Development of a Spatiotemporal Data Model for Management and Visualization of Surface Movement Data

Spatiotemporal data is a part of geographical data required in Geographical Information System (GIS). Generally, the existing GIS are not suited to manage changes occurring in the data with time. The capability of managing geographic data with time depends on the underlying data model in which the data model has to take into account the spatiotemporal aspects of the geographic data. Thus, a Spatiotemporal Data Model is required to manage changes in GIS data. Spatiotemporal Data Model represents the abstraction of data management in GIS. Surface movement on three dimensional objects is one of the spatiotemporal data which represents changes of the surface taking place in geographic phenomena. However, current Spatiotemporal Data Model and current GIS software are not adequate for managing the surface movement of three dimensional objects while representing the data. Most of the existing data models brought us to the conclusion that a new Spatiotemporal Data Model is needed to improve the management of three dimensional data with temporal element. Therefore, a new Spatiotemporal Data Model, Surface Movement Spatiotemporal (SMST) Data Model is proposed, which supports the management and visualization of surface movement data in three dimensional objects such as terrain model. The data model were developed under consideration of real world events together with current data collection, for example, a terrain model in the geographic phenomena which deals with changes from time to time based on natural phenomena and human activity. The data were collected by capturing images from time to time. Formalization of the surface movement reconstruction is a fundamental knowledge to develop the SMST Data Model. Currently, in many fields, surface reconstruction does not consider the temporal element. Therefore, the surface movement of three dimensional objects is formalized by enhancing the surface reconstruction method; that is by integrating it with temporal element. In order to test and evaluate the SMST Data Model, a database management system with a loading and a retrieval algorithm suitable to this model were developed. The retrieved data from the database system is saved in the proposed data format for surface movement visualization. In developing the visualization tool, visualization algorithm was used by employing the morphing technique which uses parametric equation. The proposed model was tested using digital terrain model digitized from a series of aerial photos. The model can store and manage surface movement data while reducing the redundancy of data in the database system. Percentage of reduced data redundancy is based on the number of points involved in the movement process. The model stores only the movement points in the surface. Besides, the proposed model can retrieve data for simulating surface movement on the three dimensional object. Therefore, the major contributions of this research are the formalization of surface movement data and the proposed SMST Data Model which can manage surface movement data on three dimensional objects with respect to time

Visualization and exploratory analysis of epidemiologic data using a novel space time information system

Abstract Background Recent years have seen an expansion in the use of Geographic Information Systems (GIS) in environmental health research. In this field GIS can be used to detect disease clustering, to analyze access to hospital emergency care, to predict environmental outbreaks, and to estimate exposure to toxic compounds. Despite these advances the inability of GIS to properly handle temporal information is increasingly recognised as a significant constraint. The effective representation and visualization of both spatial and temporal dimensions therefore is expected to significantly enhance our ability to undertake environmental health research using time-referenced geospatial data. Especially for diseases with long latency periods (such as cancer) the ability to represent, quantify and model individual exposure through time is a critical component of risk estimation. In response to this need a STIS – a Space Time Information System has been developed to visualize and analyze objects simultaneously through space and time. Results In this paper we present a "first use" of a STIS in a case-control study of the relationship between arsenic exposure and bladder cancer in south eastern Michigan. Individual arsenic exposure is reconstructed by incorporating spatiotemporal data including residential mobility and drinking water habits. The unique contribution of the STIS is its ability to visualize and analyze residential histories over different temporal scales. Participant information is viewed and statistically analyzed using dynamic views in which values of an attribute change through time. These views include tables, graphs (such as histograms and scatterplots), and maps. In addition, these views can be linked and synchronized for complex data exploration using cartographic brushing, statistical brushing, and animation. Conclusion The STIS provides new and powerful ways to visualize and analyze how individual exposure and associated environmental variables change through time. We expect to see innovative space-time methods being utilized in future environmental health research now that the successful "first use" of a STIS in exposure reconstruction has been accomplished.http://deepblue.lib.umich.edu/bitstream/2027.42/112824/1/12942_2004_Article_41.pd

A Conceptual View on Trajectories

Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context

Spatial Data Warehouse Modelling

is concerned with multidimensional data models for spatial data warehouses. It first draws a picture of the research area, and then introduces a novel spatial multidimensional data model for spatial objects with geometry: the Multigranular Spatial Data warehouse (MuSD). The main novelty of the model is the representation of spatial measures at multiple levels of geometric granularit

Data Mining in Data Stream

Tato práce pojednává o dolování v proudu dat, což představuje v současné době velice rychle se rozvíjející oblast informačních technologií. Jsou vysvětleny obecné principy dolování v datech, pojem proud dat, a jsou popsány metody jeho předzpracování a následně algoritmy pro dolování v proudu dat. Podrobně jsou rozebrány algoritmy VFDT a CVFDT pro klasifikaci. Dále je pozornost věnována časoprostorovým datům a možnostem jejich dolování. V rámci praktické části práce byla navrhnuta a implementována aplikace pro klasifikaci a predikci časoprostorových událostí (dopravních zácep) v proudu dat ze silničního provozu. Pro klasifikaci byly použity algoritmy VFDT a CVFDT. Program byl otestován na datech generovaných simulačním nástrojem SUMO.This thesis deals with the data mining in data stream which represents fast developing area of information technology. The text describes common principles of data mining, explains what data stream is and shows methods for its preprocessing and algorithms for following data mining. The special attention is given to the VFDT and the CVDT algorithm. The next mentioned are the spatiotemporal data and related data mining. The second part describes the design and implementation of the application for classification over spatiotemporal data stream represented by road traffic data and following prediction of spatiotemporal events (traffic-jams). The classification is performed by the VFDT and CVFDT algorithm. The application has been tested on the data set obtained by the simulation tool SUMO.