871 resultados para housing metrics


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This paper explores the provision of homes for less wealthy households in rural England. By allowing 'exceptions' to UK planning law to provide low-income housing for local residents, the national government seeks to secure dwellings for the less wealthy and so sustain socially mixed rural villages. This paper explores how the production of homes through the exception policy is not conducive to the construction of many new houses. The particular emphasis in the paper is on how responsible agents are discouraged from being more active in erecting new village homes for low-income households. Empirically, the paper draws on documents, interviews and a social survey in the counties of Bedfordshire, Cambridgeshire and Norfolk to investigate the process of delivering rural exception homes. It is concluded that, despite Government assertions that a socially mixed countryside is desirable, the decision-making criteria that dominate the worldviews of agents in social housing provision work against this outcome. (c) 2005 Elsevier Ltd. All rights reserved.

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Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.