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The spatial determinants of health refer to the geographic and environmental factors that influence an individual’s or population’s health. These factors include the physical environment, social environment, and access to resources, all of which can affect health outcomes at multiple geographic scales. Understanding these determinants is crucial for addressing health inequities, improving overall population health, and to developing targeted interventions and policies to support healthier communities for all.

1. Physical Environment: This encompasses aspects like air and water quality, access to green spaces, and the built environment (e.g., , high-quality green-space, or noise pollution). For instance, exposure to air pollution can , while can mediate health outcomes and behaviours.

2. Access to Resources: This refers to the availability and accessibility of essential resources like healthcare services, healthy food options, and safe and affordable housing. For example, can create and compound health disparities, and can also impact how different populations are served by policy.

3. Social Environment: This includes social structures, community resources, and social interactions that shape health.  that young women residing in rural areas face unique barriers in accessing health services, but that these are often shared between communities and across countries. An international collaboration of rural researchers has used a Dirt Research perspective to on topics including eHealth and .

4. Spatial Scales: Underlying the analysis of health process and patterns is a need for reliable data at multiple levels of aggregation, from the micro-level (individuals within neighbourhoods and communities), the meso-level (regions or cities), to the macro-level (provinces of countries). These data can be used to and health inequalities. There is also potential for the use of spatial methods and models to examine these patterns and processes, many of which are not being fully utilized in research.

On the one hand, researchers require quantitative measures that reflect the issues under study, are scalable over time and place, and sensitive enough to evaluate underlying health inequalities. On the other, there is also a need for data to be grounded in the experiences of the individuals and communities under study. The recognition of this duality guides the focus of the Spatial Determinants of Health Lab, where data-informed analysis is used to expose the changes can promote health equity.

Perhaps counterintuitively, the patterns and processes of these inequalities at the local level can be measured using large data sources, sometimes termed “big data”. These data sets provide for a range of health measures, including morbidity and mortality, and encompass enough of the local population to allow for representation of standard rates within small, sub-regional areas. Quantitative data can then act as starting point from where more nuanced, can guide analysis and inform our interpretation. Alternately, these data can be used to augment deep, qualitative experience gained from immersive research.

Whether analysing problems manifest in small urban areas (neighbourhoods), or in small rural places (villages), our research seeks to engage the communities under study. This research process, described as , is an attempt to produce thick description of phenomenon that might be typically represented by quantitative data. This is not only the process of providing detail via qualitative data, but is about seeing beyond the obvious, and making links between different sources of data and different observations.

Research in small spaces and small places presents challenges for both quantitative and qualitative methods. The quantitative record tends to be incomplete at smaller levels, and the impact of otherwise minimal changes on quantitative measures (such as disease or hospitalization rates) mean quantitative description alone provides a poor understanding of the health dynamics of small neighbourhoods and villages. At the same time, qualitative research methods have difficulty in ‘scaling-up’ results and aligning findings with quantitative measures. Our research works between these areas, by informing our quantitative analysis with engaged participants and grounding our qualitative work with key ‘data points.’