Drawing network maps automatically comprises two challenging steps, namely laying out the map and placing non-overlapping labels. In this paper we tackle the problem of labeling an already existing network map considering the application of metro maps. We present a flexible and versatile labeling model. Despite its simplicity, we prove that it is NP-complete to label a single line of the network. For a restricted variant of that model, we introduce an efficient algorithm that optimally labels a single line. Based on that algorithm, we present a general and sophisticated workflow for multiple metro lines, which is experimentally evaluated on real-world metro maps.

doi:10.1007/s00453-017-0350-0

@article{HaunertN15a,
    abstract = {Drawing network maps automatically comprises two challenging steps, namely laying out the map and placing non-overlapping labels. In this paper we tackle the problem of labeling an already existing network map considering the application of metro maps. We present a flexible and versatile labeling model. Despite its simplicity, we prove that it is NP-complete to label a single line of the network. For a restricted variant of that model, we introduce an efficient algorithm that optimally labels a single line. Based on that algorithm, we present a general and sophisticated workflow for multiple metro lines, which is experimentally evaluated on real-world metro maps.},
    author = {Niedermann, B. and Haunert, J.{-}H.},
    doi = {10.1007/s00453-017-0350-0},
    journal = {Algorithmica},
    number = {5},
    pages = {1493--1533},
    title = {An Algorithmic Framework for Labeling Network Maps},
    volume = {80},
    year = {2018}
}

Green spaces in urban areas offer great possibilities of recreation, provided that they are easily accessible. Therefore, an ideal city should offer large green spaces close to where its residents live. Although there are several measures for the assessment of urban green spaces, the existing measures usually focus either on the total size of green spaces or on their accessibility. Hence, in this paper, we present a new methodology for assessing green-space provision and accessibility in an integrated way. The core of our methodology is an algorithm based on linear programming that computes an optimal assignment between residential areas and green spaces. In a basic setting, it assigns a green space of a prescribed size exclusively to each resident such that the average distance between residents and assigned green spaces is minimized. We contribute a detailed presentation on how to engineer an assignment-based method such that it yields reasonable results (e.g., by considering distances in the road network) and becomes efficient enough for the analysis of large metropolitan areas (e.g., we were able to process an instance of Berlin with about 130000 polygons representing green spaces, 18000 polygons representing residential areas, and 6 million road segments). Furthermore, we show that the optimal assignments resulting from our method enable a subsequent analysis that reveals both interesting global properties of a city as well as spatial patterns. For example, our method allows us to identify neighborhoods with a shortage of green spaces, which will help spatial planners in their decision making.

doi:10.4230/LIPIcs.GISCIENCE.2018.13

@inproceedings{NiedermannEtAl2018,
    abstract = {Green spaces in urban areas offer great possibilities of recreation, provided that they are easily accessible. Therefore, an ideal city should offer large green spaces close to where its residents live. Although there are several measures for the assessment of urban green spaces, the existing measures usually focus either on the total size of green spaces or on their accessibility. Hence, in this paper, we present a new methodology for assessing green-space provision and accessibility in an integrated way. The core of our methodology is an algorithm based on linear programming that computes an optimal assignment between residential areas and green spaces. In a basic setting, it assigns a green space of a prescribed size exclusively to each resident such that the average distance between residents and assigned green spaces is minimized. We contribute a detailed presentation on how to engineer an assignment-based method such that it yields reasonable results (e.g., by considering distances in the road network) and becomes efficient enough for the analysis of large metropolitan areas (e.g., we were able to process an instance of Berlin with about 130000 polygons representing green spaces, 18000 polygons representing residential areas, and 6 million road segments). Furthermore, we show that the optimal assignments resulting from our method enable a subsequent analysis that reveals both interesting global properties of a city as well as spatial patterns. For example, our method allows us to identify neighborhoods with a shortage of green spaces, which will help spatial planners in their decision making.},
    author = {Niedermann, B. and Oehrlein, J. and Lautenbach, S. and Haunert, J.-H.},
    booktitle = {Proc. 10th International Conference on Geographic Information Science (GIScience '18)},
    doi = {10.4230/LIPIcs.GISCIENCE.2018.13},
    pages = {13:1--13:16},
    series = {Leibniz International Proceedings in Informatics (LIPIcs)},
    title = {A network flow model for the analysis of green spaces in urban areas},
    volume = {114},
    year = {2018}
}

Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.

