Urban Growth Modeling Using Logistic Regression and Geo-informatics: A Case of Jaipur, India

Pratyush Tripathy, Rohit Raman, Aritra Bandopadhyay, Suraj Kumar Singh | 31 March 2018


The study comprises of the logistic regression based urban growth modeling of Jaipur, Rajasthan, India to demarcate the places having higher probabilities of growth in future and also to understand the dependency of urban growth on different driving parameters. Various physical and socio-economic parameters prepared using remotely sensed spatial data were taken into consideration which showed varying level of contribution in the growth process. The built-up data for two different time periods (2008 and 2017) and other geospatial datasets were taken to perform the logistic regression modeling and to obtain the future urban growth probability map as well as to rank the participation of driving forces. The Receiver Operating Characteristics (ROC) curve was plotted to ensure the accuracy of the model. Also, a graph of overall accuracy versus cut value is proposed to determine the change in the behavior of the logistic regression model with varying probability threshold. The optimum cut of value for logistic regression model for the considered parameters was examined. Despite its inability to deal with the temporal dynamics, logistic regression is an empirical formula based robust method for modeling the unplanned urban growth, especially for developing countries like India where the growth is desultory.