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Prof. Juan Duque
EAFIT University, Medellín, Colombia.

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Research Keywords & Expertise

0 spatial econometrics
0 Operation Research (Optimization)
0 Regional Science
0 Urban and Regional Plannning
0 Spatial Analysis and Modelling

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Urban and Regional Plannning

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Short Biography

Juan C. Duque is a Full Professor at EAFIT University (Colombia) since 2007. He is founder-director of RiSE (Research in Spatial Economics), a group devoted to the development of quantitative methods for spatial data analysis. In 2000 he received his M.Sc. in Management and Economics from Pompeu Fabra University (Spain), and in 2004 he received his Ph.D. in Management from University of Barcelona. He spent the last year of his Ph.D. in the Department of Geography at University of California at Santa Barbara, and in 2005 he moved to the Department of Geography at San Diego State University as a Postdoctoral Researcher. Editorial Appointments: * Remote Sensing * Investigaciones Regionales – Journal of Regional Research * Computers Environment and Urban Systems * International Regional Science Review * Papers in Regional Science (2011-2015)

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Short communication
Published: 17 June 2021 in Cities
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This paper contributes to the discussion on policies for providing utilities and on their contribution to reducing inequality. The uniqueness of the Colombian scheme to target subsidy beneficiaries and contributors provides valuable lessons for policymakers, academics, and urban planners regarding the difficulties and implications of such a segmenting government intervention in countries of the Global South. Among the unintended consequences of the scheme are deepening spatial segregation, distorted economic incentives, and poor correspondence of the welfare system with stratification categories.

ACS Style

Mauricio Quiñones; Lina M. Martínez; Juan C. Duque; Oscar Mejía. A targeting policy for tackling inequality in the developing world: Lessons learned from the system of cross-subsidies to fund utilities in Colombia. Cities 2021, 116, 103306 .

AMA Style

Mauricio Quiñones, Lina M. Martínez, Juan C. Duque, Oscar Mejía. A targeting policy for tackling inequality in the developing world: Lessons learned from the system of cross-subsidies to fund utilities in Colombia. Cities. 2021; 116 ():103306.

Chicago/Turabian Style

Mauricio Quiñones; Lina M. Martínez; Juan C. Duque; Oscar Mejía. 2021. "A targeting policy for tackling inequality in the developing world: Lessons learned from the system of cross-subsidies to fund utilities in Colombia." Cities 116, no. : 103306.

Research article
Published: 08 March 2021 in Environment and Planning B: Urban Analytics and City Science
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This paper examines the linkages between urban form and city productivity using seven alternative metrics for urban form and applying them to a comprehensive sample of Latin-American cities. While most of the literature has concentrated on the effects of population density (compact vs. sprawling urban development), this paper seeks to assess whether different dimensions of a city’s urban form, such as shape, structure, and land use, affect its economic performance. We found that both the shape of the urban extent and the inner-city connectedness have a statistically significant association with the productivity level of a city.

ACS Style

Juan C Duque; Nancy Lozano-Gracia; Jorge E Patino; Paula Restrepo. Urban form and productivity: What shapes are Latin-American cities? Environment and Planning B: Urban Analytics and City Science 2021, 1 .

AMA Style

Juan C Duque, Nancy Lozano-Gracia, Jorge E Patino, Paula Restrepo. Urban form and productivity: What shapes are Latin-American cities? Environment and Planning B: Urban Analytics and City Science. 2021; ():1.

Chicago/Turabian Style

Juan C Duque; Nancy Lozano-Gracia; Jorge E Patino; Paula Restrepo. 2021. "Urban form and productivity: What shapes are Latin-American cities?" Environment and Planning B: Urban Analytics and City Science , no. : 1.

Research article
Published: 24 February 2021 in PLOS ONE
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Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or “heartbeat” of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City. On aggregate, our findings show that the heartbeats of crime and crashes follow a similar pattern. We observe valleys during the night and peaks in the evening, where the intensity during a peak is 7.5 times the intensity of valleys in terms of crime and 12.3 times in terms of road accidents. Although distinct types of events, crimes and crashes reach their respective intensity peak on Friday night and valley on Tuesday morning, the result of a hyper-synchronised society. Next, heartbeats are computed for city neighbourhood ‘tiles’, a division of space within the city based on the distance to Metro and other public transport stations. We find that heartbeats are spatially heterogeneous with some diffusion, so that nearby tiles have similar heartbeats. Tiles are then clustered based on the shape of their heartbeat, e.g., tiles within groups suffer peaks and valleys of crime or crashes at similar times during the week. The clusters found are similar to those based on economic activities. This enables us to anticipate temporal traces of crime and crashes based on local amenities.

