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Presentation on data, open data how we use data and how we can use data to create knowledge and wisdom
Open : Data : Culture
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Julian Tait
Mapbox performed a Vision Zero data analysis to identify high risk areas to prioritize in DDOTs street design and safety measures in Washington, DC. A custom collision frequency model took into account a wide range of datasets, not just crash counts. Model results identify clearly which areas of DC are high risk areas Outcome: Lively urban streets, with higher density of users, in cars and pedestrians, due businesses and restaurants, are prone to accidents. Intersection density is another factor to increase this risk of accidents. This can directly be used to inform DDOT’s Vision Zero resource distribution and is ultimately making urban areas of DC safer for pedestrians & cyclists.
Mapbox Cities tackles Vision Zero in collaboration with DDOT
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Examining challenges in data collection and use for better urban transport policy. Presented by Jerome Pourbaix at Transforming Transportation 2015. Transforming Transportation 2015: Smart Cities for Shared Prosperity is the annual conference co-organized by the World Resources Institute and the World Bank.
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Présentation de Mohamed Hegazi lors de l'évènement "Vers une plateforme de données collaborative et ouverte" Comment utiliser l'Open Data pour mapper les réseaux de transport informel au Caire et en faire un atout pour le système de transport.
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Early American cities were built around dense street networks and relied upon a wide variety of modes for transportation. For more than 50 years, however, many small cities have been physically restructured in efforts to provide automobile access comparable to that in the surrounding suburbs. In those cities, the level of automobile use, the physical form, and the level of activity are now very different from cities that, instead, made greater efforts to preserve their existing urban form. This study looks at 11 small cities that have exhibited very different trends in terms of automobile use and infrastructure provision since 1960 in order to gauge how these differences have impacted long-term urban vitality in those cities. This study relies on socioeconomic and demographic data dating back to 1960, historical travel mode share data, maps depicting the amount of land used for transportation purposes, and policy review. This approach provides unique insight regarding the aggregate impacts of automobile use on urban land consumption and urban vitality as well as a historical perspective revealing how these cities evolved and key policies that enabled these changes. In our study, we found that higher levels of automobile use correspond with lower concentrations of activities (residential and employment). This is due in large part to the amount of land needed for automobile infrastructure. The cities were divided into two groups: “low automobile use” and “automobile dependent.” On average, parking consumes more than twice as much urban land per activity in automobile dependent cities. These cities also have fewer than half as many productive activities per square mile. The study also reveals that incomes and automobile ownerships rates are higher in cities with low automobile use, suggesting that individuals will choose not to use their automobiles if there are diverse transportation choices and if non-automobile modes of transportation are attractive options. This lessens the amount of automobile infrastructure needed. Evidence suggests that policy decisions within each city have greatly influenced the changes they experienced over time. Based on the trends revealed in this study, a productive, long-term policy approach should incorporate measures that support diverse transportation systems and efficient use of urban space.
An Evaluation of Automobile Use, Parking Provision, and Urban Activity
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Recommended
Presentation on data, open data how we use data and how we can use data to create knowledge and wisdom
Open : Data : Culture
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Julian Tait
Mapbox performed a Vision Zero data analysis to identify high risk areas to prioritize in DDOTs street design and safety measures in Washington, DC. A custom collision frequency model took into account a wide range of datasets, not just crash counts. Model results identify clearly which areas of DC are high risk areas Outcome: Lively urban streets, with higher density of users, in cars and pedestrians, due businesses and restaurants, are prone to accidents. Intersection density is another factor to increase this risk of accidents. This can directly be used to inform DDOT’s Vision Zero resource distribution and is ultimately making urban areas of DC safer for pedestrians & cyclists.
Mapbox Cities tackles Vision Zero in collaboration with DDOT
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Data in Support of Urban Transport Policy - Jerome Pourbaix - UITP - Transfor...
