GIS Based Site Suitability Analysis for Location of a Sugar Factory in Trans Mara District

Abstract

Domestic sugar production in Kenya does not meet consumption demand. Consequently, expanded production and creation of more sugar mills within regions of high agricultural potential is necessary for expansion of the industry. Suitable arable land for such projects in Kenya is scarce, and essentially planners must select the best use of this resource to uphold sustainability. Although locating optimum sites for new industrial investments is important, it involves evaluation of conflicting criteria with large sets of alternatives. The complex nature of finding industrial investment locations requires a technique that can combine geographical data with value judgments. This research aims at finding the most suitable sites for a sugar factory in Trans Mara district in Kenya, using GIS based Multi-criteria evaluation. Nine factors including slope, an existing factory location, roads, rivers, electricity sources, land use, soil texture, rainfall, and temperature were selected based on reviewed literature and opinion of experts. Selected factors were then organized into two broad principal classes including physical site conditions and sugarcane crop requirements. Digital data on selected factors was acquired from various governmental institutions, stored, harmonized and geo-processed in Arc GIS 10.1 platform to generate factor maps.Analytical hierarchy process (AHP) was used to elicit weights which were assigned to each factor. Weighted factor maps were finally standardized, reclassified and combined by weighted linear combination (WLC) aggregation method. The composite suitability map produced shows that 2.02 % of the total area is highly suitable, 13.54% is moderately suitable, 82.33% is marginally suitable and 2.11 % is unsuitable. Highly suitable sites fall outside ecologically sensitive areas, and lie within reasonable distance to power lines and major roads. The implication of these results is that GIS-MCE application in industrial sites selection minimizes environmental risks and reduces engineering costs.