How feasible is violence early-warning prediction? Colombia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These "best-case" scenarios with annual data fall short of workable early-warning systems.
机构:
Univ York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, EnglandUniv York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
Kreif, Noemi
Mirelman, Andrew
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Univ York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, EnglandUniv York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
Mirelman, Andrew
Suhrcke, Marc
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Univ York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
Luxembourg Inst Socioecon Res, 11 Porte Sci, L-4366 Esch Sur Alzette, LuxembourgUniv York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
Suhrcke, Marc
Buitrago, Giancarlo
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Univ Nacl Colombia, Hosp Univ Nacl Colombia, Clin Res Inst, Bogota, DC, ColombiaUniv York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
Buitrago, Giancarlo
Moreno-Serra, Rodrigo
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Univ York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, EnglandUniv York, Ctr Hlth Econ, York YO10 5DD, N Yorkshire, England
机构:
New York Univ Abu Dhabi, Div Social Sci, Abu Dhabi, U Arab Emirates
Wageningen Univ & Res, Dev Econ Grp, Wageningen, NetherlandsUniv Antwerp, Inst Dev Policy, Antwerp, Belgium