Advances in GPS technology have facilitated efficient collection of spatiotemporal data of visitor travel patterns. However, analyses of these data has evolved at a slower rate. In particular, past analyses typically have not analyzed temporal variations to understand how visitor travel patterns change with time. It is necessary to understand spatial variations of temporally segmented data to gain insight into the dynamic nature of visitor behavior, because change is a function of time. Therefore, this manuscript focuses on elements of this critical co-analysis and advocates for incorporating richer temporal analysis at various scales to gain a fuller understanding of visitor travel patterns. The researchers guided this work using the concept of “time-geography” to evaluate day-visitors’ travel patterns at Cumberland Island National Seashore. Data were collected using GPS data loggers and analyzed by conducting kernel density analyses of temporally-segmented data, and then visitors’ temporal allocation within seven management zones were compared using a One-Way Analysis of Variance (ANOVA) with a Bonferroni Post Hoc test. Results reveal that visitor use at Cumberland Island is heavily concentrated, transient, and are dependent on location of attraction sites as a function of time from ferry disembarkment. The analysis also identified the most frequented management zones and the length of stay within each zone. These methods and results contribute an illustrative example to the importance of analyzing temporal patterns in conjunction with spatial patterns. Lastly, this research revealed the punctuated rhythm of visitor spatiotemporal behavior when stringently constrained by time.
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