Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

Системные основы интеллектуального анализа геопространственных данных

Здійснено оглядове дослідження наукового напряму інтелектуального аналізу геопросторових даних (ІАГД). Виявлено основні передумови формування цього напряму і його зв’язок із геоінформатикою, системним аналізом та інтелектуальним аналізом даних. Проведено бібліографічне дослідження зарубіжних і вітчизняних публікацій в галузі ІАГД. У ході дослідження подано визначення ІАГД, виявлено основні завдання, функції та етапи його проведення, визначено коло перспективних напрямків розвитку та його зв’язок з підтримкою ухвалення рішень у регіональному управлінні. Із використанням ІАГД методів кластеризації гарячих точок проведено дослідження перевищення гранично допустимих концентрацій урану в підземних водах на території України на основі даних геологічних зйомок і виявлено зони обмеженнями використання підземних вод.A survey of geospatial data mining (GSDM) research was conducted. The basic prerequisites for the emergence of this research area and its relation to geoinformatics, systems analysis, and data mining were discovered. A bibliographic study of foreign and Ukrainian publications in the field of GSDM was conducted. During this study, a definition for GSDM was provided. The main tasks, functions and stages of GSDM were identified, range of promising directions of development GSDM and its relationship to support decision-making in the regional administration were determined. The study of exceeding the maximum permissible concentrations of uranium in groundwater in the territory of Ukraine on the basis of geological survey was conducted using GSDM clustering hotspots analysis methods and areas with limited use of groundwater were detected.Проведено обзорное исследование научного направления интеллектуального анализа геопространственных данных (ИАГД). Выявлены основные предпосылки формирования этого направления и его связь с геоинформатикой, системным анализом и интеллектуальным анализом данных. Проведено библиографическое исследование зарубежных и отечественных публикаций в области ИАГД. В ходе исследования было дано определение ИАГД, выявлены основные задачи, функции и этапы его проведения, определен круг перспективных направлений развития и его связь с поддержкой принятия решений в региональном управлении. С использованием ИАГД методов кластеризации горячих точек проведено исследование превышения предельно допустимых концентраций урана в подземных водах на территории Украины на основе данных геологической съемки и выявлено зоны ограничениями использования подземных вод

Visualization of multidimensional data with collocated paired coordinates and general line coordinates

Often multidimensional data are visualized by splitting n-D data to a set of low dimensional data. While it is useful it destroys integrity of n-D data, and leads to a shallow understanding complex n-D data. To mitigate this challenge a difficult perceptual task of assembling low-dimensional visualized pieces to the whole n-D vectors must be solved. Another way is a lossy dimension reduction by mapping n-D vectors to 2-D vectors (e.g., Principal Component Analysis). Such 2-D vectors carry only a part of information from n-D vectors, without a way to restore n-D vectors exactly from it. An alternative way for deeper understanding of n-D data is visual representations in 2-D that fully preserve n-D data. Methods of Parallel and Radial coordinates are such methods. Developing new methods that preserve dimensions is a long standing and challenging task that we address by proposing Paired Coordinates that is a new type of n-D data visual representation and by generalizing Parallel and Radial coordinates as a General Line coordinates. The important novelty of the concept of the Paired Coordinates is that it uses a single 2-D plot to represent n-D data as an oriented graph based on the idea of collocation of pairs of attributes. The advantage of the General Line Coordinates and Paired Coordinates is in providing a common framework that includes Parallel and Radial coordinates and generating a large number of new visual representations of multidimensional data without lossy dimension reduction

GeoCAM: A geovisual analytics workspace to contextualize and interpret statements about movement

This article focuses on integrating computational and visual methods in a system that supports analysts to identify extract map and relate linguistic accounts of movement. We address two objectives: (1) build the conceptual theoretical and empirical framework needed to represent and interpret human-generated directions; and (2) design and implement a geovisual analytics workspace for direction document analysis. We have built a set of geo-enabled computational methods to identify documents containing movement statements and a visual analytics environment that uses natural language processing methods iteratively with geographic database support to extract interpret and map geographic movement references in context. Additionally analysts can provide feedback to improve computational results. To demonstrate the value of this integrative approach we have realized a proof-of-concept implementation focusing on identifying and processing documents that contain human-generated route directions. Using our visual analytic interface an analyst can explore the results provide feedback to improve those results pose queries against a database of route directions and interactively represent the route on a map

