EuroSDR research on state-of-the-art of automated generalisation in commercial software: main findings and conclusions

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Social Welfare to Assess the Global Legibility of a Generalized Map

International audienceCartographic generalization seeks to summarize geographical information from a geo-database to produce a less detailed and readable map. The specifications of a legible map are translated into a set of constraints to guide the generalization process and evaluate it. The global evaluation of the map, or of a part of it, consisting in aggregating all the single constraints satisfactions, is still to tackle for the generalization community. This paper deals with the use of the social welfare theory to handle the aggregation of the single satisfactions on the map level. The social welfare theory deals with the evaluation of the economical global welfare of a society, based on the individual welfare. Different social welfare orderings are adapted to generalization, compared and some are chosen for several generalization use cases. Experiments with topographic maps are carried out to validate the choices

A ZONE-BASED ITERATIVE BUILDING DISPLACEMENT METHOD THROUGH THE COLLECTIVE USE OF VORONOI TESSELLATION, SPATIAL ANALYSIS AND MULTICRITERIA DECISION MAKING

An iterative displacement method working based on generalisation zones is proposed as a part of contextual  building generalisation in topographic map production at medium scales. Displacement is very complicated operation since a compromise ought to be found between several conflicting criteria. Displacement requirement mainly arises from the violation of minimum distances imposed bygraphic limits after the enlargement of map objects for target scale. It is also important to maintain positional accuracy within scale limits and to propagate the changes to the related neighbouring objects by preserving spatial configurations asfar as possible. In the proposed method, first it is decided where and when to initiate building displacement based on spatial analysis in the generalisation zones created for building clusters in the blocks. Secondly, relevant criteria are defined to control the displacement. Finally displacement candidate and vector are decided by means of Voronoi tessellation, spatial analysis techniques and combined multiple criteria (i.e. displacement controls) in each iteration. The evaluation of the findings demonstrates that this method is largely effective in zone-based displacement of buildings

Evaluation of river network generalization methods for preserving the drainage pattern

The drainage pattern of a river network is the arrangement in which a stream erodes the channels of its network of tributaries. It can reflect the geographical characteristics of a river network to a certain extent because it depends on the topography and geology of the land and as such should be considered during the river network generalization process. There are many methods for river network generalization in tributary selection but most do not explicitly consider the network pattern. Validation of the generalized result is performed visually by an expert and may not be done systematically. An automatic validation technique may help to better understand the results obtained with each method and check whether the pattern has been preserved. This paper proposes an approach to evaluate the quality of a generalized river network by assessing how well its original drainage pattern is preserved. The membership to a drainage pattern is evaluated by a set of geometric indicators, making use of a fuzzy logic approach which allows for a compromise between different criteria depending on the membership values. Three tributary selection methods are tested in this work: selection by stroke and length, catchment area, and a manually generalized network. Assessing the quality of a generalization is done by comparing pattern memberships before and after generalization. This research provides a quantitative indicator to assess the generalized river network in preserving geographical information

Context awareness and typification in building generalisation

The objective of this thesis is the development of an automated process to perform the generalisation of buildings from 1:5000 to 1:50000 scale. The strategy adopted is applied to partitions of the dataset (blocks) and differs between urban and rural context; ad-hoc typifcation algorithms have been developed to cope with high-density blocks, medium-density blocks and spatial patterns. Low-density blocks that do not fitt the previous classifications are treated with a best-effort approachope

