Skip to main content
main-content

Über dieses Buch

This book is an earnest effort in understanding the slums and their needs by taking a case study of Kalaburagi, India. This study aims to contribute sustainable methodologies to advance the living conditions of slum dwellers and for better execution of slum policies. The core objectives are: 1) mapping the existing slums of Kalaburagi (formerly Gulbarga) city using slum ontology from very high-resolution data and validating the slum map through ground survey and using reliable data; 2) developing a model to understand the factors which are responsible for the present growth as well as to predict the future growth of slums; 3) estimating the housing demand of urban poor and suggesting a suitable site for the rehabilitation program; and 4) suggestions for the better intervention of government policies with special reference to in-situ program.

Urban is the future, and slums are its reality. Sustainable development goals are directly and indirectly concerned about the increasing urbanization and the slums. Housing the urban poor and affordable housing to all are the national missions. Practically making these plans successful depends on a deep understanding of urban issues and proper methodology and technology to handle it. The participatory slum mapping, cellular automata slum model, housing demand analysis, and the spatial decision support system demonstrated in the book help in monitoring and managing the slums and thus lead towards a slum-free India.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Urbanization in India

Abstract
In terms of population size, the urban agglomerations of India are the second-largest in the world, ranking behind only China. The Indian urban population has steadily increased since independence with an urban growth rate (31.8%) that is higher than the average population growth rate (17.64%). Industrialization, planned development and globalization are the major factors that induced urban development in India. The urban population undergoes the natural increase expected of any population and this increase is further augmented by in-migration. The lopsided growth of the urban population has resulted in a “top-heavy” urban system where Class-I cities and million-plus cities, which are crowded to begin with, are getting even more crowded instead of this population increase being distributed across other cities and towns. The level of urbanization is uneven among the states and UTs with even intra-state variations. The census data are considered a reliable source of information yet recent studies show that the urban population is underestimated in India. Given the persistent growth of the population in urban areas, slums are one of the major problems of urban India. The housing problem will further deteriorate the slums unless intensive measures are taken to upgrade the living conditions of the slum dwellers. The first chapter insists on a detailed study on slums and their problems so that they will be included in city planning and urges for sustainable measures to prevent the future formation of slums.
Sulochana Shekhar

Chapter 2. Slums in India

Abstract
Urban areas are engines of economic growth, centers of production, and support the lives of millions. These, however, do not paint a complete picture of the urban phenomenon. Urban areas are also home to millions of people who are neglected and helpless. The invisible population of urban areas was counted for the first time in India, in 2011 and included in the urban population. The present chapter describes the efforts taken by the government to count the slum population and the distribution of the slum population at the national level. It also elucidates the definitions and types of slums. Though the 2011 census was done at the national level, it did not account for the entire slum population as it neglected census towns. It was the census of 2001 that initiated slum population counting but it was restricted to towns that had a population of 50,000 and above as counted by the 1991 census. Therefore, both these censuses did not bring out a clear picture of the slum population in India. Despite these gaps, the census data did serve the purpose of drawing the poor slum situation into the limelight. Mere counting, however, will not help in improving the living conditions of the slum population. Spatial information and insight into their living conditions are needed for proper and inclusive planning. To showcase the need for spatial information and the use of geospatial technology in extracting spatial information and mapping slums, sample scenes (satellite images) of various slum areas have been taken from Google Earth and explained in this chapter.
Sulochana Shekhar

Chapter 3. Case Study: Kalaburagi

Abstract
Case studies are in-depth investigations of an event or a community. Case studies explore complicated issues in their natural environment. They also help in experimenting with new policy ideas and government schemes. The output helps us understand the loopholes or lacunas and assists in refining the policy and fine-tuning the existing schemes. They offer insight into a phenomenon or a process that cannot be attained by any other approach. Hence, to understand the characteristics of slums, problems of slum dwellers, issues of planners, we need to study at least the slums of one city in detail. This will also elucidate the success and failures of the efforts taken by the central, state, and local governments in uplifting the slum dwellers. This, in turn, will help us plan sustainable cities. With this background, it was decided to study the slums of a non-metropolitan city. Under the HUDCO (Housing and Urban Development Corporation) project, the city of Kalaburagi had been chosen as a case study. Mapping of slums experimented with new combinatory methods like slum ontology and spatial decision support systems combined with a participatory GIS. This chapter creates a base for building slum ontology, participatory slum mapping, and spatial decision support system (SDSS) for slums in the present work. The field photographs give a visual tour of the slums of Kalaburagi. The detailed study on slums helps in understanding the role of multi-stakeholders in the sustainable management of slums.
Sulochana Shekhar

Chapter 4. Slum Identification and Validation

Abstract
With increasing urbanization at the global level, slums too are going to grow. Hence, a solution is urgently required. When we look at slum development, we should begin by putting them on the map. Mapping their spatial extent and locating them in the cityscape will help planners prepare for their betterment. Mapping with outdated technologies will provide us with the needed information in the required format to aid in the planning process. Therefore, it becomes necessary to go for geospatial technologies such as earth observation data and GIS. Identifying slums from satellite data needs domain expertise and training. In the present chapter, slums were identified from high-resolution satellite data with the help of cognitive-based technology such as ontology. The slum ontology was built with the help of domain experts, planners, local administrators, and slum dwellers. The ontology building process has been explained in different phases. Through the participatory GIS approach, using slum ontology, the slums of Kalaburagi city were mapped and the city’s first slum map was prepared. The Urban Frame Survey (UFS) maps were used for validating the slum map. Thus, the slums of Kalaburagi city were mapped using a slum ontology built exclusively for that city. It can also be extended to mapping slums of any Indian city since the slum ontology is based on the Indian scenario. The same has been explained with sample images of the top 10 cities of India.
Sulochana Shekhar