ACS Style

Rafael Prieto Curiel; Jorge Eduardo Patino; Juan Carlos Duque; Neave O’Clery. The heartbeat of the city. PLOS ONE 2021, 16, e0246714 .

AMA Style

Rafael Prieto Curiel, Jorge Eduardo Patino, Juan Carlos Duque, Neave O’Clery. The heartbeat of the city. PLOS ONE. 2021; 16 (2):e0246714.

Chicago/Turabian Style

Rafael Prieto Curiel; Jorge Eduardo Patino; Juan Carlos Duque; Neave O’Clery. 2021. "The heartbeat of the city." PLOS ONE 16, no. 2: e0246714.

Journal article
Published: 01 February 2021 in Remote Sensing
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With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

ACS Style

Jairo Gómez; Chenghe Guan; Pratyush Tripathy; Juan Duque; Santiago Passos; Michael Keith; Jialin Liu. Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions. Remote Sensing 2021, 13, 512 .

AMA Style

Jairo Gómez, Chenghe Guan, Pratyush Tripathy, Juan Duque, Santiago Passos, Michael Keith, Jialin Liu. Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions. Remote Sensing. 2021; 13 (3):512.

Chicago/Turabian Style

Jairo Gómez; Chenghe Guan; Pratyush Tripathy; Juan Duque; Santiago Passos; Michael Keith; Jialin Liu. 2021. "Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions." Remote Sensing 13, no. 3: 512.

Journal article
Published: 18 December 2020 in Building and Environment
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In this paper, we explore the potential of convolutional neural networks to classify street-level imagery of one-story unreinforced masonry buildings (MURs) according to the flexibility of the roof diaphragm (rigid or flexible). This information is critical for vulnerability studies, disaster risk assessments, disaster management strategies, etc., and is of great relevance in cities where unreinforced masonry is the most common building typology or where the majority of the population resides in such buildings. Our contribution could be useful for local governments of cities in developing countries seeking to significantly reduce the number of deaths caused by disasters. Our research results indicate that VGG19 is the convolutional neural network architecture with the best performance, with an accuracy of 0.80, a precision of 0.88, and a recall of 0.84. The results are encouraging and could be used to reduce the amount of resources (both human and economic) for the development of detailed exposure models for unreinforced masonry buildings.

ACS Style

D. Rueda-Plata; D. González; A.B. Acevedo; J.C. Duque; R. Ramos-Pollán. Use of deep learning models in street-level images to classify one-story unreinforced masonry buildings based on roof diaphragms. Building and Environment 2020, 189, 107517 .

AMA Style

D. Rueda-Plata, D. González, A.B. Acevedo, J.C. Duque, R. Ramos-Pollán. Use of deep learning models in street-level images to classify one-story unreinforced masonry buildings based on roof diaphragms. Building and Environment. 2020; 189 ():107517.

Chicago/Turabian Style

D. Rueda-Plata; D. González; A.B. Acevedo; J.C. Duque; R. Ramos-Pollán. 2020. "Use of deep learning models in street-level images to classify one-story unreinforced masonry buildings based on roof diaphragms." Building and Environment 189, no. : 107517.

Data article
Published: 08 August 2020 in Data in Brief
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A bicycle route questionnaire was designed to collect information about the characteristics of cyclists and the routes they take. Medellin is used as a case study in this paper due to its strong sociodemographic inequality, land use, urban form diversity, and topographical variability. The survey execution targeted bicycle commuters in the city by distributing the questionnaires online, personally by telephone, and personally on the street. These data will be useful to support strategies aiming to promote bicycling as a mode of transportation. Several types of analysis may be derived from the data, including an explanation of the factors determining the route choice and route comparisons according to the sociodemographics and locations of users. For instance, these data have already been used by Ospina et al. (2020) [1] where they sought to understand cycling travel distance in Medellin city.

ACS Style

Juan P. Ospina-Zapata; Víctor I. López-Ríos; Veronica Botero-Fernandez; Juan C. Duque. A database to analyze cycling routes in Medellin, Colombia. Data in Brief 2020, 32, 106162 .

AMA Style

Juan P. Ospina-Zapata, Víctor I. López-Ríos, Veronica Botero-Fernandez, Juan C. Duque. A database to analyze cycling routes in Medellin, Colombia. Data in Brief. 2020; 32 ():106162.

