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 Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, Indonesia
Univ Nusa Cendana, Fac Social & Polit Sci, Dept Publ Adm, Kupang, IndonesiaUniv Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, Indonesia
Pradana, I. Putu Yoga Bumi
Kumorotomo, Wahyudi
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Univ Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, IndonesiaUniv Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, Indonesia
Kumorotomo, Wahyudi
Susanto, Ely
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Univ Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, Indonesia
Univ Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, 1 Bulaksumur, Yogyakarta 55281, IndonesiaUniv Gadjah Mada, Fac Social & Polit Sci, Dept Publ Policy & Management, Yogyakarta, Indonesia