Examinando por Materia "spatial analysis"
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Ítem Análisis Espacial de la Informalidad Laboral a Nivel Intra-urbano(Universidad EAFIT, 2018) Gallego Ortiz, Stefany; Muñoz González, Edgar Julián; García, Gustavo A.; sgalle20@eafit.edu.co; emunozg2@eafit.edu.co; ggarci24@eafit.edu.coIn this paper, we study the spatial dimension of labor informality at the intra-urban level. By using data for the city of Medellín (Colombia) as a case study, we analyze the spatial dimension of informality and estimating regression models with spatial dependence, we determine the main factors that affect this phenomenon. The results show that there are marked spatial patterns of informality at the intra-urban level in Medellín, where there is a socio-spatial segmentation between north and south of the city in terms of job quality, education, employment opportunities and housing conditions. We also found that a higher percentage of women and informality housing (slums) imply higher informality levels. In contrast, variables related to education and modern employment have a negative effect on informality.Ítem A computationally efficient method for delineating irregularly shaped spatial clusters(Springer Berlin Heidelberg, 2011-12-01) Duque, Juan C.; Aldstadt, Jared; Velasquez, Ermilson; Franco, Jose L.; Betancourt, Alejandro; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)In this paper, we present an efficiency improvement for the algorithm called AMOEBA, A Multidirectional Optimum Ecotope-Based Algorithm, devised by Aldstadt and Getis (Geogr Anal 38(4):327-343, 2006). AMOEBA embeds a local spatial autocorrelation statistic in an iterative procedure in order to identify spatial clusters (ecotopes) of related spatial units. We provide an analysis of the computational complexity of the original AMOEBA and develop an alternative formulation that reduces computational time without losing optimality. Empirical evidence is provided using georeferenced socio-demographic data in Accra, Ghana. © 2010 Springer-Verlag.Ítem Early Detection for Dengue Using Local Indicator of Spatial Association (LISA) Analysis.(MDPI, 2016-03-29) Parra, Mayra Elizabeth; M.E PUERTA; Lisarralde, Diana Paola; Arboleda, Sair; Parra, Mayra Elizabeth; M.E PUERTA; Lisarralde, Diana Paola; Arboleda, Sair; Universidad EAFIT. Departamento de Ciencias; Matemáticas y AplicacionesDengue is a viral disease caused by a flavivirus that is transmitted by mosquitoes of the genus Aedes. There is currently no specific treatment or commercial vaccine for its control and prevention; therefore, mosquito population control is the only alternative for preventing the occurrence of dengue. For this reason, entomological surveillance is recommended by World Health Organization (WHO) to measure dengue risk in endemic areas; however, several works have shown that the current methodology (aedic indices) is not sufficient for predicting dengue. In this work, we modified indices proposed for epidemic periods. The raw value of the epidemiological wave could be useful for detecting risk in epidemic periods; however, risk can only be detected if analyses incorporate the maximum epidemiological wave. Risk classification was performed according to Local Indicators of Spatial Association (LISA) methodology. The modified indices were analyzed using several hypothetical scenarios to evaluate their sensitivity. We found that modified indices could detect spatial and differential risks in epidemic and endemic years, which makes them a useful tool for the early detection of a dengue outbreak. In conclusion, the modified indices could predict risk at the spatio-temporal level in endemic years and could be incorporated in surveillance activities in endemic places.Ítem The Network-Max-P-Regions model(TAYLOR & FRANCIS LTD, 2017-05-04) She, B.; Duque, J.C.; Ye, X.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper introduces a new p-regions model called the Network-Max-P-Regions (NMPR) model. The NMPR is a regionalization model that aims to aggregate n areas into the maximum number of regions (max-p) that satisfy a threshold constraint and to minimize the heterogeneity while taking into account the influence of a street network. The exact formulation of the NMPR is presented, and a heuristic solution is proposed to effectively compute the near-optimized partitions in several simulation datasets and a case study in Wuhan, China. © 2016 Informa UK Limited, trading as Taylor & Francis Group.