Chicago/Turabian Style

Juan P. Ospina-Zapata; Víctor I. López-Ríos; Veronica Botero-Fernandez; Juan C. Duque. 2020. "A database to analyze cycling routes in Medellin, Colombia." Data in Brief 32, no. : 106162.

Original article
Published: 08 July 2020 in Regional Science Policy & Practice
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This paper provides empirical evidence on the impact of institutional fragmentation and metropolitan coordination on urban productivity in Latin American Cities. The use of night‐time lights satellite imagery and high resolution population data allow us to use a definition of metropolitan area based on the urban extents that result from the union between the formally defined metropolitan areas and the contiguous patches of urbanized areas with more than 500,000 inhabitants. Initial results suggest that the presence of multiple local governments within metropolitan areas generate opposite effects in urban productivity. On the one hand, smaller governments tend to be more responsive and efficient, which increases productivity. But, on the other hand, multiple local governments face coordination costs that result in lower productivity levels.

ACS Style

Juan Carlos Duque; Nancy Lozano‐Gracia; Jorge E. Patino; Paula Restrepo Cadavid. Institutional fragmentation and metropolitan coordination in Latin American cities: Are there links with city productivity? Regional Science Policy & Practice 2020, 13, 1096 -1128.

AMA Style

Juan Carlos Duque, Nancy Lozano‐Gracia, Jorge E. Patino, Paula Restrepo Cadavid. Institutional fragmentation and metropolitan coordination in Latin American cities: Are there links with city productivity? Regional Science Policy & Practice. 2020; 13 (4):1096-1128.

Chicago/Turabian Style

Juan Carlos Duque; Nancy Lozano‐Gracia; Jorge E. Patino; Paula Restrepo Cadavid. 2020. "Institutional fragmentation and metropolitan coordination in Latin American cities: Are there links with city productivity?" Regional Science Policy & Practice 13, no. 4: 1096-1128.

Journal article
Published: 23 June 2020 in Transportation Research Part D: Transport and Environment
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The relevance of cycling as a mode of transportation is increasingly being recognized in many cities around the world, and the city of Medellin (Colombia) is no exception. To better understand cycling travel behavior in Medellin, we perform a multiple regression to analyze the importance of route characteristics in explaining cycling travel distance. We control for socioeconomic and built environment variables at the origin and destination. Our results reveal that the effects of the socio-economic and built environment characteristics at the origin and destination are modest or statistically insignificant in explaining travel distance. However, the variables that characterize the built and natural environment along the route are significant and appreciably improve the explanatory power of the baseline econometric model. An analysis of interacting effects shows that the interaction between the dedicated infrastructure along the route and the degree of deviation from direct routes has a relevant effect on explaining travel distance. The findings of this work are useful for designing cycling policy and developing more usable cycling infrastructure.

ACS Style

Juan P. Ospina; Verónica Botero-Fernández; Juan C. Duque; Mark Brussel; Anna Grigolon. Understanding cycling travel distance: The case of Medellin city (Colombia). Transportation Research Part D: Transport and Environment 2020, 86, 102423 .

AMA Style

Juan P. Ospina, Verónica Botero-Fernández, Juan C. Duque, Mark Brussel, Anna Grigolon. Understanding cycling travel distance: The case of Medellin city (Colombia). Transportation Research Part D: Transport and Environment. 2020; 86 ():102423.

Chicago/Turabian Style

Juan P. Ospina; Verónica Botero-Fernández; Juan C. Duque; Mark Brussel; Anna Grigolon. 2020. "Understanding cycling travel distance: The case of Medellin city (Colombia)." Transportation Research Part D: Transport and Environment 86, no. : 102423.

Journal article
Published: 20 March 2020 in Building and Environment
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An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models.

ACS Style

Daniela Gonzalez; Diego Rueda-Plata; Ana B. Acevedo; Juan C. Duque; Raúl Ramos-Pollán; Alejandro Betancourt; Sebastian Garcia-Valencia. Automatic detection of building typology using deep learning methods on street level images. Building and Environment 2020, 177, 106805 .

AMA Style

Daniela Gonzalez, Diego Rueda-Plata, Ana B. Acevedo, Juan C. Duque, Raúl Ramos-Pollán, Alejandro Betancourt, Sebastian Garcia-Valencia. Automatic detection of building typology using deep learning methods on street level images. Building and Environment. 2020; 177 ():106805.

