Learning Objective #1: Identify components of Geographic Information Systems (GIS) applicable to policy evaluation studies

Learning Objective #2: Articulate GIS data requirements

---

In order to facilitate analysis of data sets which include spatial information, the use of Geographic Information Systems (GIS) is beneficial. GIS are information systems capable of capturing, storing, analyzing and displaying location based information. Within the GIS, data layers are created, data relationships are made, location and attribute queries are built, and descriptive displays of information (maps) are generated. An analogy for how data layers work within GIS is an anatomy text which has vellum overlays with different anatomical layers. Looking through the layers of the book allows exploration of spatial relationships within the body and in a similar fashion, the use of GIS facilitates exploration of spatial relationships within communities such as population density and community health indicators.

Increasingly, GIS are being utilized by nursing researchers on a variety of topics including: access to care; social networks and health determinants; and community health assessments. Newer applications of GIS to nursing research questions have included facility investigations of nosocomial infections and policy evaluation. A recent GIS policy evaluation study evaluated the policy effectiveness of Missouri's nurse recruitment and retention policy for underserved areas between 1991 and 2001. Following IRB approval, group differences (policy targeted versus non-targeted counties) were explored using GIS data visualization, spatial statistics and classic statistics. The results suggest that current policy definitions of underserved areas may not be effective in defining areas of nursing shortages and the existing policy implementation may not be achieving the stated goals.

While GIS hold great promise as tools for nursing research, it is critical to remember that GIS results are highly dependent on the quality of the input data. The issue of scale or data granularity is important in GIS data sets. Researchers must be cognizant of the potential for ecological fallacy and the potential artificial effects of political boundaries.