22,583 research outputs found

    Visions, Values, and Videos: Revisiting Envisionings in Service of UbiComp Design for the Home

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    UbiComp has been envisioned to bring about a future dominated by calm computing technologies making our everyday lives ever more convenient. Yet the same vision has also attracted criticism for encouraging a solitary and passive lifestyle. The aim of this paper is to explore and elaborate these tensions further by examining the human values surrounding future domestic UbiComp solutions. Drawing on envisioning and contravisioning, we probe members of the public (N=28) through the presentation and focus group discussion of two contrasting animated video scenarios, where one is inspired by "calm" and the other by "engaging" visions of future UbiComp technology. By analysing the reasoning of our participants, we identify and elaborate a number of relevant values involved in balancing the two perspectives. In conclusion, we articulate practically applicable takeaways in the form of a set of key design questions and challenges.Comment: DIS'20, July 6-10, 2020, Eindhoven, Netherland

    Mining large-scale human mobility data for long-term crime prediction

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    Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

    The role of urban living labs in a smart city

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    In a rapidly changing socio-technical environment cities are increasingly seen as main drivers for change. Against this backdrop, this paper studies the emerging Urban Living Lab and Smart City concepts from a project based perspective, by assessing a series of five Smart City initiatives within one local city ecosystem. A conceptual and analytical framework is used to analyse the architecture, nature and outcomes of the Smart City Ghent and the role of Urban Living Labs. The results of our analysis highlight the potential for social value creation and urban transition. However, current Smart City initiatives face the challenge of evolving from demonstrators towards real sustainable value. Furthermore, Smart Cities often have a technological deterministic, project-based approach, which forecloses a sustainable, permanent and growing future for the project outcomes. ‘City-governed’ Urban Living Labs have an interesting potential to overcome some of the identified challenges
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