From taxonomies to ontologies: formalizing generalization knowledge for on-demand mapping

© 2015 Cartography and Geographic Information Society Automation of the cartographic design process is central to the delivery of bespoke maps via the web. In this paper, ontological modeling is used to explicitly represent and articulate the knowledge used in this decision-making process. A use case focuses on the visualization of road traffic accident data as a way of illustrating how ontologies provide a framework by which salient and contextual information can be integrated in a meaningful manner. Such systems are in anticipation of web-based services in which the user knows what they need, but do not have the cartographic ability to get what they want

Automatic identification of urban settlement boundaries for multiple representation databases

Intuitive and meaningful interpretation of geographical phenomena requires their representation at multiple levels of detail. This is due to the scale dependent nature of their properties. Considerable interest remains in capturing once geographical information at the fine scale, and from this, automatically deriving information at various levels of detail and scale via the process of generalisation. Prior to the cartographic portrayal of that information, model generalisation is required in order to derive higher order phenomena associated with the smaller scales. This paper presents a technique for automatically identifying settlement boundaries based on our understanding of what constitutes ‘citiness’. From this, partonomic structures can be created that link the broad settlement with its constituent parts. The benefits of the resultant system include the automated populating of multiple representation databases (MRDB), better spatial analysis and the creation of semantic reference systems capable of supporting intelligent query or zoom. The creation of such hierarchical partonomic structures provides a very useful framework within which generalisation can take place. The methodology and implementation are presented together with an evaluation of the results. Future developments are proposed

Representing forested regions at small scales: automatic derivation from very large scale data

As with any class of feature, it is important to be able to view woodland or forest at multiple levels of detail. At the detailed level, a map can show clusters of trees, tree types, tracks and paths; at the small scale, say 1:250 000, we can discern broad patterns of forests and other land use, which can inform planners and act as input to land resource models. Rather than store such information in separate databases (requiring multiple points of maintenance), the vision is that the information has a single point of storage and maintenance, and that from this detailed level, various, more generalised forms can be automatically derived. This paper presents a methodology and algorithm for automatically deriving forest patches suitable for representation at 1:250 000 scale directly from a detailed dataset. In addition to evaluation of the output, the paper demonstrates how such algorithms can be shared and utilised via 'generalisation web services', arguing that the sharing of such algorithms can help accelerate developments in map generalisation, and increase the uptake of research solutions within commercial systems

A functional perspective on map generalisation

In the context of map generalisation, the ambition is to store once and then maintain a very detailed geographic database. Using a mix of modelling and cartographic generalisation techniques, the intention is to derive map products at varying levels of detail – from the fine scale to the highly synoptic. We argue that in modelling this process, it is highly advantageous to take a ‘functional perspective’ on map generalisation – rather than a geometric one. In other words to model the function as it manifests itself in the shapes and patterns of distribution of the phenomena being mapped – whether it be hospitals, airports, or cities. By modelling the functional composition of such features we can create relationships (partonomic, taxonomic and topological) that lend themselves directly to modelling, to analysis and most importantly to the process of generalisation. Borrowing from ideas in robotic vision this paper presents an approach for the automatic identification of functional sites (a collection of topographic features that perform a collective function) and demonstrates their utility in multi-scale representation and generalisation

Report of the ICA Workshop on Map Generalization (97-3)

This paper reports the discussions and key findings of a three day workshop held in Gävle, Sweden immediately prior to the 18th ICA/ACI International Cartographic Conference in Stock- holm. The workshop mirrored the considerable success of the first workshop help in Barcelona in 1995 and comprised demonstrations, presentations, and parallel sessions. Thirty nine delegates came from seventeen countries comprising 20% from National Mapping Agencies (NMAs), 10% from the private sector and 70% from research institutions (Geography, Cartography, Surveying, Computer Science, Physics, Planning and Linguistics). The objective of the workshop was to hold a series of discussions focused on impediments to automated map generalization, the current state of knowledge, and progress on specific problem areas. Whenever possible, discussion pressed to define specific areas for research in the coming two years. These will be noted throughout the report.This report is structured around the plenary session headings, includes a summary of one session held jointly with the ICA Visualization group meeting concurrently in Gävle, and concludes by listing the intended themes for discussion at the next workshop in 1999