Abstract
The current understanding of the causes of land-use change is dominated by simplifications, which in turn, underline many environment-development policies that led to unsustainable situation in Gaza City. Therefore, this research aims at establishing an integrated land-use management framework for Gaza city based on cause-effect relationship within sustainability context. It also aims at establishing a prediction model for the relationship between the most influential socio-economic, environmental and institutional indicators that had shaped the current land-use status of the city for the period 1967-2003. The methodology presented in this research work, offers opportunities to simulate the future demand of the different land-uses based upon actual land-use conditions and other determinant factors. The determinant variables of land-use changes have been identified and prioritized using statistical analysis and Artificial Neural Network (ANN). The results were compared with other statistical techniques and expert opinions. ANN prediction model helped in drawing scenarios for future development. Combinations of socio-economic, environmental and institutional variables in addition to the actual land use for the last four years are used as a basis of land-use change explanations and modeling. These pathways indicate that land-use policies and projections for the future must not only capture the population indicators as the only drivers for land-use change but also account for the specific human resource development indicators and urban-environmental conditions. This recognition requires moving beyond some of the simplifications that persist in much of the current understanding of the causes of land-use change and its driving forces. The analysis of the local expert’s opinions provide evidence support the conclusion that the simple answers found in population growth, poverty and infrastructure rarely provide an adequate explanations of land-use changes. Rather, social responses follow from changing economic conditions, mediated by institutional factors are the real causes for land-use changes in Gaza.
Highlights
Sustainable land-use management aims at integrating socio-economic, environmental and institutional factors into one framework that leads to formulate the appropriate policies and activities
The statistical analysis resulted with 10 indicates that have significant correlation with the different land-uses in Gaza and contributed by major role in shaping the land-uses for the last 37 years. These correlations vary from 0.7 to 0.95 in both directions. These indicators are Log population under 19 years (P19), Log population density (PD), Log households connected to the wastewater (HWW), Log labour forces (LF), unemployment rate (UE), poverty rate (PR), households used cesspits for wastewater disposal (WWC), groundwater abstraction (GWA), groundwater salinity (TDS), and funds for development (FUND)
From the second column (Eigenvalue) of the table above, we find the variance on the new factors that were successively extracted
Summary
Sustainable land-use management aims at integrating socio-economic, environmental and institutional factors into one framework that leads to formulate the appropriate policies and activities. The researches done until now for developing a conceptual framework for modeling sustainable land-use management are still fragmented and incomplete attempts. For the efficient analysis of land-use change problems, it is essential to establish a conceptual integrated framework that aims at achieving sustainable land-use management. It is essential to look for the appropriate modeling approaches and tools that could give acceptable results in analysis and predicting the available data. Traditional statistical models such as multiple regression analysis, principal component analysis and factor analysis have been very successful in interpreting socio-economic activities. Attempts have been made to develop spatially explicit models of land-use and land cover change by using several tools such as satellite imagery, aerial photography, GIS, remote sensing, agent-based modeling and ANN based modeling [2]
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