [8] computed a deletion sequence for a road network by integer linear programming and efficient approximation algorithms. They wanted to delete a stroke, which is a sequence of edges, at each step while keeping the remaining network connected.…”

Section: Optimization In Continuous Map Generalizationmentioning

confidence: 99%

Finding Optimal Sequences for Area Aggregation—A ^⋆ vs. Integer Linear Programming

Peng

Wolff

Haunert

2020

ACM Trans. Spatial Algorithms Syst.

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To provide users with maps of different scales and to allow them to zoom in and out without losing context, automatic methods for map generalization are needed. We approach this problem for land-cover maps. Given two land-cover maps at two different scales, we want to find a sequence of small incremental changes that gradually transforms one map into the other. We assume that the two input maps consist of polygons, each of which belongs to a given land-cover type. Every polygon on the smaller-scale map is the union of a set of adjacent polygons on the larger-scale map. In each step of the computed sequence, the smallest area is merged with one of its neighbors. We do not select that neighbor according to a prescribed rule but compute the whole sequence of pairwise merges at once, based on global optimization. We have proved that this problem is NP-hard. We formalize this optimization problem as that of finding a shortest path in a (very large) graph. We present the A ⋆ algorithm and integer linear programming to solve this optimization problem. To avoid long computing times, we allow the two methods to return non-optimal results. In addition, we present a greedy algorithm as a benchmark. We tested the three methods with a dataset of the official German topographic database ATKIS. Our main result is that A ⋆ finds optimal aggregation sequences for more instances than the other two methods within a given time frame.

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“…For the remaining chapters it is good to keep in mind that vario-scale produce a sequence of successively more generalized maps, so that these maps go well together, similar to (Chimani et al, 2014); instead of considering each level of generalization independent. We aim at providing more degrees of freedom in data use.…”

Section: More Compact Tgap Storagementioning

confidence: 99%

“…FIGURE 2.4 Deformation of two regions over time / scale (taken from (Danciger et al, 2009, p. 5)). Chimani et al (2014) apply a method where they remove edges of the road network map one by one. Therefore, the map is ge ing gradually simpler and simpler.…”

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confidence: 99%

Design and development of a system for vario-scale maps

Šuba

2017

Architecture and the Built Environment

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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.

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How to eat a graph

Cited by 18 publications

References 23 publications

Interactive Optimization for Cartographic Aggregation of Building Features

Interactive Optimization for Cartographic Aggregation of Building Features

Finding Optimal Sequences for Area Aggregation—A ^⋆ vs. Integer Linear Programming

Design and development of a system for vario-scale maps

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How to eat a graph

Cited by 18 publications

References 23 publications

Interactive Optimization for Cartographic Aggregation of Building Features

Interactive Optimization for Cartographic Aggregation of Building Features

Finding Optimal Sequences for Area Aggregation—A ⋆ vs. Integer Linear Programming

Design and development of a system for vario-scale maps

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Product

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Finding Optimal Sequences for Area Aggregation—A ^⋆ vs. Integer Linear Programming