Examinando por Autor "Duque JC"
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Ítem A stepwise procedure to determinate a suitable scale for the spatial delimitation of urban slums(SPRINGER, 2012-01-01) Duque JC; Royuela, Vicente; Noreña, MiguelÍtem Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery(MDPI AG, 2017-09-01) Duque JC; Patiño, Jorge; Betancourt, Alejandro; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)Slum identification in urban settlements is a crucial step in the process of formulation of pro-poor policies. However, the use of conventional methods for slum detection such as field surveys can be time-consuming and costly. This paper explores the possibility of implementing a low-cost standardized method for slum detection. We use spectral, texture and structural features extracted from very high spatial resolution imagery as input data and evaluate the capability of three machine learning algorithms (Logistic Regression, Support Vector Machine and Random Forest) to classify urban areas as slum or no-slum. Using data from Buenos Aires (Argentina), Medellin (Colombia) and Recife (Brazil), we found that Support Vector Machine with radial basis kernel delivers the best performance (with F2-scores over 0.81). We also found that singularities within cities preclude the use of a unified classification model.Ítem Heterogeneidad regional en las diferencias por género de las tasas de desempleo(Banco de la República, BID, 2016-01-01) Duque JC; Garcia, Gustavo Adolfo; Herrera, PaulaÍtem 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 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.