Karin Pfeffer

Understanding heterogeneity in metropolitan India : the added value of remote sensing data for analyzing sub - standard residential areas

Abstract Analyzing the heterogeneity in metropolitan areas of India utilizing remote sensing data can help to identify more precise patterns of sub-standard residential areas. Earlier work analyzing inequalities in Indian cities employed a constructed index of multiple deprivations (IMDs) utilizing data from the Census of India 2001 ( http://censusindia.gov.in ). While that index, described in an earlier paper, provided a first approach to identify heterogeneity at the citywide scale, it neither provided information on spatial variations within the geographical boundaries of the Census database, nor about physical characteristics, such as green spaces and the variation in housing density and quality. In this article, we analyze whether different types of sub-standard residential areas can be identified through remote sensing data, combined, where relevant, with ground-truthing and local knowledge. The specific questions address: (1) the extent to which types of residential sub-standard areas can be drawn from remote sensing data, based on patterns of green space, structure of layout, density of built-up areas, size of buildings and other site characteristics; (2) the spatial diversity of these residential types for selected electoral wards; and (3) the correlation between different types of sub-standard residential areas and the results of the index of multiple deprivations utilized at electoral ward level found previously. The results of a limited number of test wards in Delhi showed that it was possible to extract different residential types matching existing settlement categories using the physical indicators structure of layout, built-up density, building size and other site characteristics. However, the indicator ‘amount of green spaces’ was not useful to identify informal areas. The analysis of heterogeneity showed that wards with higher IMD scores displayed more or less the full range of residential types, implying that visual image interpretation is able to zoom in on clusters of deprivation of varying size. Finally, the visual interpretation of the diversity of residential types matched the results of the IMD analysis quite well, although the limited number of test wards would need to be expanded to strengthen this statement. Visual image analysis strengthens the robustness of the IMD, and in addition, gives a better idea of the degree of heterogeneity in deprivations within a ward.

Slums from Space: 15 Years of Slum Mapping Using Remote Sensing

The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical 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 spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity 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 machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.

Participatory spatial knowledge management tools: empowerment and upscaling or exclusion?

Different types of spatial knowledge (expert, sectoral, tacit and community) are strategic resources in urban planning and management. Participatory spatial knowledge management is a major method for eliciting various types of knowledge, providing a platform for knowledge integration and informing local action and public policy. Knowledge types linked to a specific geographical locality can be integrated through geographical information systems. Recent developments in geographical information and communication technology (geoICT) have extended the opportunities for participatory spatial knowledge production, use and exchange. However, data reliability of user-generated content, social exclusion due to dependence on technology and the interpretation and implications of digital maps are major concerns. The challenge is how to integrate and utilize multiple knowledge sources for improving urban management and governance. This paper integrates the literature on knowledge types and knowledge production processes with available geoICT tools for the production, use and exchange of knowledge sources and applies it to examples from Asia, Africa and Latin America. From this review, we provide a heuristic framework for assessing the extent to which participatory spatial knowledge management tools can be instrumental on several fronts. We argue that technological developments of knowledge production have not fully addressed important issues related to accountability, empowerment, control and use of knowledge. Moreover, these developments may foster social exclusion, which could detract from the benefits of participatory spatial knowledge management in the context of urban sustainability.

Extraction of Slum Areas From VHR Imagery Using GLCM Variance

Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.

Review: The emergence of slums: A contemporary view on simulation models

The existence of slums or informal settlements is common to most cities of developing countries. Its role as single housing delivery mechanism has seriously challenged the popular notion held by policy makers, planners and architects. Today informality is a paradigm of city making and economic growth in Africa, Asia and Latin America. This paper discusses the role of computer simulation models to understand the emergence and growth of slums in developing countries. We have identified the key factors influencing the growth of slums and formulated a standardized set of criteria for evaluating slum models. The review of existing computer simulation models designed to understand slum formation and expansion enabled us to define model requirements and to identify new research questions with respect to exploring the dynamics of slums.

