An Ecosystem of Applications for Modeling Political Violence

Conflict researchers face many challenges, including (1) how to model conflicts, (2) how to measure them, (3) how to manage their spatio-temporal character, and (4) how to handle a potential abundance of information and explanation. In this paper, we describe an ecosystem of tools designed for use by subject matter experts that addresses these challenges. Three case studies show workflows that are facilitated by this ecosystem.

[1]  C. War,et al.  Civil War , 2002 .

[2]  Aravind Srinivasan,et al.  'Beating the news' with EMBERS: forecasting civil unrest using open source indicators , 2014, KDD.

[3]  T. Sandler,et al.  What We Have Learned about Terrorism since 9/11 , 2019, Journal of Economic Literature.

[4]  A. Karpatne,et al.  Spatio-Temporal Data Mining: A Survey of Problems and Methods , 2017, ArXiv.

[5]  D. Lazer,et al.  Growing pains for global monitoring of societal events , 2016, Science.

[6]  Matthew Hayes,et al.  A Progressive Supervised-learning Approach to Generating Rich Civil Strife Data , 2015 .

[7]  Dan Brickley,et al.  Google Dataset Search: Building a search engine for datasets in an open Web ecosystem , 2019, WWW.

[8]  Gerald Schneider,et al.  ViEWS: A political violence early-warning system , 2019, Journal of Peace Research.

[9]  Thomas Chadefaux,et al.  Conflict Forecasting and Its Limits , 2017, Data Sci..

[10]  G. King,et al.  Improving Quantitative Studies of International Conflict: A Conjecture , 2000, American Political Science Review.

[11]  Clionadh Raleigh,et al.  Introducing ACLED: An Armed Conflict Location and Event Dataset , 2010 .

[12]  Juliana Freire,et al.  AlphaD3M: Machine Learning Pipeline Synthesis , 2021, ArXiv.

[13]  Kristian Skrede Gleditsch,et al.  The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data , 2017 .

[14]  Juliana Freire,et al.  PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines , 2020, IEEE Transactions on Visualization and Computer Graphics.

[15]  Trang T. Le,et al.  Scaling tree-based automated machine learning to biomedical big data with a feature set selector , 2019, Bioinform..

[16]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[17]  Juliana Freire,et al.  Visus: An Interactive System for Automatic Machine Learning Model Building and Curation , 2019, HILDA@SIGMOD.

[18]  Ernest Koh,et al.  UN Office for the Coordination of Humanitarian Affairs , 2006 .

[19]  Jay Ulfelder,et al.  Can Structural Conditions Explain the Onset of Nonviolent Uprisings? , 2017 .

[20]  Mia M. Bloom.  Constructing Expertise: Terrorist Recruitment and “Talent Spotting” in the PIRA, Al Qaeda, and ISIS , 2017 .

[21]  Yingcai Wu,et al.  A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges , 2013, Journal of Computer Science and Technology.

[22]  Elena Simperl,et al.  Dataset search: a survey , 2019, The VLDB Journal.

[23]  D. Collier,et al.  Measurement Validity: A Shared Standard for Qualitative and Quantitative Research , 2001, American Political Science Review.

[24]  C. Davenport,et al.  Introducing xSub: A new portal for cross-national data on subnational violence , 2019, Journal of Peace Research.

[25]  Leilani Battle,et al.  Characterizing Exploratory Visual Analysis: A Literature Review and Evaluation of Analytic Provenance in Tableau , 2019, Comput. Graph. Forum.

[26]  Nicholas Sambanis,et al.  Sensitivity Analysis of Empirical Results on Civil War Onset , 2006 .

[27]  Erik Melander,et al.  Introducing the UCDP Georeferenced Event Dataset , 2013 .

[28]  Vito D'Orazio,et al.  TwoRavens for Event Data , 2018, 2018 IEEE International Conference on Information Reuse and Integration (IRI).