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We analyze the built-environment and public health outcomes in Medellin (CO). Greenness showed the strongest negative association with the mortality measures. We found curvilinear relationships with some urban features. Intersection density relates negatively to the mortality rates after 200 int./km2. The densities of amenities and population show the opposite: U-shape relationships.
Jorge E. Patino; Andy Hong; Juan C. Duque; Kazem Rahimi; Silvana Zapata; Verónica M. Lopera. Built environment and mortality risk from cardiovascular disease and diabetes in Medellín, Colombia: An ecological study. Landscape and Urban Planning 2021, 213, 104126 .
AMA StyleJorge E. Patino, Andy Hong, Juan C. Duque, Kazem Rahimi, Silvana Zapata, Verónica M. Lopera. Built environment and mortality risk from cardiovascular disease and diabetes in Medellín, Colombia: An ecological study. Landscape and Urban Planning. 2021; 213 ():104126.
Chicago/Turabian StyleJorge E. Patino; Andy Hong; Juan C. Duque; Kazem Rahimi; Silvana Zapata; Verónica M. Lopera. 2021. "Built environment and mortality risk from cardiovascular disease and diabetes in Medellín, Colombia: An ecological study." Landscape and Urban Planning 213, no. : 104126.
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.
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 StyleJairo 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 StyleJairo 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.
El cambio climático y el calentamiento global son provocados principalmente por las actividades antrópicas. Por esta razón, conocer las líneas de investigación que relacionen Series de Tiempo de Temperatura Superficial e Índices de Vegetación es de suma importancia, dada la amplitud de las diferentes áreas científicas abiertas sobre el calentamiento global. Se presenta a la comunidad académica, por tanto, el resultado de la presente clasificación, la cual divide los estudios en dos áreas principales representativas en el estudio del cambio climático: (1) Modelado y Análisis de Geodatos y (2) Teledetección. De este último se derivan dos tipos, unos construidos con Análisis de Regresión Lineal (RL) y otros con Análisis de Regresión No Lineal (RNL). En el Modelado y Análisis de Geodatos, los Modelos Climáticos Globales (GCM) no son la herramienta adecuada para estos análisis debido a su gruesa resolución espacial. Esto implica el desarrollo de modelos híbridos con teledetección, que están también limitados por las diferencias de resolución. Por el contrario, la teledetección es la herramienta de mayor difusión para este tipo de estudios. Finalmente, se abre una prometedora ventana para el desarrollo en las series de tiempo con análisis de Regresión No Lineal.
Oscar Arley Zuluaga Gómez; Jorge Eduardo Patiño Quinchía; German Mauricio Valencia Hernández. Modelos implementados en el análisis de series de tiempo de temperatura superficial e índices de vegetación: Una propuesta taxonómica en el contexto de cambio climático global. Revista de geografía Norte Grande 2021, 323 -344.
AMA StyleOscar Arley Zuluaga Gómez, Jorge Eduardo Patiño Quinchía, German Mauricio Valencia Hernández. Modelos implementados en el análisis de series de tiempo de temperatura superficial e índices de vegetación: Una propuesta taxonómica en el contexto de cambio climático global. Revista de geografía Norte Grande. 2021; (78):323-344.
Chicago/Turabian StyleOscar Arley Zuluaga Gómez; Jorge Eduardo Patiño Quinchía; German Mauricio Valencia Hernández. 2021. "Modelos implementados en el análisis de series de tiempo de temperatura superficial e índices de vegetación: Una propuesta taxonómica en el contexto de cambio climático global." Revista de geografía Norte Grande , no. 78: 323-344.
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.
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 StyleJuan 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 StyleJuan 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.
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.
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 StyleJairo 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 StyleJairo 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.
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.
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 StyleJuan 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 StyleJuan 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.
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.
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 StyleJuan 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 StyleJuan 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.
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
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 StyleJuan 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 StyleJuan 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.
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
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 StyleJorge 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 StyleJorge 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.
This paper reviews the potential applications of satellite remote sensing to regional science research in urban settings. Regional science is the study of social problems that have a spatial dimension. The availability of satellite remote sensing data has increased significantly in the last two decades, and these data constitute a useful data source for mapping the composition of urban settings and analyzing changes over time. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation and urban social vulnerability assessment. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution, such as images from Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird. Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. Many regional scientists remain skeptical that satellite remote sensing will produce useful information for their work. More local research is needed to demonstrate the real potential and utility of satellite remote sensing for regional science in urban environments.
Jorge E. Patino; Juan C. Duque. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems 2013, 37, 1 -17.
AMA StyleJorge E. Patino, Juan C. Duque. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems. 2013; 37 ():1-17.
Chicago/Turabian StyleJorge E. Patino; Juan C. Duque. 2013. "A review of regional science applications of satellite remote sensing in urban settings." Computers, Environment and Urban Systems 37, no. : 1-17.