Bringing micro to the macro: how citizen science data enrich geospatial visualizations to advance health equity

Social and spatial contexts affect health, and understanding nuances of context is key to informing successful interventions for health equity. Layering mixed methods and mixed scale data sources to visualize patterns of health outcomes facilitates analysis of both broad trends and person-level experiences across time and space. We used micro-scale citizen scientist-collected data from four Bay Area communities along with aggregate epidemiologic and population-level data sets to illustrate barriers to, and facilitators of, physical activity in low-income aging adults. These data integrations highlight the synergistic value added by combining data sources, and what might be missed by relying on either a micro- or macro-level data source alone. Mixed methods and granularity data integration can generate a deeper understanding of environmental context, which in turn can inform more relevant and attainable community, advocacy, and policy improvements.

Automated summary

Social and spatial contexts have an impact on health, and understanding the nuances of these contexts is crucial for successful interventions to achieve health equity. By combining mixed methods and mixed scale data sources, we can visualize patterns of health outcomes and analyze broad trends as well as individual experiences across time and space. The integration of micro-scale citizen scientist-collected data with aggregate epidemiologic and population-level data sets allows us to identify barriers to and facilitators of physical activity among low-income aging adults. This approach highlights the added value of combining data sources and the potential limitations of relying solely on micro- or macro-level data. The use of mixed methods and granularity data integration can provide a deeper understanding of the environmental context, which can inform community, advocacy, and policy improvements that are more relevant and achievable.