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:
World Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, TunisiaWorld Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, Tunisia
Cali, Massimiliano
Miaari, Sami H.
论文数: 0引用数: 0
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机构:
Tel Aviv Univ, Dept Lab Studies, Tel Aviv, Israel
Univ Oxford, Blavatnik Sch Govt, Oxford, EnglandWorld Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, Tunisia
机构:
World Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, TunisiaWorld Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, Tunisia
Cali, Massimiliano
Miaari, Sami H.
论文数: 0引用数: 0
h-index: 0
机构:
Tel Aviv Univ, Dept Lab Studies, Tel Aviv, Israel
Univ Oxford, Blavatnik Sch Govt, Oxford, EnglandWorld Bank, Bldg Le Blvd,3rd Floor,Cite Les Pins,Les Berges du, Tunis 1053, Tunisia