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The 21st century urbanization under neoliberalisation unfolding in the countries of the Global South is characterized by unprecedented increase in population and infrastructure demand, and by dramatic spatial and institutional transformation, which has escalated disparities at multiple scales. As per United Nations World Urbanization Prospects: The 2018 Revision, urbanization level is set to raise to almost 70% by 2050 with majority of the countries in the Global South doubling their population. India is predicted to surpass China and become the most populous country by 2050. Despite the adoption of spatial distribution initiatives such as development of Delhi-Mumbai Industrial Corridor and spatial decentralisation by the national government, Indian urban landscape is marred by disparities. Under the prevalent disparities, accommodating population growth will be an enormous challenge for India, given its limited institutional capacity to manage growth and provide infrastructure. This research takes the states in India through which the Delhi-Mumbai Industrial Corridor will pass as a study area and aims to develop an analytical framework hinged on a theoretical foundation for reducing disparities by integrating infrastructure provision with settlement structure. This framework using mixed methods and multiscale approach enables discerning
and explaining spatial disparities across space and time. The development of such a framework makes two novel contributions to urban research: first, it underscores the relevance of classic urban theories and models for investigating and interpreting the spatial disparities in the regions of the Global South. Second, given data scarcity in these regions, the employment of mixed methods for understanding spatial disparities can be used as a proactive planning tool by policy makers to formulate evidence-based policies for reducing disparities by integrating growth with infrastructure provision. This research applied classical urban theories and models at multiple scales to describe the manifestation of spatial disparities in India. It established the relevance of these theories and models to understand the settlement system as well as to establish important gaps in infrastructure provision while predicting future growth. The papers presented here provide ample evidence that the mixed methods approach can be usefully applied to explain the context-specific peculiarities of spatial disparities. A further contribution of this research is to show that the development of a dataset well synchronised with spatial information on socioeconomic and infrastructure variables is essential for empirically establishing spatial disparities. This research explained the manifestation of spatial disparities at multiple scales. It applied several indicators such as accessibility, connectivity and commuting patterns to establish the weakness of spatial links at multiple scales (such as metropolitan, regional and inter-state). This can be considered an important contribution since improved transport links and access to employment and public services reduces spatial barriers to development. Variables on social and physical infrastructure were examined to determine a lack of adequate services in small and intermediate cities predicted to grow. This is also a crucial finding, as sufficient infrastructure and other amenities have long been considered essential to reduce spatial disparities. This research provides evidence-based policy reforms at multiple scale for curtailing spatial disparities. It argues for the introduction of spatial planning at the national level and its integration with economic plans. This integration needs to be promoted at lower tiers of government. At regional scale, the findings recommend an empowered regional authority to develop and implement a regional spatial strategy, which is not only integrated with plans of
the respective state governments but is also legally binding for the states. At the lowest scale, it is crucial to enhance the potential of small and intermediate settlements, and to move towards an integrated rural-urban governance that treats these areas as one unit for the planning and implementation of infrastructure. This research recommends implementing the 73rd and 74th Constitutional Amendment Act to facilitate integrated planning and governance at multiple scales.

Objective: The study was designed to compare intentions to receive COVID-19 vaccination by race–ethnicity, to identify beliefs that may mediate the association between race–ethnicity and intention to receive the vaccine and to identify the demographic factors and beliefs most strongly predictive of intention to receive a vaccine. Design: Cross-sectional survey conducted from November 2020 to January 2021, nested within a longitudinal cohort study of the prevalence and incidence of SARS-CoV-2 among a general population-based sample of adults in six San Francisco Bay Area counties (called TrackCOVID). Study Cohort: In total, 3161 participants among the 3935 in the TrackCOVID parent cohort responded. Results: Rates of high vaccine willingness were significantly lower among Black (41%), Latinx (55%), Asian (58%), Multi-racial (59%), and Other race (58%) respondents than among White respondents (72%). Black, Latinx, and Asian respondents were significantly more likely than White respondents to endorse lack of trust of government and health agencies as a reason not to get vaccinated. Participants' motivations and concerns about COVID-19 vaccination only partially explained racial–ethnic differences in vaccination willingness. Concerns about a rushed government vaccine approval process and potential bad reactions to the vaccine were the two most important factors predicting vaccination intention. Conclusions: Vaccine outreach campaigns must ensure that the disproportionate toll of COVID-19 on historically marginalized racial–ethnic communities is not compounded by inequities in vaccination. Efforts must emphasize messages that speak to the motivations and concerns of groups suffering most from health inequities to earn their trust to support informed decision making.