Quantifying slumness with remote sensing data

The presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008; Taubenb¨ock et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all

The urban footprint of rural forced displacement

The rapid growth of marginal settlements in the Global South, largely fueled by the resettlement of millions of internally displaced people (IDPs), underscores the urgent need for tailored housing solutions for these vulnerable populations. However, prevailing approaches have often relied on a one-size-fits-all model, overlooking the diverse socio-spatial realities of IDP communities. Drawing on a case study in Medellin, Colombia, where a significant portion of the population consists of forced migrants, this interdisciplinary study merges concepts from human geography and urban theory with computational methods in remote sensing and exploratory spatial data analysis. By integrating socio-spatial theory with quantitative analysis, we challenge the conventional housing paradigm and propose a novel framework for addressing the housing needs of IDPs. Employing a three-phase methodology rooted in Lefebvre’s theoretical framework on the production of space, including participatory mapping, urban morphology characterization, and similarity analysis, we identify distinct patterns within urban IDP settlements and advocate for culturally sensitive housing policies. Our analysis, focusing on Colombia, the country with the largest IDP population globally, reveals the limitations of standardized approaches and highlights the importance of recognizing and accommodating socio-cultural diversity in urban planning. By contesting standardized socio-spatial practices, our research aims not only to promote equality but also to foster recognition and inclusivity within marginalized communities

Análisis de problemáticas urbanas a escala continental basado en datos abiertos: espacios verdes, forma urbana y sostenibilidad futura de las ciudades en África

Las próximas décadas serán de rápida urbanización y estrés climático en las ciudades africanas. Los espacios verdes pueden aumentar la resiliencia de las ciudades frente a las olas de calor, las inundaciones, los deslizamientos de tierra e incluso la erosión costera, además de mejorar la sostenibilidad al reparar la calidad del aire, proteger la biodiversidad y absorber carbono. Sin embargo, datos cuantitativos sobre la forma urbana, la disponibilidad de espacios verdes y la contaminación del aire son muy escasos y de difícil acceso para ciudades en África. En este trabajo usamos datos geoespaciales abiertos para analizar cuantitativamente las relaciones entre la forma urbana, la presencia de espacios verdes y la calidad del aire. Los resultados del análisis indican que la presencia de espacios verdes se relaciona con mejor calidad del aire, pero que deben estar acompañados de otras políticas para que su presencia sea realmente efectiva

Empiric recommendations for population disaggregation under different data scenarios

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping

Identifying the White Matter Pathways Involved in Multiple Sclerosis-Related Tremor Using Diffusion Tensor Imaging

Background Tremor affects up to 45% of patients with Multiple Sclerosis (PwMS). Current understanding is based on insights from other neurological disorders, thus, not fully addressing the distinctive aspects of MS pathology. Objective To characterize the brain white matter (WM) correlates of MS-related tremor using diffusion tensor imaging (DTI). Methods In a prospective case-control study, PwMS with tremor were assessed for tremor severity and underwent MRI scans including DTI. PwMS without tremor served as matched controls. After tract selection and segmentation, the resulting diffusivity measures were used to calculate group differences and correlations with tremor severity. Results This study included 72 PwMS. The tremor group (n = 36) exhibited significant changes in several pathways, notably in the right inferior longitudinal fasciculus (Cohen\u27s d = 1.53, q \u3c 0.001) and left corticospinal tract (d = 1.32, q \u3c 0.001), compared to controls (n = 36). Furthermore, specific tracts showed a significant correlation with tremor severity, notably in the left medial lemniscus (Spearman\u27s coefficient [rsp] = −0.56, p \u3c 0.001), and forceps minor of corpus callosum (rsp = -0.45, p \u3c 0.01). Conclusion MS-related tremor is associated with widespread diffusivity changes in WM pathways and its severity correlates with commissural and sensory projection pathways, which suggests a role for proprioception or involvement of the dentato-rubro-olivary circuit

Proteomic analysis of low-grade, early-stage endometrial carcinoma reveals new dysregulated pathways associated with cell death and cell signaling

