It is unclear how to determine any unevenness in Spatiotemporal variability with only a simple glance at datasets. Real-time annual average daily traffic (AADT) data continues to be the primary operations data needed to perfect the exactness of the performance of transportation systems valuation. Despite the enormous investment in data collection procedures (automated and manual), data collection seems sparse and has varying levels of accuracy. The disparity from unoptimized AADT data collection from low-volume roads has stimulated the end-users to close gaps through modeling. However, it is convenient to use statistical techniques to assess the dataset’s local/global trends and normality before optimization processes can conveniently be successful. Therefore, exploratory spatial data analysis (ESDA) is explored to identify statistically significant similarities and disparate within datasets collected from the various locations. In addition, the spatial clustering assessment is completed using Getis-Ord Gi statistics. In contrast, spatial autocorrelation is explored with Moran’s Index. AADT datasets (2009 to 2016) from low-volume roads in Montana, Minnesota, and Washington is explored. The ESDA results indicate that none of the datasets exhibits global trends of global spatial autocorrelation. Nonetheless, there were some indications of local spatial autocorrelation in the datasets. Additionally, the AADT datasets exhibit spatial heterogeneity. The Getis-Ord Gi statistics indicate that most data points are not statistically significant, and the minor hotspot regions are not statistically significant. Moran’s Index reveals perfect clustering in the datasets. Though values were dissimilar (alternating values), it expresses the clustering as perfect with spatial outliers or spatial heterogeneity. As a first step before any model is completed, the process will help resolve the variability and divergence within the dataset.