Healthy Access, Healthy Regions

How can we better integrate data and spatial analysis in a flexible technological framework to continuously assess health programs and outcomes? Web-based health platforms often visualize static data. Updating these health data manually is cumbersome, resulting in online results that are often out of date. At the same time, these platforms often do not integrate spatial analytics to move beyond map visualization toward more complex findings such as disease rate uncertainty or modeling results about changes in outcomes before and after a health intervention.

While surveillance of diseases and injuries is routine in public health strategies in the U.S., simultaneous real-time monitoring of associated built environment features remains rare. There is an increasingly urgent need to create “distributed, interoperable spatial data infrastructures to integrate health research data across and within disparate health research programs,” for generating hypotheses, detecting spatial patterns, and responding to health threats (Richardson et al. 2013). Our goal is to address this need so that  spatial analytic results update automatically as new data are added.



Spatial data science decision support application

To address this challenge, Marynia Kolak of the Center for Spatial Data Science and colleagues are developing the "Healthy Access, Healthy Regions" spatial data science decision support application with Chicago community and public health partners. The goal is to generate asset maps that support needs assessments, identify areas for treatment based on risk assessments and evaluate policies and interventions within a causal spatial analytic framework.


Multiple datasets are being included such as health provider locations, transportation networks, administrative boundaries, health statistics, and population and neighborhood characteristics. Data is sourced from the Chicago Department of Public Health, Chicago Data Portal, Medicaid Data Portal, IDOT (Illinois Department of Transportation) Data Portal, Cook County Data Portal, GTFS feed (General Transit Feed Service), America Fact Finder via Data feeds with automated updates are privileged over static data, whenever possible. In some cases, data was migrated from static spreadsheets to dynamic versions on the web.

By improving how data is accessed, cleaned, and shared in a dynamic environment, analysis and understanding are also improved. Time normally spent on manual data preparation can instead be spent on assessment, testing, evaluation of uncertainties, and developing meaningful models. Data and the resulting explorations, visualizations, and analysis are more easily shared, validated, confirmed or challenged, and ultimately improved in a transparent, open process.