Nicholas Gould

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

ABSTRACT 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.

Process Modelling, Web Services and Geoprocessing

Process modelling has always been an important part of research in generalisation. In the early days this would take the form of a static sequence of generalisation actions, but currently the focus is on modelling much more complex processes, capable of generalising geographic data into various maps according to specific user requirements. To channel the growing complexity of the processes required, better process models had to be developed. This chapter discusses several aspects of the problem of building such systems. As the system gets more complex, it becomes important to be able to reuse components which already exist. Web services have been used to encapsulate generalisation processes in a way that maximises their interoperability and therefore reusability. However, for a system to discover and trigger such a service, it needs to be formalised and described in a machine understandable way, and the system needs to have the knowledge about where and when to use such tools. This chapter therefore explores the requirements and potential approaches to the design and building of such systems.

Towards a Semantic Layer to Support Road and Public Transport User Decision-Making

Municipalities now collect large amounts of data that could be potentially used to guide road users and public transport users in their decision-making. However, these data sources are heterogeneous and a semantic layer, expressed in an ontology, is proposed to match data sources, traveler needs, and the context of their journeys in order to support decision-making.

Modelling road congestion using ontologies for big data analytics in smart cities

Intelligent Transport Systems are a vital component within Smart Cities but rarely provide the context that is required by the road user or network manager that will help support decision making. Such systems need to be able to collect data from multiple heterogeneous sources and analyse this information, providing it to stakeholders in a timely manner. The focus of this work is to use Big Data analytics to gain knowledge about road accidents, which are a major contributor to non-recurrent congestion. The aim is to develop a model capable of capturing the semantics of road accidents within an ontology. With the support of the ontology, selective dimensions and Big Data sources will be chosen to populate a model of non-recurrent congestion. Initial Big Data analysis will be performed on the data collected from two different sensor types in Greater Manchester, UK to determine whether it is possible to identify clusters based on journey time and traffic volumes.

Big Data for Urban Studies: Opportunities and Challenges: A Comparative Perspective

A city is a complex system with complicated interactions between transport, land use, environment, population at a variety of scales. Understanding these interactions is the prerequisite for predicting urban changes, supporting sustainable urban development planning. In this paper, we divide the evolution of urban data into four stages. Then the opportunities of big data (new stage) for urban studies application are explored, followed by case studies from both the UK, China. The main challenges are evaluated, the solutions to which are further discussed, compared between both countries.