Predicted Probability of State-based Deadly Violence i
To check the subnational predictions, click on the country or select from the drop down menu.
Predicted probability of at least 25 fatalities per country and month from deadly violence involving a government of a state.The ViEWS-ESCWA model generates monthly predictions of the risk of violence. The model is specialised to predict ‘state-based deadly violence’, meaning incidences of fatal violence occurring between two or more states, or between a state and one or more organised groups, in relation to a contested incompatibility over government and/or territory.
The regional map shows the predicted probability of at least 25 fatalities per country and month from state-based deadly violence.
With a geographic coverage spanning the Middle East and parts of Africa, the ViEWS-ESCWA forecasts are presented at two levels of analysis – for national (entire countries) and sub-national locations.
The map shows the national level forecasts for state-based deadly violence in the Arab region between {date}. Seen from the bright red or orange colours, the model suggests a very high risk of deadly violence in {highrisk}. Armed activity also remains highly likely in {mediumrisk} throughout this period. Furthermore, the figure suggests low-level risks of deadly violence over the next three years in {lowrisk}.
Trend of Violence Over Time
Countries Overview
To select/unselect a country for comparison, please click on its name in the legend below the chart.
Predicted probability of at least 25 fatalities per country and month from deadly violence involving a government of a state.Thematic Sub-Models i
Predicted probability of at least one fatality per grid cell per month from deadly violence involving a government of a state, as predicted by the selected thematic sub-model.Predictors that pertain to a relevant topic are grouped into a set of ‘thematic groups’ or ‘themes’, which reflect the performance of indicators on the risk of violence in the location at hand.
In capturing different drivers of violence, or drivers of peace, each of these themes addresses the forecast from a different angle and thus offers a distinct insight into the relationship between the predictors and the outcome of interest in a given location.
The Drought and Vulnerability sub-model captures the frequency and intensity of droughts and their impact on the main crops harvest and yield, as well as communities’ vulnerability to climate-related impacts, agricultural dependence and adaptive capacity in agriculture.
The Natural and Social Geography sub-model includes variables describing the type of terrain, the presence of exploitable resources, as well as proxies for the profitability of each location and its centrality. It is further informed by demographic, socio-economic, and socio-political features.
The Conflict History sub-model captures both time and space proximity to past deadly violence, using conflict data at the sub-national level.
Each grid cell in the map corresponds to an area of 0.5x0.5 decimal degrees, or approximately 55x55 km at the Equator.
