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How dynamic are slums? EO-based assessment of Kibera’s morphologic transformation
Abstract
Urban morphologies change over time. The dynamics and nature of morphological changes in informal settlements or slums have largely not been scientifically investigated. Consequently, it is necessary to fill the gap of the international demand for timeline analysis. In this work, earth observation (EO) is used to discover morphologic changes within eight years (2006-2014) in Nairobi’s major slum Kibera. Research mostly handles automated detection but in this study the classical visual image interpretation is applied on a very high level of detail capturing buildings in three dimensions. Consistencies and deviations in time are measured according to morphological variables. We find dynamics in the slum area high in terms of a 77% rise in number of buildings due to arising, splitting, upgrading or demolishing; at the same time, density increases only by 10%. Overall, the general pattern of complex, organic structure remains mostly unchanged.
... Thus the data must be able to represent the physical properties of slum settlements. For example, since many slum buildings are considerably below 100m 2 and slum areas often only have a size of 1ha [10], [15], [16], the related images for their identification require a high spatial resolution. Moreover, roof surfaces are frequently not homogeneous in shape and color; when using high-resolution data, some of the roof pixels will consist of mixed roofing materials. ...
... In [22] only the street network was used to predict slum areas in a combination of traditional machine learning and artificial neural networks. In [7], [8], [16], poor urban areas were analyzed on the level of individual buildings using an object-based approach to identify the varieties of slums and their temporal changes. ...
In the course of global urbanization, poverty in cities has been observed to increase, especially in the Global South. Poverty is one of the major challenges for our society in the upcoming decades, making it one of the most important issues in the Sustainable Development Goals defined by the United Nations. Satellite-based mapping can provide valuable information about slums where insights about the location and size are still missing. Large-scale slum mapping remains a challenge, fuzzy feature spaces between formal and informal settlements, significant imbalance of slum occurrences opposed to formal settlements, and various categories of multiple morphological slum features. We propose a transfer learned fully convolutional Xception network (XFCN), which is able to differentiate between formal built-up structures and the various categories of slums in high resolution satellite data. The XFCN is trained on a large sample of globally distributed slums, located in cities of, Cape Town, Caracas, Delhi, Lagos, Medellin, Mumbai, Nairobi, Rio de Janeiro, Sao Paulo, and Shenzhen. Slums in these cities are greatly heterogeneous in its morphological feature space and differ to a varying degree to formal settlements. Transfer learning can help to improve segmentation results when learning on a variety of slum morphologies, with high F1 scores of up to 89%.
... Furthermore, deprived areas are commonly located along major infrastructure (e.g., railways, main roads) and in flood prone zones. Deprived areas are dominated by high built-up and population densities (Kraff et al., 2019). As a consequence, the majority of Nairobi's urban inhabitants concentrate within around 5 % of the total built-up area of Nairobi (Mutisya & Yarime, 2011). ...
Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed “EO + Morphometrics” approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world.
... Topics such as understanding daily activity spaces; how the journey from a water point to a residence adds risk; how proximity to a trash dump is linked to disease; the interconnection between human activity and fecal contamination of drainage channels [8] or mosquito breeding all require granular spatial data. Further complexity is added when we consider changes across time, especially as SIS environments tend to be highly dynamic in nature [9]. Different methods to address this data gap have included the use of high-resolution imagery [10,11], UAV flights [12], on-the-ground surveys [7], participatory mapping [13], web-based crowdsourcing [14] and open source data mining [15,16]. ...
In this paper, we provide an overview of how spatial video data collection enriched with contextual mapping can be used as a universal tool to investigate sub-neighborhood scale health risks, including cholera, in challenging environments. To illustrate the method’s flexibility, we consider the life cycle of the Mujoga relief camp set up after the Nyiragongo volcanic eruption in the Democratic Republic of Congo on 22 May 2021. More specifically we investigate how these methods have captured the deteriorating conditions in a camp which is also experiencing lab-confirmed cholera cases. Spatial video data are collected every month from June 2021 to March 2022. These coordinate-tagged images are used to make monthly camp maps, which are then returned to the field teams for added contextual insights. At the same time, a zoom-based geonarrative is used to discuss the camp’s changes, including the cessation of free water supplies and the visible deterioration of toilet facilities. The paper concludes by highlighting the next data science advances to be made with SV mapping, including machine learning to automatically identify and map risks, and how these are already being applied in Mujoga.
