Longitudinal measurement invariance in urbanization index of Chinese communities across 2000 and 2015: a Bayesian approximate measurement invariance approach

Background The Urbanicity Scale was developed based on the China Health and Nutrition Survey (CHNS) to measure the urbanization index of communities according to 12 components. The present study was designed to systematically investigate the factorial validity, reliability, and longitudinal measurement invariance (LMI) of the Urbanicity Scale. Methods Six waves of CHNS data from 2000 to 2015 were adopted. The factor structure and reliability of the Urbanicity Scale for 301 communities were examined using Bayesian exploratory factor analysis. Metric and scalar LMIs were evaluated using both the conventional exact and a novel approximate LMI approach via Bayesian structural equation modeling across various timeframes. Results The findings verified the one-factor structure for the Urbanicity Scale, with adequate reliability. LMI was established for the Urbanicity Scale only over a shorter timeframe from 2006 to 2009 but not over a longer timeframe from 2000 to 2015. Partial LMI was found in the factor loadings and item intercepts for the Urbanicity Scale over the 2004 to 2011 period. Conclusion Interpretation of the temporal change in urbanicity was supported only for a shorter (2006 to 2009) but not a longer timeframe (2000 to 2015). Adjustments addressing the partial non-invariance of the measurement parameters are needed for the analysis of temporal changes in urbanicity between 2004 and 2011.

Inequalities in urban exposure to infrastructure, services, and environment in million-plus cities of India

Unplanned spatial development, unregulated migration, and changing energy consumption patterns are likely to increase the vulnerability to climate change of populations inhabiting in urban areas. This study aims to estimate urban exposure level and examine the inequalities in the availability of infrastructure and the provision of services in million-plus cities in India. Using data from Census 2011 for 40 million-plus cities, this study measured urban exposure through the urbanicity scale ranging from 0 to 70 points. The urbanicity scores revealed a transparent gradient in the level of urban exposure across these 40 million-plus cities, with the scores ranging from 45.59 (the lowest, in Meerut) to 61.47 (the highest, in Delhi). The economic activity scores were similar for all the million-plus cities, whereas the health infrastructure scores showed a wide variation from 1.0 to 8.8 points. Population, health, educational infrastructure, and built environment contributed the most to the inequality. Unless addressed urgently, these inequalities in infrastructure and services will affect the sustainability of these million-plus cities and may hinder the country’s achievement of Sustainable Development Goal 13 on climate change.