Slum types and adaptation strategies: identifying policy-relevant differences in Bangalore

Krishna, Anirudh; Sriram, M S; Prakash, Purnima

doi:10.1177/0956247814537958

Cited by 74 publications

(51 citation statements)

References 11 publications

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“…The analysis of the employed methods in the reviewed slum publications shows that most studies used commercial and rather expensive imagery. Only very few studies used free data sources such as GE image, mostly for visual image interpretation (e.g., [69,84,119]), visualization of slums [90] or combining GE with commercial imagery [72,80], whereas Praptono et al [98] used GE images to automatically detect slums employing a Gabor filter and GLCM with a promising accuracy of 74%. Many of the methods used commercial software solutions, but to some extent also open-source software.…”

Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

“…Furthermore, relying on physical proxies for population estimations can lead to errors for areas that have not yet been fully occupied, e.g., new developments in outskirts [70]. Other what-related issues deal with boundaries and effectiveness of policies for health campaigns [84,135], allocation of public services and protection of environmentally sensitive areas [68] or spatial planning and policy formulation [69]. These efforts are related to the fact that local planning authorities often lack elementary information on slums, which "has led to a deficit in policy for these areas, as without quality map data, it is often difficult to plan effectively for these areas" ( [69], p. 390), leading to ad hoc plans that do not consider the specific locational context.…”

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

“…These domains reflect the reported spatial information needs and lack of information on slums in locally available data sets [84]. Quite a rich body of literature is connected to social issues, where "remotely sensed imagery can serve as data source for inferring socio-economic variables" ( [93], p. 69).…”

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

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

2016

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Abstract: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.Keywords: slums; informal areas; urban remote sensing; Global South; VHR imagery Global Urbanization and Slum Dynamics: The ContextCurrently, about one-quarter of the world's urban population lives in slums, which are defined by UN-Habitat as informal settlements [1] or areas deprived of access to safe water, acceptable sanitation, and durable housing; in addition to being areas that are overcrowded and lack land tenure security [2]. Over the last 15 years, there has been renewed interest in slum improvement and eradication by local and international organizations dealing with development issues. During this period, slums became a more prominent subject of remote sensing (RS) image analysis. Supported by increased availability of very-high-resolution (VHR) data and methodological advances, many RS studies [3][4][5][6][7][8] aimed to produce information on the geography and dynamics of slums. ...

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Section: Methods Employed For Slum Mappingmentioning

confidence: 99%

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

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

2016

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“…See Dunning (2008) and Kapizewski, MacLean, and Read (2015) on how qualitative methods can inform field experimental design and explanations of why interventions have the effects that they do. 2 On variation among low-income settlements in Bangalore, see Krishna, Sriram, and Prakash (2014). 3 Because there were many fewer valve closed and supply canceled notifications, and the valve opening time information was most useful for NextDrop's notification system, we focused our analysis on the valve opening notifications.…”

Section: Note Smentioning

confidence: 99%

Frontline worker compliance with transparency reforms: Barriers posed by family and financial responsibilities

2017

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Significant development funding flows to informational interventions intended to improve public services. Such "transparency fixes" often depend on the cooperation of frontline workers who produce or disseminate information for citizens. This article examines frontline worker compliance with a transparency intervention in Bangalore's water sector. Why did compliance vary across neighborhoods, and why did workers exhibit modest rates of compliance overall? Drawing on ethnographic observation and an original data set, this article finds that variation in workers' family responsibilities and financial circumstances largely explains variation in compliance with the intervention. Furthermore, workers often prioritize long-standing responsibilities over new tasks seen as add-ons, leading to modest rates of compliance overall. Perceptions of "core" jobs can be stickyespecially when reaffirmed through interactions with citizens. This article represents one of the first multimethod companions to a field experiment, and illustrates how the analysis of qualitative and observational data can contribute to impact evaluation. | I NTRO DUC TIO NMore information for lay citizens, cheaply provided and easily accessed, is at the heart of global efforts to "make services work for poor people" (World Bank, 2004). The underlying assumption is that transparency improves citizens' experience with service delivery; information about services positions citizens to make better use of them. In addition, citizens armed with information about service provider performance are better placed to press for improvements and to demand accountability. Improved transparency, in other words, promotes a virtuous cycle leading to improved service delivery. Development institutions, telecommunications companies, and national governments have channeled significant funding into informational interventions to improve the

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“…A study by Salvati et al () used high‐resolution Google Earth © imagery to analyse the correlation between identified pools and class segregation. Other studies visually interpreted Google Earth © to identify and delineate slums (Gunter 2009; Krishna et al ; Marghany & van Genderen ).…”

Section: Methodsmentioning

confidence: 99%

Detection and Simulation of Urban Expansion and Socioeconomic Segregation in the Greater Paramaribo Region, Suriname

Fung-Loy

Rompaey

Hemerijckx

2019

Tijd voor Econ & Soc Geog

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This paper analyses built‐up area expansion and socioeconomic segregation within the Greater Paramaribo Region, Suriname. Built‐up expansion between 1987 and 2015 was assessed via time‐series analysis of Landsat images. By identifying visible spatial residential characteristics in Google Earth© images, the residential built‐up area was differentiated into rich, middle, middle to low, and poor residences, signifying different socioeconomic groups. Results show that the built‐up expansion of the region is primarily controlled by the distance to the previously built‐up area, city centre, and roads, as well as land price. The observed expansion mainly consisted of middle and middle to low residences. Dissimilarity indices demonstrate an increasing socioeconomic segregation, especially between rich and poor. A business‐as‐usual model simulation for 2030 indicates that this segregation is likely to remain.

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Slum types and adaptation strategies: identifying policy-relevant differences in Bangalore

Cited by 74 publications

References 11 publications

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

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

Frontline worker compliance with transparency reforms: Barriers posed by family and financial responsibilities

Detection and Simulation of Urban Expansion and Socioeconomic Segregation in the Greater Paramaribo Region, Suriname

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