E. Buard



As a consequence of human development, human beings and wildlife share natural resources such as water. Thus they face with land competition: wildlife enters into human lands, damaging crops and killing livestock whereas local communities continue killing and trapping wildlife in spite of international laws (Rerimoi 2002).

The objective of this study is to cartography the animals habitats and visualises the land conflicts as a spatial problem (Smith 2000). It provides tools to understand the wildlife presence in the area and then to ease the co-habitation in adopting relevant solutions. A methodology of wildlife cartography is presented, all the more difficult as animals are always in move. My study gives a prediction of their habitats, it means their probable location. What is more, a link is done between land (visible by GIS), wildlife (data collected including a census) and social data.

The first step is to carry out the aerial photograph interpretation. This work has been done in Kenya, next to the Maasai Mara game reserve. Different features of analysis have been identified, underlining the most important factors: drainage network, human settlements, density of vegetation and land cover. A land cover classification has been created and tested on the ground in a sample survey. It includes artificial land, arable land, bushes land and riverine forest. In addition, two transects has been followed to assess the conflicts magnitude in interviewing people or in seeing evidences and to evaluate the number of animals in the area by their droppings, feet, tracks, resting sites and direct visualisation (Kushwaha 2002). We could noticed that one transect passed through elephant migratory corridors.

The second part, the data analysis has been undertaken in England, at Cranfield University. The features from photo interpretation have been digitised into ArcGIS to become GIS layers. They have been combined together with the Raster Calculator tool to create final maps namely human density map, habitats suitability maps and conflicts intensity map. Each habitat suitability map has been calculated according to the best attributes wanted: water is vital means an important weight to the drainage shapefile. The final step was to analyse the questionnaires to give statistics about population concerns or their proposal to mitigate the conflicts. The social survey has contributed to the GIS explanation (Muchemi 2004).

Finally my study gives a systematic methodology to study wildlife in a particular region, with other external data such as statistics.


Kushwaha S.P.S., 2002, Head of forestry and ecology division, Geoinformatics for wildlife habitat characterisation, unpublished application paper. Dehradun, India.

Muchemi J.G., Lwenga R., Nyambura A. M., 2005, Issues in Kuria and Transmara districts, Kenya: the circumstances and outcomes of the eviction of Kuria people from their ancestral claims in Transmara district, ERMIS Africa

Rerimoi J., 2002, Effect of Human-Wildlife conflicts on land-use practices in Salama location, Laikipia district, Kenya. PhD report of Environmental Studies, Egerton University, geography department, Kenya.

Smith R.J. and Kasiki S.M., 2000, A spatial analysis of human elephant conflict in the Tsavo ecosystem, Kenya. IUCN.