Ítem On the Performance of the Subtour Elimination Constraints Approach for the p-Regions Problem: A Computational Study(WILEY-BLACKWELL, 2018-01-01) Duque JC; Mario C. Vélez-Gallego; Echeverri, Laura Catalina; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)The p-regions is a mixed integer programming (MIP) model for the exhaustive clustering of a set of n geographic areas into p spatially contiguous regions while minimizing measures of intraregional heterogeneity. This is an NP-hard problem that requires a constant research of strategies to increase the size of instances that can be solved using exact optimization techniques. In this article, we explore the benefits of an iterative process that begins by solving the relaxed version of the p-regions that removes the constraints that guarantee the spatial contiguity of the regions. Then, additional constraints are incorporated iteratively to solve spatial discontinuities in the regions. In particular we explore the relationship between the level of spatial autocorrelation of the aggregation variable and the benefits obtained from this iterative process. The results show that high levels of spatial autocorrelation reduce computational times because the spatial patterns tend to create spatially contiguous regions. However, we found that the greatest benefits are obtained in two situations: (1) when n/p=3; and (2) when the parameter p is close to the number of clusters in the spatial pattern of the aggregation variable. © 2017 The Ohio State UniversityÍtem Recent deforestation causes rapid increase in river sediment load in the Colombian Andes(Elsevier Ltd, 2015-06-01) Restrepo, J.D.; Kettner, A.J.; Syvitski, J.P.M.; Universidad EAFIT. Departamento de Geología; Ciencias del MarHuman induced soil erosion reduces soil productivity; compromises freshwater ecosystem services, and drives geomorphic and ecological change in rivers and their floodplains. The Andes of Colombia have witnessed severe changes in land-cover and forest loss during the last three decades with the period 2000 and 2010 being the highest on record. We address the following: (1) what are the cumulative impacts of tropical forest loss on soil erosion? and (2) what effects has deforestation had on sediment production, availability, and the transport capacity of Andean rivers? Models and observations are combined to estimate the amount of sediment liberated from the landscape by deforestation within a major Andean basin, the Magdalena. We use a scaling model BQART that combines natural and human forces, like basin area, relief, temperature, runoff, lithology, and sediment trapping and soil erosion induced by humans. Model adjustments in terms of land cover change were used to establish the anthropogenic-deforestation factor for each of the sub-basins. Deforestation patterns across 1980-2010 were obtained from satellite imagery. Models were employed to simulate scenarios with and without human impacts. We estimate that, 9% of the sediment load in the Magdalena River basin is due to deforestation; 482 Mt of sediments was produced due to forest clearance over the last three decades. Erosion rates within the Magdalena drainage basin have increased 33% between 1972 and 2010; increasing the river's sediment load by 44 Mt y-1. Much of the river catchment (79%) is under severe erosional conditions due in part to the clearance of more than 70% natural forest between 1980 and 2010. © 2015 Elsevier Ltd. All rights reserved.Ítem SpMorph: An exploratory space-time analysis tool for describing processes of spatial redistribution(Blackwell Publishing Ltd, 2015-08-01) Duque, J.C.; Ye, X.; Folch, D.C.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)This paper introduces an exploratory space-time analysis tool for determining the two components of a spatial redistribution process: (i) the shock, which is the moment that triggers a spatial redistribution process; for example, a new policy, a war, an earthquake, etc.; and (ii) the duration of the regime fade, which is the time between the shock and the moment in which a new regime emerges as a better representation of the spatial distribution of the attribute. Two examples are provided: the first uses China's provincial per capitaGDP between 1978 and 2008, and the second uses state level housing price and unemployment rate data for the US between 2002 and 2012. Resumen: Este artículo presenta una herramienta preliminar de análisis espacio-temporal para determinar los dos componentes de un proceso de redistribución espacial: (i) la perturbación, que es el momento que desencadena un proceso de redistribución espacial; por ejemplo, una nueva política, una guerra, un terremoto, etc.; y (ii) la duración del desvanecimiento del régimen, que es el tiempo entre la perturbación y el momento en que emerge un nuevo régimen, como una mejor representación de la distribución espacial del atributo. Se ofrecen dos ejemplos: el primero utiliza el PIB per cápita provincial de China entre 1978 y 2008, y el segundo utiliza precios de la vivienda a nivel estatal y datos de la tasa de desempleo de los EE.UU. entre 2002 y 2012. © 2015 The Author(s). Papers in Regional Science © 2015 RSAI.