The food system’s interconnectivity with almost every aspect of society makes
accurately characterizing it very important. This same interconnectivity also makes the
problem of accurately characterizing the food system very complex. Indicator sets that
attempt to capture the holistic nature of the food system and are repeated across
location and time to allow for comparisons and stability testing are inevitably very large.
Using a large data set of state-level food system indicators collected for 1997, 2002, and
2007, this thesis explores the possibility of using Principal Component Analysis to
develop summary measures for groups of indicators. The results show that it is possible
to characterize the information presented by groups of individual indicators by
component scores, although the process is very difficult. Through Principal Component
Analysis and Partial Common Principal Component Analysis techniques, selected
groups of indicators for each state over the three years are reduced in dimensionality
and shown to be stable over time. This then allows for states to be compared nationally,
regionally, and temporally on specific aspects of their food systems.
University of Minnesota M.S. thesis. March 2012. Major: Applied Economics. Advisor: Robert King. 1 computer file (PDF); viii, 124 pages, appendices A-C.
Scharadin, Benjamin Paul.
Principal component analysis of state level food system Indicators..
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