Examinando por Materia "Population statistics"
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Ítem Analyzing Long-Term Availability of Urban Green Space by Socioeconomic Status in Medellin, Colombia, Using Open Data and Tools(Institute of Electrical and Electronics Engineers Inc., 2020-01-01) Patino J.E.; Patino J.E.; Universidad EAFIT. Departamento de Ciencias; Research in Spatial Economics (RISE)The availability of green spaces is an important issue for urban populations worldwide, given the benefits that the green spaces provide for health, well-being, and quality of life. But urban green spaces are not always distributed equally for different population groups within cities. Latin America is the second most urbanized region of the world, but there are few published studies analysing the green space availability for different urban population groups, and less so analysing the long-term trends. This work presents an analysis of long-term availability of urban green spaces by different socioeconomic status population groups in Medellin city, Colombia, using open geospatial data and open software tools. The results indicate that disparities between different groups have been decreasing in the last years, but there are still efforts to do. Showing this kind of analysis based on open data and tools is essential as it opens the possibility for replicating it in other cities with scarce budgets. © 2020 IEEE.Ítem Development of structural debris flow fragility curves (debris flow buildings resistance) using momentum flux rate as a hazard parameter(Elsevier B.V., 2018-05-18) Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de IngenieríaSocietal risks associated with debris flow hazards are significant and likely to escalate due to global population growth trends and the compounding effects of climate change. Quantitative risk assessment methods (QRA) provide a means of anticipating the likely impacts and consequences of settlement in areas susceptible to landslide activity and are increasingly being used to inform land use decisions that seek to increase disaster resilience through mitigation and/or adaptation. Current QRA methods for debris flow hazards are based primarily on empirical vulnerability functions that relate hazard intensity (depth, velocity, etc.) to expected levels of loss for a given asset of concern, i.e. most of current methods are dedicated to loss-intensity relations. Though grounded in observed cause-effect relationships, empirical vulnerability functions are not designed to predict the capacity of a building to withstand the physical impacts of a debris flow event, or the related uncertainties associated with modelling building performance as a function of variable debris flow parameters. This paper describes a methodology for developing functions that relate hazard intensity to probability of structural damage, i.e., fragility functions, rather than vulnerability functions, based on the combined hydrodynamic forces of a debris flow event (hazard level) and the inherent structural resistance of building typologies that are common in rural mountainous settings (building performance). Hazard level includes a hydrodynamic force variable (FDF), which accounts for the combined effects of debris flow depth and velocity, i.e. momentum flux (hv2), material density (?) and related flow characteristics including drag (Cd) and impact coefficient (Kd). Building performance is measured in terms of yield strength (Ay), ultimate lateral capacity (AU) and weight to breadth ratios (W/B) defined for a portfolio building types that are common in mountain settlements. Collectively, these model parameters are combined using probabilistic methods to produce building-specific fragility functions that describe the probability of reaching or exceeding successive thresholds of structural damage over a range of hazard intensity values, expressed in terms of momentum flux. Validation of the proposed fragility model is based on a comparison between model outputs and observed cause-effect relationships for recent debris flow events in South Korea and in Colombia. Debris flow impact momentum fluxes, capable of resulting in complete damage to unreinforced masonry buildings (URM) in those regions are estimated to be on the order of 24 m3/s2, consistent with field-based observations. Results of our study offer additional capabilities for assessing risks associated with urban growth and development in areas exposed to debris flow hazards. © 2018 Elsevier B.V.Ítem Development of structural debris flow fragility curves (debris flow buildings resistance) using momentum flux rate as a hazard parameter(Elsevier B.V., 2018-05-18) Prieto, Jorge Alonso; Journeay, Murray; Acevedo A.B.; Arbelaez, Juan; Ulmi, Malaika; Mecánica AplicadaSocietal risks associated with debris flow hazards are significant and likely to escalate due to global population growth trends and the compounding effects of climate change. Quantitative risk assessment methods (QRA) provide a means of anticipating the likely impacts and consequences of settlement in areas susceptible to landslide activity and are increasingly being used to inform land use decisions that seek to increase disaster resilience through mitigation and/or adaptation. Current QRA methods for debris flow hazards are based primarily on empirical vulnerability functions that relate hazard intensity (depth, velocity, etc.) to expected levels of loss for a given asset of concern, i.e. most of current methods are dedicated to loss-intensity relations. Though grounded in observed cause-effect relationships, empirical vulnerability functions are not designed to predict the capacity of a building to withstand the physical impacts of a debris flow event, or the related uncertainties associated with modelling building performance as a function of variable debris flow parameters. This paper describes a methodology for developing functions that relate hazard intensity to probability of structural damage, i.e., fragility functions, rather than vulnerability functions, based on the combined hydrodynamic forces of a debris flow event (hazard level) and the inherent structural resistance of building typologies that are common in rural mountainous settings (building performance). Hazard level includes a hydrodynamic force variable (FDF), which accounts for the combined effects of debris flow depth and velocity, i.e. momentum flux (hv2), material density (?) and related flow characteristics including drag (Cd) and impact coefficient (Kd). Building performance is measured in terms of yield strength (Ay), ultimate lateral capacity (AU) and weight to breadth ratios (W/B) defined for a portfolio building types that are common in mountain settlements. Collectively, these model parameters are combined using probabilistic methods to produce building-specific fragility functions that describe the probability of reaching or exceeding successive thresholds of structural damage over a range of hazard intensity values, expressed in terms of momentum flux. Validation of the proposed fragility model is based on a comparison between model outputs and observed cause-effect relationships for recent debris flow events in South Korea and in Colombia. Debris flow impact momentum fluxes, capable of resulting in complete damage to unreinforced masonry buildings (URM) in those regions are estimated to be on the order of 24 m3/s2, consistent with field-based observations. Results of our study offer additional capabilities for assessing risks associated with urban growth and development in areas exposed to debris flow hazards. © 2018 Elsevier B.V.Ítem Spatiotemporal Modeling of Urban Growth Using Machine Learning(MDPI AG, 2019-12-28) Duque, J.; Jorge E. Patino; Gomez, J.; Passos, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson's correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities. © 2019 by the authors.Ítem Understanding epidemics from mathematical models: Details of the 2010 dengue epidemic in Bello (Antioquia, Colombia)(Elsevier Inc., 2017-03-01) Lizarralde-Bejarano, D.P.; Arboleda-Sánchez, S.; Puerta-Yepes, M.E.; Lizarralde-Bejarano, D.P.; Arboleda-Sánchez, S.; Puerta-Yepes, M.E.; Universidad EAFIT. Departamento de Ciencias; Matemáticas y AplicacionesDengue is the most threatening vector-borne viral disease in Colombia. At the moment, there is no treatment or vaccine available for its control or prevention; therefore, the main measure is to exert control over mosquito population. To reduce the economic impact of control measures, it is important to focus on specific characteristics related to local dengue epidemiology at the local level, and know the main factors involved in an epidemic. To this end, we used a mathematical model based on ordinary differential equations and experimental data regarding mosquito populations from Bello (Antioquia, Colombia) to simulate the epidemic occurred in 2010. The results showed that the parameters to which the incidence of dengue cases are most sensitive are the biting and mortality rates of adult mosquitoes as well as the virus transmission probabilities. Finally, we found that the Basic Reproductive Number (R0) of this epidemic was between 1.5 and 2.7, with an infection force (?) of 0.061, meaning that R0 values slightly above one are sufficient to result in a significant dengue outbreak in this region. © 2016 Elsevier Inc.