742 research outputs found

    Defining Cultural Data Science

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    Since 2019 a new term called “cultural data science” has emerged as a result of the increased utilisation of data within the fields of art and humanities, cultural studies and the built environment. It differs from a similar term called “cultural analytics” in that the latter is principally concerned with the “analysis and visualization of massive cultural data sets and flows” (Manovich, 2007). Cultural data science, on the other hand, can be thought of as an extension of cultural analytics, urban studies and computational social science with a particular focus on applying advanced computational methods to various forms of cultural data to test hypotheses and devise new frameworks for policy, planning and market action within the creative and cultural industries. At present there is no clear definition of cultural data science, therefore, this paper presents the first definition of the field. Adapted from Irizarry (2020), cultural data science is defined as the umbrella term used to describe the entire complex and multistep process used to extract value from cultural data with real-world implications to influence policy, placemaking and market dynamics within the creative and cultural industries. This new work is a unification of several fields and hopes to respect and honour the ideas, methodologies and tools developed by these adjacent fields. Its intention is to assist purpose-driven collaboration between public and private organisations and has the potential to become increasingly important for stakeholders in the built environment and policy makers who want to apply novel frameworks and evidence-based methods to influence the artistic and cultural vibrancy of place, space, societies and our local economies

    EFFICIENT SCALE INVARIENT FEATURE BASED METHOD FOR CROWD LOCALIZATION

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    Visual surveillance has been a very active research topic in the last few decade due to growing importance for security in the public areas. With the increasing number of CCTV networks in public areas, the enhancement in the computing power of modern computers and increase the possibility to entrust an automatic system with the security and the monitoring of events involving large crowds is within reach. Crowd detection and localization in the surveillance video is the first step in automatic crowd monitoring system. The performance of the whole system depends on this step. Detecting the crowd is a challenging task because the crowds come in different shape, size and color, against cluttered background and varying illumination conditions. As the size of the crowd increases managing the crowd becomes more complex

    Human Crowds Estimation based on Mobile Sensing

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    University of Tokyo(東京大学

    Visualization of Crowd Trajectory, Geospatial Sets, and Audience Prediction at Roskilde Festival 2018

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    We present a large-scale study on Geospatial Big Data Analytics in a festival management and crowd safety scenario based on our volunteer work at the largest music festival in Northern Europe, the 2017 and 2018 Roskilde music festival. As large crowds move between concerts, campsites, private parties and public viewing of the FIFA world cup soccer matches across the vast festival area, previously available visualization solutions for the crowd safety staff at Roskilde Festival lack a real-time visualization of crowd trajectory for monitoring of previously known chokepoints and the discovery of potential future chokepoints. We present a real-time visualization of crowd trajectory based on mobile device GPS data that is collected through the festival’s smartphone app from a significant subset of all festivalgoers. Hence, we present a recent case for the applications of real-time visualization of geospatial data for large-scale open-air events outside the confinements of urban architecture. Furthermore, we investigate and quantify the phenomenon of festivalgoers staying at the camping areas and sometimes not once visiting the musical performances in the inner perimeters of the festival, thereby finding that up to 3,000 festivalgoers both in 2017 and 2018 chose to not attend musical performances at all. Subsequently, we evaluate a Facebook-based approach on concert audience prediction based on social media audience overlaps between artist and festival Facebook pages, and benchmark its predictive power with a GPS-based measurement of audience sizes based on mobile device GPS data
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