In the last century, many mobile pastoralists have transitioned to more sedentary lifestyles.
Mobile people can be both pushed into a more settled existence by environmental or political
forces, or pulled by new economic opportunities. While researchers have examined the
causes and consequences of growing sedentarization, few contemporary studies have examined
the patterns of settlement among mobile groups who are shifting to sedentary lifestyles and how
these patterns may be related to socio-economic outcomes. This research examines settlement
site selection by using GIS and remote sensing techniques to quantify settlement patterns in four
Maasai villages in northern Tanzania, exploring the environmental and infrastructure correlates
of settlement locations. A subset of these geographic variables is used with social survey data for
111 Maasai households in the study site to test the hypothesis that settlement location impacts
livelihood strategies and economic outcomes by creating and constraining access to important
resources and infrastructure. Landscape level evaluation of settlement patterns show that certain
soil types limit occupation and the potential for agricultural expansion in 30% of the study area.
Settlement density and existing agriculture are also clustered in certain parts of the landscape.
The spatial models support the hypothesis that proximity to roads and village centers plays an
important role in shaping overall settlement patterns. However, models that combine these factors
with environmental and geophysical elements show improved explanatory performance,
suggesting that competing factors are at play in influencing settlement patterns. Spatial models
also indicate that agricultural development may be limiting land available for settlement in some
parts of the study area. Results of the household level outcomes are more ambiguous, with few
relationships between geographic variables and household livestock holdings, land under cultivation,
annual income. Rather, these factors are influenced largely by demographic variables such
as household size, age of the household head, and asset allocation. However, there appears to be
less income diversity in households more distant from permanent water sources.