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Exploring Big Urban Data| old_uid | 17386 |
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| title | Exploring Big Urban Data |
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| start_date | 2019/03/18 |
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| schedule | 10h30-13h |
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| online | no |
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| location_info | Room Gilles Kahn |
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| summary | The ability to collect data from urban environments through a variety of sensors, coupled with a push towards openness and transparency by governments, has resulted in the availability of numerous spatio-temporal datasets containing information about diverse components of the cities, including their residents, infrastructure, and the environment. By analyzing the data exhaust from these components, we have the opportunity to better understand how they interact and obtain insights to help address important challenges brought about by urbanization with respect to transportation, resource consumption, housing affordability, and inadequate or aging infrastructure. While there have been successful efforts where data was used to improve operations, policies, and the quality of life for residents, these have been few and far between, because analyzing urban data often requires a staggering amount of work, from identifying relevant data sets, cleaning and integrating them, to performing exploratory analyses over complex, spatio-temporal data.
Our long-term research goal is to enable domain experts to crack the code of cities by freely exploring the vast amounts of urban data. In this talk, I will present methods and systems which combine data management, analytics, and visualization to increase the level of interactivity, scalability, and usability for spatio-temporal data analyses.
This work was supported in part by the National Science Foundation, DARPA, a Google Faculty Research award, the Moore-Sloan Data Science Environment at NYU, IBM Faculty Awards, NYU School of Engineering and Center for Urban Science and Progress. |
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| responsibles | Zweigenbaum |
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