Analysis of Spatial Patterns of Urbanisation Using Geoinformatics and Spatial Metrics
Ramachandra, T. V., Bharath, H. A., Sowmyashree, M. V., Theoretical and Empirical Researches in Urban Management
Urbanisation is the social process referring to the physical growth of urban areas with the increase in population either due to migration or amalgamation of peri-urban areas into cities. The urban population in India has increased from 10.8% in 1901 to 17.3% in 1951, 28.5% in 2001 and 31.5% in 2011. More than 50% of the world population residing in urban areas (United Nations, 2009) consume more than 65% of the world's energy and emit 75% of global greenhouse gas (GHG) emissions. Large scale land cover changes have led to the loss of habitats, ecosystem's productivity and also the ability to sequester carbon. Urban areas have a very high ecological footprint (Herold et al., 2003; Liu & Lathrop, 2002) as urban expansions are associated with problems such as destruction of vegetation, changes in local and global climate and the environmental factors in and around the region (Grimm et al., 2000, Ramachandra et al., 2012a). Unplanned urbanisation have led to a much skewed growth in the region, evident from the dispersed growth in peri-urban areas with the higher consumption of land and without basic amenities and infrastructure.
Unplanned urbanization has brought huge environmental impacts and developed various problems in modern growing cities in India. The urban pattern growth analysis aids in understanding the underlying effects of urbanisation such as sprawl, loss of rural land (Huang et al., 2009) and sensitive habitats. The absence of prior visualization of sprawl regions leads to ineffective administration as these areas are not documented in the administrative policy documents and hence deprived of basic amenities. Sprawl refers to disordered and unplanned growth of urban areas often used to describe the awareness of an unsuitable development (Sudhira et al., 2003; Sudhira et al., 2004; Ramachandra et al., 2012a, 2012b). Agents responsible for sprawl are intense urbanisation in core areas, population increase, and population migration. Environmental problems associated with urban sprawl necessitates better techniques to understand the spatial patterns of temporal urbanisation for sustainable management of natural resources in rapidly urbanizing regions (Lambin et al, 2000). Remote sensing data acquired through space borne sensors at high temporal and spatial resolution available for four decades aids in assessing the spatial patterns of urbanisation (Hall et al, 1995; Lewis et al., 1997; Narumalani et al., 1998; Netzband et al., 2005; Ji, 2000; Steele and Redmond, 2001; Herold et al., 2002; Herold et al., 2003; Bhatta et al., 2009, Ramachandra et al., 2012a, 2012b).
Remote sensing techniques provide economical and reliable spatial data (with diverse spectral and temporal resolutions) required to derive useful information for city managers and planners (Jensen & Cowen, 1999; Yuan et al, 2005; Jat et al, 2008; Wu et al, 2006; Bhatta et al., 2009; Taubenbock et al., 2009a, Taubenbock et al., 2009b, Ramachandra et al., 2012a, 2012b) through the quantification of land use changes, especially quantifying the urban form and to monitor the dynamic changes at regular intervals, (Turner, 2003; Milesi et al, 2003; Sudhira et al., 2004; Anindita et al., 2010; Ramachandra et al., 2012a). Availability of the temporal remote sensing data of the earth's surface helps in mapping and monitoring of landscape. The gradient approach is adopted to identify the local pockets of urbanisation and the spatial patterns of urbanisation are assessed through spatial metrics. Spatial metrics aid in quantifying the urban structure and patterns of urban growth.
The historical spatial data available since 1970's aids to visualize the urban growth, quantify and understand its pattern (Netzband et al., 2005; Ramachandra et al., 2012a). Landscape geometric pattern analysis through spatial metrics based on land use classifications is gaining importance in recent years (Fu and Chen 2000; Wu, 2004; Li and Wu 2007; Bharath et al. …