Contouring Human Development
Methods and Applications Using an Indian District as Case Study
(Sprache: Englisch)
This book acquaints readers with a range of techniques to help them effectively identify, record, map, analyze and report on patterns in various dimensions of human development (HD) with spatial scales down to the village level. It is impossible to capture...
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Klappentext zu „Contouring Human Development “
This book acquaints readers with a range of techniques to help them effectively identify, record, map, analyze and report on patterns in various dimensions of human development (HD) with spatial scales down to the village level. It is impossible to capture HD at the local and global scale with only a single index, because differences in HD at the international scale are caused by 'general' factors, whereas local-scale differences are influenced by 'specific' factors. This book offers a variety of methods for scientifically mapping HD at any spatial scale. It covers how to rationally select variables; how to test the models; how to validate the results, and how to analyze them. For this purpose, it employs a case study on an Indian district. The socio-economic factors regulating the patterns of HD are now more complex than they were only a few decades ago, making it essential to incorporate newer models in order to successfully 'replicate' the real-world situation. Accordingly, the book offers essential methodological tools & techniques for mapping HD. It sheds new light on a handful of statistical multivariate analysis and machine learning algorithms that are rarely used in the social sciences when dealing with HD, yet have sound mathematical and statistical bases. These techniques can be successfully used for predictive analysis in the earth & natural sciences, decision sciences and management disciplines, and are equally effective in terms of capturing, predicting and projecting the composite HD 'landscape.'
This book will especially benefit two groups of readers: firstly, HD practitioners who want to find out 'why some areas are doing better than others' by exploring the complex interactions of spatially linked variables with different HD parameters. And secondly, practitioners in other branches of the social sciences who are not concerned with HD but are looking for 'hands-on training' with techniques they can apply in their
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respective field of spatial investigations.
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Inhaltsverzeichnis zu „Contouring Human Development “
1. Does geography matter in human development? 1.1. Introduction
1.2. Conceptualizing human development
1.3. 'Geography' as discipline & 'Geography' as perspective
1.4. Location matters: revisiting the history of civilization
1.5. The mosaic of human development
1.5.1. International scenario
1.5.2. National scenario 1.5.3. Down to local scale
1.6. Why does geography mater in human development
1.7. Conclusion
2. On contouring human development
2.1. Introduction
2.1.1. Analyzing human development: Post 1960's new synthesis
2.1.2. The UN Human Development Report: changing style to discuss human development
2.1.3. Micro-spatial analysis of Human Development: Limitations of HDI
2.1.4. Toward alternative methods for mapping micro-spatial Human Development
2.1.5. Assumptions: contouring human development with micro-spatial datasets 2.1.6. Prospects of the alternative methods
2.3. Materials for mapping
2.3.1. Datasets
2.3.2. Software
2.4. Multi-criteria based predictive mapping
2.4.1. Single criteria vs multi-criteria mapping
2.4.2. Predictive mapping: the new dimension
2.4.3. Multi-criteria based predictive mapping: how to draw?
2.5. A glimpse of relevant investigations done
2.6. Conclusion
3. Briefing the area for case study
3.1. Introduction
3.2. Location of the study area
3.2. Physical set-up of the study area
3.3. Socio-economic-cultural characteristics of the study area
3.4 Livelihood, Economy and its modification over time
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3.4.1. Pre National Planning phase
3.4.2. Post 1950s scenario
3.5. Conclusion
4. Mapping the components of human development
4.1. Introduction
4.2. Principal Component Analysis (PCA)
4.2.1. Principle of PCA
4.2.2. Application of PCA on socio-economic datasets 4.2.3. Advantages & Limitations of PCA
4.2.3. Selecting variables & arranging datasets for PCA
4.3. Case Study: Mapping components of human development in Purulia district using PCA
4.4. Conclusion
5. Mapping economic inequality
5.1. Introduction
5.2. Commentary on non-spatial economic inequality
5.2.1. Income distribution curve
5.2.2. How to draw the income distribution curve
5.3. Case Study: Examining non-spatial economic inequality in Purulia district using
income curve
5.4. The relative deprivation of income
5.4.1. About relative deprivation
5.4.2. Graphing the relative deprivation of income
5.5. Case Study: Assessing relative deprivation of income in Purulia District
5.6. Mapping economic inequality with workforce datasets
5.6.1. Per capita income as classical measure of economic development & inequality
5.6.2. Linking income with workforce
5.6.3. Workforce based measurement of economic inequality
