Introduction
The population growth and climate is experiencing rapid change which leads to receiving increasing attention, not only from the scientists but also from policymakers. They are growing in area, population and at the same time they are acquiring a new character as their people perform new tasks in the physical environment that increasingly reflect the use of new technology (Allefsen 1962). As research progresses, it is becoming clear that large-scale changes of the global climate system would seriously affect large numbers of people in various ways. Fundamental for the estimation of the extent of such impacts is population forecasts and predictions of changes in human habitation patterns. Demographic changes are also of prime concern for studies of human impacts on their local environments. The impact of large- scale climatic changes on humans (Sea level Change) and the impact of humans on the local and regional hydrology (Flood). Population changes, including the spatial distribution of people, are therefore essential for assessments of future water resources, in addition to climatic and hydrological parameters (Bengtsson et al. 2006).
According to census 2011 the average density of India is 382 persons/km2. On an average, 57 more people inhibit every square kilometre in the country as compared to a decade ago (Census of India 2011). Population maps have a long history, but the recent development of powerful computers and software in combination with the increasing availability of various kinds of remote sensing data has led to a growing research activity in this area. In the last few decades several efforts to generate grid maps of population have thus been seen. On the global scale, Dobson et al. (2000), developed a global population dataset in 30 arc-seconds resolution (LandScan). The LandScan dataset is made by adopting an empirical model, which distributes sub-national census data to grids by using various remote sensing and ancillary data. In recent years it has been found that remote sensing is a cost-effective, technologically sound and an increasingly used technique for the analysis of population growth (Yeh et al. 2001). In light of above research, attention is being directed to the mapping and assessment of population growth using remote sensing and geographical Information system techniques. Using LandScan data it is easy to identify spatial changes of population which have occurred over the city landscape.
The scholars developed number of techniques for mapping globally variations of parameters within countries. As these techniques have become more sophisticated, and the capacity of computers to handle very large datasets with great speed has increased, the interest in developing methods for distributing population data to the grid cells of GIS maps has also increased. Initially, GIS specialists tended to direct their efforts towards establishing the coordinates of coastlines and country boundaries, and generating georeferenced datasets for physical and environmental variables that could be derived from high-resolution aerial photography and satellite imagery. Less effort was directed towards the development of georefer- enced socio-economic datasets, mainly because such data is collected by censuses and surveys and compiled for political or administrative units, and direct interpolation techniques to estimate the spatial distribution of socio-economic variables are still lacking (Clark and Rhind 1992). Despite these limitations, improvements in the quality and accessibility of georeferenced environmental data have generated growing demand for more accurate and up-to-date spatial information about the global distribution of population variables. This demand has been driven by two different concerns within the development community. One relates to the interest of demographers, sociologists and urban planners in mapping urbanization processes and defining the location and socio-economic characteristics of population growth and population at hazard risk with more accuracy (Jeffrey and Tschirley 2005).
India has an area and population equal to that of Europe (excluding USSR) or that of agricultural China. According to the census 2011 India shares 17.5 % population of the world. The population of India is almost equal to the combined population of U.S.A., Brazil, Indonesia, Pakistan, Bangladesh and Japan put together (Census 2011). However, the moist alluvial soil regions of India have highest densities of population in the world (Hoffman 1948). Accuracy assessment of large-scale population datasets is always challenging due to the use of all geographically specific datasets to produce the population dataset, leaving little independent data for testing. However, simple comparison tests with existing gridded population datasets were undertaken. The 2008 version of LandScan is the most widely used population datasets, and was acquired and compared to the enumerated census datasets, 2011. To make the comparisons possible, population datasets were adjusted to the same year, after calculating exponential growth rate. Different methods were used to compare the Census population and LandScan datasets (Linard et al. 2010).
Scholars have produced a set of maps and represented hazard exposures that spatially delimit the populations that are at risk from various natural hazards (cyclones, tornadoes, earthquakes, floods, drought, volcanoes and landslides) (Dilley et al. 2005). Some of these, maps use LandScan to calculate disaster risk at the sub-national level in order to contribute to development planning and disaster prevention. Some populations are at multiple risks and thus their overall exposure to natural hazards is additive. This type of a vulnerability analysis requires the existence of sub-national population attribute data and hazard data for areas of interest, whether at a county level (Cutter and Emrich 2006), city level (Pelling 2003) or for a small island nation (Pelling and Uitto 2002).
