Evaluation of Methods for Comparison of Spatiotemporal and Time Series Datasets

Performing analysis in any domain of research can involve the problem of selecting data from a collection of seemingly similar spatial and/or temporal datasets. In some cases these data can be highly variable in terms of their resolution (both in space and in time), quality, geographic coverage, or availability. In others, the differences are much less obvious and may present challenges when int int is critical to expanding research from data-rich (domestic) to data-poor (international) analysis domains. Of particular interest is reducing the cost of unnecessarily high temporal or spatial resolution in data collection and acquisition. The objective of the research described in this report is to develop statistical methods for the cross-comparison and relative-quality evaluation of large spatiotemporal datasets. This research outlines several methods that can be used to facilitate interpretation of analytical results and perform validation of modeled data through comparison to known or source datasets. within and across these datasets, researchers can ensure their analysis take advantage of the strengths, and avoids the weaknesses, of the available data. The methodology described in this report can be used to compare the results of different analysis methods to determine the impact of using fewer or different datasets.