5.6.4. Validation of the workforce based measurement
5.7. Case Study: Mapping economic inequality in Purulia district using workforce datasets
5.8. Mapping income insecurity 5.8.1. Susceptibility of income insecurity
5.8.2. How to capture income insecurity data from field
5.8.3. Analytical Hierarchy Process (AHP) in income insecurity analysis
5.9. Case Study: Mapping susceptibility of income insecurity in Purulia district using
AHP
5.10. Conclusion
6. Drawing the contours of educational attainment
6.1. Introduction
6.2. Dealing with the non-spatial inequality of educational attainment
5.2.1. Mean Years of Schooling (MYS) as the measurement of attainment
5.2.2. Calculating MYS from field datasets 6.3. Case Study: Examining non-spatial inequality in educational attainment in Purulia district using MYS
6.4. Spatial pattern of educational attainment
5.4.1. Graphical presentation of literacy & public education facilities 5.4.2. The rural-urban disparity in educational attainment
6.5. Case Study: Village level mapping of spatial disparity of literacy level in Purulia district
6.6. Educational attainment favorability analysis
5.6.1. Conceptualizing the Fuzzy Logic
5.6.2. Factors influencing spatial pattern of educational attainment 5.6.2. Arranging input and variables for fuzzy algorithm
5.6.3. Assigning membership functions & setting fuzzy rules
6.7. Case Study: Educational attainment suitability analysis of Purulia district using fuzzy logic
6.8. Case Study: School drop-out susceptibility analysis of Purulia district using fuzzy logic
6.9. Conclusion
7. Mapping public health scenario
7.1. Introduction
7.2. Non-spatial inequality of public health
7.2.1. Life expectancy at birth (LEB) as the indicator of public health status
7.2.2. On calculating LEB from field datasets
7.3. Case study: Assessing non-spatial inequality of health condition in Purulia district using LEB datasets
7.4. Spatial pattern of public health condition
7.3.1. Graphical presentation of spatial pattern of public healthcare services
7.3.2. Rural-urban differentials of public healthcare facilities
7.5. Case Study: Village level mapping of spatial disparity of public health services in Purulia district
7.6. Multi-criteria mapping of public health condition
7.6.1. Conceptualizing Binary Logistic Regression (BLR)
7.6.2. Factors influencing the spatial pattern of public health
7.6.3. Selection of predictors for BLR model
7.6.4. Preparing the dependent variable
7.6.4.1. Average Years of Life Lost per Head ()
7.6.4.2. Indicator of Bad Health (IBH)
7.6.4.3. People's perception to present healthcare system: Indicator of satisfaction to present healthcare system (OHS)
7.6.4.4. Dependent variable: Indicator of Health Status (IHS)
7.7. Case Study: Mapping spatial pattern of public health inequality in Purulia district
7.5. Conclusion
8. Strategy mapping with predictive modelers
8.1. Introduction
8.2. Predictive analysis & strategies for development
8.3. Classification and Regression Tree (CART)
8.3.1. Conceptualizing the CART
8.3.2. Applicability of CART analysis in strategy mapping
8.4. Case Study: The CART analysis on the income insecurity factors of Purulia for suggesting development strategies
8.5. Multiple Adaptive Regression Splines (MARS)
7.5.1. Conceptualizing the MARS
7.5.2. Applicability of MARS analysis in strategy mapping
8.6. Case Study: The MARS analysis on the educational variables of Purulia for suggesting development strategies
8.7. Partial Least Square Path Model (PLS Path) 8.7.1. Conceptualizing the PLS Path
7.7.2. Applicability of PLS Path analysis in strategy mapping 8.8. Case Study: The PLS Path analysis on the health variables of Purulia for suggesting development strategies
8.9. Conclusion
3.4.1. Pre National Planning phase
3.4.2. Post 1950s scenario
3.5. Conclusion
4. Mapping the components of human development
4.1. Introduction
4.2. Principal Component Analysis (PCA)
4.2.1. Principle of PCA
4.2.2. Application of PCA on socio-economic datasets 4.2.3. Advantages & Limitations of PCA
4.2.3. Selecting variables & arranging datasets for PCA
4.3. Case Study: Mapping components of human development in Purulia district using PCA
4.4. Conclusion
5. Mapping economic inequality
5.1. Introduction
5.2. Commentary on non-spatial economic inequality
5.2.1. Income distribution curve
5.2.2. How to draw the income distribution curve
5.3. Case Study: Examining non-spatial economic inequality in Purulia district using
income curve
5.4. The relative deprivation of income
5.4.1. About relative deprivation
5.4.2. Graphing the relative deprivation of income
5.5. Case Study: Assessing relative deprivation of income in Purulia District
5.6. Mapping economic inequality with workforce datasets
5.6.1. Per capita income as classical measure of economic development & inequality
5.6.2. Linking income with workforce
5.6.3. Workforce based measurement of economic inequality
5.6.4. Validation of the workforce based measurement
5.7. Case Study: Mapping economic inequality in Purulia district using workforce datasets
5.8. Mapping income insecurity 5.8.1. Susceptibility of income insecurity
5.8.2. How to capture income insecurity data from field
5.8.3. Analytical Hierarchy Process (AHP) in income insecurity analysis
