Assessing Land Use Suitability in Relation to Ecologically Sensitive Areas: a Case of Kisii Central Ward in Kisii County
Abstract
Kisii County is presently witnessing a remarkable and somewhat concerning surge in its growth rate, resulting in a noticeable transformation of land utilization. According to the most recent data available in the Kenya National Bureau of Statistics (KNBS) Census report from 2019 (Volume II), the estimated population density within the county has reached a striking 2000 individuals per square kilometer. This statistic reflects a significant demographic shift, totaling 1,266,860 people in 2019. This rapid population expansion places Kisii County among the fastest-growing regions in the country, demanding a strategic approach to address the challenges and opportunities associated with this remarkable growth. Various factors, including socio-demographic, economic, and environmental considerations, such as food security and political stability, drive this growth. Given these trends, it is imperative to assess the potential encroachment of land use into ecologically sensitive areas that have resulted from unplanned developments and formulate policies and strategies for land use concerning these ecologically sensitive zones. This study aims to visualize the historical changes in land use to help predict future scenarios regarding land utilization. The study utilizes remote sensing information and Geographic Information System (GIS) technology to examine the topographical features, land-use classifications, and ecological sensitivity of the Kisii Central ward, facilitating the development of meaningful evaluation criteria for assessing the influence of urban development in this rapidly expanding area. To achieve this, the study employs the Multi-Criteria Decision Analysis (MCDA) alongside the Land Use Conflict Identification Strategy (LUCIS) model, devised by Margret Carr Zwick. This model is applied to produce Conflict Space Diagrams that depict the consequences of urban land use on ecological sensitivity. The model requires input data from various factors, including water bodies, built-up areas, and road networks. The study adopts multiple criteria for weighting, including Single Utility Assignment (SUA), Multiple Utility Assignment (MUA), Complex Multi Utility Assignment (CMUA), as well as the Analytical Hierarchy Process (AHP) and Pairwise Comparison. Geographically weighted regression analysis is also employed to understand the relationship between non-spatial population data and existing land-use patterns, which aids in delineating specific zones. In the process, both random and non-random sampling methods are utilized. Household and enterprise surveys employ simple random sampling, while key informants are selected through purposive sampling. The research methodology encompasses a comprehensive approach, including literature reviews, interviews, field observations, photography, and mapping. Data analysis is conducted using descriptive, qualitative, and....................................................
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
- Faculty of Arts [979]
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