Chapter 5. Slum Modeling for Growth Prediction

Abstract
Modeling helps in the understanding of complex spatial phenomenon in a simple manner. All spatial problems are complicated and need a thorough understanding of their causes and consequences. Slum formation is one such problem that includes not only economic aspects but also social, cultural, and most importantly, behavioral aspects. Modeling all such factors requires the use of a sophisticated model which can imitate human thinking and accurately portray the ground reality. A high-end model demands valid input data for the expected output to have reasonably high accuracy. In the present chapter, the Cellular Automata (CA) model has been used to model the conditions that favor slum formation in the study area, Kalaburagi city. The model was built in an ArcGIS environment with various thematic layers generated using satellite data and spatial analysis. The core part of the model is transitional rules that reflect the actual situations that lead to slum formation. These generated results were close to the ground situation. Accordingly, the most favorable sites for slum formation as identified by the model accounted for 97% of the actual slums and moderate areas were identified as probable sites for future slum formation. Since prevention is better than rehabilitation, the open land available in the moderate zone was suggested for affordable housing. Meanwhile, the Master Plan Map of Kalaburagi city prepared by the Kalaburagi Urban Development Authority for 2021 was used to identify the areas planned for future residential expansion. The identified and economically viable sites were compared with the sites favoring slum formation as per the CA model and recommended for affordable housing.
Sulochana Shekhar

Chapter 6. Slum Housing Demand Assessment and Analysis

Abstract
Offering affordable housing is the best possible way of solving the problem of future slum formation or slum expansion/extension. So, how many houses need to be constructed to stop the formation of new slums? This is the first question that would arise in the minds of urban planners and no one can accurately answer this question. Whatever the answer, the most important thing is that we do not give up on searching for an answer. We may not be able to estimate the actual inflow of migrants but we can project the present slum population and assess the requirements of existing slums and their housing demands. The present chapter tries to project the slum population of Kalaburagi city but due to the dearth of slum population data, it did not yield good results. Despite the anomalies, we could arrive at a projection with an expected growth rate of 1.29%. Along with this, estimation of housing demand was carried out using the principle, “Willingness to stay instead of a willingness to pay”. Since the demand for housing depends on many factors; for this present study, only six basic parameters were selected for estimating the housing demand. One slum area, Borabai Nagar, was taken as a sample to demonstrate the methodology and the result is encouraging. This chapter also brought out another issue in developing affordable housing since the houses constructed under various housing schemes for slum dwellers in Kalaburagi city were evaluated for their success. It was observed that the houses meant for improving the slum situation were constructed in the city’s peripheries, far away from their employable opportunities, and thus failed to achieve their purpose.
Sulochana Shekhar

Chapter 7. Slum Development Programs—An Overview

Abstract
The United Nations development goals insist that developing countries take steps to improve the living conditions of slum dwellers and eradicate poverty. India has taken major efforts and also achieved the target of MDGs but the slum populations keep on increasing. The present chapter discusses how slum development planning began in independent India and the schemes and policies that were introduced for the betterment of slum dwellers. The five-year plans of the Government of India aimed for urban development and had special programs for slum dwellers. As the land of a state comes under the purview of the state’s government, the central government only sponsored the schemes financially in most cases while the implementation of the scheme was deemed to be the responsibility of the state. Though various schemes and policies exist, the results were not up to expectations. Hence, a detailed study on popular slum development programs and the reasons for their success and failures was carried out based on government reports and published research articles. The study revealed that government projects followed the “top-down” approach and lacked community participation. The latest slum development program, Prime Minister’s Awas Yojana (earlier known as Rajiv Awas Yojana), has taken this into account and incorporated the multi-stakeholder approach, including slum dwellers in its processes. One of the reasons for the failure of early programs is the lack of spatial data and the use of outdated technology for mapping. In an effort to overcome these limitations, in this study, geospatial technology has been used as the main source for data input, surveying, and mapping.
Sulochana Shekhar

Chapter 8. Slum-Spatial Decision Support System

Abstract
Spatial decision-making plays a vital role in resolving spatial problems over standard decision support systems. Since spatial problems are complicated, there is no single correct solution and it needs to provide alternatives to let the user choose the solution that is appropriate to the given conditions. Slum development is a multidimensional problem and involves various stakeholders. To improve the slum situation, there were many schemes and programs by the central and state governments and many non-government organizations also worked toward the same. Despite all this, the desired results were not achieved and research was needed for identifying why this was so and to also find out what was required to attain the target. Lack of spatial component and multi-stakeholder participation in decision-making were identified as two of the important causes of such failures. The present chapter discusses the need for the spatial component in the decision support system and introduces the Spatial Decision Support System (SDSS) for slums. It also insists upon multi-stakeholder participation in spatial decision-making. The detailed structure of SDSS was explained and to showcase the efficacy of the slum-SDSS, a sample slum area was chosen from within the 60 slums of Kalaburagi city. The selection of the sample slum was not done arbitrarily but instead done scientifically using spatial analysis in the GIS environment. The system was developed with slum community participation at the core along with other stakeholders ranging from GIS experts to local administrators. The system developed alternative scenarios based on community input and the results were visualized as a three-dimensional model using appropriate software. Involving all the stakeholders in the spatial decision support system will ensure the successful implementation of slum development programs and achievement of the national mission.
Sulochana Shekhar
Weitere Informationen

Premium Partner

    Bildnachweise