Collaboration on an Ontology for Generalisation

workshopInternational audienceTo move beyond the current plateau in automated cartography we need greater sophistication in the process of selecting generalisation algorithms. This is particularly so in the context of machine comprehension. We also need to build on existing algorithm development instead of duplication. More broadly we need to model the geographical context that drives the selection, sequencing and degree of application of generalisation algorithms. We argue that a collaborative effort is required to create and share an ontology for cartographic generalisation focused on supporting the algorithm selection process. The benefits of developing a collective ontology will be the increased sharing of algorithms and support for on-demand mapping and generalisation web services

Collaboration on an Ontology for Generalisation

To move beyond the current plateau in automated cartography we need greater sophistication in the process of selecting generalisation algorithms. This is particularly so in the context of machine comprehension. We also need to build on existing algorithm development instead of duplication. More broadly we need to model the geographical context that drives the selection, sequencing and degree of application of generalisation algorithms. We argue that a collaborative effort is required to create and share an ontology for cartographic generalisation focused on supporting the algorithm selection process. The benefits of developing a collective ontology will be the increased sharing of algorithms and support for on-demand mapping and generalisation web services

TOWARDS A SCALE DEPENDENT FRAMEWORK FOR CREATING VARIO-SCALE MAPS

Traditionally, the content for vario-scale maps has been created using a ‘one fits all’ approach equal for all scales. Initially only the delete/merge operation was used to create the vario-scale data using the importance and the compatibility functions defined at class level (and evaluated at instance level) to create the tGAP structure with planar partition as basis. In order to improve the generalization quality other operators and techniques have been added during the past years; e.g. simplify, collapse (change area to line representation), split, attractiveness regions and the introduction of the concept of linear network topology. However, the decision which operation to apply has been hard coded in our software, making it not very flexible. Further, we want to include awareness of the current scale when deciding what generalization operation to apply. For this purpose we propose the scale dependent framework (SDF), which at its core contains the encoding of the generalization knowledge in the SDF conceptual model. This SDF model covers the representation of scale dependent class importance, scale dependent class compatibility values, scale dependent attractiveness regions and last but not least specification of generalization operations that are scale and class dependent. By changing the settings in the SDF configuration and re-running the vario-scale generalization process, we can easily experiment in order to find best settings (for specific map user needs). In this paper we design the SDF conceptual model and explicitly motivate and define the scope of its expressiveness. We further present the improved scale dependent tGAP creation software and present initial results in the form of better created vario-scale map content

Selecção omissiva de strokes aplicada à generalização cartográfica de vias

Tese de mestrado em Engenharia Geográfica, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2011A primeira tarefa a executar para efectuar a Generalização Cartográfica de uma rede viária é a selecção omissiva das respectivas vias, operação onde são seleccionadas aquelas que se considerem suficientemente importantes para serem representadas na escala a que se destina a Generalização. Se tal problema está implicitamente resolvido para vias de reconhecida importância, o mesmo não se passa com aquelas que hierarquicamente são consideradas inferiores e cuja utilidade depende do enquadramento que têm com a sua vizinhança ou de factores estratégicos que tornem a sua representação indispensável. Para se poder definir a utilidade de cada elemento da rede viária, a avaliação a que cada um deles deve ser sujeito deve levar em consideração quatro tipos de informação: geométrica, topológica, temática e estatística, devendo cada um deles ter associadas características que sejam inicialmente disponibilizadas ou que sejam possíveis de medir de forma automatizada. Atendendo à estrutura da informação utilizada actualmente para a produção da carta série M782, Escala 1:50000 pelo IGeoE, foi utilizada pelo projecto aqui descrito uma metodologia de selecção omissiva que tem como unidade básica de medida o “Stroke”, que consiste num elemento linear criado a partir de diversos troços de via com continuidade direccional e ao qual pode ser atribuída uma relação funcional única. Definido o objecto de trabalho, foi necessário atribuir a cada Stroke características que representassem cada um dos grupos referidos para no fim lhe associar um valor único representante da sua importância para efeitos de reprodução gráfica. Para isso foi também necessário o estudo de diferentes metodologias para cálculo de densidade, de forma a providenciar ao algoritmo informação estatística que distinguisse a generalização cartográfica em áreas com diferentes tipos de densidades. Finalmente, foi necessária a aplicação persistente do algoritmo a uma amostra, cujos diferentes resultados comparados com os obtidos por meio do processo semi-automático possibilitaram a definição dos parâmetros ideais para o seu processamento. Por proposta paralela e como complemento do projecto principal, foi ainda efectuado um estudo sobre a identificação e substituição automática de rotundas e triângulos na rede viária, acabando essa tarefa por se demonstrar extremamente importante estando intimamente relacionada com a própria selecção omissiva.The objective of this project is to develop a methodological approach for the cartographic generalization of the geographic entity roads. The project was focused on the strategy for road selection. The first task to be performed in cartographic generalization of a road network is the selective omission procedure in which the roads are selected accordingly to their importance within the context and the scale. If this problem is implicitly solved for the most important roads (p.e. high-way, IPs “Itenerarios Principais”, ICs “Itenerarios Complementares”), the same cannot be assumed for less important roads, as the local urban roads or agricultural paths. In order to define the usefulness of each element of the road network, we have considered the following characteristics: geometry , topology, and theme. Considering the present day structure of cartographic data used to produce the 1:50000 scale (series M782) maps in the IGeoE, the selective omission was implemented using the “stroke” measure. The stroke measure is a sequence of segments (of the road network) built from several linear segments with directional continuity and the same function. For each stroke a set of characteristics was assigned related with the geometry, topology and theme aimed at the definition of a score. This score was later used to decide if the stroke will be or not selected to be represented given an empirical threshold. Several tests were made to select the “best” threshold. The threshold was chosen comparing the generalized map with a man made generalized map. Additionally to the main project several other small implementation procedures and algorithms were implemented. Among these, the identification and automatic replacement of round bouts and triangles on the road network was implement with success and is now routinely implement in the production chain of the IGeoE

Formalising cartographic generalisation knowledge in an ontology to support on-demand mapping

This thesis proposes that on-demand mapping - where the user can choose the geographic features to map and the scale at which to map them - can be supported by formalising, and making explicit, cartographic generalisation knowledge in an ontology. The aim was to capture the semantics of generalisation, in the form of declarative knowledge, in an ontology so that it could be used by an on-demand mapping system to make decisions about what generalisation algorithms are required to resolve a given map condition, such as feature congestion, caused by a change in scale. The lack of a suitable methodology for designing an application ontology was identified and remedied by the development of a new methodology that was a hybrid of existing domain ontology design methodologies. Using this methodology an ontology that described not only the geographic features but also the concepts of generalisation such as geometric conditions, operators and algorithms was built. A key part of the evaluation phase of the methodology was the implementation of the ontology in a prototype on-demand mapping system. The prototype system was used successfully to map road accidents and the underlying road network at three different scales. A major barrier to on-demand mapping is the need to automatically provide parameter values for generalisation algorithms. A set of measure algorithms were developed to identify the geometric conditions in the features, caused by a change in scale. From this a Degree of Generalisation (DoG) is calculated, which represents the “amount” of generalisation required. The DoG is used as an input to a number of bespoke generalisation algorithms. In particular a road network pruning algorithm was developed that respected the relationship between accidents and road segments. The development of bespoke algorithms is not a sustainable solution and a method for employing the DoG concept with existing generalisation algorithms is required. Consideration was given to how the ontology-driven prototype on-demand mapping system could be extended to use cases other than mapping road accidents and a need for collaboration with domain experts on an ontology for generalisation was identified. Although further testing using different uses cases is required, this work has demonstrated that an ontological approach to on-demand mapping has promise