Chicago/Turabian Style

Daniela Gonzalez; Diego Rueda-Plata; Ana B. Acevedo; Juan C. Duque; Raúl Ramos-Pollán; Alejandro Betancourt; Sebastian Garcia-Valencia. 2020. "Automatic detection of building typology using deep learning methods on street level images." Building and Environment 177, no. : 106805.

Journal article
Published: 28 December 2019 in Remote Sensing
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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 f 1 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.

ACS Style

Jairo A. Gómez; Santiago Passos Patiño; Juan C. Duque; Santiago Passos. Spatiotemporal Modeling of Urban Growth Using Machine Learning. Remote Sensing 2019, 12, 109 .

AMA Style

Jairo A. Gómez, Santiago Passos Patiño, Juan C. Duque, Santiago Passos. Spatiotemporal Modeling of Urban Growth Using Machine Learning. Remote Sensing. 2019; 12 (1):109.

Chicago/Turabian Style

Jairo A. Gómez; Santiago Passos Patiño; Juan C. Duque; Santiago Passos. 2019. "Spatiotemporal Modeling of Urban Growth Using Machine Learning." Remote Sensing 12, no. 1: 109.

Journal article
Published: 28 August 2019 in Landscape and Urban Planning
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The impact of urban form on economic performance and quality of life has been widely recognized. Studies regarding urban form have focused on developed countries; only a small number of cities in developing countries have been studied. This paper utilizes nighttime light imagery and information regarding street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. We study the relationship between the urban form metrics and several factors that can correlate with urban form (topography, size, colony, and economic performance) and perform a spatiotemporal analysis of urban growth from 1996 to 2010. Among the results, we highlight the tendency of a group of cities to grow on steeper slopes and several worrying aspects, specifically urban growth in protected areas and a trend to sprawl-growing in certain Latin American and Caribbean cities.

ACS Style

Juan C. Duque; Nancy Lozano-Gracia; Jorge E. Patino; Paula Restrepo; Wilson A. Velasquez. Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery. Landscape and Urban Planning 2019, 191, 103640 .

AMA Style

Juan C. Duque, Nancy Lozano-Gracia, Jorge E. Patino, Paula Restrepo, Wilson A. Velasquez. Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery. Landscape and Urban Planning. 2019; 191 ():103640.

Chicago/Turabian Style

Juan C. Duque; Nancy Lozano-Gracia; Jorge E. Patino; Paula Restrepo; Wilson A. Velasquez. 2019. "Spatiotemporal dynamics of urban growth in Latin American cities: An analysis using nighttime light imagery." Landscape and Urban Planning 191, no. : 103640.

Research article
Published: 27 November 2018 in PLOS ONE
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This work presents a nonparametric statistical test, S-maup, to measure the sensitivity of a spatially intensive variable to the effects of the Modifiable Areal Unit Problem (MAUP). To the best of our knowledge, S-maup is the first statistic of its type and focuses on determining how much the distribution of the variable, at its highest level of spatial disaggregation, will change when it is spatially aggregated. Through a computational experiment, we obtain the basis for the design of the statistical test under the null hypothesis of non-sensitivity to MAUP. We performed an exhaustive simulation study for approaching the empirical distribution of the statistical test, obtaining its critical values, and computing its power and size. The results indicate that, in general, both the statistical size and power improve with increasing sample size. Finally, for illustrative purposes, an empirical application is made using the Mincer equation in South Africa, where starting from 206 municipalities, the S-maup statistic is used to find the maximum level of spatial aggregation that avoids the negative consequences of the MAUP.

ACS Style

Juan C. Duque; Henry Laniado; Adriano Polo. S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem. PLOS ONE 2018, 13, e0207377 .

AMA Style

Juan C. Duque, Henry Laniado, Adriano Polo. S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem. PLOS ONE. 2018; 13 (11):e0207377.

Chicago/Turabian Style

Juan C. Duque; Henry Laniado; Adriano Polo. 2018. "S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem." PLOS ONE 13, no. 11: e0207377.

Preprint
Published: 21 June 2018
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This work presents a nonparametric statistical test, $S$-maup, to measure the sensitivity of a spatially intensive variable to the effects of the Modifiable Areal Unit Problem (MAUP). $S$-maup is the first statistic of its type and focuses on determining how much the distribution of the variable, at its highest level of spatial disaggregation, will change when it is spatially aggregated. Through a computational experiment, we obtain the basis for the design of the statistical test under the null hypothesis of non-sensitivity to MAUP. We performed a simulation study for approaching the empirical distribution of the statistical test, obtaining its critical values, and computing its power and size. The results indicate that the power of the statistic is good if the sample (number of areas) grows, and in general, the size decreases with increasing sample number. Finally, an empirical application is made using the Mincer equation in South Africa.

