Geospatial Modelling of Land Valuation in Adenta Municipal Assembly, Ghana
Abstract
This study focuses on land valuation in the Adenta Municipal Assembly, Ghana, addressing a critical knowledge gap of creating a standard model for factors that determined value for land. The study seeks to identify and analyze the factors influencing land value and generate a geospatial model for accurate land valuation. Socioeconomic data were obtained by administering 400 copies of a structured questionnaire and utility maps such as (electricity line map, Road Map, educational facility map, fire station, police centers, financial institution and recreational area maps) were obtained. Descriptive Statistical analysis was performed using SPSS software, Geographic Information System (GIS) techniques was used to produce the factor maps, Multi-Criteria Analysis (MCA) was used to buffer around the factor maps and carried out comparison and normalized matrix, and ArcGIS Model builder in generating geospatial model. The results revealed eleven key factors determinants of land value. These include access to electricity, transport, educational centers, health services, land use type, police stations, city centers, financial institutions, fire stations, recreation centers, parks, and government buildings. The study also established correlations between these factors and variations in land value, resulting in a geospatial and mathematical model for value zones in monetary terms, producing a mathematical model adaptable for diverse valuation categories. The mathematical model produces new values for land value in the municipality range from ?52,947.00 to ?92,890.00 as restricted area to very high area. In conclusion, the research highlights the importance of Geospatial modelling in land valuation, providing practical tools for stakeholders, planners, valuers, and policymakers in decision-making related to land use development and taxation in the Adenta Municipal Assembly and beyond.
Keywords
Geospatial model, Land value, Value zone, Multi-Criteria Analysis (MCA), GIS
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