... Urban growth can be measured by comparing old land surveying maps, but one of the most useful data is remote sensing imagery from satellites since they capture large areas with a snapshot of their sensors. There is ample of research on utilising remote sensing data for urban growth [17][18][19] and mapping of many risk aspects, ranging from climate change affecting land cover change and contributing to flooding or rock falls risk [20], over earthquake risk in urban areas [21], or specific risks to slums [22]. Remote sensing imagery is also increasingly used for monitoring and documenting disaster losses, for example within international 'space charter' calls which provide high-resolution imagery often free of charge of disaster-affected areas immediately after the events [23]. ...
Open Access: https://www.mdpi.com/2072-4292/12/19/3246
Urban growth and natural hazard events are continuous trends and reliable monitoring is demanded by organisations such as the Intergovernmental Panel on Climate Change, the United Nations Office for Disaster Risk Reduction, or the United Nations Human Settlements Programme. CORONA is the program name of photoreconnaissance satellite imagery available from 1960 to 1984 provides an extension of monitoring ranges in comparison to later satellite data such as Landsat that are more widely used. Providing visual comparisons with aerial or high-resolution OrbView satellite imagery, this article demonstrates applications of CORONA images for change detection of urban growth and sprawl and natural hazard exposure. Cases from El Alto/ La Paz in Bolivia, Santiago de Chile, Yungay in Peru, Qazvin in Iran, and Mount St. Helens in the USA are analysed. After a preassessment of over 20 disaster events, the 1970 Yungay earthquake-triggered debris avalanche and the natural hazard processes of the 1980 Mt St. Helens volcanic eruption are further analysed. Usability and limitations of CORONA data are analysed, including the availability of data depending on flight missions, cloud cover, spatial and temporal resolution, but also rather scarce documentation of natural hazards in the 1960s and 70s. Results include the identification of urban borders expanding into hazard-prone areas such as mountains, riverbeds or erosion channels. These are important areas for future research, making more usage of this valuable but little-used data source. The article addresses geographers, spatial planners, political decision makers and other scientific areas dealing with remote sensing.
... However, only very few capture slums at the level of individual buildings, as e.g., [26] explicitly refer to the single building "object" and its environment. A morphologic categorization based on LoD-1 data is provided by [3] and in a follow-up, they describe the change over time [27]. However, although nowadays images reach resolutions in centimeter ranges, textural patterns, spectral differences and clarity of object transitions in complex urban environments remain challenging. ...
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 paper 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, in-situ- or 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; further data and methods are needed to suspend uncertainty.
... However, there are already approaches that can be used in the future. Aside from the discussion on the definition of slums, the focuses of recent publications are the temporal change of slums [40,41] and the adaption of machine-learning methods [26,40,42,43], which automatically record slums combining remote sensing data with other sources [23]. Remote sensing datasets are also used to improve infrastructure planning for slums [44,45]. ...
Approximately 1 billion slum dwellers worldwide are exposed to increased health risks due to their spatial environment. Recent studies have therefore called for the spatial environment to be introduced as a separate dimension in medical studies. Hence, this study investigates how and on which spatial scale relationships between the settlement morphology and the health status of the inhabitants can be identified. To this end, we summarize the current literature on the identification of slums from a geographical perspective and review the current literature on slums and health of the last five years (376 studies) focusing on the considered scales in the studies. We show that the majority of medical studies are restricted to certain geographical regions. It is desirable that the number of studies be adapted to the number of the respective population. On the basis of these studies, we develop a framework to investigate the relationship between space and health. Finally, we apply our methodology to investigate the relationship between the prevalence of slums and different health metrics using data of the global burden of diseases for different prefectures in Brazil on a subnational level.
Most earth observation (EO) approaches only yield a binary delineation of deprived/non-deprived areas – an oversimplified characterisation with little information inferred regarding the diversity of intra-urban deprivation. In this study, we attempt to explore the potential of using VHR EO-based data to predict the degrees of intra-urban deprivation in Nairobi, Kenya. This involves a two-step workflow of characterising and predicting a continuous index of deprivation degrees. First, a principal component analysis (PCA) is conducted to characterise the multi-dimensionality of deprivation using open geospatial datasets as a set of continuous indices. Next, a convolution neural network (CNN) based regression model is trained to directly predict the deprivation indices using SPOT-7 images. The best prediction of the proposed CNN regression model is obtained in the morphology-based domain, with an R² of 0.6543. We demonstrate the potential of an EO-based method to directly capture the degrees of morphological deprivation with relatively high accuracy, while also acknowledging its limitations in predicting the complexity of deprivation.
Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolu-tional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86-88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution .
More than half of the world's population currently resides in urban areas. In the majority of developing countries slums are a defining part of the urban scape. Their supply with energy, basic infrastructure, among others is one of the main challenges of modern civilizations. To provide an optimal supply, the spatial patterns of slums in cities have to be explored. While most of current literature is focused on inter-urban dynamics, this paper is focused on intra-urban pattern (i.e. the spatial pattern of morphological slums within a city) and its link to the inter-urban ones. Therefore, census and remote sensing data are analyzed to create rank size distributions of morphological slums in different cities of developing countries. The observations were compared to rank size distributions of cities in a respective developing country. It is found that typical inter-urban pattern can be transferred to intra-urban pattern. Surprisingly is the fact that the size of slums is independent from city and global region in the analyzed cities. The slums in Mumbai, Manila, Rio de Janeiro and Cape Town have an average area of 0.016 km² with a standard deviation of only 0.004 km².
When we think about living environments of the urban poor, slums might be the most immediate association. These slums evoke a more or less stereotype impression of built environments: complex, high dense alignments of small makeshift or run-down shelters. However, this perceived characteristic morphology is neither globally homogeneous nor is this perception covering morphologic appearances of urban poverty in a comprehensive way. This research provides an empirical baseline study of existing morphologies, their similarities and differences across the globe. To do so, we conceptually approach urban poverty as places which provide relatively cheap living spaces serving as possible access to the city, to its society and to its functions-so called Arrival Cities. Based on a systematic literature survey we select a sample of 44 Arrival Cities across the globe. Using very high resolution optical satellite data in combination with street view images and field work we derive level of detail-1 3D-building models for all study areas. We measure the spatial structure of these settlements by the spatial pattern (by three features-building density, building orientation and heterogeneity of the pattern) and the morphology of individual buildings (by two features-building size and height). We develop a morphologic settlement type index based on all five features allowing categorization of Arrival Cities. We find a large mor-phologic variety for built environments of the urban poor, from slum and slum-like structures to formal and planned structures. This variability is found on all continents, within countries and even within a single city. At the same time detected categories (such as slums) are found to have very similar physical features across the globe.
Over the last decades, massive urbanization processes lead to the emergence of large slum areas making them home to about a seventh of the global population. Although the variety of morphological characteristics varies significantly within as well as across cities, common determinants exist. Informal, or unplanned settlements in particular, do show similar morphologies over the world. They are characterized mostly by extremely high building densities and small building sizes, irregular arrangement of buildings and street network and are often located at exposed sites . Based on these characteristics, we deploy satellite images for a systematic mapping of morphological slum areas in the city of Rio de Janeiro, Brazil based solely on physical characteristics and analyse the mapping result with the official census data. Outcomes show first that morphological slums are a semantic and spatial sub-group of all slum areas contained by the Brazilian census and that remote sensing-based mapping yields accuracies of almost 94%. Second, analysis of census-based income data proofs that while almost 45% of all mapped slum blocks are characterized by incomes below the poverty line, as defined by the Organisation for Economic Co-operation and Development (OECD), this holds true for only about 6% of the formal urban neighbourhoods.
Driven by massive urbanization processes, particularly in developing countries, people flock into the cities resulting in an evolution of large slum areas. Mapping and monitoring of slum areas have become an invaluable source for decision-making processes to implement policies related to improve living conditions. Space-borne remotely sensed data has been explored in the past for slum mapping, however, to a large extent supported by optical imagery. In this paper, we explore the capabilities of dual-polarized (HH/VV and VV/VH) X-band Synthetic Aperture Radar (SAR) from TerraSAR-X images for slum extent mapping using the Kennaugh element framework for image preprocessing. In this way, spatial image descriptors based on texture, morphological profiles and polarimetric features have been tested at various window sizes [11 × 11, …, 161 × 161] for mapping slums using the random forest classifier in a series of experiments. For benchmarking the classification results, LDA as parametric linear classifier is used for comparison. Classification performance was evaluated by comparison with a reference map indicating that texture features hold the highest contribution to discriminating slums from other urban structures. Best window size was found using a spatial neighborhood of 81 × 81 pixels resulting in Overall Accuracy of 88.58 and Kappa of 0.7809 for RF classifier. A patch-based analysis of classification results reveals areal dependencies of the classifier in terms of larger slum patches that are mapped with higher precision than smaller patches. Analyses including additional spatial image descriptors based on mathematical profiles reveal no significant contribution to the classification result.