ACS Style

Juan C. Duque; Henry Laniado; Adriano Polo. S-maup: Statistic test to measure the sensitivity to the Modifiable Areal Unit Problem. 2018, 1 .

AMA Style

Juan C. Duque, Henry Laniado, Adriano Polo. S-maup: Statistic test to measure the sensitivity to the Modifiable Areal Unit Problem. . 2018; ():1.

Chicago/Turabian Style

Juan C. Duque; Henry Laniado; Adriano Polo. 2018. "S-maup: Statistic test to measure the sensitivity to the Modifiable Areal Unit Problem." , no. : 1.

Technical note
Published: 30 August 2017 in Remote Sensing
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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.

ACS Style

Juan C. Duque; Jorge E. Patino; Alejandro Betancourt. Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery. Remote Sensing 2017, 9, 895 .

AMA Style

Juan C. Duque, Jorge E. Patino, Alejandro Betancourt. Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery. Remote Sensing. 2017; 9 (9):895.

Chicago/Turabian Style

Juan C. Duque; Jorge E. Patino; Alejandro Betancourt. 2017. "Exploring the Potential of Machine Learning for Automatic Slum Identification from VHR Imagery." Remote Sensing 9, no. 9: 895.

Chapter
Published: 31 July 2017 in Advances in Geographic Information Science
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There is a wide variety of computational experiments, or statistical simulations, in which regional scientists require regular and irregular lattices with a predefined number of polygons. While most commercial and free GIS software offer the possibility of generating regular lattices of any size, the generation of instances of irregular lattices is not a straightforward task. The most common strategy in this case is to find a real map that matches as closely as possible the required number of polygons. This practice is usually conducted without considering whether the topological characteristics of the selected map are close to those for an “average” map sampled from different parts of the world. In this paper, we propose an algorithm, RI-Maps, that combines fractal theory, stochastic calculus and computational geometry for simulating realistic irregular lattices with a predefined number of polygons. The irregular lattices generated with RI-Maps have guaranteed consistency in their topological characteristics, which reduces the potential distortions in the computational or statistical results due to an inappropriate selection of the lattices.

ACS Style

Juan C. Duque; Alejandro Betancourt; Freddy H. Marin. An Algorithmic Approach for Simulating Realistic Irregular Lattices. Advances in Geographic Information Science 2017, 277 -303.

AMA Style

Juan C. Duque, Alejandro Betancourt, Freddy H. Marin. An Algorithmic Approach for Simulating Realistic Irregular Lattices. Advances in Geographic Information Science. 2017; ():277-303.

Chicago/Turabian Style

Juan C. Duque; Alejandro Betancourt; Freddy H. Marin. 2017. "An Algorithmic Approach for Simulating Realistic Irregular Lattices." Advances in Geographic Information Science , no. : 277-303.

Journal article
Published: 20 June 2017 in Geographical Analysis
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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.

ACS Style

Juan Carlos Duque; Mario C. Velez-Gallego; Laura Catalina Echeverri. On the Performance of the Subtour Elimination Constraints Approach for the p -Regions Problem: A Computational Study. Geographical Analysis 2017, 50, 32 -52.

AMA Style

Juan Carlos Duque, Mario C. Velez-Gallego, Laura Catalina Echeverri. On the Performance of the Subtour Elimination Constraints Approach for the p -Regions Problem: A Computational Study. Geographical Analysis. 2017; 50 (1):32-52.

Chicago/Turabian Style

Juan Carlos Duque; Mario C. Velez-Gallego; Laura Catalina Echeverri. 2017. "On the Performance of the Subtour Elimination Constraints Approach for the p -Regions Problem: A Computational Study." Geographical Analysis 50, no. 1: 32-52.

Journal article
Published: 04 November 2016 in International Journal of Geographical Information Science
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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.

ACS Style

Bing She; Juan C. Duque; Xinyue Ye. The Network-Max-P-Regions model. International Journal of Geographical Information Science 2016, 31, 962 -981.

AMA Style

Bing She, Juan C. Duque, Xinyue Ye. The Network-Max-P-Regions model. International Journal of Geographical Information Science. 2016; 31 (5):962-981.

Chicago/Turabian Style

Bing She; Juan C. Duque; Xinyue Ye. 2016. "The Network-Max-P-Regions model." International Journal of Geographical Information Science 31, no. 5: 962-981.

Journal article
Published: 01 March 2015 in Landscape and Urban Planning
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This paper contributes empirical evidence aboutthe usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demographic characteristics. We use a very high spatial resolution (VHR) image from one of the most socioeconomically divergent cities in the world, Medellin (Colombia), to extract information on land cover composition using perpixel classification and on urban texture and structure using an automated tool for texture and structure feature extraction at object level. We evaluate the potential of these descriptors to explain a measure of poverty known as the Slum Index. We found thatthese variables explain up to 59% ofthe variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index map

ACS Style

Juan C. Duque; Jorge E. Patino; Luis A. Ruiz; Josep E. Pardo-Pascual. Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning 2015, 135, 11 -21.

AMA Style

Juan C. Duque, Jorge E. Patino, Luis A. Ruiz, Josep E. Pardo-Pascual. Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data. Landscape and Urban Planning. 2015; 135 ():11-21.

Chicago/Turabian Style

Juan C. Duque; Jorge E. Patino; Luis A. Ruiz; Josep E. Pardo-Pascual. 2015. "Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data." Landscape and Urban Planning 135, no. : 11-21.

Journal article
Published: 01 December 2014 in Applied Geography
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The link between place and crime is at the base of social ecology theories of crime that focus in the relationship of the characteristics of geographical areas and crime rates. The broken windows theory states that visible cues of physical and social disorder in a neighborhood can lead to an increase in more serious crime. The crime prevention through environmental design (CPTED) planning approach seeks to deter criminal behavior by creating defensible spaces. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighborhood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from very high spatial-resolution imagery. We tested which land cover, structure and texture descriptors were significantly related to intra-urban homicide rates in Medellin, Colombia, while controlling for socioeconomic confounders. The percentage of impervious surfaces other than clay roofs, the fraction of clay roofs to impervious surfaces, two structure descriptors related to the homogeneity of the urban layout, and the uniformity texture descriptor were all statistically significant. Areas with higher homicide rates tended to have higher local variation and less general homogeneity; that is, the urban layouts were more crowded and cluttered, with small dwellings with different roofing materials located in close proximity to one another, and these regions often lacked other homogeneous surfaces such as open green spaces, wide roads, or large facilities. These results seem to be in agreement with the broken windows theory and CPTED in the sense that more heterogeneous and disordered urban layouts are associated with higher homicide rates

ACS Style

Jorge E. Patino; Juan C. Duque; Josep E. Pardo-Pascual; Luis A. Ruiz. Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography 2014, 55, 48 -60.

AMA Style

Jorge E. Patino, Juan C. Duque, Josep E. Pardo-Pascual, Luis A. Ruiz. Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography. 2014; 55 ():48-60.

Chicago/Turabian Style

Jorge E. Patino; Juan C. Duque; Josep E. Pardo-Pascual; Luis A. Ruiz. 2014. "Using remote sensing to assess the relationship between crime and the urban layout." Applied Geography 55, no. : 48-60.

Journal article
Published: 01 August 2014 in The Review of International Organizations
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This paper argues that UN military interventions are geographically biased. For every 1,000 kilometers of distance from the three permanent Western UNSC members (France, UK, US), the probability of a UN military intervention decreases by 4 percent. We are able to rule out several alternative explanations for the distance finding, such as differences by continent, colonial origin, bilateral trade relationships, foreign aid flows, political regime forms, or the characteristics of the Cold War. We do not observe this geographical bias for non-military interventions, providing evidence that practical considerations could be important factors for UNSC decisions to intervene militarily. In fact, UNSC interventions are also more likely in smaller and poorer countries – both of which are indications of less costly interventions and higher chances of success, everything else equal.

ACS Style

Juan C. Duque; Michael Jetter; Santiago Sosa. UN interventions: The role of geography. The Review of International Organizations 2014, 10, 67 -95.

AMA Style

Juan C. Duque, Michael Jetter, Santiago Sosa. UN interventions: The role of geography. The Review of International Organizations. 2014; 10 (1):67-95.

Chicago/Turabian Style

Juan C. Duque; Michael Jetter; Santiago Sosa. 2014. "UN interventions: The role of geography." The Review of International Organizations 10, no. 1: 67-95.