| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | March 27, 2017 |
| Latest Amendment Date: | May 10, 2021 |
| Award Number: | 1652525 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2017 |
| End Date: | April 30, 2021 (Estimated) |
| Total Intended Award Amount: | $497,272.00 |
| Total Awarded Amount to Date: | $123,533.00 |
| Funds Obligated to Date: |
FY 2018 = $20,956.00 FY 2019 = $0.00 FY 2020 = $0.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
201 OLD MAIN UNIVERSITY PARK PA US 16802-1503 (814)865-1372 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
State College PA US 16802-1503 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Info Integration & Informatics |
| Primary Program Source: |
01001819DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT 01002122DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
According to U.S. 2010 Census, about 80.7% of the U.S. population live in urban area. Urbanization has modernized people's lives but also generated many urban issues such as traffic congestion, air pollution, health, education, and life quality. In the meantime, with the rapid progress in sensing technologies and widely-used digital documentation, increasing amount of urban data are being accumulated in the digital form, including human traces, traffic, air quality, local events, vehicle collisions, noise reports, and many more. Many cities in the U.S. (e.g., New York City, Chicago, and Los Angeles) have joined the open data initiative and created websites to release the city data to the public. Such big data implies rich knowledge about a city and could empower us to address many critical urban challenges.
This project develops novel data mining techniques to help people uncover the complicated correlations in the big urban data. While each type of urban data has been previously analyzed in its own domain, we lack a principled approach to integrate and analyze the data collected from different domains in order to better understand the urban issues from multiple aspects. The project investigates systematic solutions to integrate and model the urban data, discover the hidden patterns, and present and visualize the results in an interpretable way. The key innovation lies in how to effectively harness the heterogeneous urban data and learn mutually reinforced knowledge from such data. The project explores motivating real-world problems from other research fields such as social science, transportation, ecology, and urban planning, and promises interdisciplinary impacts in these fields. Ultimately, this project strives to advance the techniques in urban computing, a nascent interdisciplinary research field that addresses the challenges and opportunities in the fast-evolving urban environments.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
According to U.S. 2010 Census, about 80.7% of the U.S. population live in urban area. Urbanization has modernized people's lives but also generated many urban issues such as traffic congestion, air pollution, health, education, and life quality. In the meantime, with the rapid progress in sensing technologies and widely-used digital documentation, increasing amount of urban data are being accumulated in the digital form, including human traces, traffic, air quality, local events, vehicle collisions, noise reports, and many more. Many cities in the U.S. (e.g., New York City, Chicago, and Los Angeles) have joined the open data initiative and created websites to release the city data to the public. Such big data implies rich knowledge about a city and could empower us to address many critical urban challenges.
The key outcomes of this project are as follows. (1) The project has produced methods for urban data anomaly detection and interpretation. (2) The project has proposed methods to detect spatial communities based on cross-domain urban data. (3) The project has resulted in different spatial-temporal embedding methods using various urban data to model the relationships between spatial regions. The project has advanced the state of the art in urban data mining and has enriched general data mining principles.
Last Modified: 06/06/2021
Modified by: Zhenhui Li
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