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

Baud, I.S.A.; Kuffer, Monika; Pfeffer, Karin; Sliuzas, Richard; Karuppannan, Sadasivam

doi:10.1016/j.jag.2010.04.008

Cited by 101 publications

(79 citation statements)

References 18 publications

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“…This showed that large administrative units have limited use in mapping fine-grained patterns of deprivation in a complex megacity [15]. The ward boundaries sometimes cut across larger clusters of deprivation, splitting them into smaller subunits.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the four morphological dimensions of deprived areas in Mumbai, i.e., environment, density, geometry, and texture pattern (building on the earlier work of [4,15,22,25,[55][56][57]), image features are created with the potential to capture the diversity of such areas (Figure 2). This list of image features (Figure 7) is generated based on distinguishing features reported in slum mapping studies (e.g., [4,10,22,23,56,[58][59][60]), as well as by considering the local characteristics of deprived areas in Mumbai.…”

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

“…Earlier studies [15,25] showed that deprived areas display diversity in terms of environmental (environ) features such as location on steep slope. Furthermore, land cover/use characteristics often vary among deprived areas; e.g., large and very densely built-up areas have little land cover/use heterogeneity (measured e.g., by SHDI and SHEI) while small slum pockets are often surrounded by vegetation or other land cover/use types.…”

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

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

et al. 2017

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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municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated.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.

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Section: Discussionmentioning

confidence: 99%

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

Section: Extraction Of Features To Map the Diversity Of Deprivationmentioning

confidence: 99%

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

et al. 2017

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“…Additionally, remote sensing has been used to characterize living conditions of poor urban neighborhoods such as slums, informal settlements, marginal areas and low income neighborhoods through a combination of fine and coarse resolution data and often ancillary data [86][87][88]. Poverty and sub-standard housing in complex, cluttered, uncontrolled, and fast growing urbanized regions can be measured with very high spatial resolution remotely sensed data and associated geospatial techniques [89], however many challenges remain. The building materials in slums are very heterogeneous and thus are difficult to classify.…”

Section: Social and Economic Indicesmentioning

confidence: 99%

“…Research estimated poverty using spatial indicators, such as roof densities, irregular road structures, and vegetation/impervious surface indices to characterize the physical environment (e.g., V-I-S) [88,90]. Based on these indicators, most researchers have identified, delineated, and rated neighborhoods, often to find correlations between economic variables and -positive‖ (e.g., vegetation) or -negative‖ (e.g., asphalt) environmental conditions [89,90]. In terms of the urban vegetation pattern (often analyzed with the NDVI) existing vegetation and other open areas are considered as positive urban structure elements regarding their ecological (biodiversity, production of oxygen) as well as their social function for individual recreational purposes and as socializing meeting-points.…”

Section: Social and Economic Indicesmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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Mapping the Morphology of Urban Deprivation

Kuffer

Grippa²,

Persello

et al. 2021

Urban Remote Sensing

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Globally, about one billion urban dwellers live in deprived areas (commonly referred to as slums). However, this figure is highly uncertain due to large data gaps. For example, in many cities, systematic underreporting occurs, which hampers the monitoring of SDG indicators. Earth observation (EO) data can be used to extract consistent spatial information on important aspects of the physical domain of deprivation and can offer essential proxies to not well-covered (e.g., social and economic) domains. However, for the development of a global data repository on deprived areas, several conceptual and methodological issues need to be solved. First, the relationship between concepts of a slum household and a deprived area needs to be defined in the context of information available in EO images. Second, the costs and benefits of different types of EO-data need to be established. Third, at different scales (ranging from communities, city to global scales), meaningful spatial aggregation units need to be established that are suitable to deal with uncertainties, privacy, ethics, and user needs. Fourth, computationally feasible, scalable, and transferable methods are required to produce a global data repository on deprived areas. This chapter provides an overview of methodological advances to address these four major challenges.

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

Cited by 101 publications

References 18 publications

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

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

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Mapping the Morphology of Urban Deprivation

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