Urban Proximity and Spatial Pattern of Land Use and Development in Rural India
TL;DR: In this article, the effect of urban proximity in terms of distance and connectivity on spatial pattern of land-use and development in the context of rural India was analyzed using nonparametric methods and India Human Development Survey 2004-2005.
Abstract: Using non-parametric methods and India Human Development Survey 2004–2005, this study analyses the effect of urban proximity in terms of distance and connectivity on spatial pattern of land-use and development in the context of rural India. We find that, in urban surroundings, rural areas have diversified economic activities, intensive agriculture with higher spending on various inputs, higher economic wellbeing in terms of wages, income and consumption expenditure. We also document that transport and tele-connectivity are important for improved rural-urban linkages and changing rural landscape. These findings suggest that urban settlements are keys to development and inclusive growth in rural areas.
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TL;DR: Haggblade, Peter B.R. Hazell and Thomas Reardon as discussed by the authors, 2008, pp. xxii+−490, £46.50 and £20.00, ISBN-13 978 0 8018 8663 8 and 978 0 8664 5
Abstract: Edited by Steven Haggblade, Peter B.R. Hazell and Thomas Reardon Baltimore, MD: Johns Hopkins, 2008, pp. xxii + 490, £46.50 and £20.00, ISBN-13 978 0 8018 8663 8 and 978 0 8018 8664 5 The rural non...
114 citations
TL;DR: Wang et al. as discussed by the authors constructed a conceptual model of rural land use transition and analyzed the process characteristics and coupling relationship between cultivated land and rural residential land in China in four periods from 1996 to 2016.
87 citations
TL;DR: Wang et al. as mentioned in this paper developed a new way to quantify the rural settlement transition (RST) through analysis of the interaction between population movements and land use changes and identification of the coupling relationship between the RST and rural development.
40 citations
TL;DR: In this paper, a unified framework is proposed to understand and conceptualize the rural vulnerability with respect to the dynamics of rural-urban linkages in the case of flood events, highlighting the role of spatial factors such as city size and proximity to the city.
Abstract: Rural areas are highly vulnerable to floods due to limited social, economic, and physical resources. Understanding rural vulnerability is vital for developing effective disaster risk reduction strategies. Even though rural areas and cities are intrinsically linked, rural vulnerability was assessed without considering its relation to cities. Numerous theoretical frameworks on systemizing and assessing vulnerability were developed with varying level of scope and depth in terms of scale, dimensions, and components. Nevertheless, these frameworks did not explicitly mention the impact of flood or other hazards on the linkages between spatial units i.e., rural and urban. This study aims to understand and conceptualize the rural vulnerability with respect to the dynamics of rural–urban linkages in the case of flood events. To do so, current literature on rural–urban linkages, vulnerability, as well as factors that influence them were critically reviewed. Taking into account the main elements of rural–urban linkages (flow of people, information, finances, goods and services), components of vulnerability (exposure, susceptibility, and capacity), and factors (social, economic, institutional, infrastructural, spatial, and environmental), a unified framework is proposed. The framework underscores that the role of rural–urban linkages is essential to fully understand rural flood vulnerability. Moreover, the framework highlights the role of spatial factors—city size and proximity to the city—as crucial to comprehend rural vulnerability. This framework can be used as a tool for understanding multifaceted rural vulnerability for climate change adaptation and disaster risk reduction considering spatial development perspective. In this context, empirical investigations can be made to validate the proposed framework and policies can be introduced accordingly. Overall, the proposed framework can help recognize concepts and links of vulnerability, rural–urban dependencies, and rural development dynamics.
35 citations
Cites background from "Urban Proximity and Spatial Pattern..."
...Rural households in close proximity to cities are economically well-off due to diversified livelihoods [128,137,138]....
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...Quality and access to infrastructure and communication facilities in rural areas are modified by distance to cities in India and Ethiopia [129,137]....
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...Distance to cities can alter mobility and information patterns as well as education, skill, and income level of rural communities [39,136,137]....
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TL;DR: Based on the typical case area household survey and Google Earth high-definition remote sensing image data, the authors adopts participatory rural appraisal (PRA), a participatorial rural mapping method and a driving mechanism framework for the structural and functional evolution of rural homesteads.
30 citations
References
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TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
Abstract: The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x i , y i ), i = 1, …, n, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i , y i ) is large if x i is close to x k and small if it is not. A robust fitting procedure is used that guards against deviant points distorting the smoothed points. Visual, computational, and statistical issues of robust locally weighted regression are discussed. Several examples, including data on lead intoxication, are used to illustrate the methodology.
10,225 citations
"Urban Proximity and Spatial Pattern..." refers methods in this paper
...There are various methods used for non parametric analysis: local mean smoothing (Nadaraya, 1964; Watson, 1964; Gasser & Müller, 1979), locally weighted scatterplot smoothing-LOWESS (Cleveland, 1979), and local polynomial smoothing (Fan, 1992)....
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Book•
01 Jan 1994TL;DR: Applications of Local Polynomial Modeling in Nonlinear Time Series and Automatic Determination of Model Complexity and Framework for Local polynomial regression.
Abstract: Data-analytic approaches to regression problems, arising from many scientific disciplines are described in this book. The aim of these nonparametric methods is to relax assumptions on the form of a regression function and to let data search for a suitable function that describes the data well. The use of these nonparametric functions with parametric techniques can yield very powerful data analysis tools. Local polynomial modeling and its applications provides an up-to-date picture on state-of-the-art nonparametric regression techniques. The emphasis of the book is on methodologies rather than on theory, with a particular focus on applications of nonparametric techniques to various statistical problems. High-dimensional data-analytic tools are presented, and the book includes a variety of examples. This will be a valuable reference for research and applied statisticians, and will serve as a textbook for graduate students and others interested in nonparametric regression.
3,885 citations
TL;DR: In this article, a study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly, i.e.
Abstract: A study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly.
3,464 citations
"Urban Proximity and Spatial Pattern..." refers methods in this paper
...There are various methods used for non parametric analysis: local mean smoothing (Nadaraya, 1964; Watson, 1964; Gasser & Müller, 1979), locally weighted scatterplot smoothing-LOWESS (Cleveland, 1979), and local polynomial smoothing (Fan, 1992)....
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01 Jan 1964
2,985 citations
"Urban Proximity and Spatial Pattern..." refers methods in this paper
...There are various methods used for non parametric analysis: local mean smoothing (Nadaraya, 1964; Watson, 1964; Gasser & Müller, 1979), locally weighted scatterplot smoothing-LOWESS (Cleveland, 1979), and local polynomial smoothing (Fan, 1992)....
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