134 research outputs found
Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data
Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
Anålise de comportamento de motoristas através de trajetórias de objetos móveis
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-graduação em CiĂȘncia da Computação, FlorianĂłpolis, 2014Todos os dias o nĂșmero de veĂculos circulando pelas estradas Ă© maior e, se estiverem equipados com dispositivos mĂłveis tais como GPS, Ă© gerado um novo tipo de dado chamado de trajetĂłria. VĂĄrios estudos buscam descobrir padrĂ”es em trajetĂłrias, mas poucos tĂȘm focado na anĂĄlise do comportamento de motoristas. AtravĂ©s das trajetĂłrias geradas pelos veĂculos Ă© possĂvel inferir bons e maus condutores e encontrar locais problemĂĄticos ao longo das vias caso existam padrĂ”es de comportamento bem definidos. Esta dissertação propĂ”e um mĂ©todo para anĂĄlise de comportamento de motoristas atravĂ©s das suas trajetĂłrias, com base em caracterĂsticas como aceleraçÔes, desaceleraçÔes e mudanças bruscas de direção, excesso de velocidade, comportamento de costura, limites de velocidade das vias, bem como locais que possam ser causadores de um movimento anĂŽmalo, tal qual um semĂĄforo. Trabalhos na literatura nĂŁo classificam motoristas utilizando dados de GPS e nĂŁo detectam ou analisam movimentos anĂŽmalos com justificativas para as anomalias. O mĂ©todo Ă© avaliado atravĂ©s de experimentos com dados reais que mostram ser possĂvel classificar motoristas com base em suas trajetĂłrias.Abstract: Every day the number of vehicles driving on the roads is increasing, and if equipped with mobile devices such as GPS, a new data type called trajectory is generated. Several studies seek to discover patterns in trajectories, but only a few have focused on analyzing the behavior of drivers. Through the trajectories generated by the vehicles it is possible to infer good and bad drivers, as well as problematic places along the roads if there are well-defined behavior patterns. This thesis proposes a method for analyzing the behavior of drivers over their trajectories, based on characteristics such as acceleration, deceleration and abrupt changes of direction, speed, behavior of lane cutting and places that may be causing an anomalous movement, like a traffic light. Existing works in the literature do not classify drivers using GPS data considering repetitive anomalous movement or justifications of anomalies. The proposed method is evaluated through experiments with real data showing the possibility to classify drivers based on their trajectories
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Extracting Computational Representations of Place with Social Sensing
Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
USING SOCIALLY SENSED BIG DATA TO MODEL PATTERNS AND GEOGRAPHIC CONTEXT OF HUMAN ACTIVITIES IN CITIES
Understanding dynamic interactions between human activities and land-use structure in a city is a key lens to explore the city as a complex system. This dissertation contributes to understanding the complexity of urban dynamics by gaining knowledge of the interactions between human activities and city land-use structures by utilizing free-accessible socially sensed data sources, and building upon recent research trend and technologies in geographical information science, urban study, and computer science. This dissertation addresses three main questions related to human dynamics: 1) how human activities in an urban environment are shaped by socioeconomic status and the intra-city land-use structure, and how in turn, the knowledge of socioeconomic status-activity relationships can contribute to understanding the social landscape of a city; 2) how different types of activities are located in space and time in three U.S. cities and how the spatiotemporal activity patterns in these cities characterize the activity profile of different neighborhoods in the cities; and 3) how recent socially sensed information on human activities can be integrated with widely-used remotely sensed geographical data to create a novel approach for discovering patterns of land use in cities that are otherwise lacking in up to date land use information. This dissertation models the associations between socioeconomics and mobility in the Washington, D.C. metropolitan area as a case study and applies the learned associations for inferring geographical patterns of socioeconomic status (SES) solely using the socially sensed data. This dissertation also implements a semi-automated workflow to retrieve activity details from socially sensed Twitter data in Washington, D.C., the City of Baltimore, and New York City. The dissertation integrates remotely-sensed imagery and socially sensed data to model the dynamics associated with changing land-use types in the Washington, D.C.-Baltimore metropolitan area over time
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