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Dominant climate models suggest that large parts of Africa will experience greater climatic variability and increasing rates of drought in coming decades. This could have severe societal consequences, because the economies and food supplies of most African countries depend on rain-fed agriculture. According to leading environmental security scholars, policymakers, and nongovernmental organizations, an increase in scarcity-driven armed conflicts should also be expected. A conditional theory of environmental conflict predicts that drought increases the risk of civil war primarily when it strikes vulnerable and politically marginalized populations in agrarian societies. However, an empirical evaluation of this general proposition through a unique gridded dataset of postcolonial Africa, which combines high-resolution meteorological data with georeferenced data on civil war onset and the local ethnopolitical context, shows little evidence of a drought-conflict connection. Instead, the local risk of civil war can be explained by sociopolitical and geographic factors: a politically marginalized population, high infant mortality, proximity to international borders, and high local population density.

Contributions to the quantitative civil war literature increasingly rely on geo-referenced data and disaggregated research designs. While this is a welcome trend, it necessitates geographic information systems (GIS) skills and imposes new challenges for data collection and analysis. So far, solutions to these challenges differ between studies, obstructing direct comparison of findings and hampering replication and extension of earlier work. This article presents a standardized structure for storing, manipulating, and analyzing high-resolution spatial data. PRIO-GRID is a vector grid network with a resolution of 0.5 x 0.5 decimal degrees, covering all terrestrial areas of the world. Gridded data comprise inherently apolitical entities; the grid cells are fixed in time and space, they are insensitive to political boundaries and developments, and they are completely exogenous to likely features of interest, such as civil war outbreak, ethnic settlement patterns, extreme weather events, or the spatial distribution of wealth. Moreover, unlike other disaggregated approaches, gridded data may be scaled up or down in a consistent manner by varying the resolution of the grid. The released dataset comes with cell-specific information on a large selection of political, economic, demographic, environmental, and conflict variables for all years, 1946–2008. A simple descriptive data assessment of population density and economic activity is offered to demonstrate how PRIO-GRID may be applied in quantitative social science research.

The world's population is increasingly concentrated in cities. Research on urbanization's implications for peace and security has been hampered by a lack of comparable data on political mobilization and violence at the city level across space and through time, however. Urban Social Disorder 3.0 is a detailed event dataset covering 186 national capitals and major urban centers from 1960 to 2014. It includes 12 types of nonviolent and violent events, detailing the actors involved and their targets, start and end dates of each event, and the number of participants and deaths. We provide an overview of the main features of these data, and trends in urban social disorder across space and time. We demonstrate the utility of the dataset by analyzing the relationship between city size and the frequency of lethal disorder events. We find a positive relationship between city population and lethal urban social disorder, unlike previous studies. These new data raise promising avenues for future research on democratization; climate change and food security; and spillovers between different forms of mobilization and violence.

The world's population is increasingly concentrated in cities. Research on urbanization's implications for peace and security has been hampered by a lack of comparable data on political mobilization and violence at the city level across space and through time, however. Urban Social Disorder 3.0 is a detailed event dataset covering 186 national capitals and major urban centers from 1960 to 2014. It includes 12 types of nonviolent and violent events, detailing the actors involved and their targets, start and end dates of each event, and the number of participants and deaths. We provide an overview of the main features of these data, and trends in urban social disorder across space and time. We demonstrate the utility of the dataset by analyzing the relationship between city size and the frequency of lethal disorder events. We find a positive relationship between city population and lethal urban social disorder, unlike previous studies. These new data raise promising avenues for future research on democratization; climate change and food security; and spillovers between different forms of mobilization and violence.

Recent research suggests that climate variability and change significantly affect forced migration, within and across borders. Yet, migration is also informed by a range of non-climatic factors, and current assessments are impeded by a poor understanding of the relative importance of these determinants. Here, we evaluate the eligibility of climatic conditions relative to economic, political, and contextual factors for predicting bilateral asylum migration to the European Union—form of forced migration that has been causally linked to climate variability. Results from a machine-learning prediction framework reveal that drought and temperature anomalies are weak predictors of asylum migration, challenging simplistic notions of climate-driven refugee flows. Instead, core contextual characteristics shape latent migration potential whereas political violence and repression are the most powerful predictors of time-varying migration flows. Future asylum migration flows are likely to respond much more to political changes in vulnerable societies than to climate change.

Much of the recent research on civil war treats explanations rooted in political and economic grievances with considerable suspicion and claims that there is little empirical evidence of any relationship between ethnicity or inequality and political violence. We argue that common indicators used in previous research, such as the ethno-linguistic fractionalization (ELF) and the Gini coefficient for income dispersion, fail to capture fundamental aspects of political exclusion and economic inequality that can motivate conflict. Drawing on insights from group-level research, we develop new country-level indices that directly reflect inequalities among ethnic groups, including political discrimination and wealth differentials along ethnic lines. Our analysis reveals that these theoretically informed country profiles are much better predictors of civil war onset than conventional inequality indicators, even when we control for a number of alternative factors potentially related to grievances or opportunities for conflict. Adapted from the source document.