Mapping the Components of Human Development

Abstract

Blocks are important administrative units below the district that disseminate crucial roles in designing, supervising, monitoring as well as evaluating the state-sponsored planning processes. As the present study primarily focuses on assessing the disparity in terms of human development at the micro-spatial scale of investigation, it attempts to examine the inequality issues with block-level datasets of two consecutive census years, 2001 and 2011. Principal Component Analysis (PCA) identifies the variables explaining most variation within a given dataset. As the sub-district-level administrative units are concerned and the sets of input variables account for the issue of development, PCA can identify the variables explaining mostly the variation of development. This chapter will try to analyze the spatial pattern of human development with a range of 25 variables indicating different dimensions of human development using block-level datasets. The use of PCA will reduce the dimension to a manageable form, worth for a logical interpretation of the development scenario existing therein. The outcome of the analysis is significant that sets the direction for future analyses of human development in the district. 

Appendix

See Tables 4.5, 4.6, 4.7, 4.8, 4.9, 4.10, 4.11 and 4.12.

Table 4.5 Block-level dataset for Principal Component Analysis 2001

Table 4.6 Correlation coefficient matrix 2001

Table 4.7 Total variance explained componentwise 2001

Table 4.8 Scores of Principal Components 2001

Table 4.9 Blocklevel dataset for principal component analysis 2011

Table 4.10 Correlation coefficient matrix 2011

Table 4.11 Total variance explained componentwise 2011

Table 4.12 Scores of principal components 2011