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，技术和金融系统的稳定以及组织跨境工作的能力，需要跨司法管辖区的公共政策一致。但是，进步取决于对社会问题的创新政策解决方案。法律和法规跨越政治边界的传播避免了创新和一致性的激励措施之间的冲突。 当前项目将提供一个数据库，允许跟踪通过美国各州的策略传播。数据库将通过项目网页传播。 Web界面还将包括阐明政策传播的途径的高级分析。用户将能够下载数据和软件，以便自己在美国各州进行策略传输的自定义分析。几十年来，政策扩散一直是整个社会科学的重点话题，但是由于数据和计算技术的局限性，研究人员尚未全面地研究了许多不同政策的扩散模式。研究人员建议将文本和网络分析的尖端方法结合在一起，以了解数字化文本中代表的政策如何通过连接美国国家的网络传播。研究人员将同时开发数据和计算方法，以解决政策扩散网络研究最紧迫的局限性。该项目将产生两个关键的贡献：（1）综合数据来源，该数据来源是在三个管理分支机构中跟踪政策从一个州政府到另一个州政府的移动，以及（2）一个用户友好的开源软件包，以理解和可视化从政策数字化记录中的扩散网络。这些资源将帮助研究人员，公职人员，倡导团体以及其他相关利益相关者了解如何平衡创新和公共政策中一致性的竞争目标。

项目成果

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

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

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

FairSight: Visual Analytics for Fairness in Decision Making

• DOI: 10.1109/tvcg.2019.2934262
• 发表时间: 2020-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Ahn, Yongsu;Lin, Yu-Ru
• 通讯作者: Lin, Yu-Ru