Methodische Aspekte der Generalisierung von Geodaten

Diese Arbeit stellt die Generalisierung in der Kartographie auf eine theoretische Grundlage im Zusammenhang mit mathematischen Strukturen. Ausgegangen wird von einem erfolgreich entwickelten Verfahren zur Modellgeneralisierung von Digitalen Landschaftsmodellen. Die Verallgemeinerung dieses Verfahrens ist wünschenswert, um auch andere Anwendungen von den Ergebnissen profitieren zu lassen. Gezeigt wird, dass in bestimmten Bereichen eine Verallgemeinerung des gesamten Verfahrens prinzipiell nicht zu erreichen ist. Um der Verallgemeinerung der Modellgeneralisierung dennoch näher zu kommen, wird das Problem der Generalisierung auf einer abstrakteren Ebene definiert. Das erste Ziel ist die Formalisierung der Generalisierungsziele. Hierbei wird auch die Formalisierung der Geodaten und des Generalisierungsvorganges angestrebt. Das zweite Ziel ist die Abstraktion und Verallgemeinerung der entwickelten Formalisierungen, um Gemeinsamkeiten zwischen verschiedenen Generalisierungsanwendungen zu finden. Das dritte Ziel ist die Erstellung von Bedingungen, die sich auf einzelne Komponenten der Geodaten beziehen. Die Erfüllung dieser Bedingungen ist Voraussetzung für eine Generalisierung mit korrekten und qualitativ hochwertigen Ergebnissen. Die Methodik stützt sich auf folgende Mittel: (1) Es wird ein Datenmodell entwickelt, das auf eine große Bandbreite unterschiedlicher Arten von Geodaten anwendbar ist. Zum einen ermöglicht es durch seine klare Strukturierung in Thematik, Topologie und Geometrie eine differenzierte Gruppierung der Formalisierungen. Zum anderen ermöglicht es eine Multirepräsentation von Geodaten in unterschiedlichen Auflösungen. (2) Die Generalisierung wird durch eine mathematische Funktion ausgedrückt, die aus der ungeneralisierten Menge von Geodaten auf die Menge der generalisierten Geodaten abbildet. Die Formulierung der Generalisierung als mathematische Funktion bildet die Grundlage für die Abstraktion der Generalisierung. (3) Die Generalisierungsfunktion wird als Morphismus formuliert. Dazu werden Eigenschaften auf den ungeneralisierten Daten identifiziert, die durch eine Generalisierung nicht verändert werden dürfen oder sollen. Diese Eigenschaften bilden Invarianzen der Generalisierung. Beispiele für invariante Eigenschaften sind die Zugehörigkeit zu Objektklassen, die topologische Adjazenz und Inzidenz, der Netzzusammenhang sowie die Flächendeckung. Die gefundenen invarianten Eigenschaften werden als Bedingungen formuliert, und zwar so, dass sie durch einen Rechner automatisch überprüfbar sind. Sie sind unabhängig von einer konkreten Generalisierungsanwendung, da sie abstrahiert und allgemein formuliert sind. Mit der abstrakten und formalisierten Erstellung von Bedingungen zu invarianten Eigenschaften der Generalisierung leistet diese Arbeit einen Beitrag zur bedingungsbasierten Modellierung der Generalisierung. Mit der Anwendung auf reale Daten wird die Praktikabilität der entwickelten Formalisierungen, Abstraktionen und Verallgemeinerungen gezeigt. Die Anwendung geschieht auf Basis der Implementation des eingangs erwähnten Verfahrens zur Modellgeneralisierung. An Hand von zwei weiteren Anwendungsbeispielen - der Generalisierung von geologischen Karten und der Generalisierung von Straßenkarten - wird gezeigt, wie die abstrahiert formulierten Bedingungen der Invarianzen auf andere Generalisierungsanwendungen übertragen werden können.Methodological Aspects of the Generalisation of Spatial Data This thesis establishes a theoretical framework for the generalisation in cartography in the context of mathematical structures. The starting point is a procedure of model generalisation developed for digital landscape models that has been successfully implemented. A more general procedure that lets other generalisation applications benefit from the results would be desirable. It is shown that for some aspects it's impossible to generate a most general level of this procedure. To bypass this problem, an attempt is made to approximate a general level by defining the task of model generalisation in a more abstract way. The first aim of this thesis is the formalisation of the goals of generalisation. Subordinate objectives are the formalisation of the spatial data and the formalisation of the generalisation process. The second aim is the abstraction of the formalisations developed and the identification of analogies that hold between different generalisation applications. The third aim is to establish constraints. These constraints concern different components of spatial data. The satisfaction of these constraints is a prerequisite for a generalisation that leads to correct and high quality results. The following methods are used to reach these aims: (1) A data model is developed which is applicable to a large variety of different types of spatial data. It makes possible a differentiation of the formalisations by a clear classification of the spatial data into thematic, topological and geometrical elements. It also supports multiple representations of spatial data in different resolutions. (2) The generalisation is expressed by a mathematical function that maps an ungeneralised set of spatial data onto a generalised set of spatial data. The formulation as a mathematical function provides the basis for the abstraction of the generalisation. (3) The generalisation function is formulated as a morphism. For this purpose invariant properties of the sets of spatial data are identified. They must---or at least should---be preserved within the generalisation process. These properties function as the invariant parts of the generalisation. Examples of such invariants are: Membership of object classes, topological adjacency and incidence, net connectivity and area coverage. The identified invariants are formulated as constraints in such a way that they can be verified automatically by a computer. They are independent of the specification of a generalisation application because they are abstracted and formalised in a general way. With the abstracted and formalised constraints of invariant properties this thesis makes a contribution to the constrained-based modelling of generalisation. The practicability of the developed formalisations and abstractions is shown by applying them to real data. Here the above mentioned procedure of model generalisation of digital landscape models is used. Two further examples---the generalisation of geological maps and the generalisation of road maps---show how the abstractly formulated constraints can be adapted to other generalisation applications

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps