The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

The dynamics of poor urban areas - analyzing morphologic transformations across the globe using Earth observation data

The urban environment is in constant motion, mostly through construction but also through destruction of urban elements. While formal development is a process with long planning periods and thus the built landscape appears static, informal or spontaneous settlements seem to be subject to high dynamics in their ever unfinished urban form. However, the dynamics and morphological characteristics of physical transformation in such settlements of urban poverty have been hardly empirically studied on a global scale or temporal consistent foundation. This paper aims at filling this gap by using Earth observation data to provide a temporal analysis of builtup transformation over a period of ~7 years in 16 documented manifestations of urban poverty. This work applies visual image interpretation using very high resolution optical satellite data in combination with in-situ and Google Street View images to derive 3D city models. We measure physical spatial structures through six spatial morphologic variables - number of buildings, size, height, orientation, heterogeneity and density. Our temporal assessment reveals inter- as well intra-urban differences and we find different, yet generally high morphologic dynamic across study sites. This is expressed in manifold ways: from demolished and reconstructed areas to such where changes appeared within the given structures. Geographically, we find advanced dynamics among our sample specifically in areas of the global south. At the same time, we observe a high spatial variability of morphological transformations within the studied areas. Despite partly high morphologic dynamics, spatial patterns of building alignments, streets and open spaces remain predominantly constant

Slum mapping : a comparison of single class learning and expert system object-oriented classification for mapping slum settlements in Addis Ababa city, Ethiopia

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesUpdated spatial information on the dynamics of slums can be helpful to measure and evaluate the progress of urban upgrading projects and policies. Earlier studies have shown that remote sensing techniques, with the help of very-high resolution imagery, can play a significant role in detecting slums, and providing timely spatial information. The main objective of this thesis is to develop a reliable object-oriented slum identification technique that enables the provision of timely spatial information about slum settlements in Addis Ababa city. It compares the one-class support vector machines algorithm with the expert defined classification rule set in the discrimination of slums, using GeoEye-1 imagery. Two different approaches, called manual and automatic fine-tuning, were deployed to determine the best value of parameters in one-class support vector machines algorithm. The manual fine-tuning of the parameters is done using extensive manual trial. The automatic tuning is done using cross-validation grid search with the overall accuracy as the performance metric. Two regions of study were defined with different landscape compositions, providing different classification scenarios to compare the classification approaches. After image segmentation, twenty predictive variables were computed to characterize the objects in both study areas. An image analyst collected one hundred sample objects of a slum to be used as training for the single-class learner. In parallel, an image analyst has defined a hierarchical rule set to discriminate the class of interest. Results in both study areas indicate that the one-class support vector machine with manual tuning yields higher overall accuracy (97.7% in subset 1, and 92% in subset 2) and requiring much less application effort and computing time than the expert system

Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796

Cityscape, poverty and crime: a quantitative assessment using VHR imagery

[EN] The first part of this work reviews the potential applications of satellite remote sensing to regional science research in urban settings. The availability of satellite remote sensing data has increased significantly in the last two decades. 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, urban social vulnerability assessment, and the variability of intra-urban crime rates. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution (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. This work contributes empirical evidence about the 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 demo- graphic characteristics. We evaluate the potential of the image-derived urban fabric descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the 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 maps. The last part of this work analyzes the relation between the urban layout and crime. 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. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighbor- hood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from VHR imagery. 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.[ES] La primera parte aporta una revisión de las aplicaciones de la teledetección satelital en la investigación de ciencia regional en entornos urbanos. La disponibilidad de imágenes satelitales se ha incrementado significativamente en las dos últimas décadas, al tiempo que la resolución espacial ha venido aumentando, lo que ha influenciado el surgimiento de investigaciones y aplicaciones de ciencia regional en zonas urbanas. Las aplicaciones más comunes son la detección de hot spots de pobreza urbana, la evaluación de índices de calidad de vida, el análisis del crecimiento urbano, la estimación de valores de vivienda, la estimación de población urbana, la evaluación de la vulnerabilidad social y las variaciones intra-urbanas en tasas de crimen. Las imágenes satelitales usadas tienen resolución espacial media, alta o muy alta (Landsat MSS, Landsat TM y ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). Se han encontrado relaciones consistentes entre variables socio-económicas obtenidas de censos y encuestas y variables de la cobertura de vegetación en varias ciudades de Estados Unidos. Algunas de las técnicas que se han implementado y obtenido buenos resultados son el análisis de mezcla espectral, las clasificaciones orientadas a objetos y las medidas de textura de la imagen. Se aporta evidencia empírica acerca de la utilidad de las imágenes satelitales para cuantificar el grado de pobreza a escala intra-urbana. Se basa en dos premisas: primero, que la apariencia física de un asentamiento urbano es un reflejo de la sociedad que lo habita; y segundo, que la población de áreas urbanas con condiciones físicas de vivienda parecidas tiene características sociales y demográficas similares. Evaluamos el potencial de los descriptores del tejido urbano extraídos de la imagen para explicar una medida de pobreza conocida como el índice Slum. Encontramos que esas variables explican hasta un 59% de la variabilidad en el índice Slum. Aproximaciones similares a esta podrían usarse para disminuir el costo de encuestas socioeconómicas por medio del desarrollo de un modelo econométrico usando una muestra y luego aplicando el modelo al resto de la ciudad, y para elaborar estimaciones inter-censales o inter-encuestas de mapas intra-urbanos del índice Slum. La última parte analiza la relación entre el trazado urbano y crimen. El enlace entre el lugar y el crimen está en la base de las teorías socio-ecológicas de crimen que se enfocan en la relación de las características de las áreas geográficas y las tasas de crimen. La teoría de las ventanas rotas afirma que las evidencias visibles de desorden físico y social en un barrio pueden llevar al incremento de crímenes más serios. Con base en la premisa de que la apariencia de un asentamiento es un reflejo de la sociedad, nos preguntamos si el diseño del barrio tiene un impacto cuantificable cuando se observa desde el espacio usando descriptores del tejido urbano obtenidos de imágenes de muy alta resolución. El porcentaje de superficies impermeables diferentes a los techos de arcilla, la fracción de techos de arcilla sobre las superficies impermeables, dos variables de estructura relacionadas con la homogeneidad del trazado urbano y la variable de textura uniformidad resultaron estadísticamente significativas. Las áreas con tasas de homicidio más altas tienden a tener mayor variación local y menor homogeneidad general; esto es, los trazados urbanos son más desordenados y hacinados, con pequeñas viviendas que tienen materiales diferentes en sus techos localizadas muy cerca unas de otras, y estas áreas carecen a menudo de otras superficies homogéneas tales como espacios verdes abiertos, vías amplias y grandes construcciones industriales o institucionales. Estos resultados parecen estar en acuerdo con la teoría de las ventanas rotas y CPTED en el sentido de que los trazados urbanos más desordenados y heterogéneos están asociados con tasas de homicid[CA] La primera part aporta una revisió de les potencials aplicacions de la teledetecció espacial a la investigació en ciència regional en entorns urbans. La disponibilitat de dades de percepció remota des de satèl·lits s'ha incrementat significativament a les dues últimes dècades. La resolució espacial de les imatges de satèl·lit comercials també han anat augmentant i això, ha influït en l'aparició de investigacions i aplicacions a la ciència regional en assentaments urbans. Les aplicacions més comunes trobades a la literatura són la detecció de punts calents de pobresa urbana, l'avaluació dels índex de qualitat de vida, les anàlisis de creixement urbà, l'avaluació de la vulnerabilitat social i les variacions intraurbanes de les taxes de crims. Les imatges de satèl·lit emprades tenen resolució espacial mitjana, alta o molt alta (Landsat MSS, Landsat TM i ETM+, SPOT, ASTER, IRS, Ikonos y Quickbird). S'han torbat relacions consistents entre variables socioeconòmiques obtingudes de censos i enquestes i variables de la cobertura de vegetació en varies ciutats del Estats Units. Algunes de les tècniques que s'han implementat i han donat bons resultats són l'anàlisi de mescla espectral, les classificacions orientades a objecte i les mesures de textura de les imatges. Es aporta evidència empírica sobre la utilitat de les imatges de satèl·lit per quantificar el grau de pobresa a escala intraurbana. Es bassa en dues premisses: primer, que l'aparença física d'un assentament urbà n'és un reflex de la societat que l'habita; i segon, que les persones que resideixen en àrees urbanes amb condicions físiques de vivenda paregudes tenen també característiques socials i demogràfiques similars. Avaluem el potencial dels descriptors del teixit urbà extrets de la imatge per explicar una mesura de pobresa coneguda com index Slum. Trobem que aquestes variables expliquen fins un 59% de la variabilitat de l'índex Slum. Aproximacions semblants a aquesta es podrien emprar per a disminuir el cost de les enquestes socioeconòmiques mitjançant el desenvolupament d'un model economètric utilitzant una mostra i després aplicant el model a la resta de la ciutat, i per elaborar estimacions inter-censals o inter-enquestes de mapes intraurbans de l'índex Slum. La darrera part analitza la relació entre el traçat urbà i el crim. L'enllaç entre el lloc i el crim està a la base de les teories socio-ecològiques del crim que es centren en la relació de les característiques de les àrees geogràfiques i les taxes de crims. La teoria de les finestres trencades afirma que les evidències visibles de desordre físic i social d'un barri pot portar a l'augment de crims més greus. Basant-se en la premissa de que l'aparença d'un assentament n'és el reflex de la societat, ens hi preguntem si el disseny del barri té un impacte quantificable quan s'observa des de el espai, utilitzant descriptors del teixit urbà obtinguts de imatges de molt alta resolució. Han resultat estadísticament significatius el percentatge de superfícies impermeables diferents a les teulades de argila, la fracció de teulades d'argila sobre les superfícies impermeables, dues variables d'estructura relacionades amb la homogeneïtat del traçat urbà i la variable de textura de uniformitat. Les àrees amb taxes d'homicidi més altes tendeixen a presentar una major variació local i una menor homogeneïtat general; és a dir, el traçats urbans són més desordenats i amuntonats, amb petites vivendes que tenen materials diferents a les seues teulades localitzades molt prop unes d'altres, i aquestes àrees manquen sovint d'altres superfícies homogènies, com ara espais verds oberts, vies amplies i grans construccions industrials o institucionals. Aquests resultats pareixen estar-hi d'acord amb la teoria de les finestres trencades i CPTED en el sentit de que els traçats urbans més desordenats i heterogenis estan associats amb taxes d'homicides mPatiño Quinchía, JE. (2015). Cityscape, poverty and crime: a quantitative assessment using VHR imagery [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59453TESI

