The availability of spatial data on the web has greatly increased through the availability of user-generated community data and geosensor networks. The integration of such multi-source data is providing promising opportunities, as integrated information is richer than can be found in only one data source, but also poses new challenges due to the heterogeneity of the data, the differences in quality and in respect of tag-based semantic modelling. The chapter describes approaches for the integration of official and informal sources, and discusses the impact of integrating user-generated data on automated generalisation and visualisation.

doi:10.1007/978-3-319-00203-3_5

@inproceedings{SesterEtAl2014,
    abstract = {The availability of spatial data on the web has greatly increased through the availability of user-generated community data and geosensor networks. The integration of such multi-source data is providing promising opportunities, as integrated information is richer than can be found in only one data source, but also poses new challenges due to the heterogeneity of the data, the differences in quality and in respect of tag-based semantic modelling. The chapter describes approaches for the integration of official and informal sources, and discusses the impact of integrating user-generated data on automated generalisation and visualisation.},
    author = {M. Sester and J. {Jokar~Arsanjani} and R. Klammer and D. Burghardt and J.-H. Haunert},
    booktitle = {Abstracting Geographic Information in a Data Rich World},
    doi = {10.1007/978-3-319-00203-3_5},
    editor = {Burghardt, D. and Duchene, C. and Mackaness, W.},
    publisher = {Springer-Verlag, Berlin, Germany},
    series = {Lecture Notes in Geoinformation and Cartography},
    title = {Integrating and Generalising Volunteered Geographic Information},
    year = {2014}
}

We present a new algorithm that enlarges a focus region in a given network map without removing non-focus (i.e., context) network parts from the map or changing the map's size. In cartography, this problem is usually tackled with fish-eye projections, which, however, introduce severe distortion. Our new algorithm minimizes distortion and, with respect to this objective, produces results of similar quality compared to an existing algorithm. In contrast to the existing algorithm, the new algorithm achieves real-time performance that allows its application in interactive systems. We target applications where a user sets a focus by brushing parts of the network or the focus region is defined as the neighborhood of a moving user. A crucial feature of the algorithm is its capability of avoiding unwanted edge crossings. Basically, we first solve a least-squares optimization problem without constraints for avoiding edge crossings. The solution we find is then used to identify a small set of constraints needed for a crossing-free solution and, beyond this, allows us to start an animation enlarging the focus region before the final, crossing-free solution is found. Moreover, memorizing the non-crossing constraints from an initial run of the algorithm allows us to achieve a better runtime on any further run - assuming that the focus region does not move too much between two consecutive runs. As we show with experiments on real-world data, this enables response times well below 1 second.

doi:10.1080/13658816.2014.887718

@article{VanDijkHaunert,
    abstract = {We present a new algorithm that enlarges a focus region in a given network map without removing non-focus (i.e., context) network parts from the map or changing the map's size. In cartography, this problem is usually tackled with fish-eye projections, which, however, introduce severe distortion. Our new algorithm minimizes distortion and, with respect to this objective, produces results of similar quality compared to an existing algorithm. In contrast to the existing algorithm, the new algorithm achieves real-time performance that allows its application in interactive systems. We target applications where a user sets a focus by brushing parts of the network or the focus region is defined as the neighborhood of a moving user. A crucial feature of the algorithm is its capability of avoiding unwanted edge crossings. Basically, we first solve a least-squares optimization problem without constraints for avoiding edge crossings. The solution we find is then used to identify a small set of constraints needed for a crossing-free solution and, beyond this, allows us to start an animation enlarging the focus region before the final, crossing-free solution is found. Moreover, memorizing the non-crossing constraints from an initial run of the algorithm allows us to achieve a better runtime on any further run - assuming that the focus region does not move too much between two consecutive runs. As we show with experiments on real-world data, this enables response times well below 1 second.},
    author = {T. C. van Dijk and J.-H. Haunert},
    doi = {10.1080/13658816.2014.887718},
    journal = {International Journal of Geographical Information Science},
    number = {10},
    title = {Interactive Focus Maps Using Least-Squares Optimization},
    volume = {28},
    year = {2014}
}

