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      Forecasting Rebel Violence: How Diffusion Patterns of Violence Improve the Prediction of Future Conflicts

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      https://www.riss.kr/link?id=A106182985

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      다국어 초록 (Multilingual Abstract)

      Ensuring the safety of civilians and security personnel in conflict zones remain one of the most significant challenges to the United Nations and troop contributors. This study addresses this issue by predicting future locations of violent event based on empirical investigation of rebel activities. Making such predictions includes a three-step process. First, diffusion patterns of rebel activities are analysed using the contagion model of near-repeat victimization. This method enables to assess how the risk of violent events at a given location is transmitted to neighbouring locations within a limited time. Next, an autoregressive distributed lag model is specified based on diffusion patterns of violence identified in the first stage. This model is then applied to monthly, disaggregated data on African civil wars over the period 1997-2012. Finally, estimation results are used to predict the location of conflict in the following month at the level of 55×55km grids. The generated predictions pose both promises and challenges of conflict prediction, with the potential to provide security personnel with timely and policy-relevant information on future conflict.
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      Ensuring the safety of civilians and security personnel in conflict zones remain one of the most significant challenges to the United Nations and troop contributors. This study addresses this issue by predicting future locations of violent event based...

      Ensuring the safety of civilians and security personnel in conflict zones remain one of the most significant challenges to the United Nations and troop contributors. This study addresses this issue by predicting future locations of violent event based on empirical investigation of rebel activities. Making such predictions includes a three-step process. First, diffusion patterns of rebel activities are analysed using the contagion model of near-repeat victimization. This method enables to assess how the risk of violent events at a given location is transmitted to neighbouring locations within a limited time. Next, an autoregressive distributed lag model is specified based on diffusion patterns of violence identified in the first stage. This model is then applied to monthly, disaggregated data on African civil wars over the period 1997-2012. Finally, estimation results are used to predict the location of conflict in the following month at the level of 55×55km grids. The generated predictions pose both promises and challenges of conflict prediction, with the potential to provide security personnel with timely and policy-relevant information on future conflict.

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      참고문헌 (Reference)

      1 이진영, "지리정보데이터를 활용한 전투와 폭력시위 결정 요인 분석: 미얀마 사례를 중심으로" 국제지역연구원 25 (25): 287-314, 2018

      2 United Nations, "United Nations Peacekeeing: Fatalities"

      3 Ward, Michael, "The Perils of Policy by p-Value: Predicting Civil Conflicts" 47 (47): 363-375, 2010

      4 International Peace Institute, "The Accountability System for the Protection of Civilians: A Shared Responsibility"

      5 Witmer, Frank, "Subnational Violent Conflict Forecasts for Sub-Saharan Africa, 2015–65, Using Climate-Sensitive Models" 54 (54): 175-192, 2017

      6 Johnson, Shane, "Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization" 23 (23): 201-219, 2007

      7 United Nations, "Report of the High-Level Independent Panel on Peace Operations"

      8 Weidmann, Nils, "Predicting Conflict in Space and Time" 54 (54): 883-901, 2010

      9 Hegre, Håvard, "Predicting Armed Conflict, 2010–2050" 57 (57): 250-270, 2013

      10 Tollefsen, Andreas Forø, "PRIO-GRID: A Unified Spatial Data Structure" 49 (49): 363-374, 2012

      1 이진영, "지리정보데이터를 활용한 전투와 폭력시위 결정 요인 분석: 미얀마 사례를 중심으로" 국제지역연구원 25 (25): 287-314, 2018

      2 United Nations, "United Nations Peacekeeing: Fatalities"

      3 Ward, Michael, "The Perils of Policy by p-Value: Predicting Civil Conflicts" 47 (47): 363-375, 2010

      4 International Peace Institute, "The Accountability System for the Protection of Civilians: A Shared Responsibility"

      5 Witmer, Frank, "Subnational Violent Conflict Forecasts for Sub-Saharan Africa, 2015–65, Using Climate-Sensitive Models" 54 (54): 175-192, 2017

      6 Johnson, Shane, "Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization" 23 (23): 201-219, 2007

      7 United Nations, "Report of the High-Level Independent Panel on Peace Operations"

      8 Weidmann, Nils, "Predicting Conflict in Space and Time" 54 (54): 883-901, 2010

      9 Hegre, Håvard, "Predicting Armed Conflict, 2010–2050" 57 (57): 250-270, 2013

      10 Tollefsen, Andreas Forø, "PRIO-GRID: A Unified Spatial Data Structure" 49 (49): 363-374, 2012

      11 Perry, Chris, "Machine Learning and Conflict Prediction: A Use Case" 2 (2): 2013

      12 Ward, Michael, "Learning from the Past and Stepping into the Future: Toward a New Generation of Conflict Prediction" 15 (15): 473-490, 2013

      13 Raleigh, Clionadh, "Introducing ACLED: An Armed Conflict Location and Event Dataset: Special Data Feature" 47 (47): 651-660, 2010

      14 Townsley, Michael, "Infectious Burglaries. A Test of the Near Repeat Hypothesis" 43 (43): 615-633, 2003

      15 Hegre, Håvard, "Forecasting Civil Conflict along the Shared Socioeconomic Pathways" 11 (11): 054002/1-054002/8, 2016

      16 Knox, G., "Epidemiology of childhood Leukemia in Northumberland and Durham" 18 : 17-24, 1964

      17 Choi, Hyun Jin, "Dominant Forms of Conflict in Changing Political Systems" 59 (59): 158-171, 2015

      18 Colaresi, Michael, "Do the Robot: Lessons from Machine Learning to Improve Conflict Forecasting" 54 (54): 193-214, 2017

      19 Schutte, Sebastian, "Diffusion Patterns of Violence in Civil Wars" 30 (30): 143-152, 2011

      20 Raleigh, Clionadh, "Conflict Dynamics and Feedback: Explaining Change in Violence against Civilians within Conflicts" 43 (43): 848-878, 2017

      21 Lalkhen, Abdul Ghaaliq, "Clinical Tests: Sensitivity and Specificity" 8 (8): 221-223, 2008

      22 Youstin, Tasha, "Assessing the Generalizability of the Near Repeat Phenomenon" 38 (38): 1042-1063, 2011

      23 Gleditsch, Nils Petter, "Armed Conflict 1946-2001: A New Dataset" 39 (39): 615-637, 2002

      24 Wang, Zengli, "Analysis of Burglary Hot Spots and Near-Repeat Victimization in a Large Chinese City" 6 (6): 148-, 2017

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2019-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2016-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2014-01-14 학술지명변경 외국어명 : 미등록 -> Peace Studies KCI등재
      2012-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-05-28 학회명변경 한글명 : 평화연구소 -> 평화와 민주주의연구소
      영문명 : Institute for Peace Studies, Korea University -> Peace & Democracy Institute
      KCI등재
      2009-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2008-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2006-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.82 0.82 0.72
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.69 0.74 1.224 0.27
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