5.9. Case Study: Mapping susceptibility of income insecurity in Purulia district using
AHP
5.10. Conclusion
6. Drawing the contours of educational attainment
6.1. Introduction
6.2. Dealing with the non-spatial inequality of educational attainment
5.2.1. Mean Years of Schooling (MYS) as the measurement of attainment
5.2.2. Calculating MYS from field datasets 6.3. Case Study: Examining non-spatial inequality in educational attainment in Purulia district using MYS
6.4. Spatial pattern of educational attainment
5.4.1. Graphical presentation of literacy & public education facilities 5.4.2. The rural-urban disparity in educational attainment
6.5. Case Study: Village level mapping of spatial disparity of literacy level in Purulia district
6.6. Educational attainment favorability analysis
5.6.1. Conceptualizing the Fuzzy Logic
5.6.2. Factors influencing spatial pattern of educational attainment 5.6.2. Arranging input and variables for fuzzy algorithm
5.6.3. Assigning membership functions & setting fuzzy rules
6.7. Case Study: Educational attainment suitability analysis of Purulia district using fuzzy logic
6.8. Case Study: School drop-out susceptibility analysis of Purulia district using fuzzy logic
6.9. Conclusion
7. Mapping public health scenario
7.1. Introduction
7.2. Non-spatial inequality of public health
7.2.1. Life expectancy at birth (LEB) as the indicator of public health status
7.2.2. On calculating LEB from field datasets
7.3. Case study: Assessing non-spatial inequality of health condition in Purulia district using LEB datasets
7.4. Spatial pattern of public health condition
7.3.1. Graphical presentation of spatial pattern of public healthcare services
7.3.2. Rural-urban differentials of public healthcare facilities
7.5. Case Study: Village level mapping of spatial disparity of public health services in Purulia district
7.6. Multi-criteria mapping of public health condition
7.6.1. Conceptualizing Binary Logistic Regression (BLR)
7.6.2. Factors influencing the spatial pattern of public health
7.6.3. Selection of predictors for BLR model
7.6.4. Preparing the dependent variable
7.6.4.1. Average Years of Life Lost per Head ()
7.6.4.2. Indicator of Bad Health (IBH)
7.6.4.3. People's perception to present healthcare system: Indicator of satisfaction to present healthcare system (OHS)
7.6.4.4. Dependent variable: Indicator of Health Status (IHS)
7.7. Case Study: Mapping spatial pattern of public health inequality in Purulia district
7.5. Conclusion
8. Strategy mapping with predictive modelers
8.1. Introduction
8.2. Predictive analysis & strategies for development
8.3. Classification and Regression Tree (CART)
8.3.1. Conceptualizing the CART
8.3.2. Applicability of CART analysis in strategy mapping
8.4. Case Study: The CART analysis on the income insecurity factors of Purulia for suggesting development strategies
8.5. Multiple Adaptive Regression Splines (MARS)
7.5.1. Conceptualizing the MARS
7.5.2. Applicability of MARS analysis in strategy mapping
8.6. Case Study: The MARS analysis on the educational variables of Purulia for suggesting development strategies
8.7. Partial Least Square Path Model (PLS Path) 8.7.1. Conceptualizing the PLS Path
7.7.2. Applicability of PLS Path analysis in strategy mapping 8.8. Case Study: The PLS Path analysis on the health variables of Purulia for suggesting development strategies
8.9. Conclusion
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Autoren-Porträt von Mukunda Mishra, Soumendu Chatterjee
Mukunda Mishra is an Assistant Professor, Department of Geography, at Dr. Meghnad Saha College in West Bengal, India. The college is affiliated to the University of Gour Banga. Dr. Mishra completed his postgraduate studies in Geography and Environmental Management at Vidyasagar University (receiving top rank in both the B.Sc. and M.Sc. panels of merit) and holds a Ph.D. in Geography from the same University. He was selected for the prestigious National Merit Scholarship by the Ministry of Human Resource Development, Government of India. His research chiefly focuses on analyzing unequal human development, and on creating multi-criteria predictive models. He has more than ten years of hands-on experience in dealing with development issues at the ground level in various districts of eastern India. Soumendu Chatterjee is a Professor and Head of the Department of Geography at Presidency University in Kolkata, India. He has been teaching Geographical Science at the undergraduate and graduate levels for more than twenty years. His primary research interest is in creating scientific models for predicting complex physical and human processes on the Earth's surface. He has more than fifty publications in national and international journals of repute to his credit, and has headed several research projects funded by the University Grants Commission (of India), Department of Science & Technology (GoI), Indian Council of Social Science Research (ICSSR) and other respected agencies in India and abroad.
Bibliographische Angaben
- Autoren: Mukunda Mishra , Soumendu Chatterjee
- 2021, 1st ed. 2020, XXXIII, 358 Seiten, 102 farbige Abbildungen, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 9811540853
- ISBN-13: 9789811540851
Sprache:
Englisch
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