Spatial patterns of deprivation in cities of the global south in very high resolution imagery

Kuffer, Monika

doi:10.3990/1.9789036543699

Cited by 2 publications

(2 citation statements)

References 162 publications

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“…Overall, all the wards showed a heterogeneous character. This is in line with the study by Kuffer where she highlighted that wards in India are much more diverse (Kuffer, 2017). The slums are not clumped in restricted wards but distributed throughout the city.…”

Section: Resultssupporting

confidence: 85%

“…However, it is also observed that a particular city usually replicate a similar slum structure (Taubenböck et al, 2018). Another study by Kuffer using high resolution satellite images highlighted that deprived areas are not homogeneous in their dimensions and considering them as one class ignores their vast diversity (Kuffer, 2017). A study on slum settlement in Hyderabad, India used remote sensing data (lacunarity concept) and advanced image processing techniques (line detection algorithms) to identify slum boundaries aiming for urban decision makers (Kit et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

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Analyzing Social Stratification through Spatial Characteristics: Case Study of Bhubaneswar, India

Dash

Rashid²

2022

EJDEVELOP

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Objective: The overall goal of this investigation is to describe the pattern of distribution of various socio-economic groups in Bhubaneswar, India. Background: Social stratification studies are inconsistent across countries. While the deprived communities are clustered around the center of the city in the US and Shanghai, recent Indian studies showed that they are mostly located at the periphery of the city. Most of these studies have used census data to differentiate the socio-economic groups. However, there are limited studies that examine the spatial structure of various socio-economic neighborhoods in India. This study is unique because it combines census data and satellite images to examine how different socio-economic neighborhoods are different geographically and spatially. Method: Census tract or ward is considered representative of neighborhoods. Census data on socio-economic indicators such as household size, literacy, home ownership, car-jeep ownership, availing banking facility, and workers population are used for an empirical PCA analysis. The wards are divided into five quintiles based on their deprivation index. A spatial study is performed for two of the most deprived neighborhoods and two of the least deprived neighborhoods using satellite maps. Results: The least deprived neighborhoods are located mostly around airports. However, the most deprived neighborhoods are located at the periphery. Informal housing or slums are not clumped in deprived neighborhoods only but are distributed throughout the city. All four wards studied are heterogeneous with informal housing combined with independent housing and apartments. Conclusion: Most deprived and least deprived neighborhoods are not differentiated based on informal housing locations but on parcel and apartment size.

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Section: Resultssupporting

confidence: 85%

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Social Stratification through Spatial Characteristics: Case Study of Bhubaneswar, India

Dash

Rashid²

2022

EJDEVELOP

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Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

Ranguelova

Weel

Roy

et al. 2018

European Journal of Remote Sensing

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Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods -Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.

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Spatial patterns of deprivation in cities of the global south in very high resolution imagery

Cited by 2 publications

References 162 publications

Analyzing Social Stratification through Spatial Characteristics: Case Study of Bhubaneswar, India

Analyzing Social Stratification through Spatial Characteristics: Case Study of Bhubaneswar, India

Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

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