Modelling for suitable locations lower income hosing

Abstract

Cities in developing countries have seen massive in-migration of people from the countryside looking for work and a place to call home. Poor individuals (lower income migrants), the majority of whom are from rural regions, cannot afford the high rents in cities, the lack of adequate low-cost housing, and poor government planning that support the supply side of informal housing. As a result, wherever they find land, public or private, they begin to live there in temporary hutments; as years progress, more people join them, and the region quickly becomes informal housing (slums). According to the World Bank survey in 2018, 35.2 % of India's urban population lives in informal housing. Insufficient resources to provide suitable housing and essential services to residents lead to poverty, unemployment, traffic congestion, environmental pollution, scarcity of land, inappropriate land use, skyrocketing land value, insecure tenure, etc. It poses a burden to the government and planners. Moreover, informal housing restoration and upliftment programs require special funds, affecting the economy. Also, it indirectly affects the nation socially, economically, and politically and creates pressure on un-used, unprotected, and un-suitable public land. Due to a lack of timely information and people's lack of consideration for such things in the absence of any other option, planning regulations in informal urban housing are typically ineffective. The lack of reliable, up-to-date informal housing information and maps in most local organizations poses a problem in decision making. Therefore, preventive measures to minimize the burden are more practical than elimination. Identifying the current and future informal housing will help us plan better and reduce the burden on the economy. The research will study the various paraments for the location preference for forming lower-income housing (Informal housing) through literature and validating the relationship through Geographical Weighted Regression (GWR). This study aims to identify and map current informal housings and predict the more prone location to the formation of informal housings in Pune city through GIS and CA Markov Chain modeling. The study will help urban planners and policymakers proactively address the issue by integrating the Model with the master plan preparation and framework for better sustainable urban planning. Pune is one of the growing metro cities in India, attracting lakhs of the population in search of job opportunities. Economic expansion leads to a large amount of in migration from surrounding regions. Most migrants belong to the lower strata and cannot enter the formal housing market. These lead to 40% of the Pune city population living in informal settlements (Slums). The study identifies the local factors for the location choice of lower-income housing and predicts the suitable location using the GIS and CA-Markov chain model. Key Words: Informal housings, Location preference, GWR, GIS, CA-Markov model, Master Plan.