mortality (Haux, 2006). These systems provide the type of data necessary for defining public health priorities and thus play an important role directing expenditure.

The research agenda outlined above can also help to identify opportunities to deploy novel approaches to reduce health care disparities across both urban and rural communities. For example, mobile health clinics and temporary facilities can provide health care for remote populations (Free et al., 2013). Improving the quality of select, less advanced health facilities in rural areas and small urban centers, including by strengthening primary health care, can prevent populations from having to travel long distances for basic services (Buykx, Humphreys, Wakerman, & Pashen, 2010; Chan, 2009). Internetenabled mobile phones offer the potential for expanding telemedicine services (Africa-EU Partnership, 2015; Martínez, Villarroel, Seoane, & Pozo, 2004). Culturally-sensitive interventions can enhance trust and vouchers for transportation or health services can improve affordability.

In summary, context-specific health care resource optimization is possible through the collection of data sets and application of analyses that address the challenges of localized health needs, geographic distributions of health care resources and populations, constraints, and indirect benefits. By combining multiple, spatially-explicit approaches, improved characterizations of disparities in access to care along the rural-urban continuum are possible. Overall, having geographic-specific information ensures interventions can be deployed to target specific gaps in health system performance, maximizing the impact of investments on health outcomes.

_Addressing rural–urban disparities in education_ Differences in access to educational services and educational outcomes along the urban–rural continuum are sizeable. New data that provide finer gradations between urban centers and the most remote locations enable innovative research that can improve education policy and outcomes. While educational attainment in rural areas may deliver lower returns on average than in urban areas, in part due to less economic opportunity in rural areas, a lack of data means that research to date has not been able to parse how returns vary across the spectrum of urbanization. New estimates of the returns to schooling, combining household survey data with geolocated data, could illuminate how these returns vary not only across the urban–rural divide but also as students get further from roads that provide access to urban centers (and thus, the formal job market). It could also illustrate the extent to which these returns are mediated through complementary behaviors like migration or inputs like virtual technologies.

A deeper understanding about how policy makers, educators, caregivers, and students see the role of education across the urban–rural continuum can also help improve the quality of education and reduce potential inequalities across children in different geographic spaces. For instance, rural areas often face teacher-specific challenges related to attendance, resources, training, and recruitment. Students in rural areas also access post-secondary education at much lower rates. Rural and remote-area schools also have less access to modern construction materials, transportation, electricity, water, and proper infrastructure; good infrastructure can promote attendance and foster a productive environment that is conducive for learning, particularly when designed to serve the basic needs of its beneficiaries (Beteille et al., 2020). Likewise, teacher and student absenteeism may vary non-linearly and even nonmonotonically across the urbanization spectrum. However, very few research studies examine absenteeism across the full spectrum of urbanization. This would be possible, for example, by 24