doi:10.5194/isprs-annals-IV-4-W7-107-2018

@inproceedings{OehrleinEtAl2018,
    abstract = {Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.},
    author = {Oehrlein, J. and Förster, A. and Schunck, D. and Dehbi, Y. and Roscher, R. and Haunert, J.-H.},
    booktitle = {Proc. 3rd International Conference on Smart Data and Smart Cities},
    doi = {10.5194/isprs-annals-IV-4-W7-107-2018},
    pages = {107--114},
    series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    title = {Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories},
    volume = {IV-4/W7},
    year = {2018}
}

Many researchers used expensive 3D laser scanning techniques to derive indoor models. Few papers describe the derivation of indoor models based on sparse data such as footprints. They assume that floorplans and rooms are rather rectangular and that information on functional use is given. This paper addresses the automatic learning of a classifier which predicts the functional use of housing rooms. The classification is based on features which are widely available such as room areas and orientation. These features are extracted from an extensive database of annotated rooms. A Bayesian classifier is applied which delivers probabilities of competing class hypotheses. In a second step, functional uses are used to predict the shape of the rooms in a further classification.

doi:10.5194/isprs-annals-IV-4-33-2018

@inproceedings{DehbiEtAl2018,
    abstract = {Many researchers used expensive 3D laser scanning techniques to derive indoor models. Few papers describe the derivation of indoor models based on sparse data such as footprints. They assume that floorplans and rooms are rather rectangular and that information on functional use is given. This paper addresses the automatic learning of a classifier which predicts the functional use of housing rooms. The classification is based on features which are widely available such as room areas and orientation. These features are extracted from an extensive database of annotated rooms. A Bayesian classifier is applied which delivers probabilities of competing class hypotheses. In a second step, functional uses are used to predict the shape of the rooms in a further classification.},
    author = {Dehbi, Y. and Gojayeva, N. and Pickert, A. R. and Haunert, J.-H. and Pl\"{u}mer, L.},
    booktitle = {Proc. ISPRS Technical Commission IV Symposium},
    doi = {10.5194/isprs-annals-IV-4-33-2018},
    series = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
    title = {Room shapes and functional uses predicted from sparse data},
    volume = {IV-4:33--40},
    year = {2018}
}

Maize (Zea mays, L.) displays an exceptional degree of structural genomic diversity. In addition, variation in gene expression further contributes to the extraordinary phenotypic diversity and plasticity of maize. This study provides a systematic investigation on how distantly related homozygous maize inbred lines affect the transcriptomic plasticity of their highly heterozygous F1 hybrids.

doi:10.1016/j.cub.2017.12.027

@article{Vedder2018,
    abstract = {Maize (Zea mays, L.) displays an exceptional degree of structural genomic diversity. In addition, variation in gene expression further contributes to the extraordinary phenotypic diversity and plasticity of maize. This study provides a systematic investigation on how distantly related homozygous maize inbred lines affect the transcriptomic plasticity of their highly heterozygous F1 hybrids.},
    author = {Baldauf, Jutta A. and Marcon, Caroline and Lithio, Andrew and Vedder, Lucia and Altrogge, Lena and Piepho, Hans-Peter and Schoof, Heiko and Nettleton, Dan and Hochholdinger, Frank},
    doi = {10.1016/j.cub.2017.12.027},
    journal = {Current Biology},
    number = {3},
    pages = {431--437},
    title = {Single-Parent Expression Is a General Mechanism Driving Extensive Complementation of Non-syntenic Genes in Maize Hybrids},
    volume = {28},
    year = {2018}
}

@inproceedings{nrw-eaorg-preprint-18,
    author = {Niedermann, B. and Rutter, I. and Wolf, M.},
    booktitle = {Proceedings of the 34rd European Workshop on Computational Geometry (EuroCG'18)},
    file = {nrw-eaorg-preprint-18.pdf:http\://i11www.ira.uka.de/extra/publications/nrw-eaorg-preprint-18.pdf:PDF},
    note = {Preprint.},
    title = {{Efficient Algorithms for Ortho-Radial Graph Drawing}},
    url = {https://conference.imp.fu-berlin.de/eurocg18/download/paper_71.pdf},
    year = {2018}
}

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.

doi:10.3390/s18082541

@article{Wierig_2018,
    abstract = {In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.},
    author = {Wierig, M. and Mandtler, L. and Rottmann, P. and Stroh, V. and Müller, U. and Büscher, W. and Plümer, L.},
    doi = {10.3390/s18082541},
    issn = {1424-8220},
    journal = {Sensors},
    month = {Aug},
    number = {8},
    pages = {2541},
    publisher = {MDPI AG},
    title = {Recording Heart Rate Variability of Dairy Cows to the Cloud—Why Smartphones Provide Smart Solutions},
    url = {http://dx.doi.org/10.3390/s18082541},
    volume = {18},
    year = {2018}
}