RIDIR: Collaborative Research: DAPPR: Diffusion Analytics for Public Policy Research

RIDIR：协作研究：DAPPR：公共政策研究的扩散分析

• 批准号: 1637067
• 负责人: Yu-Ru Lin
• 金额: $ 18.84万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637067&HistoricalAwards=false
• 关键词: RIDIR; Collaborative; Research; DAPPR; Diffusion

Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. However, progress depends upon innovative policy solutions to society's problems. The transmission of laws and regulations across political boundaries averts a conflict between the incentives for innovation and consistency. The current project will offer a database that allows the tracking of the spread of policies through the US states. The database will be disseminated through project webpages. The web interface will also include advanced analytics that illuminate the pathways through which policies spread. Users will be able to download the data and software for their own custom analysis of policy transmission in the US states. Policy diffusion has been a topic of focus across the social sciences for several decades, but due to limitations of data and computational technology, researchers have not taken a comprehensive look at patterns of diffusion across many different policies. The investigators propose to combine cutting-edge methods of text and network analysis to understand how policies, as represented in digitized text, spread through networks connecting the American states. The investigators will develop both data and computational methods that address the most pressing limitations to the study of policy diffusion networks. The project will result in two key contributions: (1) a comprehensive source of data with which to track the movement of policies from one state government to another, across three governing branches, and (2) a user-friendly, open source software package for understanding and visualizing diffusion networks from digitized records of policies. These resources will help researchers, public officials, advocacy groups, and other relevant stakeholders understand how to balance the competing goals of innovation and consistency in public policy.

社会、技术和金融体系的稳定，以及各组织跨境工作的能力，要求各司法管辖区的公共政策保持一致。然而，能否取得进展取决于对社会问题采取创新的政策解决办法。法律法规的跨政治边界传播避免了创新激励与一致性之间的冲突。 目前的项目将提供一个数据库，可以跟踪政策在美国各州的传播情况。该数据库将通过项目网页传播。网络界面还将包括先进的分析，阐明政策传播的途径。用户将能够下载数据和软件，用于对美国各州的政策传播进行自定义分析。几十年来，政策扩散一直是社会科学的焦点话题，但由于数据和计算技术的限制，研究人员还没有全面研究许多不同政策的扩散模式。研究人员建议将联合收割机结合文本和网络分析的尖端方法，以了解数字化文本所代表的政策如何通过连接美国各州的网络传播。研究人员将开发数据和计算方法，以解决政策扩散网络研究中最紧迫的限制。该项目将产生两个关键贡献：（1）一个全面的数据源，用于跟踪政策从一个州政府到另一个州政府、跨三个管理部门的移动，以及（2）一个用户友好的开源软件包，用于理解和可视化来自数字化政策记录的扩散网络。这些资源将帮助研究人员、政府官员、倡导团体和其他相关利益攸关方了解如何平衡创新和公共政策一致性这两个相互竞争的目标。

项目成果

Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects

• DOI: 10.1002/jcop.21975
• 发表时间: 2018-09-01
• 期刊: JOURNAL OF COMMUNITY PSYCHOLOGY
• 影响因子: 2.3
• 作者: Booth, Jaime M.;Lin, Yu-Ru;Wei, Kai
• 通讯作者: Wei, Kai

Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning

• DOI: 10.1145/3132847.3132964
• 发表时间: 2017-11
• 期刊: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
• 作者: Xian Teng;Y. Lin;Xidao Wen

FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns

• DOI: 10.1109/visual.2019.8933750
• 发表时间: 2019-10
• 期刊: 2019 IEEE Visualization Conference (VIS)
• 作者: Xidao Wen;K. Pelechrinis;Y. Lin;Xi Liu;Yongsu Ahn;Nan Cao

PolicyFlow: Interpreting Policy Diffusion in Context

• DOI: 10.1145/3385729
• 发表时间: 2020-06-01
• 期刊: ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS
• 影响因子: 3.4
• 作者: Ahn, Yongsu;Lin, Yu-Ru
• 通讯作者: Lin, Yu-Ru

Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data

瞧：使用流时空数据进行视觉异常检测和监控

• DOI: 10.1109/tvcg.2017.2744419
• 发表时间: 2018-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Cao, Nan;Lin, Chaoguang;Wen, Xidao
• 通讯作者: Wen, Xidao