@inproceedings{gnn-lprm-14,
    author = {A. Gemsa and B. Niedermann and M. N{\"o}llenburg},
    booktitle = {Proceedings of the 30th European Workshop on Computational Geometry (EuroCG'14)},
    file = {gnn-lprm-14.pdf:http\://i11www.ira.uka.de/extra/publications/gnn-lprm-14.pdf:PDF},
    month = {March},
    note = {Preprint.},
    title = {{Label Placement in Road Maps}},
    year = {2014}
}

@inproceedings{bknn-pigra-14,
    author = {T. Bl{\"a}sius and F. Klute and B. Niedermann and M. N{\"o}llenburg},
    booktitle = {Proceedings of the 22nd International Symposium on Graph Drawing (GD'14)},
    editor = {Christian A. Duncan and Antonios Symvonis},
    file = {bknn-pigra-14.pdf:http\://i11www.ira.uka.de/extra/publications/bknn-pigra-14.pdf:PDF},
    note = {Poster abstract.},
    pages = {513--514},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {{PIGRA -- A Tool for Pixelated Graph Representations}},
    volume = {8871},
    year = {2014}
}

@inproceedings{vanDijkEtAl2014,
    author = {T. C. van Dijk and A. van Goethem and J.-H. Haunert and W. Meulemans and B. Speckmann},
    booktitle = {Abstracts of the Schematic Mapping Workshop 2014},
    title = {An Automated Method for Circular-Arc Metro Maps},
    url = {https://sites.google.com/site/schematicmapping/Dijk-Circular.pdf},
    year = {2014}
}

doi:10.1145/2666310.2666414

@inproceedings{ChimaniEtAl2014,
    author = {M. Chimani and T. C. van Dijk and J.-H. Haunert},
    booktitle = {Proc. 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS '14)},
    doi = {10.1145/2666310.2666414},
    pages = {243--252},
    title = {How to Eat a Graph: Computing Selection Sequences for the Continuous Generalization of Road Networks},
    year = {2014}
}

doi:10.1007/978-3-319-11593-1_1

@inproceedings{DijkEtAl2014,
    author = {T. C. van Dijk and A. van Goethem and J.-H. Haunert and W. Meulemans and B. Speckmann},
    booktitle = {Proc. 8th International Conference on Geographic Information Science (GIScience'14)},
    doi = {10.1007/978-3-319-11593-1_1},
    pages = {1--17},
    publisher = {Springer-Verlag, Berlin, Germany},
    series = {Lecture Notes in Computer Science},
    title = {Map Schematization with Circular Arcs},
    volume = {8728},
    year = {2014}
}

doi:10.1145/2677068.2677069

@inproceedings{HaunertHermes2014,
    author = {J.-H. Haunert and T. Hermes},
    booktitle = {Proc. 2nd ACM SIGSPATIAL Workshop on MapInteraction},
    doi = {10.1145/2677068.2677069},
    numpages = {7},
    title = {Labeling Circular Focus Regions Based on a Tractable Case of Maximum Weight Independent Set of Rectangles},
    year = {2014}
}

doi:10.1007/978-3-319-03611-3_17

@inproceedings{SchwartgesEtAl2014,
    author = {N. Schwartges and J.-H. Haunert and D. Zwiebler and A. Wolff},
    booktitle = {Proc. 17th AGILE International Conference on Geographic Information Science},
    doi = {10.1007/978-3-319-03611-3_17},
    pages = {295--310},
    publisher = {Springer-Verlag, Berlin, Germany},
    series = {Lecture Notes in Geoinformation and Cartography},
    title = {Point Labeling with Sliding Labels in Interactive Maps},
    year = {2014}
}

doi:10.1145/2666310.2666494

@inproceedings{SchwartgesEtAl2014b,
    author = {N. Schwartges and A. Wolff and J.-H. Haunert},
    booktitle = {Proc. 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS '14)},
    doi = {10.1145/2666310.2666494},
    pages = {517--520},
    title = {Labeling Streets in Interactive Maps Using Embedded Labels},
    year = {2014}
}