A Geospatial Approach to Mapping and Monitoring Real Estate-Induced Urban Expansion in the National Capital Region of Delhi

Abstract

Monitoring of real estate growth is essential with the increasing demand for housing and working space in cities. In this study, a new methodological framework is proposed to map the area under real estate using geospatial techniques. In this framework, the built-up area and open land at successive stages of development are used to map the area under real estate. Three machine learning algorithms were used, namely random forest (RF), support vector machine (SVM), and feedforward neural networks (FFNN), to classify the land use and land cover (LULC) map of Delhi NCR during 1990–2018, which is the basic input for real estate mapping. The results of the study show that optimized RF performed better than SVM and FFNN in LULC classification. The real estate land increased by 279% in Delhi NCR during 1990–2018. The area under real estate increased by 33%, 47%, 29%, 21%, and 22% during 1990–1996, 1996–2003, 2003–2008, 2008–2014, and 2014–2018, respectively. Among the cities surrounding Delhi, Gurgaon, Rohtak, Noida, and Faridabad have witnessed maximum real estate growth. The approach used in this study could be used for real estate mapping in other cities across the world.