Uncertainties of Human Perception in Visual Image Interpretation in Complex Urban Environments

Today satellite images are mostly exploited automatically due to advances in image classification methods. Manual visual image interpretation (MVII), however, still plays a significant role e.g., to generate training data for machine-learning algorithms or for validation purposes. In certain urban environments, however, of e.g., highest densities and structural complexity, textural and spectral complications in overlapping roof-structures still demand the human interpreter if one aims to capture individual building structures. The cognitive perception and real-world experience are still inevitable. Against these backgrounds, this article aims at quantifying and interpreting the uncertainties of mapping rooftop footprints of such areas. We focus on the agreement among interpreters and which aspects of perception and elements of image interpretation affect mapping. Ten test persons digitized six complex built-up areas. Hereby, we receive quantitative information about spatial variables of buildings to systematically check the consistency and congruence of results. An additional questionnaire reveals qualitative information about obstacles. Generally, we find large differences among interpreters’ mapping results and a high consistency of results for the same interpreter. We measure rising deviations correlate with a rising morphologic complexity. High degrees of individuality are expressed e.g., in time consumption, insitu-or geographic information system (GIS)-precognition whereas data source mostly influences the mapping procedure. By this study, we aim to fill a gap as prior research using MVII often does not implement an uncertainty analysis or quantify mapping aberrations. We conclude that remote sensing studies should not only rely unquestioned on MVII for validation; furthermore, data and methods are needed to suspend uncertainty

Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments

The Norway rat (Rattus norvegicus) is a key pest species globally and responsible for seasonal outbreaks of the zoonotic bacterial disease leptospirosis in the tropics. The city of Salvador, Brazil, has seen recent and dramatic increases in human population residing in slums, where conditions foster high rat density and increasing leptospirosis infection rates. Intervention campaigns have been used to drastically reduce rat numbers. In planning these interventions, it is important to define the eradication units ‐ the spatial scale at which rats constitute continuous populations and from where rats are likely recolonizing, post‐intervention. To provide this information, we applied spatial genetic analyses to 706 rats collected across Salvador and genotyped at 16 microsatellite loci. We performed spatially explicit analyses and estimated migration levels to identify distinct genetic units and landscape features associated with genetic divergence at different spatial scales, ranging from valleys within a slum community to city‐wide analyses. Clear genetic breaks exist between rats not only across Salvador but also between valleys of slums separated by <100 m—well within the dispersal capacity of rats. The genetic data indicate that valleys may be considered separate units and identified high‐traffic roads as strong impediments to rat movement. Migration data suggest that most (71–90%) movement is contained within valleys, with no clear source population contributing to migrant rats. We use these data to recommend eradication units and discuss the importance of carrying out individual‐based analyses at different spatial scales in urban landscapes

Remote sensing-based proxies for urban disaster risk management and resilience: A review

© 2018 by the authors. Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery