Many processes are involved in data collection. Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, it was hypothesized that the collaboratio might enhance CHA data analysis. In order to find out if this was true, the SOVAT system was test. In the end, results proved that using SOVAT allows tasks to be completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis.
For more information on this study and how it was conducted, please view the full article here.
Melissa Lawrence, Rutgers Student Intern, VERTICES, LLC