Modeling Consequence of Environmental on low income housing development with Gis

Document Type : scientific-research article

Authors

1 Student of Gazvin azad univercity

2 Azad University ofv Qazvin

Abstract
Housing for low-income groups is one of the main challenges facing urban communities, aiming to improve the quality of life and provide safe shelter. However, developing such schemes without proper planning can have irreversible environmental consequences. This research aims to model the environmental impacts of-income housing development in Zanj's Zengan neighborhood. The importance this study lies in highlighting the need sustainable development principles in urban planning, neglecting these principles leads to the destruction of natural habitats, reduced ecosystem services, spatial poverty, and decreased quality of life Meanwhile, advantages such as suitable location proximity to transport axes, affordable land, and high social capital provide positive for this project. The innovation of research lies in the combined use of Information Systems (GIS) and the Analytic Hierarchy Process (AHP) for spatial analysis of environmental data. This approach precise modeling of land-use changes, green space loss, soil and stream, and waste management challenges resulting from density, offering a novel framework for-criteria evaluation. The methodology is on the analytical-spatial and quantitative-qualitative combination, and environmental data are collected from credible sources and processed using and AHP tools. The results that inadequate planning and a lack of principles in design and implementation stages are main factors leading to habitat destruction, pressure on resources, and rapid urban growth accompanied by spatial poverty. The research findings have wide applications in urban and housing policy; for instance, using the proposed model to evaluate similar projects, integrating sustainability considerations into design, preserving green spaces, improving waste management, and reducing spatial poverty. These findings can provide a pattern for sustainable housing development in other low-income areas, contributing to improved quality of life and preventing the repetition of mistakes.

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