DocumentCode :
255308
Title :
Prediction of spatial patterns of urban dynamics in Pune, India
Author :
Aithal, B.H. ; Vinay, S. ; Venugopal, R.K. ; Ramachandra, T.V.
Author_Institution :
Energy & Wetlands Res. Group, Centre for Ecological Sci., India
fYear :
2014
fDate :
11-13 Dec. 2014
Firstpage :
1
Lastpage :
6
Abstract :
The potential of Markov chain and cellular automata model with help of agents that play a vital role in a cities urbanisation through fuzziness in the data and hierarchal weights (for principal agents) have been used to understand and predict the urban growth for the Pune city, India. The model utilizes temporal land use changes with probable growth agents such as roads drainage networks, railway connectivity, slope, bus network, industrial establishments, educational network etc., to simulate the growth of Pune for 2025 using two scenarios of development - implementation of City Development Plan (CDP) and without CDP. In the study, multi temporal land use datasets, derived from remotely-sensed images of 1992, 2000, 2010 and 2013, were used for simulation and validation. Prediction reveals that future urban expansion would be in northwest and southeast regions with intensification near the central business district. This approach provides insights to urban growth dynamics required for city planning and management.
Keywords :
Markov processes; cellular automata; multi-agent systems; town and country planning; CDP; India; Markov chain; Pune City; cellular automata model; city development plan; city planning; hierarchal weights; multitemporal land use datasets; northwest regions; southeast regions; spatial patterns prediction; urban dynamics; Accuracy; Cities and towns; Data models; Earth; Markov processes; Remote sensing; Vegetation mapping; AHP; Cellular Automata; Fuzzy; Markov Chain; Modeling; Pune; Remote Sensing; Urban sprawl;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
India Conference (INDICON), 2014 Annual IEEE
Conference_Location :
Pune
Print_ISBN :
978-1-4799-5362-2
Type :
conf
DOI :
10.1109/INDICON.2014.7030404
Filename :
7030404
Link To Document :
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