Low-grade, early-stage endometrial carcinoma (EC) is the most frequent malignant tumor of the uterine corpus. However, the molecular alterations that underlie these tumors are far from being fully understood. The purpose of this study is to describe dysregulated molecular pathways from EC patients. Sixteen samples of tumor tissue and paired healthy controls were collected and both were subjected to mass spectrometry (MS)/MS proteomic analysis. Gene ontology and pathway analysis was performed to discover dysregulated pathways and/or proteins using different databases and bioinformatic tools. Dysregulated pathways were cross-validated in an independent external cohort. Cell signaling, immune response, and cell death-associated pathways were robustly identified. The SLIT/ROBO signaling pathway demonstrated dysregulation at the proteomic and transcriptomic level. Necroptosis and ferroptosis were cell death-associated processes aberrantly regulated, in addition to apoptosis. Immune response-associated pathways showed a dominance of innate immune responses. Tumor immune infiltrates measured by immunofluorescence demonstrated diverse lymphoid and myeloid populations. Our results suggest a role of SLIT/ROBO, necroptosis, and ferroptosis, as well as a prominent role of innate immune response in low-grade, early-stage EC. These results could guide future research in this group of tumorsThis research was funded by the Instituto de Salud Carlos III (ISCIII) (PI17/01723), cofinanced by the European Development Regional Fund “A way to achieve Europe” (FEDER

Using remote sensing to assess the relationship between crime and the urban layout

[EN] 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.This research was made possible by funding from EAFIT University (EAFIT-435-000060) and the Medellin City Hall EnlazaMundos program. The authors thank the anonymous reviewers and Hermilson Velazquez, Andr es Ramírez Hassan and Gustavo Canavire for their insightful observations and suggestions during the different stages of this projectPatiño Quinchía, JE.; Duque, JC.; Pardo Pascual, JE.; Ruiz Fernández, LÁ. (2014). Using remote sensing to assess the relationship between crime and the urban layout. Applied Geography. 55:48-60. https://doi.org/10.1016/j.apgeog.2014.08.016S48605

Formación de Competencias Ciudadanas en los Patrulleros de la Policía Nacional de Colombia

El problema de investigación radica en que durante el proceso de formación de los Patrulleros de la Policía de Colombia, no existe un mecanismo que permita conocer el nivel de conocimiento de las competencias ciudadanas. Para esto se tomó a los estudiantes para Patrulleros de la Escuela Metropolitana de Bogotá a quienes se les aplicó un instrumento, con el propósito de identificar los niveles de conocimiento, las debilidades y fortalezas así como las mejoras en la formación de competencias ciudadanas. La metodología empleada en la presente investigación es cuantitativa, donde se observó variables relacionadas con la convivencia pacífica y la paz, la participación y la responsabilidad democrática, la pluralidad, multiculturalidad y valoración por la diferencias, estas asociadas a las competencias ciudadanas, cuyos resultados se analizaron de forma independiente para proceder a realizar las recomendaciones. Dentro de los principales hallazgos que evidencian el alto sentido de ciudadanía y el reconocimiento de los derechos y los deberes de estos, por los conceptos de sociedad y la importancia para de la construcción de la paz de Colombia. Así mismo es necesario que se fortalezcan conocimientos sobre los derechos fundamentales bajo el estudio de la Constitución Política de Colombia. Esta investigación concluyó que en la formación de Patrulleros es de gran importancia el estudio de las competencias ciudadanas, más aún, por los caminos de paz que busca el país en la actualidad. Esto tiene una implicación y es que una vez egresen como profesionales, deberán aplicar todos los conocimientos de las competencias de conocimiento para la contribución de la paz de Colombia

Exploring the potential of machine learning for automatic slum identification from VHR imagery

\u3cp\u3eSlum 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.\u3c/p\u3

Institutional fragmentation and metropolitan coordination in Latin American cities: Are there links with city productivity?

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 co-ordination costs that result in lower productivity levels. © 2020 The Author(s). Regional Science Policy and Practice © 2020 RSA