The use of GIS in informal settlement upgrading: its role and impact on the community and on local government

Abbott, John

doi:10.1016/s0197-3975(03)00006-7

Cited by 51 publications

(41 citation statements)

References 5 publications

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“…The potential application areas for RS-based information on the morphology and temporal dynamics of slums are grouped into four major domains (Table 3, [21,54,77,120,[138][139][140][141][142][143][144][145][146][147][148][149][150][151][152]): economy, environment, governance and planning, and social applications. These domains reflect the reported spatial information needs and lack of information on slums in locally available data sets [84].…”

Section: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

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: Purposes Of Slum Mapping Using Remote Sensingmentioning

confidence: 99%

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

2016

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“…This actions and type of system will only be legit and credible if all the process is followed and the participation of local community (Amado et al, 2016;Abbott, 2003;Rao, Sharma & Rajashekar, 2014).…”

Section: Methodsmentioning

confidence: 99%

“…Different authors (Abbott, 2003;Amado et al, 2017;Paulsson & Paasch, 2015;(Durovic & Nikolic, 2016) point the utilisation of GIS as fundamental to defined and support future land development, management and planning decisions.…”

Section: Methodsmentioning

confidence: 99%

Transformation of Informal Settlements: Case of Cape Vert

Amado¹,

Poggi²,

Martins³

et al. 2018

Preprint

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The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information

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“…These result in a sustainable technique of data capturing being established, and also a quick process of data collection, as residents are knowledgeable of problems affecting the community (Musungu et al, 2012). In return, the residents improve their knowledge concerning maps and GIS, as the goal behind developments in informal settlements is to improve living conditions of residents (Abbott, 2003). For instance at Monwabisi Park 20 members of the community volunteered in collection of spatial data, which involved the use of GPS and maps.…”

Section: Participatory Gis (Pgis)mentioning

confidence: 99%