Tsetse are the insect vectors of the African trypanosomiases. As with many diseases, transmission of trypanosomiasis varies through space and time. Capturing the variation of both vector and disease has, in the past, been attempted separately in the space and time dimensions, usually using deterministic techniques. Very few efforts have used space-time covariation and have hence missed any correlations that may exist between variation in these two dimensions. Here we propose two novel approaches to space-time analysis derived from space-time geostatistics in a kriging framework. The approaches were developed through analysis of a dataset recording the Apparent Density of Glossina palpalis gambiensis and Glossina tachinoides (Diptera: Glossinidae) in three riparian sites in Burkina Faso over 15 months between 2006 and 2007. This site is fragmented due to human activity in the area. The first approach, Space Time Ordinary Kriging, does not consider the effect of fragmentation. It is used as a benchmark to test the increased explanatory power of the second method, which does account for fragmentation. The second method, Regression Space Time Simple Kriging, is a distinct improvement over the first approach because it allows for a spatial trend in the mean trap catch; this trend is related to, and later predicted from, environmental co-variates. The results indicate the presence of space and time effects on tsetse distribution, dependent on the size of the habitat fragmentation patches. These effects occur at relatively small geographic scales within a season. Whilst such variation has long been suspected, the new methods presented here are able to quantify this variation precisely, so that seasonal and spatial comparisons can now be made both within and between species.