413 research outputs found

    Visual analytics of geo-related multidimensional data

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    In recent years, both the volume and the availability of urban data related to various social issues, such as real estate, crime and population are rapidly increasing. Analysing such urban data can help the government make evidence-based decisions leading to better-informed policies; the citizens can also benefit in many scenarios such as home-seeking. However, the analytic design process can be challenging since (i) the urban data often has multiple attributes (e.g., the distance to supermarket, the distance to work, schools zone in real estate data) that are highly related to geography; and (ii) users might have various analysis/exploration tasks that are hard to define (e.g., different home-buyers might have requirements for housing properties and many of them might not know what they want before they understand the local real estate market). In this thesis, we use visual analytics techniques to study such geo-related multidimensional urban data and answer the following research questions. In the first research question, we propose a visual analytics framework/system for geo-related multidimensional data. Since visual analytics and visualization designs are highly domain-specific, we use the real estate domain as an example to study the problem. Specifically, we first propose a problem abstraction to satisfy the requirements from users (e.g., home buyers, investors). Second, we collect, integrate and clean the last ten year's real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository. Third, we propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their personalized requirements in property seeking. Fourth, we propose a series of designs to visualize properties/suburbs in different dimensions and different granularity. Finally, we implement a system prototype for public access (http://115.146.89.158), and present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system. Our second research question extends the first one and studies the scalability problem to support cluster-based visualization for large-scale geo-related multidimensional data. Particularly, we first propose a design space for cluster-based geographic visualization. To calculate the geographic boundary of each cluster, we propose a concave hull algorithm which can avoid complex shapes, large empty area inside the boundary and overlaps among different clusters. Supported by the concave hull algorithm, we design a cluster-based data structure named ConcaveCubes to efficiently support interactive response to users' visual exploration on large-scale geo-related multidimensional data. Finally, we build a demo system (http://115.146.89.158/ConcaveCubes) to demonstrate the cluster-based geographic visualization, and present extensive experiments using real-world datasets and compare ConcaveCubes with state-of-the-art cube-based structures to verify the efficiency and effectiveness of ConcaveCubes. The last research question studies the problem related to visual analytics of urban areas of interest (AOIs), where we visualize geographic points that satisfy the user query as a limited number of regions (AOIs) instead of a large number of individual points (POIs). After proposing a design space for AOI visualization, we design a parameter-free footprint method named AOI-shapes to effectively capture the region of an AOI based on POIs that satisfy the user query and those that do not. We also propose two incremental methods which generate the AOI-shapes by reusing previous calculations as per users' update of their AOI query. Finally, we implement an online demo (http://www.aoishapes.com) and conduct extensive experiments to demonstrate the efficiency and effectiveness of the proposed AOI-shapes

    A Fast and Scalable System to Visualize Contour Gradient from Spatio-temporal Data

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    Changes in geological processes that span over the years may often go unnoticed due to their inherent noise and variability. Natural phenomena such as riverbank erosion, and climate change in general, is invisible to humans unless appropriate measures are taken to analyze the underlying data. Visualization helps geological sciences to generate scientific insights into such long-term geological events. Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the historical spatial trends. To overcome this challenge, we propose an image-gradient based approach called ContourDiff. ContourDiff overlays gradient vector over contour plots to analyze the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors (differential trends) along the contour paths, revealing the differential trends that the contour lines (isolines) experienced over time. We designed an interface, where users can interact with the generated visualization to reveal changes and trends in geospatial data. We evaluated our system using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. We show the potential of the system in detecting subtle changes from almost identical images, describe implementation challenges, speed-up techniques, and scope for improvements. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data. The expert evaluation of our system using real-life WRF (Weather Research and Forecasting) model output reveals the potential of our technique to generate useful insights on the spatio-temporal trends of geospatial variables

    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

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    In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

    Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

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    Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

    Thinking interactively with visualization

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    Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other. This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization. The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions

    A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

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    Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB
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