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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 IGN] agrégation spatiale [Termes IGN] algorithme du recuit simulé [Termes IGN] généralisation cartographique automatisée [Termes IGN] méthode heuristique [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau routier [Termes 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]

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Exact optimization algorithms for the aggregation of spatial data / Johannes Oehrlein (2020)  

Public Titre : Exact optimization algorithms for the aggregation of spatial data Type de document : Thèse/HDR Auteurs : Johannes Oehrlein, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 862 Importance : 184 p. Format : 21 x 30 cm Note générale : bibliographie Dissertation zur Erlangung des GradesDoktor-Ingenieur (Dr.-Ing.) Diese Arbeit ist gleichzeitig als elektronische Dissertationbei der Universitäts-und Landesbibliothek Bonn veröffentlicht Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation spatiale [Termes IGN] cycliste [Termes IGN] données localisées [Termes IGN] espace vert [Termes IGN] généralisation automatique de données [Termes IGN] programmation linéaire [Termes IGN] réseau routier [Termes IGN] trajet (mobilité) [Termes IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) The aggregation of spatial data is a recurring problem in geoinformation science. Aggregating data means subsuming multiple pieces of information into a less complex representation. It is pursued for various reasons, like having a less complex data structure to apply further processing algorithms or a simpler visual representation as targeted in map generalization. In this thesis, we identify aggregation problems dealing with spatial data and formalize themas optimization problems. That means we set up a function that is capable of evaluating valid solutions to the considered problem, like a cost function for minimization problems. To each problem introduced, we present an algorithm that finds a valid solution that optimizes this objective function. In general, this superiority with respect to the quality of the solution comes at the cost of computation efficiency, a reason why non-exact approaches like heuristics are widely used for optimization. Nevertheless, the higher quality of solutions yielded by exact approaches is undoubtedly important. On the one hand, “good” solutions are sometimes not sufficient. On the other hand, exact approaches yield solutions that maybe used as benchmarks for the evaluation of non-exact approaches. This kind of application is of particular interest since heuristic approaches, for example, give no guarantee on the quality of solutions found. Furthermore, algorithms that provide exact solutions to optimization problems reveal weak spots of underlying models. A result that does not satisfy the user cannot be excused with a mediocre performance of an applied heuristic. With this motivation, we developed several exact approaches for aggregation problems, which we present in this thesis. Since we deal with spatial data, for all problems considered, the aggregation is based on both geometric and semantic aspects although the focus varies. The first problem we discuss is about visualizing a road network in the context of navigation. Given a fixed location in the network, we aim for a clear representation of the surroundings. For this purpose, we introduce an equivalence relation for destinations in the network based on which we perform the aggregation. We succeed in designing an efficient algorithm that aggregates as many equivalent destinations as possible. Furthermore, we tackle a class of similar and frequently discussed problems concerning the aggregation of areal units into larger, connected regions. Since these problems are NP-complete, i.e. extraordinarily complex, we do not aim for an efficient exact algorithm (which is suspected not to exist) but present a strong improvement to existing exact approaches. In another setup, we present an efficient algorithm for the analysis of urban green-space supply. Performing a hypothetical assignment of citizens to available green spaces, it detects local shortages and patterns in the accessibility of green space within a city. Finally, we introduce and demonstrate a tool for detecting route preferences of cyclists based on a selection of given trajectories. Examining a set of criteria forming suitable candidates, we aggregate them efficiently to the best-fitting derivable criterion. Overall, we present exact approaches to various aggregation problems. In particular, the NP-complete problem we deal with firmly underscores, as expected, the need for heuristic approaches. For applications asking for an immediate solution, it may be reasonable to apply a heuristic approach. This holds in particular due to easy and generally applicable meta-heuristics being available. However, with this thesis, we argue for applying exact approaches if possible. The guaranteed superior quality of solutions speaks for itself. Besides, we give additional examples which show that exact approaches can be applied efficiently as well. Numéro de notice : 17681 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD dissertation : : Rheinische Friedrich-Wilhelms-Universität Bonn : 2020 En ligne : https://nbn-resolving.org/urn:nbn:de:hbz:5-60713 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98023