The pervasive herdsmen-farmers conflicts in the north-central region of Nigeria have changed the narrative of Nigeria's enduring ethnic crises to ideologies, which are in-controvertibly sinister. The consequences of this tension, which has defied possible military responses, political, religious and cultural strategies are potentially devastating, not just for Nigeria, but the whole of West African region. Since the particular nature of these conflicts increasingly highlights the significance and inevitability of land resources for crops farming and cattle rearing, it is imperative to create awareness of the elemental nature of soils, especially their diversities in these conflict-prone areas. This study's objective was to produce a Geographic Information System (GIS) based digital soil map (DSM) of the north-central region of Nigeria, and to delineate soil distribution and unique properties. Based on this study, the DSM offers a quick access to quantitative soil data covering the study area. It indicates that soil mapping units 15d, 18d and 24b are dominant, and constitute about 40% of the local arable lands. The broad pattern of distribution of these soils reflects both the climatic conditions and the geological structure of the region. The soils are highly weathered with limited capacities to supply essential nutrients needed by crop plants. These issues raise a number of questions, most of which focuses on the best possible way to maximize these soils to accommodate both crop farming and cattle rearing. It is our hope that taking the advantage of GIS to stimulate the knowledge and consciousness of soil distribution in the region will place the weight where it is appropriate in terms of food security through crops production and cattle rearing, and hence forge a more realistic pathway to reconciliation and conflict resolution.

Analysis of the dynamic relationship between traffic accident events and road network topology based on connectivity and graph analytics offers a new approach to identifying, ranking and profiling traffic accident high risk-locations at different levels of space and time granularities. Previous studies on traffic accident hot spots have mostly adopted spatial statistics and Geographic Information Systems (GIS) where spatial point patterns are discovered based only on spatial dependence with no recognition of the temporal dependence of the events. A limitation arises from the fact that the results are either under or over-estimated because of the temporal aggregation of the events to an absolute time point. Furthermore, the existing methods apart from the Network Kernel Density Estimation (NETKDE), consider traffic accident events as events randomly on a 2-D geographic space. However, traffic accident events are network constrained events that happens majorly on the road network space. Therefore, in this paper, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on spacetime-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented for this study. Traffic accident high risk-locations are identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. This study offers urban traffic accident analysts with a new and efficient approach to identify, rank and profile accident-prone areas in space and time at different scales.

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