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Aggregating land-use polygons considering line features as separating map elements / Sven Gedicke in Cartography and Geographic Information Science, vol 48 n° 2 (March 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 2 (March 2021) . - pp 124 - 139 Titre : Aggregating land-use polygons considering line features as separating map elements Type de document : Article/Communication Auteurs : Sven Gedicke, Auteur ; Johannes Oehrlein, Auteur ; Jan‐Henrik Haunert, Auteur Année de publication : 2021 Article en page(s) : pp 124 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] agrégation spatiale [Termes descripteurs IGN] algorithme du recuit simulé [Termes descripteurs IGN] généralisation cartographique [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] utilisation du sol [Vedettes matières IGN] Généralisation Résumé : (Auteur) Map generalization is the process of deriving small-scale target maps from a large-scale source map or database while preserving valuable information. In this paper we focus on topographic data, in particular areas of different land-use classes and line features representing the road network. When reducing the map scale, some areas need to be merged to larger composite regions. This process is known as area aggregation. Given a planar partition of areas, one usually aims to build geometrically compact regions of sufficient size while keeping class changes small. Since line features (e.g. roads) are perceived as separating elements in a map, we suggest integrating them into the process of area aggregation. Our aim is that boundaries of regions coincide with line features in such a way that strokes (i.e. chains of line features with small angles of deflection) are not broken into short sections. Complementing the criteria of compact regions and preserving land-use information, we consider this aim as a third criterion. Regarding all three criteria, we formalize an optimization problem and solve it with a heuristic approach using simulated annealing. Our evaluation is based on experiments with different parameter settings. In particular, we compare results of a baseline method that considers two criteria, namely compactness and class changes, with results of our new method that additionally considers our stroke-based criterion. Our results show that this third criterion can be substantially improved while keeping the quality with respect to the original two criteria on a similar level. Numéro de notice : A2021-180 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1851613 date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1080/15230406.2020.1851613 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97067 [article]

Extracting spatial patterns in bicycle routes from crowdsourced data / Jody Sultan in Transactions in GIS, vol 21 n° 6 (December 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 6 (December 2017) . - pp 1321 - 1340 Titre : Extracting spatial patterns in bicycle routes from crowdsourced data Type de document : Article/Communication Auteurs : Jody Sultan, Auteur ; Gev Ben‐Haim, Auteur ; Jan‐Henrik Haunert, Auteur ; Sagi Dalyot, Auteur Année de publication : 2017 Article en page(s) : pp 1321 - 1340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] Amsterdam [Termes descripteurs IGN] cycliste [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] extraction de modèle [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] trajet Résumé : (auteur) Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user‐generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road‐type use of cyclists in urban environments. Since user‐generated data shows data‐deficiencies, we introduce tailored spatial data‐handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map‐matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road‐type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabrück (Germany), proving its feasibility and reliability in mining road‐type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning. Numéro de notice : A2017-838 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12280 date de publication en ligne : 06/06/2017 En ligne : https://doi.org/10.1111/tgis.12280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89374 [article]

A symmetry detector for map generalization and urban-space analysis / Jan‐Henrik Haunert in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 74 (Novembrer 2012) . - pp 66 - 77 Titre : A symmetry detector for map generalization and urban-space analysis Type de document : Article/Communication Auteurs : Jan‐Henrik Haunert, Auteur Année de publication : 2012 Article en page(s) : pp 66 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] agrégation de détails [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] bati [Termes descripteurs IGN] Boston (Massachusetts) [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] indice de symétrie [Termes descripteurs IGN] polygone [Termes descripteurs IGN] simplification de contour [Vedettes matières IGN] Généralisation Résumé : (Auteur) This article presents an algorithmic approach to the problem of finding symmetries in building footprints, which is motivated by map generalization tasks such as symmetry-preserving building simplification and symmetry-aware grouping and aggregation. Moreover, symmetries in building footprints may be used for landmark selection and building classification. The presented method builds up on existing methods for symmetry detection in vector data that use algorithms for string matching. It detects both mirror symmetries and repetitions of geometric structures. In addition to the existing vector-based methods, the new method finds partial symmetries in polygons while allowing for small geometric errors and, based on a least-squares approach, computes optimally adjusted mirror axes and assesses their quality. Finally, the problem of grouping symmetry relations is addressed with an algorithm that finds mirror axes that are almost collinear. The presented approach was tested on a large building dataset of the metropolitan Boston area and its results were compared with results that were manually generated in an empirical test. The symmetry relations that the participants of the test considered most important were found by the algorithm. Future work will deal with the integration of information on symmetry relations into algorithms for map generalization. Numéro de notice : A2012-604 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32050 [article]

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Map generalisation / Jan‐Henrik Haunert in GIM international, vol 25 n° 1 (January 2011)

Public [article]  inGIM international > vol 25 n° 1 (January 2011) . - pp 31 - 33 Titre : Map generalisation Type de document : Article/Communication Auteurs : Jan‐Henrik Haunert, Auteur Année de publication : 2011 Article en page(s) : pp 31 - 33 Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] point d'appui [Vedettes matières IGN] Généralisation Résumé : (Editeur) Decades of research on map generalization have resulted in an abundance of heuristic algorithms, evaluation of the performance of which is vital for choosing the most suitable for a certain application. Proper evaluation methods are, however, missing. The author proposes an approach based on optimization methods adopted from the field of operation research. Numéro de notice : A2011-005 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30787 [article]

Area aggregation in map generalisation by mixed-integer programming / Jan‐Henrik Haunert in International journal of geographical information science IJGIS, vol 24 n°11-12 (december 2010)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 24 n°11-12 (december 2010) . - pp 1871 - 1897 Titre : Area aggregation in map generalisation by mixed-integer programming Type de document : Article/Communication Auteurs : Jan‐Henrik Haunert, Auteur ; A. Wolff, Auteur Année de publication : 2010 Article en page(s) : pp 1871 - 1897 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] agrégation [Termes descripteurs IGN] analyse combinatoire (maths) [Termes descripteurs IGN] ATKIS [Termes descripteurs IGN] base de données topographiques [Termes descripteurs IGN] distance [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] graphe [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] rédaction cartographique [Termes descripteurs IGN] sémiologie graphique [Vedettes matières IGN] Généralisation Résumé : (Auteur) Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method for the aggregation problem. This method aims to minimise changes of classes and to create compact shapes, subject to hard constraints ensuring aggregates of sufficient size for the target scale. To quantify class changes we apply a semantic distance measure. We give a graph theoretical problem formulation and prove that the problem is NP-hard, meaning that we cannot hope to find an efficient algorithm. Instead, we present a solution by mixed-integer programming that can be used to optimally solve small instances with existing optimisation software. In order to process large datasets, we introduce specialised heuristics that allow certain variables to be eliminated in advance and a problem instance to be decomposed into independent sub-instances. We tested our method for a dataset of the official German topographic database ATKIS with input scale 1:50,000 and output scale 1:250,000. For small instances, we compare results of this approach with optimal solutions that were obtained without heuristics. We compare results for large instances with those of an existing iterative algorithm and an alternative optimisation approach by simulated annealing. These tests allow us to conclude that, with the defined heuristics, our optimisation method yields high-quality results for large datasets in modest time. Numéro de notice : A2010-554 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810903401008 En ligne : https://doi.org/10.1080/13658810903401008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30746 [article]

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