4,204 research outputs found

    Analysing imperfect temporal information in GIS using the Triangular Model

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    Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis

    TRIANGULAR MODELS FOR STUDYING AND MEMORISING TEMPORAL KNOWLEDGE

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    The study of a lot of disciplines (e.g. history, geology, archaeology and politics) is often connected to temporal information. The nature of temporal information can be very complex. To help students to understand and memorise time-related knowledge, visualisation is a common approach. Currently, time intervals are usually visualised as linear segments. But, if a lot of information with a complex distribution or even incomplete data is displayed, this visualisation is quickly getting overcharged and confusing. The Triangular Model (TM) and its extension the Rough Triangular Model (RTM) are constituting alternatives to the limited linear model. Instead of linear segments, TM and RTM display time intervals as points, polygons or lines. The positions of them determine as well the beginning, the end and the vagueness of an interval. Additionally TM and RTM are also displaying temporal relations and topology of time intervals. Given the spread of the intervals points, polygons or lines within the TM, an easy overview about the complex distribution of time intervals can be read as a map which is helpful for students in learning and memorising temporal knowledge. Both TM and RTM deliver a compact visualisation of time-related knowledge where a huge amount of (rough) time intervals can be clearly displayed

    Interactive analysis of time intervals in a two-dimensional space

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    Time intervals are conventionally represented as linear segments in a one-dimensional space. An alternative representation of time intervals is the triangular model (TM), which represents time intervals as points in a two-dimensional space. In this paper, the use of TM in visualising and analysing time intervals is investigated. Not only does this model offer a compact visualisation of the distribution of intervals, it also supports an innovative temporal query mechanism that relies on geometries in the two-dimensional space. This query mechanism has the potential to simplify queries that are difficult to specify using traditional linear temporal query devices. Moreover, a software prototype that implements TM in a geographical information system (GIS) is introduced. This prototype has been applied in a real scenario to analyse time intervals that were detected by a Bluetooth tracking system. This application shows that TM has the potential to support a traditional GIS to analyse interval-based geographical data

    Visual and interactive exploration of point data

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    Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data

    Knowledge-based systems and geological survey

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    This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records

    Conditional Visualisation for Statistical Models

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    It is difficult to understand data and statistical models in high-dimensional space. One way to approach the problem is conditional visualisation, but methods in this area have lagged behind the considerable advances in statistical modelling in recent decades. This thesis presents a new approach to conditional visualisation which uses interactive computer graphics, and supports the exploration of a broad range of statistical models. The new approach to conditional visualisation consists of visualising a single lowdimensional section at a time, showing fitted models on the section, and enhancing the section by displaying observed data which are near the section according to a similarity measure. Two ways of choosing sections are given |choosing sections interactively using data summary graphics, and choosing sections programmatically according to some criteria. The visualisations in this thesis necessitate interactive graphics, which are implemented in the condvis package in R
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