Engineering as a tool for improving human habitat

The conventional approaches to poverty alleviation in the slums entail a cocktail of interventions in health, education, governance and physical improvements, often stretching the scarce resources far and thin. Driven by the 'poverty' mindset, physical measures such as minimal paving, public water posts and community latrines actually brand the slums apart instead of assimilating them into the urban infrastructure fabric. The concept of Slum Networking proposes comprehensive water and environmental sanitation infrastructure as the central and catalytic leverage for holistic development. At costs less than the conventional 'slum' solutions, it tries to penetrate a high quality urban infrastructure net deeply into the slums to assimilate them into the city rather than lock them in as disadvantaged islands. Further, it transcends resource barriers and 'aid' through innovative partnerships and the latent resource mobilisation potential of the so-called 'poor'. This paper examines Slum Networking as implemented in Sanjaynagar in Ahmedabad, India and compares it with a similar settlement with no interventions in Ahmedabad. It assesses the knock-on impact of physical infrastructure on health, education and poverty. Finally, it evaluates the multiplier effect of physical infrastructure and the partnerships on the subsequent investments by the community in its own shelter and habitat. Copyright © 2009 Inderscience Enterprises Ltd.

Is there a preferred classifier for operational thematic mapping?

The importance of properly exploiting a classifier's inherent geometric characteristics when developing a classification methodology is emphasized as a prerequisite to achieving near optimal performance when carrying out thematic mapping. When used properly, it is argued that the long-standing maximum likelihood approach and the more recent support vector machine can perform comparably. Both contain the flexibility to segment the spectral domain in such a manner as to match inherent class separations in the data, as do most reasonable classifiers. The choice of which classifier to use in practice is determined largely by preference and related considerations, such as ease of training, multiclass capabilities, and classification cost. © 1980-2012 IEEE.