Spatial Data Mining and Modeling for Visualisation of Rapid Urbanisation

Uttam Kumar 1, Mukhopadhyay. C 2 and Ramachandra. T. V 3*

1 Department of Management Studies and Centre for Sustainable Technologies, Indian Institute of Science, Bangalore.
2 Department of Management Studies, Indian Institute of Science, Bangalore.    
3 Centre for Ecological Sciences, Centre for Sustainable Technologies, Centre for Infrastructure, Sustainable Transportation and Urban Planning
Indian Institute of Science, Bangalore, India.
E-mail: 1 uttam@ces.iisc.ernet.in, 2 cm@mgmt.iisc.ernet.in,      * Corresponding author: 3 cestvr@ces.iisc.ernet.in
Citation: Uttam Kumar, Mukhopadhyay. C and Ramachandra. T.V, 2009. Spatial Data Mining and Modeling for Visualisation of Rapid Urbanisation, Symbiosis Centre for Information Technology SCIT Journal (ISSN 0974-5076), Volume IX, August 2009.

Abstract

Rapid urbanisation in India has posed serious challenges to the decision makers in regional planning involving plethora of issues including provision of basic amenities (like electricity, water, sanitation, transport, etc.). Urban planning entails an understanding of landscape and urban dynamics with causal factors. Identifying, delineating and mapping landscapes on temporal scale provide an opportunity to monitor the changes, which is important for natural resource management and sustainable planning activities. Multi-source, multi-sensor, multi-temporal, multi-frequency or multi-polarization remote sensing data with efficient classification algorithms and pattern recognition techniques aid in capturing these dynamics. This paper analyses the landscape dynamics of Greater Bangalore by: (i) characterisation of direct impervious surface, (ii) computation of forest fragmentation indices and (iii) modeling to quantify and categorise urban changes. Linear unmixing is used for solving the mixed pixel problem of coarse resolution super spectral MODIS data for impervious surface characterisation. Fragmentation indices were used to classify forests – interior, perforated, edge, transitional, patch and undetermined. Based on this, urban growth model was developed to determine the type of urban growth – Infill, Expansion and Outlying growth. This helped in visualising urban growth poles and consequence of earlier policy decisions that can help in evolving strategies for effective land use policies.

Keywords: Landscape, orthogonal subspace projection, forest fragmentation, urban growth model
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