Examinando por Materia "urban population"

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    Community participation in natural risk prevention: Case histories from Colombia
    (GEOLOGICAL SOC PUBLISHING HOUSE, 2008-01-01) Hermelin, M.; Bedoya, G.; Universidad EAFIT. Departamento de Geología; Ciencias del Mar
    More than 75% of Colombia's 42 million people live in urban areas located in the mountains and are exposed to numerous natural hazards: floods, flash floods, landslides, earthquakes and volcanism. The Armero disaster of 1985 triggered the creation of the National System for Disaster Prevention and Relief. National, regional and local committees started to operate across the country, accompanied by education commissions that produced diverse audiovisual materials to help educate people living in these areas. The experiences of working with local committees gained during the last two decades are presented here. Case histories are from cities such as Pereira, Manizales and Medellín, where the local committees are run by people with little or no formal education but who understand that they must participate as a group to prevent or mitigate the effects of natural disasters. The co-operation between technical experts and trained residents represents an outstanding example of good communication and co-operation for urban populations living in dangerous areas. Although many problems have yet to be resolved, these case histories show that this type of organization seems to be more effective than direct intervention from national government agencies. The models of community participation and communication developed and refined here may have application to similar social environments in other countries. © 2008 Geological Society of London.
  • No hay miniatura disponible
    Ítem
    Community participation in natural risk prevention: Case histories from Colombia
    (GEOLOGICAL SOC PUBLISHING HOUSE, 2008-01-01) Hermelin, M.; Bedoya, G.; Hermelin, M.; Bedoya, G.; Universidad EAFIT. Departamento de Ciencias; Geología Ambiental y Tectónica
    More than 75% of Colombia's 42 million people live in urban areas located in the mountains and are exposed to numerous natural hazards: floods, flash floods, landslides, earthquakes and volcanism. The Armero disaster of 1985 triggered the creation of the National System for Disaster Prevention and Relief. National, regional and local committees started to operate across the country, accompanied by education commissions that produced diverse audiovisual materials to help educate people living in these areas. The experiences of working with local committees gained during the last two decades are presented here. Case histories are from cities such as Pereira, Manizales and Medellín, where the local committees are run by people with little or no formal education but who understand that they must participate as a group to prevent or mitigate the effects of natural disasters. The co-operation between technical experts and trained residents represents an outstanding example of good communication and co-operation for urban populations living in dangerous areas. Although many problems have yet to be resolved, these case histories show that this type of organization seems to be more effective than direct intervention from national government agencies. The models of community participation and communication developed and refined here may have application to similar social environments in other countries. © 2008 Geological Society of London.
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    Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning
    (Public Library of Science, 2017-05-02) Arribas-Bel D; Patino JE; Duque JC; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively. © 2017 Arribas-Bel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.