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This paper describes the process of urbanization and land use change in the urban ecosystems of the National Capital Region (NCR) of Delhi, India. Two types of land use change are considered--from natural to urban and from agricultural to urban. Both types are explained in terms of economic variables known to be drivers of change. A panel data method was used, and economic variables were combined with GIS-based information on land use change during 1986-2004 for 11 administrative units of the NCR. The results suggest that investment in the construction sector plays a major role in converting the land from natural to urban areas, while differences in land productivity seems to be the major driver for change from agricultural to urban uses. [ABSTRACT FROM AUTHOR]

Fog affects human life in a number of ways by reducing the visibility, hence affecting critical infrastructure, transportation, tourism or by the formation of frost, thus harming the standing crops. Smog is becoming a regular phenomenon in urban areas which is highly toxic to humans. Delhi was chosen as the area of study as it encounters all these hazards of fog stated apart from other political and economic reasons. The complex relationship behind the parameters and processes behind the formation of fog makes it extremely difficult to model and forecast it accurately. It is attempted to forecast the fog and understand its dynamics through a statistical downscaling technique of artificial neural network which is deemed accurate for short-term forecasting and usually outperform time-series models. The backpropagation neural network, which is a gradient descent algorithm where the network weights are moved along the negative of the gradient of the performance function, has been used for our analysis. Indian Meteorological Department (IMD) supported National Oceanic and Atmospheric Administration (NOAA) data had been used for carrying out the simulations. The model was found to have high accuracy but lacking in skill. An attempt has been made to present the data in a binary form by determining a threshold by the contingency table approach followed by its critical analysis. It is found that the calculation of an optimum threshold was also difficult to fix as the parameters of fog formation on which the model has been has been trained had shown some changes in their trend over a period of time. [ABSTRACT FROM AUTHOR]