Urban Livelihoods in Slums of Chennai: Developing a Relational Understanding

Saharan, Tara; Pfeffer, Karin; Baud, I.S.A.

doi:10.1057/s41287-017-0095-2

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…For instance, [35] ordered categorical data, transferred them to ordinal data, and aggregated them using descriptive statistics to build a slums index. Similarly, [43] aggregated indicators of a deprivation index with equal weights. Although these methods are very common and based on experts' knowledge, they might include many assumptions and bias the result.…”

Section: Discussionmentioning

confidence: 99%

“…This framework focuses on households and the dwelling they live in but does not involve the context of the dwellings. Based on related studies and constructed indices (e.g., [2,10,14,43,44]), the contextual domain was added to the IMD-based deprivation framework to create a holistic picture of deprivation levels. The contextual domain involves indicators, which look at spatial neighborhood characteristics of the geographic area in which the dwelling is located, like accessibility to services or environmental characteristics.…”

Section: Conceptualizing Deprivationmentioning

confidence: 99%

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Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks

et al. 2019

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In the cities of the Global South, slum settlements are growing in size and number, but their locations and characteristics are often missing in official statistics and maps. Although several studies have focused on detecting slums from satellite images, only a few captured their variations. This study addresses this gap using an integrated approach that can identify a slums’ degree of deprivation in terms of socio-economic variability in Bangalore, India using image features derived from very high resolution (VHR) satellite images. To characterize deprivation, we use multiple correspondence analysis (MCA) and quantify deprivation with a data-driven index of multiple deprivation (DIMD). We take advantage of spatial features learned by a convolutional neural network (CNN) from VHR satellite images to predict the DIMD. To deal with a small training dataset of only 121 samples with known DIMD values, insufficient to train a deep CNN, we conduct a two-step transfer learning approach using 1461 delineated slum boundaries as follows. First, a CNN is trained using these samples to classify slums and formal areas. The trained network is then fine-tuned using the 121 samples to directly predict the DIMD. The best prediction is obtained by using an ensemble non-linear regression model, combining the results of the CNN and models based on hand-crafted and geographic information system (GIS) features, with R2 of 0.75. Our findings show that using the proposed two-step transfer learning approach, a deep CNN can be trained with a limited number of samples to predict the slums’ degree of deprivation. This demonstrates that the CNN-based approach can capture variations of deprivation in VHR images, providing a comprehensive understanding of the socio-economic situation of slums in Bangalore.

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Section: Discussionmentioning

confidence: 99%

Section: Conceptualizing Deprivationmentioning

confidence: 99%

Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks

et al. 2019

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“…(2020) reveal a lack of collaboration between stakeholders in the implementation of “the city without slums” programme. Within the context of India, Saharan et al . (2018) reveal cleavages between slum policies and institutional practices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Discourse concerning slums policy has piqued the interest of scholars within the sphere of public administration and policy management studies. Areas of high research interest orbit around perspectives on slum upgrading (Jones, 2017; Ren, 2017; Saharan et al ., 2018; Sandoval and Sarmiento, 2020); slum administration and governance (Adamtey et al ., 2021; Doe et al ., 2020; Oteng-Ababio and Grant, 2019; Yeboah et al ., 2021); and slum social characteristics (Smit et al , 2019). Further review of the literature indicates that slum voices do not reflect policy design and implementation (Sinharoy et al ., 2019; Yeboah et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

How do slum dwellers influence policies affecting their lives? Perspectives from Ghanaian slums

Agyabeng¹,

Bawole²,

Ahenkan³

et al. 2023

OHI

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PurposeThe study examined the influence of slums on policies affecting the slums' lives in Ghana.Design/methodology/approachAn exploratory qualitative approach based on in-depth interviews (IDIs) was used to select 24 respondents using purposive and snowball sampling techniques.FindingsThe findings show that slum dwellers have an adequate understanding of policies that affect the dwellers' lives. Furthermore, slum dwellers use statutory, technological, media and right-to-vote-based strategies to influence government policies. This also indicates that implemented policies do not align with realities in the slumsResearch limitations/implicationsThe outcome of this study cannot be generalised to represent the whole population of slums due to the inherent limitations associated with a qualitative designSocial implicationsThis study uncovers context-specific strategies through which slum residents influence policies. The study concedes that policy actors involve the slums in policies that affect their livelihoodsOriginality/valueThe results are unique not only to developing countries, but are also useful to other economies with similar characteristics.

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“…The process of building livelihoods is not only associated to how long households have resided in the settlement but also to the city and settlement histories that they are a part of (Saharan, Pfeffer, & Baud, 2018a). Anna Nagar and River Side both are characterised by winding narrow lanes and high-density housing with a range of precarity.…”

Section: Bargaining As a Strategy For Building Livelihoods In Informa...mentioning

confidence: 99%

Comparing governance and bargaining of livelihoods in informal settlements in Chennai and eThekwini

Saharan

Pfeffer

Baud

et al. 2022

Cities

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Urban Livelihoods in Slums of Chennai: Developing a Relational Understanding

Cited by 8 publications

References 31 publications

Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks

Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks

How do slum dwellers influence policies affecting their lives? Perspectives from Ghanaian slums

Comparing governance and bargaining of livelihoods in informal settlements in Chennai and eThekwini

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