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Présentation de Mohamed Hegazi lors de l'évènement "Vers une plateforme de données collaborative et ouverte" Comment utiliser l'Open Data pour mapper les réseaux de transport informel au Caire et en faire un atout pour le système de transport.
Transport for Cairo - "Vers une plateforme de données collaborative et ouverte"
Transport for Cairo - "Vers une plateforme de données collaborative et ouverte"
Ghislain Delabie
Bayesian inference, causality, machine learning
Project 2019 21
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Early American cities were built around dense street networks and relied upon a wide variety of modes for transportation. For more than 50 years, however, many small cities have been physically restructured in efforts to provide automobile access comparable to that in the surrounding suburbs. In those cities, the level of automobile use, the physical form, and the level of activity are now very different from cities that, instead, made greater efforts to preserve their existing urban form. This study looks at 11 small cities that have exhibited very different trends in terms of automobile use and infrastructure provision since 1960 in order to gauge how these differences have impacted long-term urban vitality in those cities. This study relies on socioeconomic and demographic data dating back to 1960, historical travel mode share data, maps depicting the amount of land used for transportation purposes, and policy review. This approach provides unique insight regarding the aggregate impacts of automobile use on urban land consumption and urban vitality as well as a historical perspective revealing how these cities evolved and key policies that enabled these changes. In our study, we found that higher levels of automobile use correspond with lower concentrations of activities (residential and employment). This is due in large part to the amount of land needed for automobile infrastructure. The cities were divided into two groups: “low automobile use” and “automobile dependent.” On average, parking consumes more than twice as much urban land per activity in automobile dependent cities. These cities also have fewer than half as many productive activities per square mile. The study also reveals that incomes and automobile ownerships rates are higher in cities with low automobile use, suggesting that individuals will choose not to use their automobiles if there are diverse transportation choices and if non-automobile modes of transportation are attractive options. This lessens the amount of automobile infrastructure needed. Evidence suggests that policy decisions within each city have greatly influenced the changes they experienced over time. Based on the trends revealed in this study, a productive, long-term policy approach should incorporate measures that support diverse transportation systems and efficient use of urban space.
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Aug 3, 2013 Taipei @ Code for Tomorrow's Data Weekend. Introducing "Urban Design" and why it's related to Urban Data. How does data utilization helps designer's decision making, and why designers and data engineers/scientists should collaborate.
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This was presented by Alvaro Herrero from the Buenos Aires City Government at the Impacts of Civic Technology Conference (TICTeC 2018) in Lisbon on 18th April 2018. You can find out more information about the conference here: http://tictec.mysociety.org/2018
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Francesca Froy, Honorary Research Associate, UCL Institute for Innovation and Public Purpose, UK at the OECD Conference on SMEs and the Urban Fabric, 15-16 April 2019, OECD Trento Centre, Italy. Full event info: https://oe.cd/SMEs-Cities
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Ignite presentation covering some of the approaches used at the Venice and Santa Fe WPI Project Centers using the Google ecology for project management
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A review of 25 years of research for Venice, Italy
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Keynote presentation at the University College London, Center for Advanced Spatial Analysis conference entitled: "Future Cities and Digital Technologies"
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Urban Data Management Symposium 2013 (Univ. College London)
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Presentation given at annual meeting of the American Institute for Conservation in Albuquerque, NM.
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Background on my research and a proposal for a new paradigm in Urban Data Management based on publish/subscribe agents in the cloud
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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?
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Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.
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These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
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My presentation at the Lehigh Carbon Community College (LCCC) NSA GenCyber Cyber Security Day event that is intended to foster an interest in the cyber security field amongst college students.
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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Partners Life - Insurer Innovation Award 2024
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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
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Citizen apps
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Mobile technologies in
Citizen Science Washington, DC Oct 12-13, 2010
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Fabio [email_address] venice2point0.blogspot.com
venice2point0.org www.wpi.edu/~carrera Steve [email_address] www.redfish.com www.sfcomplex.org
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