# **Appendix IV: Conflict Kernel**

In recent years, researchers studying conflict have increasingly embraced a “micro-level” approach as an alternative to macro, country–year level analyses. The micro-level approach suggests that researchers should focus on subnational or individual conflict units of analysis, which are better suited to studying the internal dynamics across time and space and to be able to draw better inferences about the local conditions which affect, and are affected by, conflict. Within the drive for empirical disaggregation, there are two trends. The usual approach of using household or individual surveys and the newer one of geographical and temporal disaggregation of conflict events. For this report the latter approach was selected. Specifically the Armed Conflict Location Events Dataset (ACLED) was used.

The ACLED project codes reported information on the location, date, and other characteristics of politically violent events in unstable and warring states. ACLED focuses specifically on: tracking rebel, militia and government activity over time and space; locating rebel group bases, headquarters, strongholds and presence; distinguishing between territorial transfers of military control from governments to rebel groups and vice versa; recording violent acts between militias; collecting information on rioting and protesting; and non-violent events that are crucial to the dynamics of political violence (e.g. rallies, recruitment drives, peace talks, high-level arrests). ACLED Version 4 data cover all countries on the African continent from 1997 to 2013.

However, due to the nature of conflict, the ACLED data set could not be used in its raw format since it brings up some technical and methodological problems. First, using points pins the conflict to a specific location and does not capture the effects of conflict to the surrounding area (for instance battles that may have been fought in a large area). Second, conflict points cannot capture conflict intensity very well (for instance one isolated conflict point versus a cluster of conflict points). Finally, ACLED is subject to some geographic imprecision due to how the data was obtained (for instance rural conflicts are often allocated to nearby villages).<sup>37</sup> To account for these methodological issues, a ‘hot spot’ strategy was followed. Hot spots are concentrations of incidents within a limited geographical area that appear over time (Braga and Weisburd 2010)<sup>38</sup> . Particularly a kernel density interpolation technique was chosen since it allows to transform conflict points into a smooth surface, and generalize conflict locations. To calculate the value at any

> 37 In ACLED geographic uncertainty level is coded with “geoprecision codes” ranging from one to three (higher numbers indicate broader geographic spans and thus greater uncertainty about where the event occurred). A geoprecision code of 1 indicates that the coordinates mark the exact location that the event took place. When a specific location is not provided, ACLED selects the provincial capital. This way ACLED may attribute violent incidents to towns when in fact they took place in rural areas and therefore introducing a systematic bias towards attributes associated with urban areas that can lead to invalid inferences.

> 38 Braga, A. & Weisburd, D. (2010). Policing Problem Places: Crime Hot Spots and Effective Prevention. Oxford: Oxford University Press.

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