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Predicting Conflict in Space and Time

Author

Listed:
  • Nils B. Weidmann

    (Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, NJ, USA, nils.weidmann@gmail.com)

  • Michael D. Ward

    (Department of Political Science, Duke University, Durham, NC, USA)

Abstract

The prediction of conflict constitutes a challenge to social scientists. This article explores whether the incorporation of geography can help us make our forecasts of political violence more accurate. The authors describe a spatially and temporally autoregressive discrete regression model, following the framework of Geyer and Thompson. This model is applied to geo-located data on attributes and conflict events in Bosnia over the period from March 1992 to October 1995. Results show that there is a strong spatial as well as temporal dimension to the outbreak of violence in Bosnia. The authors then explore the use of this model for predicting future conflict. Using a simulation approach, the predictive accuracy of the spatial—temporal model is compared to a standard regression model that only includes time lags. The results show that even in a difficult out-of-sample prediction task, the incorporation of space improves our forecasts of future conflict.

Suggested Citation

  • Nils B. Weidmann & Michael D. Ward, 2010. "Predicting Conflict in Space and Time," Journal of Conflict Resolution, Peace Science Society (International), vol. 54(6), pages 883-901, December.
    • Handle: RePEc:sae:jocore:v:54:y:2010:i:6:p:883-901
    DOI: 10.1177/0022002710371669
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    References listed on IDEAS

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    4. Zhu, Jun & Rasmussen, Jakob G. & Moller, Jesper & Aukema, Brian H. & Raffa, Kenneth F., 2008. "Spatial-Temporal Modeling of Forest Gaps Generated by Colonization From Below- and Above-Ground Bark Beetle Species," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 162-177, March.
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    Citations

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    Cited by:

    1. Çiflikli, Gökhan & Metternich, Nils W, 2019. "We predict conflict better than we thought! Taking time seriously when evaluating predictions in Binary-Time-Series-Cross-Section-Data," SocArXiv tvshu, Center for Open Science.
    2. Stijn van Weezel, 2016. "Short term effects of drought on communal conflict in Nigeria," Working Papers 201618, School of Economics, University College Dublin.
    3. Jerg Gutmann & Matthias Neuenkirch & Florian Neumeier, 2020. "Precision-guided or blunt? The effects of US economic sanctions on human rights," Public Choice, Springer, vol. 185(1), pages 161-182, October.
    4. von Borzyskowski, Inken & Wahman, Michael, 2018. "Systematic measurement error in election violence data: causes and consequences," LSE Research Online Documents on Economics 90450, London School of Economics and Political Science, LSE Library.
    5. J. M. Quinn, 2015. "Territorial contestation and repressive violence in civil war," Defence and Peace Economics, Taylor & Francis Journals, vol. 26(5), pages 536-554, October.
    6. Constantin Ruhe, 2012. "Predicting atrocities. Statistically modeling violence against civilians during civil war," NEPS Working Papers 7/2012, Network of European Peace Scientists.
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    8. Michael Jetter & Bei Li, 2017. "The Political Economy of Opposition Groups: Peace, Terrorism, or Civil Conflict," CESifo Working Paper Series 6747, CESifo.
    9. Jetter, Michael, 2016. "Peace, Terrorism, or Civil Conflict? Understanding the Decision of an Opposition Group," IZA Discussion Papers 9996, Institute of Labor Economics (IZA).

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