Header menu link for other important links
Urban sprawl modelling and prediction using regression and Seasonal ARIMA: a case study for Vellore, India
Published in Springer Science and Business Media Deutschland GmbH
Analysing the changes in various landuses during the past, identifying the direction and type of urban sprawl, modelling its causative factors and predicting the future sprawl are some of the key areas which urban planners are generally interested in as they help them to properly plan cities in a more efficient way. In the present study, an attempt has been made to address these key areas using open-source geospatial data for the city of Vellore in India which has experienced significant urban growth in recent decades. Landuse maps were prepared using aerial photos, topographic maps and satellite images for the period 1967–2019 and using that a comprehensive urban sprawl analysis was carried out. The results indicated that the major highways that pass-through Vellore have strong influence on urban development as ribbon type of sprawl was witnessed along the principal transportation corridors. This has been confirmed while modelling of causative factors that when distance from highway increases, built-up density decreases. Similarly, the location of central business district (CBD) found to play a crucial role in urban growth as built-up density decreases when distance from CBD increases. Regression models were built to calculate the built-up density at any distance from city centre or major roads in space–time domain. The present study also developed models based on Seasonal ARIMA to forecast the future built-up density for the year 2045 using the built-up densities of 1967, 1991 and 2019. The results showed that more development is expected on suburban areas as those are the places where one can expect development to take place when compared to the interior zones which were already developed much during the past years. The methodology reported in this paper is well applicable for any city or town to analyse the landuse changes, model the causative factors and predict its future sprawl using completely the open-source data. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetModeling Earth Systems and Environment
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH