Investigation of whether, how, and why inequality influences the dynamics of violent conflict has a long intellectual history. Inequality between individuals and households (vertical inequality) has dominated the literature, but recently attention has shifted to role of group-based inequalities in triggering violence. Our review of research on the relationship between conflict mobilisation, violence and "horizontal inequality" (inequalities based on group identities such as ethnicity, region, and religion) and conflict reveals solid support for the argument that high levels of horizontal economic and political inequalities among the relatively deprived make violent conflict more likely.

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A great deal of information is contained within archival maps—ranging from historic political boundaries, to mineral resources, to the locations of cultural landmarks. There are many ongoing efforts to preserve and digitize historic maps so that the information contained within them can be stored and analyzed efficiently. A major barrier to such map digitizing efforts is that the geographic location of each map is typically unknown and must be determined through an often slow and manual process known as georeferencing. To mitigate the time costs associated with the georeferencing process, this paper introduces a fully automated method based on map toponym (place name) labels. It is the first study to demonstrate these methods across a wide range of both simulated and real-world maps. We find that toponym-based georeferencing is sufficiently accurate to be used for data extraction purposes in nearly half of all cases. We make our implementation available to the wider research community through fully open-source replication code, as well as an online georeferencing tool, and highlight areas of improvement for future research. It is hoped that the practical implications of this research will allow for larger and more efficient processing and digitizing of map information for researchers, institutions, and the general public.

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.