Knowledge Production in Urban Governance Systems through Qualitative Geographical Information Systems (GIS)

Urban governance offers opportunities for more inclusive urban management, incorporating tacit knowledge and citizens’ preferences. The question is how to elicit such knowledge and preferences so that they are both inclusive as well as efficient. Field visits to Indian cities have shown that a lot of effort is put into the implementation of E-governance tools and setting up Geographic Information Systems (GIS), focusing on administrative interaction with citizens. Little attention is paid to how GIS could be included in strategic governance processes. The main question here is how a combination of GIS-based qualitative and quantitative approaches can make local embedded knowledge visible for inclusive urban governance. Therefore workshops were held in four Indian cities (Mysore, Hubli–Dharward, Kalyan and Mira–Bhayandar) with participants from local government departments and elected councillors to elicit and discuss local knowledge on urban inequalities. GIS maps were used as an input to the process and for visualizing outcomes. The workshops show that using GIS throughout the process provides an understanding of the local context, enriches knowledge obtained from local databases, and therefore supports multiple forms of knowledge. However, the outcome depends greatly on the nature of input maps, the situated knowledge of workshop participants and map literacy.

E-government tools, claimed potentials/unnamed limitations: the case of Kalyan-Dombivli

Contemporary cities are characterized by the inequality reflected in uneven geographies of quality-of-life conditions. When these inequalities are a matter of concern, local governments usually assert their intention to respond to citizen’s needs and deprivations. It is in this context that information and communication technology (ICT) tools are being incorporated in Indian cities to promote local governance by improving quality of life and increasing efficiency and transparency in the response to citizen’s demands and needs. Depending on the institutional environment and how information is created, processed and disseminated, these ‘e-government’ tools can exacerbate existing exclusionary practices. The objectives of this article are twofold. First, we want to explore how a local e-grievance redressal system reflects self-expressed needs. Second, we want to investigate whether there is a (mis)match between self-expressed needs and deprived areas. This helps to answer the question how these systems capture the requirements of the most deprived. The main methods used are geocoding and spatial visualization of the processed information. Results show that the self-expressed needs do not necessarily concentrate in the most deprived areas. This suggests that the e-grievances redressal system does not guarantee a narrowing of the gap between the different sections of the city, nor does it necessarily capture the requirements of those in the most need.

Considering sound in planning and designing public spaces a review of theory and applications and a proposed framework for integrating research and practice

We critically review the literature on the relationship between users of public spaces and their auditory environments, and how this knowledge is integrated in the planning, design, and management of public spaces as well as in technologies for acoustic and spatial data collection, analysis, and communication. To address the gaps identified in the review, we propose an activity-centered framework as a conceptual tool developed to support the integration of different types of knowledge in incorporating sound and the auditory environment in the planning and design of public spaces, by focusing on the activities that users perform in these spaces.

Capturing the Diversity of Deprived Areas with Image-Based Features : The Case of Mumbai

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.

Geo-technologies for spatial knowledge: challenges for inclusive and sustainable urban development

Critical to governance for sustainable and inclusive urban development is access to, and management of, relevant contextual spatial knowledge. Digital geo-technologies such as geographical information systems, online applications and spatial simulation models are increasingly becoming embedded in urban governance processes to produce, utilize, exchange, and monitor contextual knowledge and create scenarios for the future. This chapter provides a comprehensive state-of-the-art review of geo-technologies for spatial knowledge production and management for urban governance focusing on (1) the kinds of geo-technologies that feature in the urban governance area; (2) the discourses with respect to geo-technologies in urban governance processes; (3) the kinds of knowledge produced, used, exchanged, and contested in relation to quality of life, economic development and the ecosystem; and (4) the transformative potential of geo-technologies in urban governance processes. Through this review it draws out the capacities and challenges of geo-technologies for inclusive and sustainable urban development.