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment

Системні основи інтелектуального аналізу геопросторових даних

Здійснено оглядове дослідження наукового напряму інтелектуального аналізу геопросторових даних (ІАГД). Виявлено основні передумови формування цього напряму і його зв’язок із геоінформатикою, системним аналізом та інтелектуальним аналізом даних. Проведено бібліографічне дослідження зарубіжних і вітчизняних публікацій в галузі ІАГД. У ході дослідження подано визначення ІАГД, виявлено основні завдання, функції та етапи його проведення, визначено коло перспективних напрямків розвитку та його зв’язок з підтримкою ухвалення рішень у регіональному управлінні. Із використанням ІАГД методів кластеризації гарячих точок проведено дослідження перевищення гранично допустимих концентрацій урану в підземних водах на території України на основі даних геологічних зйомок і виявлено зони обмеженнями використання підземних вод.Проведено обзорное исследование научного направления интеллектуального анализа геопространственных данных (ИАГД). Выявлены основные предпосылки формирования этого направления и его связь с геоинформатикой, системным анализом и интеллектуальным анализом данных. Проведено библиографическое исследование зарубежных и отечественных публикаций в области ИАГД. В ходе исследования было дано определение ИАГД, выявлены основные задачи, функции и этапы его проведения, определен круг перспективных направлений развития и его связь с поддержкой принятия решений в региональном управлении. С использованием ИАГД методов кластеризации горячих точек проведено исследование превышения предельно допустимых концентраций урана в подземных водах на территории Украины на основе данных геологической съемки и выявлено зоны ограничениями использования подземныхA survey of geospatial data mining (GSDM) research was conducted. The basic prerequisites for the emergence of this research area and its relation to geoinformatics, systems analysis, and data mining were discovered. A bibliographic study of foreign and Ukrainian publications in the field of GSDM was conducted. During this study, a definition for GSDM was provided. The main tasks, functions and stages of GSDM were identified, range of promising directions of development GSDM and its relationship to support decision-making in the regional administration were determined. The study of exceeding the maximum permissible concentrations of uranium in groundwater in the territory of Ukraine on the basis of geological survey was conducted using GSDM clustering hotspots analysis methods and areas with limited use of groundwater were detected

Uso de DesktopMap para manipulação de informações biogeográficas em SIG

Com este artigo pretende-se apresentar a arquitectura e os processos de construção de um projecto em Sistemas de Informação Geográfica (SIG), desenvolvido para dar resposta à gestão e análise de informação biogeográfica. Pretendia-se que a arquitectura do SIG se moldasse aos diferentes grupos de dados de campo (inventariação de insectos, aves, vegetação e inquéritos de maneio agrícola), ser fonte de geração e gestão de dados existentes em vários tipos de suportes e com diferentes origens. Foi dado especial ênfase às questões de processamento e arquitectura em DesktopMap. A construção e desenvolvimento do SIG assumiu-se assim, como instrumento essencial na resposta aos objectivos esperados. O trabalho realizado na construção deste projecto SIG permitiu centralizar os dados, aceder a informação de uma forma expedita, e contribuir para a difusão dos resultados

An investigation into automated processes for generating focus maps

The use of geographic information for mobile applications such as wayfinding has increased rapidly, enabling users to view information on their current position in relation to the neighbouring environment. This is due to the ubiquity of small devices like mobile phones, coupled with location finding devices utilising global positioning system. However, such applications are still not attractive to users because of the difficulties in viewing and identifying the details of the immediate surroundings that help users to follow directions along a route. This results from a lack of presentation techniques to highlight the salient features (such as landmarks) among other unique features. Another problem is that since such applications do not provide any eye-catching distinction between information about the region of interest along the route and the background information, users are not tempted to focus and engage with wayfinding applications. Although several approaches have previously been attempted to solve these deficiencies by developing focus maps, such applications still need to be improved in order to provide users with a visually appealing presentation of information to assist them in wayfinding. The primary goal of this research is to investigate the processes involved in generating a visual representation that allows key features in an area of interest to stand out from the background in focus maps for wayfinding users. In order to achieve this, the automated processes in four key areas - spatial data structuring, spatial data enrichment, automatic map generalization and spatial data mining - have been thoroughly investigated by testing existing algorithms and tools. Having identified the gaps that need to be filled in these processes, the research has developed new algorithms and tools in each area through thorough testing and validation. Thus, a new triangulation data structure is developed to retrieve the adjacency relationship between polygon features required for data enrichment and automatic map generalization. Further, a new hierarchical clustering algorithm is developed to group polygon features under data enrichment required in the automatic generalization process. In addition, two generalization algorithms for polygon merging are developed for generating a generalized background for focus maps, and finally a decision tree algorithm - C4.5 - is customised for deriving salient features, including the development of a new framework to validate derived landmark saliency in order to improve the representation of focus maps