What drives urban growth in Pune? A logistic regression and relative importance analysis perspective

Proactive planning and management of rapidly urbanizing cities using up-to-date spatially explicit datasets is an urgent need. This requires a good understanding of the driving factors responsible for urban growth. Using Pune metropolis as test site, this paper presents an approach to assess the relative importance of urban growth driving factors from inexpensive geospatial datasets with respect to (i) urbanization process, (ii) urban planning (iii) urban growth modelling by utilizing relative importance analysis (RIA) as a supplement to logistic regression. Furthermore, this research proposes a new approach to reduce the parameterization and data requirement of urban growth models. Our research shows, that proximity to essential infrastructure has the highest predictive power in explaining urban growth of Pune. The importance of policy factors increase with time. Our results reveal that RIA is a suitable method, which can assist planners in deeper understanding of the urbanization process and to devise sustainable urban development strategies, utilizing a limited amount of data, which can be easily updated from geospatial datasets. The proposed break point method based on RIA to reduce parameterization of urban models performed at par with the model results achieved with the traditional AIC approach using less than half of the total number of driving factors.