The body of scientific literature on slum mapping employing remote sensing methods hasincreased since the availability of more very-high-resolution (VHR) sensors. This improves the abilityto produce information for pro-poor policy development and to build methods capable of supportingsystematic global slum monitoring required for international policy development such as theSustainable Development Goals. This review provides an overview of slum mapping-related remotesensing publications over the period of 2000–2015 regarding four dimensions: contextual factors,physical slum characteristics, data and requirements, and slum extraction methods. The review hasshown the following results. First, our contextual knowledge on the diversity of slums across theglobe is limited, and slum dynamics are not well captured. Second, a more systematic exploration ofphysical slum characteristics is required for the development of robust image-based proxies. Third,although the latest commercial sensor technologies provide image data of less than 0.5 m spatialresolution, thereby improving object recognition in slums, the complex and diverse morphology ofslums makes extraction through standard methods difficult. Fourth, successful approaches showdiversity in terms of extracted information levels (area or object based), implemented indicator sets(single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machinelearning). In the context of a global slum inventory, texture-based methods show good robustnessacross cities and imagery. Machine-learning algorithms have the highest reported accuracies andallow working with large indicator sets in a computationally efficient manner, while the upscalingof pixel-level information requires further research. For local slum mapping, OBIA approaches showgood capabilities of extracting both area- and object-based information. Ultimately, establishing a moresystematic relationship between higher-level image elements and slum characteristics is essential to trainalgorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.
The increasing development of informal settlements and slums in the years to come is a humanitarian challenge we are facing worldwide. The reasons for this development are mainly given by an urbanization of poverty and the lack of affordable housing in urban areas. Consequently, the majority of slum dwellers are forced to live in housing conditions which are simply unacceptable. In order to fight urban poverty and develop strategies to reduce the number of slum dwellers it is essential to inventory and monitor slums and to understand the underlying mechanisms of slum genesis. However, systematic spatial knowledge about these mechanisms is still absent. This paper addresses the capabilities of mapping and structural analysis of informal settlements using remote sensing data and modelling their genesis based on agent based approaches.
Cities in Africa and developing countries in general are having difficulties coping with the influx of people arriving every day. Informal settlements (slums) are growing, and governments are struggling to provide even the most fundamental services to their populations. One of the tools that can be used to study these environments is satellite imagery, especially very high-resolution (VHR) images coming from systems such as 1KONOS, Quickbird, GeoEye and similar. Detection of informal settlements from satellite imagery is a challenging task due to their microstructurc and irregular shapes of buildings. Higher spatial resolution is necessary to identify and extract individual buildings, especially in slum communities that are characterized by small, densely packed shanties and other structures. In the paper we are dealing with the Kibcra (Nairobi, Kenya) slum that is composed of varying housing sizes, where roofs can be a combination of many different materials, and mainly unpaved road and path network. Typically this produces a spectral response, which is difficult to interpret, and makes traditional classification almost impossible. We have applied object-based classification on GeoEye and QuickBird imagery over a tree year period (from 2006 to 2009) to help differentiate slum rooftops and unpaved roads from non-build land and therefore residential areas or grasslands. Object-based segmentation automatically delimits segments on the image into homogeneous elements, which correspond to the real urban geographical objects on the Earth's surface. In the stage of classification all these homogeneous elements are classified into most appropriate classes. In addition to determination of the detailed urban structure we were also interested in the expansion of slum areas with change detection, which was analysed by comparison of images taken in different time sequences. The results of object-based analysis with morphology attributes were further used to estimate the potential population density in the slum area. There is a big discrepancy between different estimations on Kibera census, ranging from 1 to 2 million people, while no field survey was ever performed to assess the population. Different parameters were tested to estimate the potential population density scenarios. The paper will discuss merits and drawbacks of object-based image analysis in dense non-formal settlements analysis with remote sensing data. Overall, the use of the object-based image analysis holds great promise for dense urban environments and could be utilized in studies of urban change structure and corresponding population estimation.
Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of κ statistics of 0.8–0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to κ statistics, which underlines the robustness of the proposed approach.
Remotely sensed-based estimates of dwelling and population characteristics can provide timely and spatially explicit information for urban planning and development in emerging cities. This exploratory analysis quantifies spatial features of built-up areas derived from high-resolution satellite imagery and directly relates them to census-derived variables in Accra, Ghana. Spatial features are image metrics that analyze pixel groups in order to describe the geometry, orientation, and patterns of objects in an image. By using spatial features in an urban setting, city infrastructure variations, such as roads and buildings, can be quantified and related to census variables, such as living standards and housing conditions. To test the associations between spatial patterns and demographic variables, five spatial features (line support regions, PanTex, histograms of oriented gradients, local binary patterns, and Fourier transform) were quantified and extracted from the imagery, and then correlated to the census variables. Findings indicate both spatial features and spectral information (such as NDVI) correlate strongly with standards of living such as population and housing density. Results from this study suggest that spatial features derived from satellite imagery can be used to help map socioeconomic characteristics within the city of Accra, Ghana, and that this methodology may be transferable to other developing cities.
This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top-down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework.
The term “slum” is difficult to define, but if we see one, we know it. Definitions for slums are qualitative such as “areas of people lacking, for example, durable housing or easy access to safe water”. This study aims at identifying characteristic physical features of the built environment that allows defining slum areas based on quantitative and measurable parameters. In general, spatial data on slums are generalized, outdated, or even nonexistent. The bird’s eye view of remotely sensed data is capable to provide an independent, area-wide spatial overview, to capture the complex morphological pattern and at the same time capture the large-scale individual objects typical for slums. Using high-resolution optical satellite data, parameters such as building density, building heights, and sizes are used to differentiate between slums and formal settlements. From it, the physical features are used to analyze structural homogeneity and heterogeneities within and across slums and to suggest characteristic physical features for spatial slum delineation at three study sites in Mumbai, India.
The primary objective of the study is to carry out an assessment of the current status of modern energy supply among the urban and peri-urban poor in Kenya and to identify viable policy options that can assist in providing cleaner and more sustainable energy services to the rapidly growing urban population in Kenya. The study also assesses prevailing energy policies that address the challenges associated with supply of modern energy services to the urban poor. The study focuses on the example of energy consumption patterns of urban poor households in Kibera – often said to be Africa's largest slum – and the trends in energy use among small and medium enterprises (SMEs) in the area, providing an empirical basis for key findings of the study.
The findings of the household survey clearly demonstrate the role that kerosene, electricity, biomass and LPG can play in cooking and lighting in low-income areas such as Kibera, Nairobi. According to the survey findings, kerosene is the most important modern energy option for the poor for both lighting and cooking. Electricity also appears to be a relatively important energy option. Biomass in the form of charcoal and LPG appear to be consumed by a relatively small segment of the urban poor in the selected sample area. The results of this survey largely reflect the situation at the national level.
The study concludes by presenting central issues related to identified key energy options for the poor in Kibera (kerosene, electricity, biomass and LPG) and presents policy measures that could enhance modern energy services among the urban poor.
This paper describes the ways that households, and particularly women, experience water scarcity in a large informal settlement in Nairobi, Kenya, through heavy expenditures of time and money, considerable investments in water storage and routinized sequences of deferred household tasks. It then delineates three phases of adaptive water and social engineering undertaken in several informal settlements by the Nairobi Water Company in an ongoing attempt to construct effective municipal institutions and infrastructure to improve residential access to water and loosen the grip that informal vendors may have on the market for water in these localities.
The megacities arising around the planet are like the Internet where many events are taking place simultaneously. The urban scape today is becoming more a space of flows—migrants, trade, capital, information, microbes—than a space of places rooted in an historical identity.
The megaurban condition today encompasses many realities, from the glittering generic city-state of Singapore to the slums climbing up the hillsides around Mexico City or Sao Paulo. In these spaces we work, love and live out the intimate moments of our lives. In these spaces we consume and spew out climate warming gases.
In this section, two of the world's “star architects”—Rem Koolhaas and Frank Gehry—the visionary “arcologist” Paolo Soleri and the Turkish novelist and Nobel laureate, Orhan Pamuk, grasp at chronicling the reality of where we live.
A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.
A blueprint for addressing the global affordable housing challenge
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The challenge of slums: Global report on human settlements
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