What Is the Level of Detail of OpenStreetMap?

workshopInternational audienc

Escaping the pushpin paradigm in geographic information science: (re)presenting national crime data

In 2011 the Home Office released the police.uk website, which provided a high-resolution map of recent crime data for the national extents of England, Wales and Northern Ireland. Through this free service, crimes were represented as points plotted on top of a Google map, visible down to a street level of resolution. However, in order to maintain confidentiality and to comply with data disclosure legislation, individual-level crimes were aggregated into points that represented clusters of events that were located over a series of streets. However, with aggregation the representation of crimes as points becomes problematic, engendering spurious precision over where crimes occurred. Given obvious public sensitivity to such information, there are social imperatives for appropriate representation of crime data, and as such, in this paper we present a method of translating the ‘point’ crime events into a new representational form that is tied to street network geography; presenting these results in an alternate national crime mapping portal http://www.policestreets.co.u

Generation and Validation of Workflows for On-demand Mapping

The paper presents a method to automatically select and sequence the tasks required to build maps according to user requirements. Workflows generated are analysed using Petri nets to assess their validity before execution. Although further work is required to select the optimal method for generating the workflow and to execute the workflow, the proposed method can be used on any workflow to assess its validity

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