Inadequate and untimely access to social services such as medical care, mental health services, shelters, and SNAP-accepting retailers is a major barrier to achieving health equity for disadvantaged communities. Creating an effective and multi-faceted social safety net requires a systems-level perspective to ensure that a broad combination of services are accessible to the people who need them the most. For this approach to succeed, it is critical for regulators and policy makers to have a detailed understanding of the communities they serve.
Government agencies and nonprofits often lack the technical resources or in-house geographic information systems (GIS) expertise necessary to conduct accessibility analyses themselves. Existing tools used to map systems at this scale are prohibitively expensive, require significant amounts of manual data processing, and are too coarse to accurately depict accessibility issues. While more sophisticated mapping, routing, and population modeling technologies in the private sector are well-established, these tools remain out of reach for the public sector. We set out to build an open-source solution that eliminates these barriers for overburdened and under-resourced government agencies and others working to build a more equitable health ecosystem.
We drew from our past expertise working with government agencies on geospatial problems to build Encompass, an analytics and mapping tool that enables policymakers, researchers, and consumer advocates to analyze how accessibility to social services varies across demographic groups. The application contains three main components: a high-resolution population model represented as a set of discrete points, a list of service locations from which we would like to measure access, and an efficient procedure for calculating the driving times between population points and service locations.
For the population model, Encompass uses satellite data from the European Commission Global Human Settlement (GHS) initiative to approximate the location of people across the world to a 250 meter resolution. This data is combined with U.S. Census data to integrate demographic information such as income, race, and age. We use Open Source Routing Machine (OSRM) to calculate driving times. The sample datasets come from a variety of publicly available data sources. This entire project is open-source and packaged to be easily adapted to support different transportation methods, different countries, and any dataset that has address or geographic information.